system

Abstract

We provide the system. [Solution] A means of performing individual settings based on authentication information obtained from the user, A means of collecting and sending message data from an external application, A means for analyzing message content using natural language processing and generating response candidates, A means of selecting and sending an automated response at the appropriate time, A system that includes means for accepting manual intervention from users.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the development of modern communication technology, users receive a large amount of information daily from a large number of messaging applications. As a result, it takes time to respond to unimportant conversations, resulting in a problem that it is difficult for users to concentrate on other important activities. To address such problems, it is required to improve the efficiency of user message management and reduce wasted time.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for making individual settings based on user authentication information, means for collecting and transmitting message data from external applications, means for analyzing message content using natural language processing and generating response candidates, means for selecting and transmitting an automated response at an appropriate time, and means for accepting manual intervention from the user. As a result, users can reduce the effort of inefficient conversational responses through automated responses and concentrate their resources on important activities.

[0006] A "user" is the entity that utilizes this system and receives services through the application.

[0007] "Authentication information" refers to information used to identify an individual user and grant them permission to access the system.

[0008] "Individual settings" refer to customized system operation settings tailored to the user's needs.

[0009] An "external application" is another software program that interacts with this system and is an application used to send and receive messages.

[0010] "Message data" refers to information exchanged through communication, including the sender, content, and timestamp.

[0011] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0012] A "suggested response" is a proposed message that can be sent on behalf of the user.

[0013] An "automated response" is a message generated by a system and sent without user intervention.

[0014] "Manual intervention" refers to the user taking action themselves to respond to a message.

[0015] The "system" is an aggregate of hardware and software that constitutes the whole of the present invention.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing apparatus and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing apparatus and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing apparatus and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing apparatus and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system designed to enable users to process messages efficiently and save time. This system is realized through the interaction of the user, terminal, and server.

[0038] When a user installs an application and enters their authentication information, the device obtains the user's personal settings data. This allows the device to create an environment optimized for each user.

[0039] The terminal retrieves message data in real time from external applications that the user uses on a daily basis. This data is sent to a server, where it is analyzed using advanced natural language processing technology.

[0040] The server analyzes the received message content and generates response candidates appropriate to the context. An algorithm is applied to automatically select the most appropriate response from a large number of candidates. This feature allows for automatic responses even while the user is concentrating on other activities.

[0041] This system also has the ability to determine timing; when it detects that a message has been addressed to a user, it sends a pre-generated response. This allows for immediate responses to user inquiries or important notifications.

[0042] Furthermore, manual intervention by the user is supported as needed. For example, manual responses take precedence over complex topics or matters that should not be automated by the system. In this case, the terminal receives user input and sends it to the server to update the conversation flow.

[0043] To give a concrete example, if a user receives a question in a work messaging application during a meeting asking "How is the project progressing?", the system can automatically generate and send a response such as "It's progressing smoothly so far." On the other hand, in response to a question in a personal chat asking "Shall we get together this weekend?", it can send a neutral response such as "No plans have been made yet."

[0044] This invention will be a useful tool to support efficient and appropriate communication for users who need to process a large volume of messages on a daily basis.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user installs the application and enters their login information. This configures the user's personal settings.

[0048] Step 2:

[0049] The terminal sends user authentication information to the server to identify the user. The server provides the terminal with the necessary configuration data.

[0050] Step 3:

[0051] The device monitors message data in real time from external chat applications used by the user. This data includes the sender, message content, and timestamp.

[0052] Step 4:

[0053] The terminal prepares to send the received message data to the server.

[0054] Step 5:

[0055] The server uses a natural language processing engine to analyze the text based on the message data received from the terminal. This analysis helps to understand the context and content of the conversation.

[0056] Step 6:

[0057] The server generates response candidates based on the analysis results. These response candidates are optimized based on the user's past response patterns and context.

[0058] Step 7:

[0059] The device monitors messages to detect mentions and questions directed at the user, preparing to provide timely responses.

[0060] Step 8:

[0061] The server selects an appropriate response based on the mention or question detected by the terminal and sends it to the terminal. The terminal then automatically replies with that response on behalf of the user.

[0062] Step 9:

[0063] If the user wishes to intervene, the terminal will accept manual message input. This data is sent to the server and will be reflected in the subsequent flow of the conversation.

[0064] (Example 1)

[0065] 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."

[0066] In today's information society, individuals and organizations receive a large number of messages daily and are required to respond efficiently and appropriately. However, manual responses are time-consuming and resource-intensive, placing a burden on busy users. In particular, there is a risk of missing important messages and being unable to respond in the right time. An effective system is needed to address these issues.

[0067] 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.

[0068] In this invention, the server includes means for obtaining configuration information based on authentication data obtained from the user, means for collecting and transferring message information from external software, and means for analyzing the message information using natural language processing to generate response candidates. This enables rapid and accurate responses to a large volume of messages. Furthermore, by supporting manual operation and enabling flexible responses to user requests, the efficiency and accuracy of communication are improved.

[0069] A "user" is an individual or organization that uses this system to send and receive messages.

[0070] "Authentication data" refers to information used to verify a user's identity and is an element that enables user identification.

[0071] "Configuration information" refers to data that defines the customized operating environment and message processing policies for each user.

[0072] "External software" refers to messaging applications and information platforms that users use on a daily basis.

[0073] "Message information" refers to communication-related data, including text and metadata, obtained from external software.

[0074] "Natural language processing technology" refers to technologies that enable computers to analyze and understand human language, and are used to interpret the intent behind messages.

[0075] "Response candidates" refer to the multiple possible responses that the system generates in response to an analyzed message, all of which are considered appropriate.

[0076] "Manual operation" refers to actions taken by the user to decide on the content of their reply based on their own judgment, rather than accepting the automated response suggested by the system.

[0077] "Context" refers to the circumstances and background information surrounding the sending and receiving of a message, and serves as a reference when generating an appropriate response.

[0078] "Mention detection" is a process that recognizes when a specific user or subject is mentioned within a message and takes an appropriate response.

[0079] This invention is a system in which a user, a terminal, and a server cooperate to provide an environment in which the user can respond to messages quickly and appropriately. Specific embodiments are described in detail below.

[0080] System Overview

[0081] The user first installs the application on their device and enters their personal authentication data. This allows the device to connect to the server and retrieve the user's configuration information. This configuration information reflects the user's profile and communication preferences and becomes a customizable element of the entire system.

[0082] The terminal is responsible for retrieving message information in real time via APIs from external software that the user uses on a daily basis (e.g., email clients and messaging applications). The retrieved message information, including content and metadata, is then transferred to the server.

[0083] The server receives the forwarded message and analyzes its content using advanced natural language processing technology. This analysis process utilizes generative AI models to understand the message's intent and generate response candidates. The evaluation of the generated response candidates takes into account the user's context and past interactions, and the AI ​​selects the most appropriate response. This enables automatic and accurate replies even while the user is concentrating on other tasks.

[0084] Automated response and manual intervention

[0085] This system not only provides automated responses for sending and receiving messages, but also retains the flexibility to allow manual intervention by the user. In particular, for complex content that the system deems inappropriate, the user's manual response takes precedence. In this case, the terminal receives the user's input, sends it to the server to update the conversation flow, and uses this information to generate the next response.

[0086] Specific examples and prompt statements

[0087] For example, if a user receives the message "Please tell me about the project's progress" during a meeting, the system can automatically generate and send a response such as "It's going well at this stage." Furthermore, an example of a prompt for the generation AI model would be, "A new message has arrived. It says, 'How is the project progressing?' Please generate the best response." This would allow the system to obtain the desired response.

[0088] In this way, users are provided with an environment that allows them to efficiently manage a large volume of messages while responding immediately to important notifications and messages.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The user installs the application on their device and enters their personal authentication data. This causes the device to connect to the server and retrieve user configuration information. This information retrieval process involves receiving profile data based on the user's individual settings from the server, and then building an environment optimized for that user on the device side. The input authentication data is login information, and the output is user-specific configuration information.

[0092] Step 2:

[0093] The terminal is responsible for retrieving message information from external software used by the user via an API. The input data is communication data containing message content and metadata. The terminal sends this data to the server in real time. The output is the message information converted into the format transferred to the server.

[0094] Step 3:

[0095] The server analyzes the received message information using natural language processing techniques. It breaks down the message content received as input, interprets its context and intent, and generates response candidates using a generative AI model. The output is a list of multiple response candidates. Through this process, the server accurately understands the intent of the message and prepares the optimal response based on the prompt.

[0096] Step 4:

[0097] The server selects the most appropriate response from the generated response candidates based on the user's past interactions and contextual information. The input is a list of generated response candidates, and the output is the selected response. The selected response is automatically sent to the original external software via the terminal.

[0098] Step 5:

[0099] When a user intervenes manually, they can input directly on the terminal. The input data is the user's manual response. The terminal sends this data to the server, which updates the conversation flow, contributing to improvements in future response generation. The output is the updated conversation history. This allows the system to provide responses that better reflect the user's intent.

[0100] (Application Example 1)

[0101] 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."

[0102] In today's communication environment, users need to respond quickly to a large volume of messages. However, when the volume of messages is enormous, responding quickly and accurately becomes difficult, posing a significant challenge, especially in customer support for e-commerce sites. Therefore, there is a need for a system that can efficiently and automatically generate and immediately provide appropriate responses.

[0103] 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.

[0104] In this invention, the server includes means for performing individual settings based on authentication information obtained from the user, means for collecting and transmitting information data from external applications, means for analyzing the information content using natural language processing technology and generating response candidates, and means for automatically generating and quickly providing the optimal answer in a dialogue with the customer. As a result, users can respond efficiently even to large amounts of information, enabling immediate responses, especially in customer support for e-commerce sites.

[0105] "Authentication information obtained from the user" refers to the information necessary for the system to identify the user and configure individual settings.

[0106] "Means for collecting and transmitting information data from external applications" refers to a function that collects data from external information sources and sends it to a server.

[0107] "Natural language processing technology" is a technology for understanding, analyzing, and extracting meaning from human language.

[0108] "Means for generating response candidates" refers to the process of generating multiple responses based on collected information.

[0109] "A means of automatically generating and quickly providing the optimal answer in customer interactions" refers to a function that automatically creates and provides a prompt and appropriate response to customer inquiries.

[0110] This invention proposes a system for achieving efficient communication in customer support for e-commerce websites. The system mainly consists of the interaction of the user's terminal, a server, and external applications.

[0111] Users install an application on a device such as a smartphone and enter authentication information through this application. This allows the device to create a unique environment configured for each user. The device then collects information data in real time from external applications, including e-commerce sites, and sends it to the server. The server is built using Python and employs NLP libraries (e.g., spaCy, NLTK) as natural language processing technology.

[0112] The server analyzes the received information and automatically generates candidate responses using a generative AI model. The most appropriate response is selected from these multiple candidates and forwarded to the terminal. The terminal then quickly provides this optimal response to the customer, enabling a smooth conversation.

[0113] As a concrete example, consider a scenario where a customer inquires about the shipping status of their order. This system can automatically generate an appropriate response, such as "We are currently checking the shipping status of your order. Please wait a moment," and provide it to the customer quickly.

[0114] Example of a prompt

[0115] User inquiry: I would like to know the shipping status of my item.

[0116] Possible response: We are currently checking the shipping status of your order. Please wait a moment.

[0117] Input to the model: Generate appropriate responses to inquiries regarding the shipping status of products.

[0118] This system enables quick and accurate responses to the large volume of customer inquiries on e-commerce sites, contributing to improved customer satisfaction.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The user launches the application on their smartphone and enters their authentication information. This information is then sent to the server by the device. The server uses this information to retrieve individual user settings and prepares to build an environment optimized for the device. The input is the user's authentication information, and the output is individual settings data.

[0122] Step 2:

[0123] The terminal collects customer inquiries in real time through the e-commerce site. The collected inquiry data is sent to the server. In this process, data obtained from external applications is the input, and the data sent to the server is the output.

