system

Abstract

We provide the system. [Solution] A means of analyzing a user's personal data communication history and learning the content of their responses, A means of evaluating incoming new data communications and determining their urgency, A means for generating and sending an automated response based on the determined urgency, In cases of high urgency, a means of immediately notifying the user, A means to organize data communications received during the holiday period and provide priority-based action guidelines upon return, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that during annual leave or refresh leave, email and chat notifications come frequently, and users cannot secure a calm time. In addition, there is a problem that a large amount of emails need to be processed after the leave, and users become depressed. [[ID=�6]]

Means for Solving the Problems

[0005] The present invention evaluates new data communications according to the urgency by using an AI agent that analyzes a user's personal data communication history and learns response contents. When the urgency is high, a user notification is immediately made, and in other cases, an automatic response is made. Furthermore, the data communications received during the leave are sorted out, and by providing action guidelines based on the priority when the user returns, efficient resumption of work is supported.

[0006] "Personal data communication history" refers to a collection of data related to communication, such as emails and chats, that a user has sent and received in the past.

[0007] "Urgency level" is an indicator that shows whether the received data communication requires immediate attention.

[0008] An "automatic response" is a predefined response message that an AI agent generates on behalf of the user in response to received data communication.

[0009] An "AI agent" is artificial intelligence that learns a user's past communication patterns and responds appropriately to received messages.

[0010] "Action guidelines" are instructions, including task prioritization based on important information, provided to users to efficiently carry out their work after returning to the system. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0012] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0013] First, the terms used in the following description will be described.

[0014] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0015] In the following embodiments, the tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by a processor.

[0016] In the following embodiments, the tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0017] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0019] [First Embodiment]

[0020] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0021] As shown in FIG. 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention provides a system that allows users to manage their work-related communications more efficiently and relax with peace of mind while on vacation. This system uses an AI agent that learns from the user's past communication history and provides a function to automatically respond to emails and chats on the user's behalf.

[0033] First, when a user starts using the system, they provide their past email and chat history. This allows the system to learn the user's communication patterns and model individual response styles. Next, the server generates an AI model based on this data and prepares for automated responses.

[0034] When a user takes leave, the device sends newly received emails and chat messages to the server in real time. The server evaluates these messages and determines whether an automated response is necessary. An algorithm is built in to assess the urgency of messages; for example, if a message contains keywords such as "urgent" or "immediate," the server prioritizes notifying the user of that message.

[0035] Furthermore, if an automated reply is appropriate but not urgent, the server will select an appropriate response and automatically send it through the device. For example, if a simple email requesting a meeting schedule arrives, the AI ​​will generate and send a response such as, "We have received your request, Mr. / Ms. XX. We will confirm by [date]."

[0036] Furthermore, to assist users returning to work after their holidays, the server organizes all communications during the holiday period and generates a task list based on priority. This list is sent to the terminal and serves as a guide for what users should do upon their return.

[0037] Thus, the present invention is a system that utilizes AI to enable users to efficiently manage their communications, thereby allowing them to spend their vacations without worry and to smoothly resume work after returning to the office.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] To begin using the system, users provide their past email and chat history data. This allows the system to obtain the basic data needed to learn the user's communication patterns.

[0041] Step 2:

[0042] The server analyzes the provided historical data and builds an AI model that learns user response patterns using natural language processing techniques. This model is used to generate responses based on specific vocabulary and context.

[0043] Step 3:

[0044] The device detects newly received emails and chat messages and sends that data to the server in real time. The received data is temporarily stored on the server in preparation for subsequent processing.

[0045] Step 4:

[0046] The server evaluates the urgency of incoming messages. This is done based on keyword analysis and sender identification, and if it determines that the message is highly urgent, it instructs the terminal to immediately notify the user.

[0047] Step 5:

[0048] For low-urgency messages, the server uses an AI model to generate an appropriate automated response. For example, for routine meeting request emails, the system constructs a response based on response styles it has learned.

[0049] Step 6:

[0050] The terminal follows instructions from the server and sends a generated automated response to the recipient. This history of automated replies is saved as a log so that the user can review it as needed.

[0051] Step 7:

[0052] After communication processing during the holiday period is complete, the server organizes all messages and generates a prioritized task list based on their content. This includes categorization by importance and deadline.

[0053] Step 8:

[0054] Upon user return, the terminal displays a task list received from the server to assist in resuming work. Users can then use this list to efficiently process tasks.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In today's information and communication environment, users need to process a large volume of electronic communications on a daily basis, and this task can affect work efficiency and mental burden. In particular, during vacations, users temporarily disconnect from work communications, making it difficult to manage communications during that time. In addition, misjudging the urgency of communications can lead to missing important information. Therefore, there is a need to provide an environment where users can relax during their vacations and quickly resume work upon their return.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for analyzing the user's electronic communication history and learning the content of responses, means for evaluating newly received electronic communications and determining their urgency, and means for sending emergency notifications to the user's computer. This enables the automatic processing of electronic communications even when the user is absent, allowing access to important information at the appropriate time.

[0060] A "user" refers to an individual or legal entity that uses this system to manage electronic communications.

[0061] "Electronic communication history" refers to all digital communication data, such as emails and chat messages, that a user has sent or received to date.

[0062] "Analysis" refers to the process of using computers to evaluate data in detail and extract specific information or patterns.

[0063] "Response content" refers to the information or message generated as a response to communication from the user.

[0064] "Learning" is the process of using AI technology to acquire specific behavioral patterns from data and update the model.

[0065] "Urgency" is a measure used to evaluate the importance and priority of a communication to a user.

[0066] "Automatic response" refers to a reply message that is generated and sent by the system without any manual action from the user.

[0067] "Notifications" refer to system functions that include messages and warnings to immediately convey information to the user.

[0068] The term "computer" refers to a broad range of devices, including all electronic equipment that performs data processing, and especially computers.

[0069] This invention provides a system that frees users from manual communication handling. The system mainly consists of three elements: a server, a terminal, and a user.

[0070] To begin using the system, users must provide their past electronic communication history to the server. This is done by importing data using email clients or chat applications. Specifically, dedicated client software can be used to transfer history data from major communication platforms to the server.

[0071] The server analyzes the received data and executes a learning process using a generative AI model. During this process, natural language processing techniques are utilized to identify patterns in user responses. By employing deep learning algorithms using libraries such as TENSORFLOW® and PyTorch, a response model optimized for each individual user is generated.

[0072] When a user sets a vacation, the device is configured to send newly received electronic communications to the server. The device is designed to operate on currently dominant operating systems (e.g., iOS, Android®, Windows, etc.) and uses secure protocols to transfer data.

[0073] The server evaluates newly received messages and determines their urgency. If the message is highly urgent, it immediately sends a notification to the user's computer. This determination is made using algorithms that perform keyword analysis of the message content. If an automated response is deemed appropriate, the server generates a reply based on the AI ​​model and sends it through the terminal. As a concrete example of a prompt, telling the server something like, "When a new email arrives, please use the AI ​​model to analyze its content and determine if an automated response is possible," will immediately generate a response.

[0074] This system allows users to respond to communications even when they are away, and provides an environment where communication processing can be carried out smoothly according to priority upon their return.

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

[0076] Step 1:

[0077] The user provides their past electronic communication history to the server. The user imports past message data into the system using an email client or chat application. The input includes data from the user's chosen communication platform, which the server receives and converts into a parseable format. This conversion includes the process of converting the file to a standard data format (e.g., a CSV file). The output is the user's communication data, ready for analysis, input into the server.

[0078] Step 2:

[0079] The server analyzes the received communication data using a specific algorithm. Specifically, it uses natural language processing techniques to extract patterns and frequently occurring phrases within the messages. As input, the server is provided with the user's communication history data, which is then analyzed. As part of the data processing, text analysis is performed to identify keywords and response styles that the user has used in the past. Based on this, a user-specific response model is generated as output and stored on the server.

[0080] Step 3:

[0081] The server uses the generated response model to assess the urgency of newly received communications. New messages received from terminals are forwarded to the server, and analysis begins. The input consists of user communication data and newly received messages, and the AI ​​model is used to determine the urgency. The output is the determined urgency level of the message, and the user is notified if necessary.

[0082] Step 4:

[0083] The terminal instantly notifies the user of emergency message notifications sent from the server. If the message is deemed highly urgent, the terminal displays an alert according to the user's default notification settings. The input is emergency message information provided by the server, and the output is a notification displayed on the user's device. This allows the user to respond immediately to particularly important communications.

[0084] Step 5:

[0085] The server uses AI to generate automated responses for non-urgent messages. As input, the server receives new messages requiring processing and user response models, and sends the generated response content via the terminal. As output, a response message tailored to the user's style is generated and sent to the recipient. An example of this is a response to a scheduling email.

[0086] Step 6:

[0087] When a user returns from vacation, the server generates a task list with priority based on the processed communication data. The input includes all communication data received during the vacation and its evaluation results, which are used to formulate action plans. The output is a task list necessary for resuming work upon returning to the office, which is sent to the user's terminal. This creates an environment where users can efficiently resume their work.

[0088] (Application Example 1)

[0089] 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."

[0090] Conventional communication management systems have difficulty providing appropriate emergency responses when users are absent, and in particular, real-time management of mechanical equipment is insufficient in production sites. Furthermore, there is a lack of means to effectively organize communications that occur while users are on vacation and to respond quickly upon their return to work.

[0091] 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.

[0092] In this invention, the server includes means for analyzing the user's personal data communication history and learning the content of responses; means for evaluating newly received data communications and determining their urgency; means for generating and transmitting an automated response based on the determined urgency; and means for delivering instructions in real time to mechanical devices in the production environment and applying the automated response. This enables appropriate emergency response at the production site even when the user is absent, and allows for efficient response based on the organization and prioritization of communications.

[0093] "Personal data communication history" refers to emails, messages, and other communication information that a user has sent in the past, and is used to analyze specific trends and patterns.

[0094] "Urgency" is an indicator used to determine whether received data communications require immediate attention, and is usually determined by keywords and content.

[0095] An "automated response" is a reply message generated by an AI model, acting on behalf of the user, and is sent with appropriate content pre-formed.

[0096] "Mechanical equipment in the production environment" refers to machinery, robots, and other equipment operating in factories and production sites, and is subject to management and operation.

[0097] "Real-time instruction delivery" refers to a process where, as soon as information is received, the appropriate instruction is immediately sent to the target device, with the aim of ensuring a response without delay.

[0098] To implement this system, the server receives the user's personal data communication history and analyzes its contents. The server uses Python and the AI ​​framework TensorFlow to generate an AI model that learns the user's response tendencies. This makes it possible to model the user's individual communication style and determine the urgency of newly received data communications.