[0124] Step 3:

[0125] The server analyzes the received message data using natural language processing (NLP) technology (NLP library). Through this analysis, it understands the intent of the query and extracts relevant information. Data processing involves text analysis and structuring, and the interpreted content is generated as output.

[0126] Step 4:

[0127] The server generates response candidates using a generative AI model. The server takes the analyzed data as input and performs calculations to generate response candidates. The output consists of multiple response candidates, each represented in text format.

[0128] Step 5:

[0129] The server selects the best response from among several generated candidates. This selection is performed using an algorithm that evaluates the semantic relevance of the response candidates. The selected response is then sent to the terminal as output.

[0130] Step 6:

[0131] The terminal receives the optimal response sent from the server and displays it to the user or customer. In this process, the selected response is used as input, and the displayed data becomes the output. Specifically, the response is displayed on the screen, which the user or customer can then review.

[0132] By following these steps, users can efficiently provide automatically generated, optimal responses, creating an environment where customer inquiries can be addressed quickly.

[0133] 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.

[0134] This invention relates to a system that generates and adjusts response candidates by individually configuring settings based on authentication information obtained from the user, collecting message data from external applications, analyzing it using natural language processing, and further recognizing the user's emotions by combining it with an emotion engine. This system automatically provides appropriate responses tailored to the user's emotional state, thereby achieving more human-like communication.

[0135] Users first install the application on their device and then enter their login information to configure their settings individually. This configuration allows users to obtain a message management environment optimized for their needs.

[0136] The device receives message data from the external chat application the user is using. This message includes sender information, content, and a timestamp, and this data is transferred to the server.

[0137] The server analyzes the received message data using a natural language processing engine. Based on this analysis, it generates response candidates, while simultaneously evaluating the user's emotional state using an emotion engine. The emotion engine estimates the user's emotions from their past message history and the current context, and uses this information to select response candidates.

[0138] The generated response options are tailored to the user's emotional state and are sent on their behalf at the appropriate time. This process is fully automated, allowing for the most appropriate communication based on the situation without manual user intervention.

[0139] For example, if the system assumes the user is in a stressful environment, it might select a friendly response such as "Are you okay?". Conversely, if the user's emotions indicate frustration, a more cautious and encouraging response will be generated. In this way, the emotion engine contributes to improving the quality of communication.

[0140] This invention aims to provide a dynamic message response function that responds to the user's emotions, thereby realizing a richer user experience.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The user installs the application on their device and logs in by entering their authentication information. This process registers the user's specific settings with the server.

[0144] Step 2:

[0145] The device monitors messages from external chat applications used by the user in real time and retrieves received message data. This data includes sender information, message content, and timestamps.

[0146] Step 3:

[0147] The terminal sends the acquired message data to the server. The server analyzes the received messages using a natural language processing engine to understand the context of the conversation. This analysis includes keyword extraction and sentiment analysis.

[0148] Step 4:

[0149] The server uses an emotion engine to evaluate the user's emotional state. Based on past message history and the current context, it estimates the emotional state and determines what response is appropriate.

[0150] Step 5:

[0151] The server generates response candidates based on the analysis results and emotional state. It creates multiple candidates as natural responses that take emotions into consideration, and selects the most appropriate one from among them.

[0152] Step 6:

[0153] The terminal receives response candidates sent from the server and replies with the message on behalf of the user. This reply is timely and does not require any manual action from the user.

[0154] Step 7:

[0155] If the user wishes to intervene manually, the terminal will accept manual input from the user. This manual response takes priority and is sent to the server, where it will be reflected in the subsequent conversation.

[0156] Through these steps, the system can automatically provide sophisticated message responses tailored to the user's emotions.

[0157] (Example 2)

[0158] 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".

[0159] In recent years, with the increasing diversification of communication, there has been a growing need for systems that provide appropriate responses tailored to the user's emotional state. However, conventional systems have struggled to generate responses that take the user's emotions into account, resulting in only mechanical responses. Furthermore, there is a demand for systems that automatically provide user-appropriate responses without manual intervention.

[0160] 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.

[0161] In this invention, the server includes means for performing individual settings based on authentication information, means for collecting and transmitting message data from an information processing device, means for analyzing message content using natural language processing and generating response candidates, means for evaluating the user's emotional state based on past communication history and the current context, means for adjusting and selecting response candidates using a generative AI model, means for selecting and transmitting an automatic response at an appropriate time, and means for accepting manual intervention by the user. This makes it possible to provide human-like communication that takes the user's emotional state into consideration.

[0162] "Authentication information" refers to information necessary for a user to identify themselves and verify their access rights, and generally includes a username and password.

[0163] "Individual settings" refers to the process of adjusting the system to suit the individual user's requirements and usage.

[0164] An "information processing device" is a device that has the ability to process data and provide results, and includes terminals and servers.

[0165] "Message data" refers to text and other forms of information exchanged between users, and may include identification information and timestamps.

[0166] "Natural language processing" refers to the technology and processes by which computers understand, analyze, and generate human language.

[0167] "Emotional state" refers to the psychological or emotional state that a user is experiencing at a particular moment.

[0168] A "generative AI model" refers to an artificial intelligence model that can learn from large amounts of data and generate or analyze new data.

[0169] "Manual intervention" refers to the act of a user intentionally taking action or making adjustments to an automated process within a system.

[0170] This invention is built around a system that automatically generates and provides responses based on the user's emotional state. Users configure their settings by installing a dedicated application on their device and entering their login information. This individual configuration allows users to utilize a message management environment optimized for them.

[0171] The terminal is responsible for collecting message data from external information processing devices. The collected message data includes sender information, message content, and timestamps, and this data is sent to the server in real time. The server analyzes the received data using a natural language processing engine (e.g., SpaCy or NLTK) to extract the intent of the message. Using this analysis result, it generates response candidates using a generative AI model (e.g., open-source generative AI).

[0172] Next, the server evaluates the user's emotional state. Based on past communication history and conversation context, the emotion engine (e.g., emotion analysis API) estimates the user's emotional state. Based on this emotional information, the generative AI model refines response candidates and selects the one that is most appropriate for the user. The selected response is automatically sent through the terminal on behalf of the user.

[0173] For example, if a user sends a message such as "I'm tired today," the server uses a natural language processing engine to extract the emotion "tired," and an emotion engine recognizes the user's emotional state as fatigue. Based on this information, a generative AI model can generate a response such as "Why don't you take a short break?" and send it at the appropriate time.

[0174] As an example of a prompt, users can input something like, "What is an appropriate response if the user is feeling stressed?" and have the AI ​​model process it. In this way, the present invention provides dynamic responses that respond to the user's emotions, realizing more human-like communication.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] Users install a dedicated application on their device and log in by entering their username and password as authentication credentials. Based on this information, the device sends the user's individual settings data to the server. The server analyzes the received authentication information and creates or updates an optimized profile for each user. This allows users to obtain a personalized message management environment.

[0178] Step 2:

[0179] The terminal monitors message data from an external information processing device and receives it in real time. The received message data includes sender information, content, and a timestamp. The terminal organizes this data and transfers it to the server. The server stores the received message data in a database and prepares it for tracking and analysis.

[0180] Step 3:

[0181] The server analyzes the stored message data using a natural language processing engine. It uses the message content and its metadata as input to extract the message's intent and keywords. Through this analysis, the server generates information that forms the basis for potential responses. The results of this analysis are then used as output to proceed to the evaluation of the emotional state.

[0182] Step 4:

[0183] The server uses an emotion engine to evaluate the user's emotional state based on the analysis results. It uses past message history and the current context as input to infer the emotional state. The output of this process is an estimate of the user's psychological tendencies and emotional state, which is used to adjust the response candidates.

[0184] Step 5:

[0185] The server generates response candidates using a generative AI model and refines them based on the output of the sentiment engine. It prompts the generative AI model with input from natural language processing analysis results and sentiment evaluation results. It selects the most appropriate response from the generated candidates. The output is a response message that takes the user's emotions into consideration.

[0186] Step 6:

[0187] The server sends the selected response message to the terminal. The terminal automatically sends the received response to an external information processing device, communicating on behalf of the user. This response transmission is performed based on the configured appropriate timing.

[0188] (Application Example 2)

[0189] 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".

[0190] In content distribution services, there is a need to improve the quality of communication with users. However, conventional systems have difficulty responding in a way that fully considers user emotions and context, which can result in a poor user experience. Furthermore, it is difficult to respond appropriately to real-time feedback from users, and this needs to be addressed.

[0191] 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.

[0192] In this invention, the server includes means for performing individual settings based on authentication information obtained from the user, means for collecting and transmitting communication data from an external application, means for analyzing the communication content using natural language processing and generating response candidates, means including an emotion recognition engine that infers the user's emotional state and adjusts the response accordingly, and means for responding in real time. This makes it possible to automatically perform personalized communication in accordance with the user's emotions.

[0193] "Authentication information" refers to data used to identify a user and grant them access to specific services or applications.

[0194] "Individual settings" refer to settings that optimize the operation of the system and applications according to the user's specific needs and preferences.

[0195] "Communication data" refers to messages and information sent and received over a network, and includes content, sender information, timestamps, etc.

[0196] "Natural language processing" refers to the techniques and methodologies used to enable computers to understand and analyze human language.

[0197] A "response candidate" is a set of candidates that represent a portion of the system's responses to user input, used to select the optimal response.

[0198] An "emotion recognition engine" is a system or function that analyzes a user's emotional state and adjusts responses and service delivery methods based on the results.

[0199] "Real-time response" refers to a system function that provides an immediate response to user input or questions.

[0200] This invention is a system that generates automated responses tailored to the user's emotions. The terminal first obtains authentication information from the user and then performs individual configuration. This allows the user to obtain an optimized response environment.

[0201] The terminal also collects communication data from external applications. This data includes sender information, message content, and timestamps. This data is then processed on the server.

[0202] The server analyzes the content of the communication data using natural language processing technology. Natural language processing APIs such as Google Cloud Natural Language API can be used for the analysis. Based on the analysis results, response candidates are generated with the help of a generative AI model (e.g., GPT-3®).

[0203] Furthermore, the server incorporates an emotion recognition engine. This engine infers the user's emotions from their past communication history and current context. As a result, the generated response candidates are optimized for the user's emotional state and sent in real time at the appropriate time.

[0204] For example, if a user posts a negative message in the video's comment section, such as "This scene isn't funny," the system can respond with a positive message like, "What aspects would you like to see improved? Thank you for your feedback!" This improves the user experience.

[0205] A concrete example of a prompt would be: "The user evaluates their emotion and generates response options: If a comment like 'Wow! This scene is my favorite part.' is received, generate a response that matches that emotion."

[0206] This allows for more natural and smoother communication with users, potentially improving their satisfaction.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The device obtains authentication information from the user and performs individual configuration. It receives user authentication information as input and processes the data to obtain configuration information. As output, it generates user-optimized configuration information and saves it as a profile.

[0210] Step 2:

[0211] The terminal collects communication data from external applications. It receives communication data such as message content, sender information, and timestamps as input, and performs format conversion and data filtering on this data. As output, it generates data in a parseable format for transfer to the server.

[0212] Step 3:

[0213] The server analyzes the collected communication data using natural language processing. It receives communication data transferred from the terminal as input and uses a natural language processing engine to analyze the subject and intent of the message. As output, response candidates are determined based on the analysis results.

[0214] Step 4:

[0215] The server uses an emotion recognition engine to infer the user's emotional state. It utilizes analysis results and the user's past communication history as input to perform the necessary data calculations for emotion inference. The output generates response adjustment information appropriate to the user's emotional state.

[0216] Step 5:

[0217] The server generates appropriate responses using a generative AI model. It uses a refined response candidate and user sentiment information as input to create a prompt. The output is a response message optimized for the user's state.

[0218] Step 6:

[0219] The server automatically sends response messages at the appropriate time. Using the generated response messages as input and the data used to determine the appropriate sending timing, a response designed to improve the user experience is sent to the external application as output.