[0099] The server evaluates the data and analyzes the urgency of new messages in real time as they arrive. It uses message queuing services such as AWS® SQS to process the necessary data and delivers appropriate instructions to mechanical equipment in the production environment in real time, supporting the continuous operation of the production line.

[0100] As a concrete example, if a production machine malfunctions in a factory, the automated response system can immediately detect the problem, generate an alternative procedure, and send it to the machine, even if the operator is on break. This minimizes production delays.

[0101] Furthermore, the server has a mechanism to organize all communications received during vacation and provide users with a priority-based task list upon their return. This allows them to smoothly resume work after returning to the office.

[0102] An example of a prompt for the generating AI model is: "If an unexpected problem occurs while the operator is on break, suggest how to deal with it and generate a method for handling it immediately, safely, and efficiently." Based on this prompt, the AI ​​forms an appropriate response and applies automated instructions to the device.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server receives past data communication history from the user. This input data includes records of sending and receiving emails and messages. The server analyzes this data to extract the user's communication trends. As part of data processing, a natural language processing library is used to recognize patterns in message content, and the output is a generative AI model that models the user's unique response style.

[0106] Step 2:

[0107] When a terminal receives new data communication, it immediately sends the content of that communication to the server. This input message includes the date, sender, and content. The server performs data calculations using a pre-configured keyword detection algorithm to determine the urgency of the received message. As output, if the urgency is high, it generates a notification flag indicating that it should be processed with priority.

[0108] Step 3:

[0109] The server generates an automated response based on the determined urgency level. Inputs include a generation AI model, the content of the received message, and an urgency flag. The server uses the model to generate an appropriate response and sends it to the terminal via the message queue service. The output is the automated response message displayed on the terminal.

[0110] Step 4:

[0111] Mechanical devices installed in the production environment receive instructions from terminals. Based on the response messages output from the server, the devices perform the specified actions. This input contains instructions on what specific operations the machine should perform. As a result of the execution, the planned production activities are maintained, and this becomes the output.

[0112] Step 5:

[0113] When a user returns from vacation, the server organizes all communications received during their vacation. Input data includes received messages, their urgency, and processing history. The server generates a task list based on priority and sends it to the user's terminal, providing guidance on actions to take upon their return. This output is the task list displayed on the user's screen.

[0114] 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.

[0115] This invention is a communication management system that takes user emotions into consideration, and by incorporating an emotion engine that analyzes the user's past and present emotions, it provides more personalized responses. This system aims to improve communication methods that respond to the user's emotional state, in addition to automated responses to emails and chats.

[0116] First, the user provides past email and chat data. This allows the system to learn not only the user's response patterns but also the emotional changes in past communications. The server analyzes this data and applies natural language processing and sentiment analysis algorithms to generate a user response model. This process helps to understand the situations in which specific emotions were triggered and the user's reactions to them.

[0117] The device detects newly received data communication and sends it to the server. At this time, the emotion engine is activated and evaluates the user's emotions that the sent message is expected to evoke. For example, if the message contains content that would cause stress, the emotion engine will identify that.

[0118] The server generates an automated response based on the urgency of the message, including emotionally sensitive content when sending it. For example, if the content might cause offense to the recipient, it selects a response that incorporates apologies and mitigating expressions.

[0119] Furthermore, the server organizes communications during vacation based on the user's emotional changes and generates a task list based on priorities that take emotional stress into account. This list, displayed on the terminal, serves as a guide for the user to smoothly resume work and helps reduce mental burden.

[0120] By incorporating the emotion engine of this invention, users can not only receive appropriate communication responses according to their emotional state, but also reduce emotional stress during vacation and improve work efficiency upon returning to work.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] Users provide the system with past email and chat data. This gives the system a foundation to learn not only individual reply patterns but also emotional responses.

[0124] Step 2:

[0125] The server analyzes the provided data and uses natural language processing techniques and sentiment analysis algorithms to build a response model that learns the user's reply style and emotional changes. This model serves as the basis for generating response content.

[0126] Step 3:

[0127] The device sends newly received emails and chat messages to the server in real time. This provides the server with the data necessary for sentiment analysis at the time of reception.

[0128] Step 4:

[0129] The server analyzes incoming messages and uses a sentiment engine to evaluate how the message might affect the user's emotions. For example, it can detect potentially stressful situations from the sender's past message history.

[0130] Step 5:

[0131] If the message is urgent or a standard automated response is appropriate, the server uses an AI model to generate a response. This response is then refined to reflect emotional assessments, such as expressing gratitude or using polite language.

[0132] Step 6:

[0133] The terminal sends a generated response to the recipient based on instructions from the server. This response is sent on behalf of the user and is emotionally sensitive.

[0134] Step 7:

[0135] After communications during the holiday period have been processed, the server organizes the received messages, prioritizes them based on their content and the emotional impact on the users, and generates a task list. This task list is created taking emotional load into consideration.

[0136] Step 8:

[0137] The terminal displays a task list and action plan received from the server upon the user's return from vacation, assisting them in resuming work. Based on these guidelines, users can efficiently carry out their tasks while reducing mental stress.

[0138] (Example 2)

[0139] 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".

[0140] Modern information and communication are constantly increasing, particularly in the workplace, leading to rising psychological burdens. In particular, accurately understanding the priority of various types of information and providing appropriate responses immediately is difficult, and users are often overwhelmed by the sheer volume of data. Furthermore, automated responses that do not consider the user's emotional state can cause additional stress. Therefore, there is a need for communication management systems that provide efficient and appropriate responses while taking user emotions into consideration.

[0141] 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.

[0142] In this invention, the server includes means for analyzing the user's information history and learning response content, means for evaluating newly received information and determining its importance, and means for analyzing the user's emotions and generating responses that take their emotional state into consideration. This enables accurate responses that take the user's emotions into account and information organization based on priority.

[0143] "Information history" refers to records of various communications that a user has experienced in the past, including exchanges such as emails and chats.

[0144] "Means of learning response content" refers to the process by which the system extracts patterns from past information history to understand how users respond.

[0145] "Means for determining importance" refers to criteria or algorithms used to identify which newly received information should be processed with priority.

[0146] "Means for analyzing emotions and generating responses that take emotional state into consideration" refers to a process that evaluates the user's emotions from past and present communications and automatically generates appropriate responses based on that evaluation.

[0147] "Priority-based information organization" refers to a method of classifying received information according to certain criteria or conditions and setting a processing order based on its importance and urgency.

[0148] This invention is a system that analyzes a user's information history and manages communication while taking the user's emotions into consideration. The following describes a specific form for implementing this system.

[0149] Users provide the system with their past email and chat history. This creates foundational data for learning the user's communication style and emotional changes. First, this information is sent to the server.

[0150] The server uses natural language processing toolkits (e.g., NLTK, spaCy) and sentiment analysis software (e.g., VADER, TextBlob) to analyze the received historical data. These tools analyze sentiment from text and learn user response patterns. This analysis is important for understanding what emotions were evoked in specific situations and for grasping the user's response to those emotions.

[0151] The device instantly detects the arrival of new emails or chat messages. This detected information is then sent back to the server. The server uses an emotion engine to evaluate how the sent message will affect the user's emotions.

[0152] Based on this assessment, the server generates an automated response. This response is designed with a particular focus on reducing stress, and includes mitigating expressions and apologies for content that the user might find offensive. This system allows users to receive communications that are appropriate to their emotional state.

[0153] For example, if a user receives an important message while on vacation, the system organizes the information and generates a task list based on priority. This task list serves as a guide for quickly resuming work upon returning to the office.

[0154] Another example of a prompt is: "What words soothe you when you're feeling stressed? Please tell me some specific phrases or content." Based on such prompts, responses tailored to the user's needs are constructed.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] Users provide the system with their past email and chat data. This input data serves as foundational data for analyzing communication patterns and emotional changes. Specifically, users upload data through a dedicated interface, which is then received by the server.

[0158] Step 2:

[0159] The server analyzes the provided data using natural language processing toolkits (e.g., NLTK, spaCy). It receives text data as input and extracts user response patterns through morphological analysis and sentiment scoring. The output provides characteristic user sentiment patterns and response tendencies. This process evaluates whether specific words or expressions evoke positive or negative emotions.

[0160] Step 3:

[0161] The device detects when a new message (email or chat) arrives. This becomes new input data and is sent to the server. This process happens automatically when the received message arrives in the user's inbox.

[0162] Step 4:

[0163] The server uses an emotion engine to analyze newly received messages. Based on the messages received as input data, it evaluates the impact on the user's emotions from the tone and content of the messages. The output is an emotion score and a predicted emotional state. Specifically, the process includes keyword extraction and the assignment of corresponding emotion labels.

[0164] Step 5:

[0165] The server generates an automated response that takes emotions into consideration, based on the results of sentiment analysis. Using a generative AI model, it creates the optimal reply based on past learning results and the user's current emotional state. When generating this prompt, it selects words and expressions that alleviate user stress. The output is an automatically generated response message.

[0166] Step 6:

[0167] The server organizes all communications received by the user during their vacation and generates a task list based on priority. It receives messages received during the vacation as input and sorts them according to importance and emotional burden. The final output is a task list that serves as a guide for work upon returning to work and is displayed on the terminal. The specific operation involves a process of visually organizing the listed tasks and presenting them to the user in an easy-to-understand format.

[0168] (Application Example 2)

[0169] 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".

[0170] In recent years, with the advancement of information and communication technology, the content of data communications has diversified, and users now receive a large volume of messages daily. This has made it difficult to adequately address the mental burden caused by information overload and security risks such as phishing emails. In particular, uniform notifications and responses that do not take into account the user's emotional state are problematic because they prevent the user from taking the optimal action in an emergency. Therefore, there is a need to provide more personalized response systems that take into account the user's emotions and the urgency of the situation.

[0171] 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.

[0172] In this invention, the server includes means for analyzing the user's personal data communication history and learning response content, means for evaluating incoming new data communication and determining its urgency, and means for analyzing the user's emotional state and evaluating security risks. This enables personalized notifications and responses according to the user's emotional state and appropriate responses according to urgency. Furthermore, by providing users with warnings that address security risks, the burden of information overload is reduced, allowing users to use communication with peace of mind.

[0173] "User's personal data communication history" refers to all communication history, such as emails and chats, that a user has made in the past. This data serves as foundational information for analyzing the user's communication patterns and emotional changes.

[0174] "Analysis means" refers to technologies that analyze the content of user communications using natural language processing and sentiment analysis algorithms based on data communication history, and generate response models.

[0175] "Means for determining urgency" refers to technology that analyzes the content of received data communications to determine how important and urgent that content is to the user.