[0220] 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.

[0221] 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 those described above. 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 shown 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] This invention is a system designed to enable users to process messages efficiently and save time. This system is realized through the interaction of the user, terminal, and server.

[0237] When a user installs an application and enters their authentication information, the device obtains the user's personal settings data. This allows the device to create an environment optimized for each user.

[0238] The terminal retrieves message data in real time from external applications that the user uses on a daily basis. This data is sent to a server, where it is analyzed using advanced natural language processing technology.

[0239] The server analyzes the received message content and generates response candidates appropriate to the context. An algorithm is applied to automatically select the most appropriate response from a large number of candidates. This feature allows for automatic responses even while the user is concentrating on other activities.

[0240] This system also has the ability to determine timing; when it detects that a message has been addressed to a user, it sends a pre-generated response. This allows for immediate responses to user inquiries or important notifications.

[0241] Furthermore, manual intervention by the user is supported as needed. For example, manual responses take precedence over complex topics or matters that should not be automated by the system. In this case, the terminal receives user input and sends it to the server to update the conversation flow.

[0242] To give a concrete example, if a user receives a question in a work messaging application during a meeting asking "How is the project progressing?", the system can automatically generate and send a response such as "It's progressing smoothly so far." On the other hand, in response to a question in a personal chat asking "Shall we get together this weekend?", it can send a neutral response such as "No plans have been made yet."

[0243] This invention will be a useful tool to support efficient and appropriate communication for users who need to process a large volume of messages on a daily basis.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The user installs the application and enters their login information. This configures the user's personal settings.

[0247] Step 2:

[0248] The terminal sends user authentication information to the server to identify the user. The server provides the terminal with the necessary configuration data.

[0249] Step 3:

[0250] The device monitors message data in real time from external chat applications used by the user. This data includes the sender, message content, and timestamp.

[0251] Step 4:

[0252] The terminal prepares to send the received message data to the server.

[0253] Step 5:

[0254] The server uses a natural language processing engine to analyze the text based on the message data received from the terminal. This analysis helps to understand the context and content of the conversation.

[0255] Step 6:

[0256] The server generates response candidates based on the analysis results. These response candidates are optimized based on the user's past response patterns and context.

[0257] Step 7:

[0258] The device monitors messages to detect mentions and questions directed at the user, preparing to provide timely responses.

[0259] Step 8:

[0260] The server selects an appropriate response based on the mention or question detected by the terminal and sends it to the terminal. The terminal then automatically replies with that response on behalf of the user.

[0261] Step 9:

[0262] If the user wishes to intervene, the terminal will accept manual message input. This data is sent to the server and will be reflected in the subsequent flow of the conversation.

[0263] (Example 1)

[0264] 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."

[0265] In today's information society, individuals and organizations receive a large number of messages daily and are required to respond efficiently and appropriately. However, manual responses are time-consuming and resource-intensive, placing a burden on busy users. In particular, there is a risk of missing important messages and being unable to respond in the right time. An effective system is needed to address these issues.

[0266] 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.

[0267] In this invention, the server includes means for obtaining configuration information based on authentication data obtained from the user, means for collecting and transferring message information from external software, and means for analyzing the message information using natural language processing to generate response candidates. This enables rapid and accurate responses to a large volume of messages. Furthermore, by supporting manual operation and enabling flexible responses to user requests, the efficiency and accuracy of communication are improved.

[0268] A "user" is an individual or organization that uses this system to send and receive messages.

[0269] "Authentication data" refers to information used to verify a user's identity and is an element that enables user identification.

[0270] "Configuration information" refers to data that defines the customized operating environment and message processing policies for each user.

[0271] "External software" refers to messaging applications and information platforms that users use on a daily basis.

[0272] "Message information" refers to communication-related data, including text and metadata, obtained from external software.

[0273] "Natural language processing technology" refers to technologies that enable computers to analyze and understand human language, and are used to interpret the intent behind messages.

[0274] "Response candidates" refer to the multiple possible responses that the system generates in response to an analyzed message, all of which are considered appropriate.

[0275] "Manual operation" refers to actions taken by the user to decide on the content of their reply based on their own judgment, rather than accepting the automated response suggested by the system.

[0276] "Context" refers to the circumstances and background information surrounding the sending and receiving of a message, and serves as a reference when generating an appropriate response.

[0277] "Mention detection" is a process that recognizes when a specific user or subject is mentioned within a message and takes an appropriate response.

[0278] This invention is a system in which three parties, namely the user, the terminal, and the server, cooperate to operate, and it provides an environment in which the user can respond to messages quickly and appropriately. The specific embodiments thereof will be described in detail below.

[0279] Overview of the System

[0280] First, the user installs an application on the terminal and inputs personal authentication data. As a result, the terminal can connect to the server and obtain the user's setting information. This setting information reflects the user's profile and communication preferences and is an element that can be customized for the entire system.

[0281] The terminal is responsible for obtaining message information in real time from external software (e.g., email client or messaging application) that the user uses daily via an API. The obtained message information includes content and metadata and is transferred to the server.

[0282] The server receives the transferred message and analyzes its content using advanced natural language technology. In this analysis process, a generative AI model is utilized to understand the intention of the message and generate response candidates. When evaluating the generated response candidates, the user's context and past interactions are considered, and the AI selects the most appropriate response. This enables automatic and accurate replies even while the user is concentrating on other tasks.

[0283] Automatic Response and Manual Intervention

[0284] This system not only realizes automatic responses for message sending and receiving but also maintains the flexibility for the user to intervene manually. In particular, for complex content that the system determines is inappropriate, the user's manual response takes precedence. In this case, the terminal receives the user's input, sends it to the server to update the conversation flow, and uses it for the generation of the next response.

[0285] Specific Examples and Prompt Sentences

[0286] As a specific example, when a user receives a message "Please tell me the progress of the project" during a meeting, the system can automatically generate and send a response such as "It is going smoothly at the current stage". Also, as an example of a prompt sentence for the generation AI model, by inputting "A new message has arrived. The content is 'How is the progress of the project?' Please generate the optimal response.", the necessary response can be obtained.

[0287] In this way, an environment is provided where the user can efficiently manage a large number of messages and immediately respond to important notifications and messages.

[0288] The flow of the specific process in Example 1 will be described using FIG. 11.

[0289] Step 1:

[0290] The user installs the application on the terminal and inputs personal authentication data. Thereby, the terminal connects to the server and acquires user setting information. This information acquisition is a process of receiving profile data based on the user's individual settings from the server and constructing an environment optimized for the user on the terminal side. The input authentication data is login information, and the output is user-specific setting information.

[0291] Step 2:

[0292] The terminal is responsible for acquiring message information from external software used by the user via the API. The input data is communication data including message content and metadata. The terminal sends this data to the server in real time. The output is message information converted into the format transferred to the server.

[0293] Step 3:

[0294] The server analyzes the received message information using natural language processing techniques. It breaks down the message content received as input, interprets its context and intent, and generates response candidates using a generative AI model. The output is a list of multiple response candidates. Through this process, the server accurately understands the intent of the message and prepares the optimal response based on the prompt.

[0295] Step 4:

[0296] The server selects the most appropriate response from the generated response candidates based on the user's past interactions and contextual information. The input is a list of generated response candidates, and the output is the selected response. The selected response is automatically sent to the original external software via the terminal.

[0297] Step 5:

[0298] When a user intervenes manually, they can input directly on the terminal. The input data is the user's manual response. The terminal sends this data to the server, which updates the conversation flow, contributing to improvements in future response generation. The output is the updated conversation history. This allows the system to provide responses that better reflect the user's intent.

[0299] (Application Example 1)

[0300] 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."

[0301] In today's communication environment, users need to respond quickly to a large volume of messages. However, when the volume of messages is enormous, responding quickly and accurately becomes difficult, posing a significant challenge, especially in customer support for e-commerce sites. Therefore, there is a need for a system that can efficiently and automatically generate and immediately provide appropriate responses.

[0302] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0303] In this invention, the server includes means for performing individual settings based on authentication information acquired from a user, means for collecting and transmitting information data from an external application, means for analyzing the information content using natural language analysis technology and generating response candidates, and means for automatically generating and quickly providing an optimal answer in the dialogue with a customer. Thereby, the user can efficiently respond to a large amount of information, and in particular, immediate response in customer support of an e-commerce site becomes possible.

[0304] The "authentication information acquired from a user" is information necessary for the system to identify the user and perform individual settings.

[0305] The "means for collecting and transmitting information data from an external application" is a function for collecting data from an external information source and sending it to the server.

[0306] The "natural language analysis technology" is a technology for understanding, analyzing, and extracting the meaning of human language.

[0307] The "means for generating response candidates" is a process for generating a plurality of responses based on the collected information.

[0308] The "means for automatically generating and quickly providing an optimal answer in the dialogue with a customer" is a function for automatically creating and providing an appropriate reply promptly to an inquiry from a customer.

[0309] This invention proposes a system for realizing efficient communication in customer support of an e-commerce site. The system is mainly composed of the cooperation of a user's terminal, a server, and an external application.

[0310] Users install an application on a device such as a smartphone and enter authentication information through this application. This allows the device to create a unique environment configured for each user. The device then collects information data in real time from external applications, including e-commerce sites, and sends it to the server. The server is built using Python and employs NLP libraries (e.g., spaCy, NLTK) as natural language processing technology.

[0311] The server analyzes the received information and automatically generates candidate responses using a generative AI model. The most appropriate response is selected from these multiple candidates and forwarded to the terminal. The terminal then quickly provides this optimal response to the customer, enabling a smooth conversation.

[0312] As a concrete example, consider a scenario where a customer inquires about the shipping status of their order. This system can automatically generate an appropriate response, such as "We are currently checking the shipping status of your order. Please wait a moment," and provide it to the customer quickly.

[0313] Example of a prompt

[0314] User inquiry: I would like to know the shipping status of my item.

[0315] Possible response: We are currently checking the shipping status of your order. Please wait a moment.

[0316] Input to the model: Generate appropriate responses to inquiries regarding the shipping status of products.

[0317] This system enables quick and accurate responses to the large volume of customer inquiries on e-commerce sites, contributing to improved customer satisfaction.

[0318] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0319] Step 1:

[0320] The user launches the application on their smartphone and enters their authentication information. This information is then sent to the server by the device. The server uses this information to retrieve individual user settings and prepares to build an environment optimized for the device. The input is the user's authentication information, and the output is individual settings data.

[0321] Step 2:

[0322] The terminal collects customer inquiries in real time through the e-commerce site. The collected inquiry data is sent to the server. In this process, data obtained from external applications is the input, and the data sent to the server is the output.

[0323] Step 3:

[0324] The server analyzes the received message data using natural language processing (NLP) technology (NLP library). Through this analysis, it understands the intent of the query and extracts relevant information. Data processing involves text analysis and structuring, and the interpreted content is generated as output.

[0325] Step 4:

[0326] The server generates response candidates using a generative AI model. The server takes the analyzed data as input and performs calculations to generate response candidates. The output consists of multiple response candidates, each represented in text format.

[0327] Step 5:

[0328] The server selects the best response from among several generated candidates. This selection is performed using an algorithm that evaluates the semantic relevance of the response candidates. The selected response is then sent to the terminal as output.

[0329] Step 6:

[0330] The terminal receives the optimal response sent from the server and displays it to the user or customer. In this process, the selected response is used as input, and the displayed data becomes the output. Specifically, the response is displayed on the screen, which the user or customer can then review.

[0331] By following these steps, users can efficiently provide automatically generated, optimal responses, creating an environment where customer inquiries can be addressed quickly.

[0332] 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.

[0333] This invention relates to a system that generates and adjusts response candidates by individually configuring settings based on authentication information obtained from the user, collecting message data from external applications, analyzing it using natural language processing, and further recognizing the user's emotions by combining it with an emotion engine. This system automatically provides appropriate responses tailored to the user's emotional state, thereby achieving more human-like communication.