[0176] "Means for generating automated responses" refers to a mechanism for automatically creating response messages in an appropriate format based on the user's communication history and the assessment of urgency.

[0177] "Means for analyzing emotional states" refers to analytical techniques that identify a user's emotions based on past and received data, and then respond appropriately to that emotional state.

[0178] "Means of assessing security risks" refers to evaluation methods for determining whether received data is potentially harmful to the user and for issuing warnings accordingly.

[0179] "Means for generating warnings" refers to technologies that create appropriate warning messages and notify users when security risks are identified.

[0180] The system of this invention enables communication management that takes user emotions into consideration, and includes means for analyzing the user's personal data communication history and learning response content. The system consists of a server, terminals, and multiple interconnected components for effectively managing each user's data.

[0181] The server is implemented in a programming language such as Python and utilizes natural language processing (NLP) libraries (e.g., NLTK, spaCy) and sentiment analysis libraries (e.g., TextBlob, IBM Watson®). The server first receives the user's past communication data and analyzes it using natural language processing techniques. This analyzes the user's unique response tendencies and emotional fluctuations, and generates a response model. Using this response model, the server evaluates the urgency of newly received data communications and generates an automated response as needed.

[0182] Furthermore, the server can analyze the user's emotional state in real time and assess security risks. This allows for the identification of potential risks, such as phishing emails, and enables the user to receive appropriate warnings. These warnings take into account the stress and emotional reactions the user might face, and are designed to support them in responding calmly.

[0183] The device displays notifications and warnings sent from the server to the user in real time. This allows the user to immediately understand information regarding the importance and risks of received communications and take swift and appropriate action. As part of this system, smartphones and other mobile devices play a crucial role and are designed to ensure that the immediacy and convenience of communications are not compromised.

[0184] For example, if a fraudulent email disguised as coming from a financial institution arrives, the server analyzes the email and, based on the user's sentiment analysis, sends a warning message stating, "This email is dangerous. Do not open any links or attachments." This allows users to detect risks in advance and prevent damage.

[0185] An example of a prompt message is: "Analyze the user's sentiment and analyze the risk based on the email content and changes in sentiment. Create an appropriate warning message as needed." This provides a foundation for effectively utilizing generative AI models to make the user experience safer and more comfortable.

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The server collects the user's past data communication history. This input data includes email and chat history. The server uses natural language processing techniques to analyze this history and identify the user's unique response tendencies and changes in emotion. The output of this process is a model of the user's emotions and responses.

[0189] Step 2:

[0190] The server detects newly received data communications. These communications become input data, and the server applies an algorithm to determine their urgency. In this process, context and keywords are analyzed, the importance of the communications is evaluated, and the results are output.

[0191] Step 3:

[0192] The server analyzes received communication data using a sentiment analysis library to re-evaluate the user's current emotional state. The input data is the content of the new communication, and the output is the result of the emotional state evaluation. Based on this evaluation, an automated response is generated as needed.

[0193] Step 4:

[0194] The server generates security alerts as needed based on the analysis results and evaluation. When an alert is necessary, such as in the case of a potentially phishing email, it creates an alert message composed of specific yet gentle language. The input is sentiment and security evaluation, and the output is the alert message.

[0195] Step 5:

[0196] The terminal displays notifications and warnings sent from the server to the user. In this process, the notification content (input) is displayed on the screen (output), making it immediately available to the user. This allows the user to take a quick and appropriate action.

[0197] 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.

[0198] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] 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.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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".

[0213] This invention provides a system that allows users to manage their work-related communications more efficiently and relax with peace of mind while on vacation. This system uses an AI agent that learns from the user's past communication history and provides a function to automatically respond to emails and chats on the user's behalf.

[0214] First, when a user starts using the system, they provide their past email and chat history. This allows the system to learn the user's communication patterns and model individual response styles. Next, the server generates an AI model based on this data and prepares for automated responses.

[0215] When a user takes leave, the device sends newly received emails and chat messages to the server in real time. The server evaluates these messages and determines whether an automated response is necessary. An algorithm is built in to assess the urgency of messages; for example, if a message contains keywords such as "urgent" or "immediate," the server prioritizes notifying the user of that message.

[0216] Furthermore, if an automated reply is appropriate but not urgent, the server will select an appropriate response and automatically send it through the device. For example, if a simple email requesting a meeting schedule arrives, the AI ​​will generate and send a response such as, "We have received your request, Mr. / Ms. XX. We will confirm by [date]."

[0217] Furthermore, to assist users returning to work after their holidays, the server organizes all communications during the holiday period and generates a task list based on priority. This list is sent to the terminal and serves as a guide for what users should do upon their return.

[0218] Thus, the present invention is a system that utilizes AI to enable users to efficiently manage their communications, thereby allowing them to spend their vacations without worry and to smoothly resume work after returning to the office.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] To begin using the system, users provide their past email and chat history data. This allows the system to obtain the basic data needed to learn the user's communication patterns.

[0222] Step 2:

[0223] The server analyzes the provided historical data and builds an AI model that learns user response patterns using natural language processing techniques. This model is used to generate responses based on specific vocabulary and context.

[0224] Step 3:

[0225] The device detects newly received emails and chat messages and sends that data to the server in real time. The received data is temporarily stored on the server in preparation for subsequent processing.

[0226] Step 4:

[0227] The server evaluates the urgency of incoming messages. This is done based on keyword analysis and sender identification, and if it determines that the message is highly urgent, it instructs the terminal to immediately notify the user.

[0228] Step 5:

[0229] For low-urgency messages, the server uses an AI model to generate an appropriate automated response. For example, for routine meeting request emails, the system constructs a response based on response styles it has learned.

[0230] Step 6:

[0231] The terminal follows instructions from the server and sends a generated automated response to the recipient. This history of automated replies is saved as a log so that the user can review it as needed.

[0232] Step 7:

[0233] After communication processing during the holiday period is complete, the server organizes all messages and generates a prioritized task list based on their content. This includes categorization by importance and deadline.

[0234] Step 8:

[0235] Upon user return, the terminal displays a task list received from the server to assist in resuming work. Users can then use this list to efficiently process tasks.

[0236] (Example 1)

[0237] 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."

[0238] In today's information and communication environment, users need to process a large volume of electronic communications on a daily basis, and this task can affect work efficiency and mental burden. In particular, during vacations, users temporarily disconnect from work communications, making it difficult to manage communications during that time. In addition, misjudging the urgency of communications can lead to missing important information. Therefore, there is a need to provide an environment where users can relax during their vacations and quickly resume work upon their return.

[0239] 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.

[0240] In this invention, the server includes means for analyzing the user's electronic communication history and learning the content of responses, means for evaluating newly received electronic communications and determining their urgency, and means for sending emergency notifications to the user's computer. This enables the automatic processing of electronic communications even when the user is absent, allowing access to important information at the appropriate time.

[0241] A "user" refers to an individual or legal entity that uses this system to manage electronic communications.

[0242] "Electronic communication history" refers to all digital communication data, such as emails and chat messages, that a user has sent or received to date.

[0243] "Analysis" refers to the process of using computers to evaluate data in detail and extract specific information or patterns.

[0244] "Response content" refers to the information or message generated as a response to communication from the user.

[0245] "Learning" is the process of using AI technology to acquire specific behavioral patterns from data and update the model.

[0246] "Urgency" is a measure used to evaluate the importance and priority of a communication to a user.

[0247] "Automatic response" refers to a reply message that is generated and sent by the system without any manual action from the user.

[0248] "Notifications" refer to system functions that include messages and warnings to immediately convey information to the user.

[0249] The term "computer" refers to a broad range of devices, including all electronic equipment that performs data processing, and especially computers.

[0250] This invention provides a system that frees users from manual communication handling. The system mainly consists of three elements: a server, a terminal, and a user.

[0251] To begin using the system, users must provide their past electronic communication history to the server. This is done by importing data using email clients or chat applications. Specifically, dedicated client software can be used to transfer history data from major communication platforms to the server.

[0252] The server analyzes the received data and executes a learning process using a generative AI model. During this process, natural language processing techniques are utilized to identify patterns in user responses. By employing deep learning algorithms using libraries such as TensorFlow and PyTorch, a response model optimized for each individual user is generated.

[0253] When a user sets a vacation, the device is configured to send newly received electronic communications to the server. The device assumes a device running a currently dominant OS (e.g., iOS, Android, Windows, etc.) and transfers data using a secure protocol.

[0254] The server evaluates newly received messages and determines their urgency. If the message is highly urgent, it immediately sends a notification to the user's computer. This determination is made using algorithms that perform keyword analysis of the message content. If an automated response is deemed appropriate, the server generates a reply based on the AI ​​model and sends it through the terminal. As a concrete example of a prompt, telling the server something like, "When a new email arrives, please use the AI ​​model to analyze its content and determine if an automated response is possible," will immediately generate a response.

[0255] This system allows users to respond to communications even when they are away, and provides an environment where communication processing can be carried out smoothly according to priority upon their return.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The user provides their past electronic communication history to the server. The user imports past message data into the system using an email client or chat application. The input includes data from the user's chosen communication platform, which the server receives and converts into a parseable format. This conversion includes the process of converting the file to a standard data format (e.g., a CSV file). The output is the user's communication data, ready for analysis, input into the server.

[0259] Step 2:

[0260] The server analyzes the received communication data using a specific algorithm. Specifically, it uses natural language processing techniques to extract patterns and frequently occurring phrases within the messages. As input, the server is provided with the user's communication history data, which is then analyzed. As part of the data processing, text analysis is performed to identify keywords and response styles that the user has used in the past. Based on this, a user-specific response model is generated as output and stored on the server.

[0261] Step 3:

[0262] The server uses the generated response model to assess the urgency of newly received communications. New messages received from terminals are forwarded to the server, and analysis begins. The input consists of user communication data and newly received messages, and the AI ​​model is used to determine the urgency. The output is the determined urgency level of the message, and the user is notified if necessary.

[0263] Step 4:

[0264] The terminal instantly notifies the user of emergency message notifications sent from the server. If the message is deemed highly urgent, the terminal displays an alert according to the user's default notification settings. The input is emergency message information provided by the server, and the output is a notification displayed on the user's device. This allows the user to respond immediately to particularly important communications.

[0265] Step 5:

[0266] The server uses AI to generate automated responses for non-urgent messages. As input, the server receives new messages requiring processing and user response models, and sends the generated response content via the terminal. As output, a response message tailored to the user's style is generated and sent to the recipient. An example of this is a response to a scheduling email.

[0267] Step 6:

[0268] When a user returns from vacation, the server generates a task list with priority based on the processed communication data. The input includes all communication data received during the vacation and its evaluation results, which are used to formulate action plans. The output is a task list necessary for resuming work upon returning to the office, which is sent to the user's terminal. This creates an environment where users can efficiently resume their work.