[0334] Users first install the application on their device and then enter their login information to configure their settings individually. This configuration allows users to obtain a message management environment optimized for their needs.

[0335] The device receives message data from the external chat application the user is using. This message includes sender information, content, and a timestamp, and this data is transferred to the server.

[0336] The server analyzes the received message data using a natural language processing engine. Based on this analysis, it generates response candidates, while simultaneously evaluating the user's emotional state using an emotion engine. The emotion engine estimates the user's emotions from their past message history and the current context, and uses this information to select response candidates.

[0337] The generated response options are tailored to the user's emotional state and are sent on their behalf at the appropriate time. This process is fully automated, allowing for the most appropriate communication based on the situation without manual user intervention.

[0338] For example, if the system assumes the user is in a stressful environment, it might select a friendly response such as "Are you okay?". Conversely, if the user's emotions indicate frustration, a more cautious and encouraging response will be generated. In this way, the emotion engine contributes to improving the quality of communication.

[0339] This invention aims to provide a dynamic message response function that responds to the user's emotions, thereby realizing a richer user experience.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] The user installs the application on their device and logs in by entering their authentication information. This process registers the user's specific settings with the server.

[0343] Step 2:

[0344] The device monitors messages from external chat applications used by the user in real time and retrieves received message data. This data includes sender information, message content, and timestamps.

[0345] Step 3:

[0346] The terminal sends the acquired message data to the server. The server analyzes the received messages using a natural language processing engine to understand the context of the conversation. This analysis includes keyword extraction and sentiment analysis.

[0347] Step 4:

[0348] The server uses an emotion engine to evaluate the user's emotional state. Based on past message history and the current context, it estimates the emotional state and determines what response is appropriate.

[0349] Step 5:

[0350] The server generates response candidates based on the analysis results and emotional state. It creates multiple candidates as natural responses that take emotions into consideration, and selects the most appropriate one from among them.

[0351] Step 6:

[0352] The terminal receives response candidates sent from the server and replies with the message on behalf of the user. This reply is timely and does not require any manual action from the user.

[0353] Step 7:

[0354] If the user wishes to intervene manually, the terminal will accept manual input from the user. This manual response takes priority and is sent to the server, where it will be reflected in the subsequent conversation.

[0355] Through these steps, the system can automatically provide sophisticated message responses tailored to the user's emotions.

[0356] (Example 2)

[0357] 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".

[0358] In recent years, with the increasing diversification of communication, there has been a growing need for systems that provide appropriate responses tailored to the user's emotional state. However, conventional systems have struggled to generate responses that take the user's emotions into account, resulting in only mechanical responses. Furthermore, there is a demand for systems that automatically provide user-appropriate responses without manual intervention.

[0359] 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.

[0360] In this invention, the server includes means for performing individual settings based on authentication information, means for collecting and transmitting message data from an information processing device, means for analyzing message content using natural language processing and generating response candidates, means for evaluating the user's emotional state based on past communication history and the current context, means for adjusting and selecting response candidates using a generative AI model, means for selecting and transmitting an automatic response at an appropriate time, and means for accepting manual intervention by the user. This makes it possible to provide human-like communication that takes the user's emotional state into consideration.

[0361] "Authentication information" refers to information necessary for a user to identify themselves and verify their access rights, and generally includes a username and password.

[0362] "Individual settings" refers to the process of adjusting the system to suit the individual user's requirements and usage.

[0363] An "information processing device" is a device that has the ability to process data and provide results, and includes terminals and servers.

[0364] "Message data" refers to text and other forms of information exchanged between users, and may include identification information and timestamps.

[0365] "Natural language processing" refers to the technology and processes by which computers understand, analyze, and generate human language.

[0366] "Emotional state" refers to the psychological or emotional state that a user is experiencing at a particular moment.

[0367] A "generative AI model" refers to an artificial intelligence model that can learn from large amounts of data and generate or analyze new data.

[0368] "Manual intervention" refers to the act of a user intentionally taking action or making adjustments to an automated process within a system.

[0369] This invention is built around a system that automatically generates and provides responses based on the user's emotional state. Users configure their settings by installing a dedicated application on their device and entering their login information. This individual configuration allows users to utilize a message management environment optimized for them.

[0370] The terminal is responsible for collecting message data from external information processing devices. The collected message data includes sender information, message content, and timestamps, and this data is sent to the server in real time. The server analyzes the received data using a natural language processing engine (e.g., SpaCy or NLTK) to extract the intent of the message. Using this analysis result, it generates response candidates using a generative AI model (e.g., open-source generative AI).

[0371] Next, the server evaluates the user's emotional state. Based on past communication history and conversation context, the emotion engine (e.g., emotion analysis API) estimates the user's emotional state. Based on this emotional information, the generative AI model refines response candidates and selects the one that is most appropriate for the user. The selected response is automatically sent through the terminal on behalf of the user.

[0372] For example, if a user sends a message such as "I'm tired today," the server uses a natural language processing engine to extract the emotion "tired," and an emotion engine recognizes the user's emotional state as fatigue. Based on this information, a generative AI model can generate a response such as "Why don't you take a short break?" and send it at the appropriate time.

[0373] As an example of a prompt, users can input something like, "What is an appropriate response if the user is feeling stressed?" and have the AI ​​model process it. In this way, the present invention provides dynamic responses that respond to the user's emotions, realizing more human-like communication.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] Users install a dedicated application on their device and log in by entering their username and password as authentication credentials. Based on this information, the device sends the user's individual settings data to the server. The server analyzes the received authentication information and creates or updates an optimized profile for each user. This allows users to obtain a personalized message management environment.

[0377] Step 2:

[0378] The terminal monitors message data from an external information processing device and receives it in real time. The received message data includes sender information, content, and a timestamp. The terminal organizes this data and transfers it to the server. The server stores the received message data in a database and prepares it for tracking and analysis.

[0379] Step 3:

[0380] The server analyzes the stored message data using a natural language processing engine. It uses the message content and its metadata as input to extract the message's intent and keywords. Through this analysis, the server generates information that forms the basis for potential responses. The results of this analysis are then used as output to proceed to the evaluation of the emotional state.

[0381] Step 4:

[0382] The server uses an emotion engine to evaluate the user's emotional state based on the analysis results. It uses past message history and the current context as input to infer the emotional state. The output of this process is an estimate of the user's psychological tendencies and emotional state, which is used to adjust the response candidates.

[0383] Step 5:

[0384] The server generates response candidates using a generative AI model and refines them based on the output of the sentiment engine. It prompts the generative AI model with input from natural language processing analysis results and sentiment evaluation results. It selects the most appropriate response from the generated candidates. The output is a response message that takes the user's emotions into consideration.

[0385] Step 6:

[0386] The server sends the selected response message to the terminal. The terminal automatically sends the received response to an external information processing device, communicating on behalf of the user. This response transmission is performed based on the configured appropriate timing.

[0387] (Application Example 2)

[0388] 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."

[0389] In content distribution services, there is a need to improve the quality of communication with users. However, conventional systems have difficulty responding in a way that fully considers user emotions and context, which can result in a poor user experience. Furthermore, it is difficult to respond appropriately to real-time feedback from users, and this needs to be addressed.

[0390] 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.

[0391] In this invention, the server includes means for performing individual settings based on authentication information obtained from the user, means for collecting and transmitting communication data from an external application, means for analyzing the communication content using natural language processing and generating response candidates, means including an emotion recognition engine that infers the user's emotional state and adjusts the response accordingly, and means for responding in real time. This makes it possible to automatically perform personalized communication in accordance with the user's emotions.

[0392] "Authentication information" refers to data used to identify a user and grant them access to specific services or applications.

[0393] "Individual settings" refer to settings that optimize the operation of the system and applications according to the user's specific needs and preferences.

[0394] "Communication data" refers to messages and information sent and received over a network, and includes content, sender information, timestamps, etc.

[0395] "Natural language processing" refers to the techniques and methodologies used to enable computers to understand and analyze human language.

[0396] A "response candidate" is a set of candidates that represent a portion of the system's responses to user input, used to select the optimal response.

[0397] An "emotion recognition engine" is a system or function that analyzes a user's emotional state and adjusts responses and service delivery methods based on the results.

[0398] "Real-time response" refers to a system function that provides an immediate response to user input or questions.

[0399] This invention is a system that generates automated responses tailored to the user's emotions. The terminal first obtains authentication information from the user and then performs individual configuration. This allows the user to obtain an optimized response environment.

[0400] The terminal also collects communication data from external applications. This data includes sender information, message content, and timestamps. This data is then processed on the server.

[0401] The server analyzes the content of the communication data using natural language processing (NLP) techniques. Natural language processing APIs such as the Google Cloud Natural Language API can be used for this analysis. Based on the analysis results, response candidates are generated with the help of a generative AI model (e.g., GPT-3).

[0402] Furthermore, the server incorporates an emotion recognition engine. This engine infers the user's emotions from their past communication history and current context. As a result, the generated response candidates are optimized for the user's emotional state and sent in real time at the appropriate time.

[0403] For example, if a user posts a negative message in the video's comment section, such as "This scene isn't funny," the system can respond with a positive message like, "What aspects would you like to see improved? Thank you for your feedback!" This improves the user experience.

[0404] A concrete example of a prompt would be: "The user evaluates their emotion and generates response options: If a comment like 'Wow! This scene is my favorite part.' is received, generate a response that matches that emotion."

[0405] This allows for more natural and smoother communication with users, potentially improving their satisfaction.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The device obtains authentication information from the user and performs individual configuration. It receives user authentication information as input and processes the data to obtain configuration information. As output, it generates user-optimized configuration information and saves it as a profile.

[0409] Step 2:

[0410] The terminal collects communication data from external applications. It receives communication data such as message content, sender information, and timestamps as input, and performs format conversion and data filtering on this data. As output, it generates data in a parseable format for transfer to the server.

[0411] Step 3:

[0412] The server analyzes the collected communication data using natural language processing. It receives communication data transferred from the terminal as input and uses a natural language processing engine to analyze the subject and intent of the message. As output, response candidates are determined based on the analysis results.

[0413] Step 4:

[0414] The server uses an emotion recognition engine to infer the user's emotional state. It utilizes analysis results and the user's past communication history as input to perform the necessary data calculations for emotion inference. The output generates response adjustment information appropriate to the user's emotional state.

[0415] Step 5:

[0416] The server generates appropriate responses using a generative AI model. It uses a refined response candidate and user sentiment information as input to create a prompt. The output is a response message optimized for the user's state.

[0417] Step 6:

[0418] The server automatically sends response messages at the appropriate time. Using the generated response messages as input and the data used to determine the appropriate sending timing, a response designed to improve the user experience is sent to the external application as output.

[0419] 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.

[0420] 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 those described above. 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 shown 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention is a system designed to enable users to process messages efficiently and save time. This system is realized through the interaction of the user, terminal, and server.

[0436] When a user installs an application and enters their authentication information, the device obtains the user's personal settings data. This allows the device to create an environment optimized for each user.

[0437] The terminal retrieves message data in real time from external applications that the user uses on a daily basis. This data is sent to a server, where it is analyzed using advanced natural language processing technology.

[0438] The server analyzes the received message content and generates response candidates appropriate to the context. An algorithm is applied to automatically select the most appropriate response from a large number of candidates. This feature allows for automatic responses even while the user is concentrating on other activities.

[0439] This system also has the ability to determine timing; when it detects that a message has been addressed to a user, it sends a pre-generated response. This allows for immediate responses to user inquiries or important notifications.

[0440] Furthermore, manual intervention by the user is supported as needed. For example, manual responses take precedence over complex topics or matters that should not be automated by the system. In this case, the terminal receives user input and sends it to the server to update the conversation flow.

[0441] To give a concrete example, if a user receives a question in a work messaging application during a meeting asking "How is the project progressing?", the system can automatically generate and send a response such as "It's progressing smoothly so far." On the other hand, in response to a question in a personal chat asking "Shall we get together this weekend?", it can send a neutral response such as "No plans have been made yet."

[0442] This invention will be a useful tool to support efficient and appropriate communication for users who need to process a large volume of messages on a daily basis.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The user installs the application and enters their login information. This configures the user's personal settings.

[0446] Step 2:

[0447] The terminal sends user authentication information to the server to identify the user. The server provides the terminal with the necessary configuration data.

[0448] Step 3:

[0449] The device monitors message data in real time from external chat applications used by the user. This data includes the sender, message content, and timestamp.

[0450] Step 4:

[0451] The terminal prepares to send the received message data to the server.

[0452] Step 5:

[0453] The server uses a natural language processing engine to analyze the text based on the message data received from the terminal. This analysis helps to understand the context and content of the conversation.

[0454] Step 6:

[0455] The server generates response candidates based on the analysis results. These response candidates are optimized based on the user's past response patterns and context.

[0456] Step 7:

[0457] The device monitors messages to detect mentions and questions directed at the user, preparing to provide timely responses.

[0458] Step 8:

[0459] The server selects an appropriate response based on the mention or question detected by the terminal and sends it to the terminal. The terminal then automatically replies with that response on behalf of the user.

[0460] Step 9:

[0461] If the user wishes to intervene, the terminal will accept manual message input. This data is sent to the server and will be reflected in the subsequent flow of the conversation.

[0462] (Example 1)

[0463] 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."

[0464] In today's information society, individuals and organizations receive a large number of messages daily and are required to respond efficiently and appropriately. However, manual responses are time-consuming and resource-intensive, placing a burden on busy users. In particular, there is a risk of missing important messages and being unable to respond in the right time. An effective system is needed to address these issues.

[0465] 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.

[0466] In this invention, the server includes means for obtaining configuration information based on authentication data obtained from the user, means for collecting and transferring message information from external software, and means for analyzing the message information using natural language processing to generate response candidates. This enables rapid and accurate responses to a large volume of messages. Furthermore, by supporting manual operation and enabling flexible responses to user requests, the efficiency and accuracy of communication are improved.

[0467] A "user" is an individual or organization that uses this system to send and receive messages.

[0468] "Authentication data" refers to information used to verify a user's identity and is an element that enables user identification.

[0469] "Configuration information" refers to data that defines the customized operating environment and message processing policies for each user.

[0470] "External software" refers to messaging applications and information platforms that users use on a daily basis.

[0471] "Message information" refers to communication-related data, including text and metadata, obtained from external software.

[0472] "Natural language processing technology" refers to technologies that enable computers to analyze and understand human language, and are used to interpret the intent behind messages.

[0473] "Response candidates" refer to the multiple possible responses that the system generates in response to an analyzed message, all of which are considered appropriate.

[0474] "Manual operation" refers to actions taken by the user to decide on the content of their reply based on their own judgment, rather than accepting the automated response suggested by the system.

[0475] "Context" refers to the circumstances and background information surrounding the sending and receiving of a message, and serves as a reference when generating an appropriate response.

[0476] "Mention detection" is a process that recognizes when a specific user or subject is mentioned within a message and takes an appropriate response.

[0477] This invention is a system in which a user, a terminal, and a server cooperate to provide an environment in which the user can respond to messages quickly and appropriately. Specific embodiments are described in detail below.

[0478] System Overview

[0479] The user first installs the application on their device and enters their personal authentication data. This allows the device to connect to the server and retrieve the user's configuration information. This configuration information reflects the user's profile and communication preferences and becomes a customizable element of the entire system.

[0480] The terminal is responsible for retrieving message information in real time via APIs from external software that the user uses on a daily basis (e.g., email clients and messaging applications). The retrieved message information, including content and metadata, is then transferred to the server.

[0481] The server receives the forwarded message and analyzes its content using advanced natural language processing technology. This analysis process utilizes generative AI models to understand the message's intent and generate response candidates. The evaluation of the generated response candidates takes into account the user's context and past interactions, and the AI ​​selects the most appropriate response. This enables automatic and accurate replies even while the user is concentrating on other tasks.

[0482] Automated response and manual intervention

[0483] This system not only provides automated responses for sending and receiving messages, but also retains the flexibility to allow manual intervention by the user. In particular, for complex content that the system deems inappropriate, the user's manual response takes precedence. In this case, the terminal receives the user's input, sends it to the server to update the conversation flow, and uses this information to generate the next response.

[0484] Specific examples and prompt statements

[0485] For example, if a user receives the message "Please tell me about the project's progress" during a meeting, the system can automatically generate and send a response such as "It's going well at this stage." Furthermore, an example of a prompt for the generation AI model would be, "A new message has arrived. It says, 'How is the project progressing?' Please generate the best response." This would allow the system to obtain the desired response.

[0486] In this way, users are provided with an environment that allows them to efficiently manage a large volume of messages while responding immediately to important notifications and messages.

[0487] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0488] Step 1:

[0489] The user installs the application on their device and enters their personal authentication data. This causes the device to connect to the server and retrieve user configuration information. This information retrieval process involves receiving profile data based on the user's individual settings from the server, and then building an environment optimized for that user on the device side. The input authentication data is login information, and the output is user-specific configuration information.

[0490] Step 2:

[0491] The terminal is responsible for retrieving message information from external software used by the user via an API. The input data is communication data containing message content and metadata. The terminal sends this data to the server in real time. The output is the message information converted into the format transferred to the server.

[0492] Step 3:

[0493] The server analyzes the received message information using natural language processing techniques. It breaks down the message content received as input, interprets its context and intent, and generates response candidates using a generative AI model. The output is a list of multiple response candidates. Through this process, the server accurately understands the intent of the message and prepares the optimal response based on the prompt.

[0494] Step 4:

[0495] The server selects the most appropriate response from the generated response candidates based on the user's past interactions and contextual information. The input is a list of generated response candidates, and the output is the selected response. The selected response is automatically sent to the original external software via the terminal.

[0496] Step 5:

[0497] When a user intervenes manually, they can input directly on the terminal. The input data is the user's manual response. The terminal sends this data to the server, which updates the conversation flow, contributing to improvements in future response generation. The output is the updated conversation history. This allows the system to provide responses that better reflect the user's intent.

[0498] (Application Example 1)

[0499] 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."

[0500] In today's communication environment, users need to respond quickly to a large volume of messages. However, when the volume of messages is enormous, responding quickly and accurately becomes difficult, posing a significant challenge, especially in customer support for e-commerce sites. Therefore, there is a need for a system that can efficiently and automatically generate and immediately provide appropriate responses.

[0501] 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.

[0502] In this invention, the server includes means for performing individual settings based on authentication information obtained from the user, means for collecting and transmitting information data from external applications, means for analyzing the information content using natural language processing technology and generating response candidates, and means for automatically generating and quickly providing the optimal answer in a dialogue with the customer. As a result, users can respond efficiently even to large amounts of information, enabling immediate responses, especially in customer support for e-commerce sites.

[0503] "Authentication information obtained from the user" refers to the information necessary for the system to identify the user and configure individual settings.

[0504] "Means for collecting and transmitting information data from external applications" refers to a function that collects data from external information sources and sends it to a server.

[0505] "Natural language processing technology" is a technology for understanding, analyzing, and extracting meaning from human language.

[0506] "Means for generating response candidates" refers to the process of generating multiple responses based on collected information.

[0507] "A means of automatically generating and quickly providing the optimal answer in customer interactions" refers to a function that automatically creates and provides a prompt and appropriate response to customer inquiries.

[0508] This invention proposes a system for achieving efficient communication in customer support for e-commerce websites. The system mainly consists of the interaction of the user's terminal, a server, and external applications.

[0509] Users install an application on a device such as a smartphone and enter authentication information through this application. This allows the device to create a unique environment configured for each user. The device then collects information data in real time from external applications, including e-commerce sites, and sends it to the server. The server is built using Python and employs NLP libraries (e.g., spaCy, NLTK) as natural language processing technology.

[0510] The server analyzes the received information and automatically generates candidate responses using a generative AI model. The most appropriate response is selected from these multiple candidates and forwarded to the terminal. The terminal then quickly provides this optimal response to the customer, enabling a smooth conversation.

[0511] As a concrete example, consider a scenario where a customer inquires about the shipping status of their order. This system can automatically generate an appropriate response, such as "We are currently checking the shipping status of your order. Please wait a moment," and provide it to the customer quickly.

[0512] Example of a prompt

[0513] User inquiry: I would like to know the shipping status of my item.

[0514] Possible response: We are currently checking the shipping status of your order. Please wait a moment.

[0515] Input to the model: Generate appropriate responses to inquiries regarding the shipping status of products.

[0516] This system enables quick and accurate responses to the large volume of customer inquiries on e-commerce sites, contributing to improved customer satisfaction.

[0517] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0518] Step 1:

[0519] The user launches the application on their smartphone and enters their authentication information. This information is then sent to the server by the device. The server uses this information to retrieve individual user settings and prepares to build an environment optimized for the device. The input is the user's authentication information, and the output is individual settings data.

[0520] Step 2:

[0521] The terminal collects customer inquiries in real time through the e-commerce site. The collected inquiry data is sent to the server. In this process, data obtained from external applications is the input, and the data sent to the server is the output.

[0522] Step 3:

[0523] The server analyzes the received message data using natural language processing (NLP) technology (NLP library). Through this analysis, it understands the intent of the query and extracts relevant information. Data processing involves text analysis and structuring, and the interpreted content is generated as output.

[0524] Step 4:

[0525] The server generates response candidates using a generative AI model. The server takes the analyzed data as input and performs calculations to generate response candidates. The output consists of multiple response candidates, each represented in text format.

[0526] Step 5:

[0527] The server selects the best response from among several generated candidates. This selection is performed using an algorithm that evaluates the semantic relevance of the response candidates. The selected response is then sent to the terminal as output.

[0528] Step 6:

[0529] The terminal receives the optimal response sent from the server and displays it to the user or customer. In this process, the selected response is used as input, and the displayed data becomes the output. Specifically, the response is displayed on the screen, which the user or customer can then review.

[0530] By following these steps, users can efficiently provide automatically generated, optimal responses, creating an environment where customer inquiries can be addressed quickly.

[0531] 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.

[0532] This invention relates to a system that generates and adjusts response candidates by individually configuring settings based on authentication information obtained from the user, collecting message data from external applications, analyzing it using natural language processing, and further recognizing the user's emotions by combining it with an emotion engine. This system automatically provides appropriate responses tailored to the user's emotional state, thereby achieving more human-like communication.

[0533] Users first install the application on their device and then enter their login information to configure their settings individually. This configuration allows users to obtain a message management environment optimized for their needs.

[0534] The device receives message data from the external chat application the user is using. This message includes sender information, content, and a timestamp, and this data is transferred to the server.

[0535] The server analyzes the received message data using a natural language processing engine. Based on this analysis, it generates response candidates, while simultaneously evaluating the user's emotional state using an emotion engine. The emotion engine estimates the user's emotions from their past message history and the current context, and uses this information to select response candidates.

[0536] The generated response options are tailored to the user's emotional state and are sent on their behalf at the appropriate time. This process is fully automated, allowing for the most appropriate communication based on the situation without manual user intervention.

[0537] For example, if the system assumes the user is in a stressful environment, it might select a friendly response such as "Are you okay?". Conversely, if the user's emotions indicate frustration, a more cautious and encouraging response will be generated. In this way, the emotion engine contributes to improving the quality of communication.

[0538] This invention aims to provide a dynamic message response function that responds to the user's emotions, thereby realizing a richer user experience.

[0539] The following describes the processing flow.

[0540] Step 1:

[0541] The user installs the application on their device and logs in by entering their authentication information. This process registers the user's specific settings with the server.

[0542] Step 2:

[0543] The device monitors messages from external chat applications used by the user in real time and retrieves received message data. This data includes sender information, message content, and timestamps.