[0269] (Application Example 1)

[0270] 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."

[0271] Conventional communication management systems have difficulty providing appropriate emergency responses when users are absent, and in particular, real-time management of mechanical equipment is insufficient in production sites. Furthermore, there is a lack of means to effectively organize communications that occur while users are on vacation and to respond quickly upon their return to work.

[0272] 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.

[0273] In this invention, the server includes means for analyzing the user's personal data communication history and learning the content of responses; means for evaluating newly received data communications and determining their urgency; means for generating and transmitting an automated response based on the determined urgency; and means for delivering instructions in real time to mechanical devices in the production environment and applying the automated response. This enables appropriate emergency response at the production site even when the user is absent, and allows for efficient response based on the organization and prioritization of communications.

[0274] "Personal data communication history" refers to emails, messages, and other communication information that a user has sent in the past, and is used to analyze specific trends and patterns.

[0275] "Urgency" is an indicator used to determine whether received data communications require immediate attention, and is usually determined by keywords and content.

[0276] An "automated response" is a reply message generated by an AI model, acting on behalf of the user, and is sent with appropriate content pre-formed.

[0277] "Mechanical equipment in the production environment" refers to machinery, robots, and other equipment operating in factories and production sites, and is subject to management and operation.

[0278] "Real-time instruction delivery" refers to a process where, as soon as information is received, the appropriate instruction is immediately sent to the target device, with the aim of ensuring a response without delay.

[0279] To implement this system, the server receives the user's personal data communication history and analyzes its content. Using Python and the AI framework TensorFlow, the server generates an AI model that learns the user's reply tendencies. This enables modeling the user's individual communication style and determining the urgency of newly received data communications.

[0280] The server evaluates the data and, when a new message is received, analyzes its urgency in real time. By using a message queue service such as AWS SQS to process the necessary data and delivering appropriate instructions to the mechanical devices in the production environment in real time, it supports the continuous operation of the production line.

[0281] As a specific example, when a malfunction occurs in a production machine in a factory, even if the operator is on break, the automatic response system immediately detects the problem, generates an alternative procedure, and sends it to the device. This makes it possible to minimize production delays.

[0282] Furthermore, the server has a mechanism to sort all communications received during vacation and provide a task list based on priority to the returning user. This enables smooth progress of work after resuming work.

[0283] An example of a prompt sentence for the generated AI model is "When an unexpected trouble occurs while the operator is on break, propose how to handle it and generate a method to process it safely and efficiently immediately." Based on this prompt sentence, the AI forms an appropriate response and applies the automated instructions to the device.

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The server receives the past data communication history from the user. This input data includes the sending and receiving records of emails and messages. The server analyzes these data and extracts the user's communication trends. As data processing, a natural language processing library is used to recognize the patterns of message content, and as output, a generative AI model that models the user-specific response style is obtained.

[0287] Step 2:

[0288] When the terminal receives a new data communication, it immediately sends the communication content to the server. This input message includes the date, sender, and content. The server performs data operations using a pre-set keyword detection algorithm to determine the urgency of the received message. As output, when the urgency is high, a notification flag indicating that it should be processed preferentially is generated.

[0289] Step 3:

[0290] Based on the determined urgency, the server generates an automatic response. The inputs are the generative AI model, the content of the received message, and the urgency flag. The server uses the model to generate an appropriate response text and sends the response to the terminal through the message queue service. The output is the automatic response message displayed on the terminal.

[0291] Step 4:

[0292] The mechanical device installed in the production environment receives instructions from the terminal. Based on the response message output from the server, the device executes the specified operation. This input includes the instruction content for the machine to perform specific operations. As a result of the execution, the production activities proceed as planned, which is the output.

[0293] Step 5:

[0294] When a user returns from vacation, the server organizes all communications received during their vacation. Input data includes received messages, their urgency, and processing history. The server generates a task list based on priority and sends it to the user's terminal, providing guidance on actions to take upon their return. This output is the task list displayed on the user's screen.

[0295] 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.

[0296] This invention is a communication management system that takes user emotions into consideration, and by incorporating an emotion engine that analyzes the user's past and present emotions, it provides more personalized responses. This system aims to improve communication methods that respond to the user's emotional state, in addition to automated responses to emails and chats.

[0297] First, the user provides past email and chat data. This allows the system to learn not only the user's response patterns but also the emotional changes in past communications. The server analyzes this data and applies natural language processing and sentiment analysis algorithms to generate a user response model. This process helps to understand the situations in which specific emotions were triggered and the user's reactions to them.

[0298] The device detects newly received data communication and sends it to the server. At this time, the emotion engine is activated and evaluates the user's emotions that the sent message is expected to evoke. For example, if the message contains content that would cause stress, the emotion engine will identify that.

[0299] The server generates an automated response based on the urgency of the message, including emotionally sensitive content when sending it. For example, if the content might cause offense to the recipient, it selects a response that incorporates apologies and mitigating expressions.

[0300] Furthermore, the server sorts out communications during vacations based on the user's emotional changes and generates a task list based on priorities considering emotional loads. This list displayed on the terminal serves as a guideline for the user to smoothly resume work and plays a role in reducing mental burdens.

[0301] By incorporating the emotion engine of the present invention, not only can the user receive appropriate communication responses according to their emotional states, but also reduce emotional stress during vacations and improve work efficiency after returning to work.