[0544] Step 3:

[0545] The terminal sends the acquired message data to the server. The server analyzes the received messages using a natural language processing engine to understand the context of the conversation. This analysis includes keyword extraction and sentiment analysis.

[0546] Step 4:

[0547] The server uses an emotion engine to evaluate the user's emotional state. Based on past message history and the current context, it estimates the emotional state and determines what response is appropriate.

[0548] Step 5:

[0549] The server generates response candidates based on the analysis results and emotional state. It creates multiple candidates as natural responses that take emotions into consideration, and selects the most appropriate one from among them.

[0550] Step 6:

[0551] The terminal receives response candidates sent from the server and replies with the message on behalf of the user. This reply is timely and does not require any manual action from the user.

[0552] Step 7:

[0553] If the user wishes to intervene manually, the terminal will accept manual input from the user. This manual response takes priority and is sent to the server, where it will be reflected in the subsequent conversation.

[0554] Through these steps, the system can automatically provide sophisticated message responses tailored to the user's emotions.

[0555] (Example 2)

[0556] 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."

[0557] In recent years, with the increasing diversification of communication, there has been a growing need for systems that provide appropriate responses tailored to the user's emotional state. However, conventional systems have struggled to generate responses that take the user's emotions into account, resulting in only mechanical responses. Furthermore, there is a demand for systems that automatically provide user-appropriate responses without manual intervention.

[0558] 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.

[0559] In this invention, the server includes means for performing individual settings based on authentication information, means for collecting and transmitting message data from an information processing device, means for analyzing message content using natural language processing and generating response candidates, means for evaluating the user's emotional state based on past communication history and the current context, means for adjusting and selecting response candidates using a generative AI model, means for selecting and transmitting an automatic response at an appropriate time, and means for accepting manual intervention by the user. This makes it possible to provide human-like communication that takes the user's emotional state into consideration.

[0560] "Authentication information" refers to information necessary for a user to identify themselves and verify their access rights, and generally includes a username and password.

[0561] "Individual settings" refers to the process of adjusting the system to suit the individual user's requirements and usage.

[0562] An "information processing device" is a device that has the ability to process data and provide results, and includes terminals and servers.

[0563] "Message data" refers to text and other forms of information exchanged between users, and may include identification information and timestamps.

[0564] "Natural language processing" refers to the technology and processes by which computers understand, analyze, and generate human language.

[0565] "Emotional state" refers to the psychological or emotional state that a user is experiencing at a particular moment.

[0566] A "generative AI model" refers to an artificial intelligence model that can learn from large amounts of data and generate or analyze new data.

[0567] "Manual intervention" refers to the act of a user intentionally taking action or making adjustments to an automated process within a system.

[0568] This invention is built around a system that automatically generates and provides responses based on the user's emotional state. Users configure their settings by installing a dedicated application on their device and entering their login information. This individual configuration allows users to utilize a message management environment optimized for them.

[0569] The terminal is responsible for collecting message data from external information processing devices. The collected message data includes sender information, message content, and timestamps, and this data is sent to the server in real time. The server analyzes the received data using a natural language processing engine (e.g., SpaCy or NLTK) to extract the intent of the message. Using this analysis result, it generates response candidates using a generative AI model (e.g., open-source generative AI).

[0570] Next, the server evaluates the user's emotional state. Based on past communication history and conversation context, the emotion engine (e.g., emotion analysis API) estimates the user's emotional state. Based on this emotional information, the generative AI model refines response candidates and selects the one that is most appropriate for the user. The selected response is automatically sent through the terminal on behalf of the user.

[0571] For example, if a user sends a message such as "I'm tired today," the server uses a natural language processing engine to extract the emotion "tired," and an emotion engine recognizes the user's emotional state as fatigue. Based on this information, a generative AI model can generate a response such as "Why don't you take a short break?" and send it at the appropriate time.

[0572] As an example of a prompt, users can input something like, "What is an appropriate response if the user is feeling stressed?" and have the AI ​​model process it. In this way, the present invention provides dynamic responses that respond to the user's emotions, realizing more human-like communication.

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1:

[0575] Users install a dedicated application on their device and log in by entering their username and password as authentication credentials. Based on this information, the device sends the user's individual settings data to the server. The server analyzes the received authentication information and creates or updates an optimized profile for each user. This allows users to obtain a personalized message management environment.

[0576] Step 2:

[0577] The terminal monitors message data from an external information processing device and receives it in real time. The received message data includes sender information, content, and a timestamp. The terminal organizes this data and transfers it to the server. The server stores the received message data in a database and prepares it for tracking and analysis.

[0578] Step 3:

[0579] The server analyzes the stored message data using a natural language processing engine. It uses the message content and its metadata as input to extract the message's intent and keywords. Through this analysis, the server generates information that forms the basis for potential responses. The results of this analysis are then used as output to proceed to the evaluation of the emotional state.

[0580] Step 4:

[0581] The server uses an emotion engine to evaluate the user's emotional state based on the analysis results. It uses past message history and the current context as input to infer the emotional state. The output of this process is an estimate of the user's psychological tendencies and emotional state, which is used to adjust the response candidates.

[0582] Step 5:

[0583] The server generates response candidates using a generative AI model and refines them based on the output of the sentiment engine. It prompts the generative AI model with input from natural language processing analysis results and sentiment evaluation results. It selects the most appropriate response from the generated candidates. The output is a response message that takes the user's emotions into consideration.

[0584] Step 6:

[0585] The server sends the selected response message to the terminal. The terminal automatically sends the received response to an external information processing device, communicating on behalf of the user. This response transmission is performed based on the configured appropriate timing.

[0586] (Application Example 2)

[0587] 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."

[0588] In content distribution services, there is a need to improve the quality of communication with users. However, conventional systems have difficulty responding in a way that fully considers user emotions and context, which can result in a poor user experience. Furthermore, it is difficult to respond appropriately to real-time feedback from users, and this needs to be addressed.

[0589] 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.

[0590] In this invention, the server includes means for performing individual settings based on authentication information obtained from the user, means for collecting and transmitting communication data from an external application, means for analyzing the communication content using natural language processing and generating response candidates, means including an emotion recognition engine that infers the user's emotional state and adjusts the response accordingly, and means for responding in real time. This makes it possible to automatically perform personalized communication in accordance with the user's emotions.

[0591] "Authentication information" refers to data used to identify a user and grant them access to specific services or applications.

[0592] "Individual settings" refer to settings that optimize the operation of the system and applications according to the user's specific needs and preferences.

[0593] "Communication data" refers to messages and information sent and received over a network, and includes content, sender information, timestamps, etc.

[0594] "Natural language processing" refers to the techniques and methodologies used to enable computers to understand and analyze human language.

[0595] A "response candidate" is a set of candidates that represent a portion of the system's responses to user input, used to select the optimal response.

[0596] An "emotion recognition engine" is a system or function that analyzes a user's emotional state and adjusts responses and service delivery methods based on the results.

[0597] "Real-time response" refers to a system function that provides an immediate response to user input or questions.

[0598] This invention is a system that generates automated responses tailored to the user's emotions. The terminal first obtains authentication information from the user and then performs individual configuration. This allows the user to obtain an optimized response environment.

[0599] The terminal also collects communication data from external applications. This data includes sender information, message content, and timestamps. This data is then processed on the server.

[0600] The server analyzes the content of the communication data using natural language processing (NLP) techniques. Natural language processing APIs such as the Google Cloud Natural Language API can be used for this analysis. Based on the analysis results, response candidates are generated with the help of a generative AI model (e.g., GPT-3).

[0601] Furthermore, the server incorporates an emotion recognition engine. This engine infers the user's emotions from their past communication history and current context. As a result, the generated response candidates are optimized for the user's emotional state and sent in real time at the appropriate time.

[0602] For example, if a user posts a negative message in the video's comment section, such as "This scene isn't funny," the system can respond with a positive message like, "What aspects would you like to see improved? Thank you for your feedback!" This improves the user experience.

[0603] A concrete example of a prompt would be: "The user evaluates their emotion and generates response options: If a comment like 'Wow! This scene is my favorite part.' is received, generate a response that matches that emotion."

[0604] This allows for more natural and smoother communication with users, potentially improving their satisfaction.

[0605] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0606] Step 1:

[0607] The device obtains authentication information from the user and performs individual configuration. It receives user authentication information as input and processes the data to obtain configuration information. As output, it generates user-optimized configuration information and saves it as a profile.

[0608] Step 2:

[0609] The terminal collects communication data from external applications. It receives communication data such as message content, sender information, and timestamps as input, and performs format conversion and data filtering on this data. As output, it generates data in a parseable format for transfer to the server.

[0610] Step 3:

[0611] The server analyzes the collected communication data using natural language processing. It receives communication data transferred from the terminal as input and uses a natural language processing engine to analyze the subject and intent of the message. As output, response candidates are determined based on the analysis results.

[0612] Step 4:

[0613] The server uses an emotion recognition engine to infer the user's emotional state. It utilizes analysis results and the user's past communication history as input to perform the necessary data calculations for emotion inference. The output generates response adjustment information appropriate to the user's emotional state.

[0614] Step 5:

[0615] The server generates appropriate responses using a generative AI model. It uses a refined response candidate and user sentiment information as input to create a prompt. The output is a response message optimized for the user's state.

[0616] Step 6:

[0617] The server automatically sends response messages at the appropriate time. Using the generated response messages as input and the data used to determine the appropriate sending timing, a response designed to improve the user experience is sent to the external application as output.

[0618] 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.

[0619] 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 those described above. 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 shown 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.

[0620] 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.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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".

[0635] This invention is a system designed to enable users to process messages efficiently and save time. This system is realized through the interaction of the user, terminal, and server.

[0636] When a user installs an application and enters their authentication information, the device obtains the user's personal settings data. This allows the device to create an environment optimized for each user.

[0637] The terminal retrieves message data in real time from external applications that the user uses on a daily basis. This data is sent to a server, where it is analyzed using advanced natural language processing technology.

[0638] The server analyzes the received message content and generates response candidates appropriate to the context. An algorithm is applied to automatically select the most appropriate response from a large number of candidates. This feature allows for automatic responses even while the user is concentrating on other activities.

[0639] This system also has the ability to determine timing; when it detects that a message has been addressed to a user, it sends a pre-generated response. This allows for immediate responses to user inquiries or important notifications.

[0640] Furthermore, manual intervention by the user is supported as needed. For example, manual responses take precedence over complex topics or matters that should not be automated by the system. In this case, the terminal receives user input and sends it to the server to update the conversation flow.

[0641] To give a concrete example, if a user receives a question in a work messaging application during a meeting asking "How is the project progressing?", the system can automatically generate and send a response such as "It's progressing smoothly so far." On the other hand, in response to a question in a personal chat asking "Shall we get together this weekend?", it can send a neutral response such as "No plans have been made yet."

[0642] This invention will be a useful tool to support efficient and appropriate communication for users who need to process a large volume of messages on a daily basis.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The user installs the application and enters their login information. This configures the user's personal settings.

[0646] Step 2:

[0647] The terminal sends user authentication information to the server to identify the user. The server provides the terminal with the necessary configuration data.

[0648] Step 3:

[0649] The device monitors message data in real time from external chat applications used by the user. This data includes the sender, message content, and timestamp.

[0650] Step 4:

[0651] The terminal prepares to send the received message data to the server.

[0652] Step 5:

[0653] The server uses a natural language processing engine to analyze the text based on the message data received from the terminal. This analysis helps to understand the context and content of the conversation.

[0654] Step 6:

[0655] The server generates response candidates based on the analysis results. These response candidates are optimized based on the user's past response patterns and context.

[0656] Step 7:

[0657] The device monitors messages to detect mentions and questions directed at the user, preparing to provide timely responses.

[0658] Step 8:

[0659] The server selects an appropriate response based on the mention or question detected by the terminal and sends it to the terminal. The terminal then automatically replies with that response on behalf of the user.

[0660] Step 9:

[0661] If the user wishes to intervene, the terminal will accept manual message input. This data is sent to the server and will be reflected in the subsequent flow of the conversation.

[0662] (Example 1)

[0663] 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".

[0664] In today's information society, individuals and organizations receive a large number of messages daily and are required to respond efficiently and appropriately. However, manual responses are time-consuming and resource-intensive, placing a burden on busy users. In particular, there is a risk of missing important messages and being unable to respond in the right time. An effective system is needed to address these issues.

[0665] 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.

[0666] In this invention, the server includes means for obtaining configuration information based on authentication data obtained from the user, means for collecting and transferring message information from external software, and means for analyzing the message information using natural language processing to generate response candidates. This enables rapid and accurate responses to a large volume of messages. Furthermore, by supporting manual operation and enabling flexible responses to user requests, the efficiency and accuracy of communication are improved.

[0667] A "user" is an individual or organization that uses this system to send and receive messages.

[0668] "Authentication data" refers to information used to verify a user's identity and is an element that enables user identification.

[0669] "Configuration information" refers to data that defines the customized operating environment and message processing policies for each user.

[0670] "External software" refers to messaging applications and information platforms that users use on a daily basis.

[0671] "Message information" refers to communication-related data, including text and metadata, obtained from external software.

[0672] "Natural language processing technology" refers to technologies that enable computers to analyze and understand human language, and are used to interpret the intent behind messages.

[0673] "Response candidates" refer to the multiple possible responses that the system generates in response to an analyzed message, all of which are considered appropriate.

[0674] "Manual operation" refers to actions taken by the user to decide on the content of their reply based on their own judgment, rather than accepting the automated response suggested by the system.

[0675] "Context" refers to the circumstances and background information surrounding the sending and receiving of a message, and serves as a reference when generating an appropriate response.

[0676] "Mention detection" is a process that recognizes when a specific user or subject is mentioned within a message and takes an appropriate response.

[0677] This invention is a system in which a user, a terminal, and a server cooperate to provide an environment in which the user can respond to messages quickly and appropriately. Specific embodiments are described in detail below.

[0678] System Overview

[0679] The user first installs the application on their device and enters their personal authentication data. This allows the device to connect to the server and retrieve the user's configuration information. This configuration information reflects the user's profile and communication preferences and becomes a customizable element of the entire system.

[0680] The terminal is responsible for retrieving message information in real time via APIs from external software that the user uses on a daily basis (e.g., email clients and messaging applications). The retrieved message information, including content and metadata, is then transferred to the server.

[0681] The server receives the forwarded message and analyzes its content using advanced natural language processing technology. This analysis process utilizes generative AI models to understand the message's intent and generate response candidates. The evaluation of the generated response candidates takes into account the user's context and past interactions, and the AI ​​selects the most appropriate response. This enables automatic and accurate replies even while the user is concentrating on other tasks.

[0682] Automated response and manual intervention

[0683] This system not only provides automated responses for sending and receiving messages, but also retains the flexibility to allow manual intervention by the user. In particular, for complex content that the system deems inappropriate, the user's manual response takes precedence. In this case, the terminal receives the user's input, sends it to the server to update the conversation flow, and uses this information to generate the next response.

[0684] Specific examples and prompt statements

[0685] For example, if a user receives the message "Please tell me about the project's progress" during a meeting, the system can automatically generate and send a response such as "It's going well at this stage." Furthermore, an example of a prompt for the generation AI model would be, "A new message has arrived. It says, 'How is the project progressing?' Please generate the best response." This would allow the system to obtain the desired response.

[0686] In this way, users are provided with an environment that allows them to efficiently manage a large volume of messages while responding immediately to important notifications and messages.

[0687] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0688] Step 1:

[0689] The user installs the application on their device and enters their personal authentication data. This causes the device to connect to the server and retrieve user configuration information. This information retrieval process involves receiving profile data based on the user's individual settings from the server, and then building an environment optimized for that user on the device side. The input authentication data is login information, and the output is user-specific configuration information.

[0690] Step 2:

[0691] The terminal is responsible for retrieving message information from external software used by the user via an API. The input data is communication data containing message content and metadata. The terminal sends this data to the server in real time. The output is the message information converted into the format transferred to the server.

[0692] Step 3:

[0693] The server analyzes the received message information using natural language processing techniques. It breaks down the message content received as input, interprets its context and intent, and generates response candidates using a generative AI model. The output is a list of multiple response candidates. Through this process, the server accurately understands the intent of the message and prepares the optimal response based on the prompt.

[0694] Step 4:

[0695] The server selects the most appropriate response from the generated response candidates based on the user's past interactions and contextual information. The input is a list of generated response candidates, and the output is the selected response. The selected response is automatically sent to the original external software via the terminal.

[0696] Step 5:

[0697] When a user intervenes manually, they can input directly on the terminal. The input data is the user's manual response. The terminal sends this data to the server, which updates the conversation flow, contributing to improvements in future response generation. The output is the updated conversation history. This allows the system to provide responses that better reflect the user's intent.

[0698] (Application Example 1)

[0699] 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".

[0700] In today's communication environment, users need to respond quickly to a large volume of messages. However, when the volume of messages is enormous, responding quickly and accurately becomes difficult, posing a significant challenge, especially in customer support for e-commerce sites. Therefore, there is a need for a system that can efficiently and automatically generate and immediately provide appropriate responses.

[0701] 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.

[0702] In this invention, the server includes means for performing individual settings based on authentication information obtained from the user, means for collecting and transmitting information data from external applications, means for analyzing the information content using natural language processing technology and generating response candidates, and means for automatically generating and quickly providing the optimal answer in a dialogue with the customer. As a result, users can respond efficiently even to large amounts of information, enabling immediate responses, especially in customer support for e-commerce sites.

[0703] "Authentication information obtained from the user" refers to the information necessary for the system to identify the user and configure individual settings.

[0704] "Means for collecting and transmitting information data from external applications" refers to a function that collects data from external information sources and sends it to a server.

[0705] "Natural language processing technology" is a technology for understanding, analyzing, and extracting meaning from human language.

[0706] "Means for generating response candidates" refers to the process of generating multiple responses based on collected information.

[0707] "A means of automatically generating and quickly providing the optimal answer in customer interactions" refers to a function that automatically creates and provides a prompt and appropriate response to customer inquiries.

[0708] This invention proposes a system for achieving efficient communication in customer support for e-commerce websites. The system mainly consists of the interaction of the user's terminal, a server, and external applications.

[0709] Users install an application on a device such as a smartphone and enter authentication information through this application. This allows the device to create a unique environment configured for each user. The device then collects information data in real time from external applications, including e-commerce sites, and sends it to the server. The server is built using Python and employs NLP libraries (e.g., spaCy, NLTK) as natural language processing technology.

[0710] The server analyzes the received information and automatically generates candidate responses using a generative AI model. The most appropriate response is selected from these multiple candidates and forwarded to the terminal. The terminal then quickly provides this optimal response to the customer, enabling a smooth conversation.

[0711] As a concrete example, consider a scenario where a customer inquires about the shipping status of their order. This system can automatically generate an appropriate response, such as "We are currently checking the shipping status of your order. Please wait a moment," and provide it to the customer quickly.

[0712] Example of a prompt

[0713] User inquiry: I would like to know the shipping status of my item.

[0714] Possible response: We are currently checking the shipping status of your order. Please wait a moment.

[0715] Input to the model: Generate appropriate responses to inquiries regarding the shipping status of products.

[0716] This system enables quick and accurate responses to the large volume of customer inquiries on e-commerce sites, contributing to improved customer satisfaction.

[0717] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0718] Step 1:

[0719] The user launches the application on their smartphone and enters their authentication information. This information is then sent to the server by the device. The server uses this information to retrieve individual user settings and prepares to build an environment optimized for the device. The input is the user's authentication information, and the output is individual settings data.

[0720] Step 2:

[0721] The terminal collects customer inquiries in real time through the e-commerce site. The collected inquiry data is sent to the server. In this process, data obtained from external applications is the input, and the data sent to the server is the output.

[0722] Step 3:

[0723] The server analyzes the received message data using natural language processing (NLP) technology (NLP library). Through this analysis, it understands the intent of the query and extracts relevant information. Data processing involves text analysis and structuring, and the interpreted content is generated as output.

[0724] Step 4:

[0725] The server generates response candidates using a generative AI model. The server takes the analyzed data as input and performs calculations to generate response candidates. The output consists of multiple response candidates, each represented in text format.

[0726] Step 5:

[0727] The server selects the best response from among several generated candidates. This selection is performed using an algorithm that evaluates the semantic relevance of the response candidates. The selected response is then sent to the terminal as output.

[0728] Step 6:

[0729] The terminal receives the optimal response sent from the server and displays it to the user or customer. In this process, the selected response is used as input, and the displayed data becomes the output. Specifically, the response is displayed on the screen, which the user or customer can then review.

[0730] By following these steps, users can efficiently provide automatically generated, optimal responses, creating an environment where customer inquiries can be addressed quickly.

[0731] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0732] This invention relates to a system that generates and adjusts response candidates by individually configuring settings based on authentication information obtained from the user, collecting message data from external applications, analyzing it using natural language processing, and further recognizing the user's emotions by combining it with an emotion engine. This system automatically provides appropriate responses tailored to the user's emotional state, thereby achieving more human-like communication.

[0733] Users first install the application on their device and then enter their login information to configure their settings individually. This configuration allows users to obtain a message management environment optimized for their needs.

[0734] The device receives message data from the external chat application the user is using. This message includes sender information, content, and a timestamp, and this data is transferred to the server.

[0735] The server analyzes the received message data using a natural language processing engine. Based on this analysis, it generates response candidates, while simultaneously evaluating the user's emotional state using an emotion engine. The emotion engine estimates the user's emotions from their past message history and the current context, and uses this information to select response candidates.

[0736] The generated response options are tailored to the user's emotional state and are sent on their behalf at the appropriate time. This process is fully automated, allowing for the most appropriate communication based on the situation without manual user intervention.

[0737] For example, if the system assumes the user is in a stressful environment, it might select a friendly response such as "Are you okay?". Conversely, if the user's emotions indicate frustration, a more cautious and encouraging response will be generated. In this way, the emotion engine contributes to improving the quality of communication.

[0738] This invention aims to provide a dynamic message response function that responds to the user's emotions, thereby realizing a richer user experience.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] The user installs the application on their device and logs in by entering their authentication information. This process registers the user's specific settings with the server.

[0742] Step 2:

[0743] The device monitors messages from external chat applications used by the user in real time and retrieves received message data. This data includes sender information, message content, and timestamps.

[0744] Step 3:

[0745] The terminal sends the acquired message data to the server. The server analyzes the received messages using a natural language processing engine to understand the context of the conversation. This analysis includes keyword extraction and sentiment analysis.

[0746] Step 4:

[0747] The server uses an emotion engine to evaluate the user's emotional state. Based on past message history and the current context, it estimates the emotional state and determines what response is appropriate.

[0748] Step 5:

[0749] The server generates response candidates based on the analysis results and emotional state. It creates multiple candidates as natural responses that take emotions into consideration, and selects the most appropriate one from among them.

[0750] Step 6:

[0751] The terminal receives response candidates sent from the server and replies with the message on behalf of the user. This reply is timely and does not require any manual action from the user.

[0752] Step 7:

[0753] If the user wishes to intervene manually, the terminal will accept manual input from the user. This manual response takes priority and is sent to the server, where it will be reflected in the subsequent conversation.

[0754] Through these steps, the system can automatically provide sophisticated message responses tailored to the user's emotions.

[0755] (Example 2)

[0756] 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".

[0757] In recent years, with the increasing diversification of communication, there has been a growing need for systems that provide appropriate responses tailored to the user's emotional state. However, conventional systems have struggled to generate responses that take the user's emotions into account, resulting in only mechanical responses. Furthermore, there is a demand for systems that automatically provide user-appropriate responses without manual intervention.

[0758] 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.

[0759] In this invention, the server includes means for performing individual settings based on authentication information, means for collecting and transmitting message data from an information processing device, means for analyzing message content using natural language processing and generating response candidates, means for evaluating the user's emotional state based on past communication history and the current context, means for adjusting and selecting response candidates using a generative AI model, means for selecting and transmitting an automatic response at an appropriate time, and means for accepting manual intervention by the user. This makes it possible to provide human-like communication that takes the user's emotional state into consideration.

[0760] "Authentication information" refers to information necessary for a user to identify themselves and verify their access rights, and generally includes a username and password.

[0761] "Individual settings" refers to the process of adjusting the system to suit the individual user's requirements and usage.

[0762] An "information processing device" is a device that has the ability to process data and provide results, and includes terminals and servers.

[0763] "Message data" refers to text and other forms of information exchanged between users, and may include identification information and timestamps.

[0764] "Natural language processing" refers to the technology and processes by which computers understand, analyze, and generate human language.

[0765] "Emotional state" refers to the psychological or emotional state that a user is experiencing at a particular moment.

[0766] A "generative AI model" refers to an artificial intelligence model that can learn from large amounts of data and generate or analyze new data.

[0767] "Manual intervention" refers to the act of a user intentionally taking action or making adjustments to an automated process within a system.

[0768] This invention is built around a system that automatically generates and provides responses based on the user's emotional state. Users configure their settings by installing a dedicated application on their device and entering their login information. This individual configuration allows users to utilize a message management environment optimized for them.

[0769] The terminal is responsible for collecting message data from external information processing devices. The collected message data includes sender information, message content, and timestamps, and this data is sent to the server in real time. The server analyzes the received data using a natural language processing engine (e.g., SpaCy or NLTK) to extract the intent of the message. Using this analysis result, it generates response candidates using a generative AI model (e.g., open-source generative AI).

[0770] Next, the server evaluates the user's emotional state. Based on past communication history and conversation context, the emotion engine (e.g., emotion analysis API) estimates the user's emotional state. Based on this emotional information, the generative AI model refines response candidates and selects the one that is most appropriate for the user. The selected response is automatically sent through the terminal on behalf of the user.

[0771] For example, if a user sends a message such as "I'm tired today," the server uses a natural language processing engine to extract the emotion "tired," and an emotion engine recognizes the user's emotional state as fatigue. Based on this information, a generative AI model can generate a response such as "Why don't you take a short break?" and send it at the appropriate time.

[0772] As an example of a prompt, users can input something like, "What is an appropriate response if the user is feeling stressed?" and have the AI ​​model process it. In this way, the present invention provides dynamic responses that respond to the user's emotions, realizing more human-like communication.

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

[0774] Step 1:

[0775] Users install a dedicated application on their device and log in by entering their username and password as authentication credentials. Based on this information, the device sends the user's individual settings data to the server. The server analyzes the received authentication information and creates or updates an optimized profile for each user. This allows users to obtain a personalized message management environment.

[0776] Step 2:

[0777] The terminal monitors message data from an external information processing device and receives it in real time. The received message data includes sender information, content, and a timestamp. The terminal organizes this data and transfers it to the server. The server stores the received message data in a database and prepares it for tracking and analysis.

[0778] Step 3:

[0779] The server analyzes the stored message data using a natural language processing engine. It uses the message content and its metadata as input to extract the message's intent and keywords. Through this analysis, the server generates information that forms the basis for potential responses. The results of this analysis are then used as output to proceed to the evaluation of the emotional state.

[0780] Step 4:

[0781] The server uses an emotion engine to evaluate the user's emotional state based on the analysis results. It uses past message history and the current context as input to infer the emotional state. The output of this process is an estimate of the user's psychological tendencies and emotional state, which is used to adjust the response candidates.

[0782] Step 5:

[0783] The server generates response candidates using a generative AI model and refines them based on the output of the sentiment engine. It prompts the generative AI model with input from natural language processing analysis results and sentiment evaluation results. It selects the most appropriate response from the generated candidates. The output is a response message that takes the user's emotions into consideration.

[0784] Step 6:

[0785] The server sends the selected response message to the terminal. The terminal automatically sends the received response to an external information processing device, communicating on behalf of the user. This response transmission is performed based on the configured appropriate timing.

[0786] (Application Example 2)

[0787] 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".

[0788] In content distribution services, there is a need to improve the quality of communication with users. However, conventional systems have difficulty responding in a way that fully considers user emotions and context, which can result in a poor user experience. Furthermore, it is difficult to respond appropriately to real-time feedback from users, and this needs to be addressed.

[0789] 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.

[0790] In this invention, the server includes means for performing individual settings based on authentication information obtained from the user, means for collecting and transmitting communication data from an external application, means for analyzing the communication content using natural language processing and generating response candidates, means including an emotion recognition engine that infers the user's emotional state and adjusts the response accordingly, and means for responding in real time. This makes it possible to automatically perform personalized communication in accordance with the user's emotions.

[0791] "Authentication information" refers to data used to identify a user and grant them access to specific services or applications.

[0792] "Individual settings" refer to settings that optimize the operation of the system and applications according to the user's specific needs and preferences.

[0793] "Communication data" refers to messages and information sent and received over a network, and includes content, sender information, timestamps, etc.

[0794] "Natural language processing" refers to the techniques and methodologies used to enable computers to understand and analyze human language.

[0795] A "response candidate" is a set of candidates that represent a portion of the system's responses to user input, used to select the optimal response.

[0796] An "emotion recognition engine" is a system or function that analyzes a user's emotional state and adjusts responses and service delivery methods based on the results.

[0797] "Real-time response" refers to a system function that provides an immediate response to user input or questions.

[0798] This invention is a system that generates automated responses tailored to the user's emotions. The terminal first obtains authentication information from the user and then performs individual configuration. This allows the user to obtain an optimized response environment.

[0799] The terminal also collects communication data from external applications. This data includes sender information, message content, and timestamps. This data is then processed on the server.

[0800] The server analyzes the content of the communication data using natural language processing (NLP) techniques. Natural language processing APIs such as the Google Cloud Natural Language API can be used for this analysis. Based on the analysis results, response candidates are generated with the help of a generative AI model (e.g., GPT-3).

[0801] Furthermore, the server incorporates an emotion recognition engine. This engine infers the user's emotions from their past communication history and current context. As a result, the generated response candidates are optimized for the user's emotional state and sent in real time at the appropriate time.

[0802] For example, if a user posts a negative message in the video's comment section, such as "This scene isn't funny," the system can respond with a positive message like, "What aspects would you like to see improved? Thank you for your feedback!" This improves the user experience.

[0803] A concrete example of a prompt would be: "The user evaluates their emotion and generates response options: If a comment like 'Wow! This scene is my favorite part.' is received, generate a response that matches that emotion."

[0804] This allows for more natural and smoother communication with users, potentially improving their satisfaction.

[0805] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0806] Step 1:

[0807] The device obtains authentication information from the user and performs individual configuration. It receives user authentication information as input and processes the data to obtain configuration information. As output, it generates user-optimized configuration information and saves it as a profile.

[0808] Step 2:

[0809] The terminal collects communication data from external applications. It receives communication data such as message content, sender information, and timestamps as input, and performs format conversion and data filtering on this data. As output, it generates data in a parseable format for transfer to the server.

[0810] Step 3:

[0811] The server analyzes the collected communication data using natural language processing. It receives communication data transferred from the terminal as input and uses a natural language processing engine to analyze the subject and intent of the message. As output, response candidates are determined based on the analysis results.

[0812] Step 4:

[0813] The server uses an emotion recognition engine to infer the user's emotional state. It utilizes analysis results and the user's past communication history as input to perform the necessary data calculations for emotion inference. The output generates response adjustment information appropriate to the user's emotional state.

[0814] Step 5:

[0815] The server generates appropriate responses using a generative AI model. It uses a refined response candidate and user sentiment information as input to create a prompt. The output is a response message optimized for the user's state.

[0816] Step 6:

[0817] The server automatically sends response messages at the appropriate time. Using the generated response messages as input and the data used to determine the appropriate sending timing, a response designed to improve the user experience is sent to the external application as output.

[0818] 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.

[0819] 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 those described above. 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 shown 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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."

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] (Claim 1)

[0841] A means of performing individual settings based on authentication information obtained from the user,

[0842] A means of collecting and sending message data from an external application,

[0843] A means for analyzing message content using natural language processing and generating response candidates,

[0844] A means of selecting and sending an automated response at the appropriate time,

[0845] A system that includes means for accepting manual intervention from users.

[0846] (Claim 2)

[0847] The system according to claim 1, having an algorithm that analyzes the context of conversational data and improves the appropriate response.

[0848] (Claim 3)

[0849] The system according to claim 1, comprising means for detecting and responding to references to a user.

[0850] "Example 1"

[0851] (Claim 1)

[0852] A means of obtaining configuration information based on authentication data obtained from the user,

[0853] A means of collecting and transferring message information from external software,

[0854] A means for analyzing message information using natural language processing technology and generating response candidates,

[0855] A means for selecting the best candidate from multiple generated response candidates and sending it automatically,

[0856] Means that allow users to perform manual operations,

[0857] A means of using a technique to improve the response based on the context of the acquired message information,

[0858] A method to automatically determine and respond to references to a user based on the message content,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, comprising a method for analyzing the context of conversational information and improving response accuracy.

[0862] (Claim 3)

[0863] The system according to claim 1, comprising means for determining whether a message addressed to a user has been detected and for taking countermeasures.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] A means of performing individual settings based on authentication information obtained from the user,

[0867] A means of collecting and transmitting information data from an external application,

[0868] A means for analyzing information content using natural language processing technology and generating response candidates,

[0869] A means of selecting and sending an automated response at the appropriate time,

[0870] A means of accepting manual intervention by the user,

[0871] A means of automatically generating and quickly providing the optimal answer in customer interactions,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which has an algorithm that analyzes the context of conversation data and improves appropriate responses, and is applicable to customer support on e-commerce sites.

[0875] (Claim 3)

[0876] The system according to claim 1, comprising means for detecting and responding to references to users and for responding quickly to inquiries.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] A means of performing individual settings based on authentication information obtained from the user,

[0880] A means for collecting and transmitting message data from an information processing device,

[0881] A means for analyzing message content using natural language processing and generating response candidates,

[0882] A means for evaluating the user's emotional state based on past communication history and current context,

[0883] A means of adjusting and selecting response candidates using a generative AI model,

[0884] A means of selecting and sending an automated response at the appropriate time,

[0885] A system that includes means for accepting manual intervention from users.

[0886] (Claim 2)

[0887] The system according to claim 1, comprising an algorithm that analyzes the context of conversational data and improves the appropriate response using a generative AI model.

[0888] (Claim 3)

[0889] The system according to claim 1, comprising means for detecting and responding to references to a user.

[0890] "Application example 2 when combining with an emotional engine"

[0891] (Claim 1)

[0892] A means of performing individual settings based on authentication information obtained from the user,

[0893] A means of collecting and transmitting communication data from an external application,

[0894] A means for analyzing communication content using natural language processing and generating response candidates,

[0895] A means of selecting and sending an automated response at the appropriate time,

[0896] A means including an emotion recognition engine that infers the user's emotional state and adjusts its response accordingly,

[0897] A system that includes means for accepting manual intervention from users.

[0898] (Claim 2)

[0899] The system according to claim 1, having an algorithm that analyzes the context of conversational information and improves the appropriate response.

[0900] (Claim 3)

[0901] The system according to claim 1, comprising means for analyzing user comment data in a content distribution service and responding in real time. [Explanation of Symbols]

[0902] 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 performing individual settings based on authentication information obtained from the user, A means of collecting and sending message data from an external application, A means for analyzing message content using natural language processing and generating response candidates, A means of selecting and sending an automated response at the appropriate time, A system that includes means for accepting manual intervention from users.

2. The system according to claim 1, comprising an algorithm that analyzes the context of conversational data and improves appropriate responses.

3. The system according to claim 1, comprising means for detecting and responding to references to a user.

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