[0302] The following describes the processing flow.

[0303] Step 1:

[0304] The user provides the system with past email and chat data. Thereby, the system obtains a basis for learning individual reply patterns as well as emotional reactions.

[0305] Step 2:

[0306] The server analyzes the provided data and constructs a response model that learns the user's reply style and emotional changes using natural language processing technology and emotion analysis algorithms. This model serves as a basis for generating response content.

[0307] Step 3:

[0308] The terminal transmits newly received emails and chat messages to the server in real time. Thereby, the data necessary for emotion analysis at the time of reception is supplied to the server.

[0309] Step 4:

[0310] The server analyzes incoming messages and uses a sentiment engine to evaluate how the message might affect the user's emotions. For example, it can detect potentially stressful situations from the sender's past message history.

[0311] Step 5:

[0312] If the message is urgent or a standard automated response is appropriate, the server uses an AI model to generate a response. This response is then refined to reflect emotional assessments, such as expressing gratitude or using polite language.

[0313] Step 6:

[0314] The terminal sends a generated response to the recipient based on instructions from the server. This response is sent on behalf of the user and is emotionally sensitive.

[0315] Step 7:

[0316] After communications during the holiday period have been processed, the server organizes the received messages, prioritizes them based on their content and the emotional impact on the users, and generates a task list. This task list is created taking emotional load into consideration.

[0317] Step 8:

[0318] The terminal displays a task list and action plan received from the server upon the user's return from vacation, assisting them in resuming work. Based on these guidelines, users can efficiently carry out their tasks while reducing mental stress.

[0319] (Example 2)

[0320] 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".

[0321] Modern information and communication are constantly increasing, particularly in the workplace, leading to rising psychological burdens. In particular, accurately understanding the priority of various types of information and providing appropriate responses immediately is difficult, and users are often overwhelmed by the sheer volume of data. Furthermore, automated responses that do not consider the user's emotional state can cause additional stress. Therefore, there is a need for communication management systems that provide efficient and appropriate responses while taking user emotions into consideration.

[0322] 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.

[0323] In this invention, the server includes means for analyzing the user's information history and learning response content, means for evaluating newly received information and determining its importance, and means for analyzing the user's emotions and generating responses that take their emotional state into consideration. This enables accurate responses that take the user's emotions into account and information organization based on priority.

[0324] "Information history" refers to records of various communications that a user has experienced in the past, including exchanges such as emails and chats.

[0325] "Means of learning response content" refers to the process by which the system extracts patterns from past information history to understand how users respond.

[0326] "Means for determining importance" refers to criteria or algorithms used to identify which newly received information should be processed with priority.

[0327] "Means for analyzing emotions and generating responses that take emotional state into consideration" refers to a process that evaluates the user's emotions from past and present communications and automatically generates appropriate responses based on that evaluation.

[0328] "Priority-based information organization" refers to a method of classifying received information according to certain criteria or conditions and setting a processing order based on its importance and urgency.

[0329] This invention is a system that analyzes a user's information history and manages communication while taking the user's emotions into consideration. The following describes a specific form for implementing this system.

[0330] Users provide the system with their past email and chat history. This creates foundational data for learning the user's communication style and emotional changes. First, this information is sent to the server.

[0331] The server uses natural language processing toolkits (e.g., NLTK, spaCy) and sentiment analysis software (e.g., VADER, TextBlob) to analyze the received historical data. These tools analyze sentiment from text and learn user response patterns. This analysis is important for understanding what emotions were evoked in specific situations and for grasping the user's response to those emotions.

[0332] The device instantly detects the arrival of new emails or chat messages. This detected information is then sent back to the server. The server uses an emotion engine to evaluate how the sent message will affect the user's emotions.

[0333] Based on this assessment, the server generates an automated response. This response is designed with a particular focus on reducing stress, and includes mitigating expressions and apologies for content that the user might find offensive. This system allows users to receive communications that are appropriate to their emotional state.

[0334] For example, if a user receives an important message while on vacation, the system organizes the information and generates a task list based on priority. This task list serves as a guide for quickly resuming work upon returning to the office.

[0335] Another example of a prompt is: "What words soothe you when you're feeling stressed? Please tell me some specific phrases or content." Based on such prompts, responses tailored to the user's needs are constructed.

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1:

[0338] Users provide the system with their past email and chat data. This input data serves as foundational data for analyzing communication patterns and emotional changes. Specifically, users upload data through a dedicated interface, which is then received by the server.

[0339] Step 2:

[0340] The server analyzes the provided data using natural language processing toolkits (e.g., NLTK, spaCy). It receives text data as input and extracts user response patterns through morphological analysis and sentiment scoring. The output provides characteristic user sentiment patterns and response tendencies. This process evaluates whether specific words or expressions evoke positive or negative emotions.

[0341] Step 3:

[0342] The device detects when a new message (email or chat) arrives. This becomes new input data and is sent to the server. This process happens automatically when the received message arrives in the user's inbox.

[0343] Step 4:

[0344] The server uses an emotion engine to analyze newly received messages. Based on the messages received as input data, it evaluates the impact on the user's emotions from the tone and content of the messages. The output is an emotion score and a predicted emotional state. Specifically, the process includes keyword extraction and the assignment of corresponding emotion labels.

[0345] Step 5:

[0346] The server generates an automated response that takes emotions into consideration, based on the results of sentiment analysis. Using a generative AI model, it creates the optimal reply based on past learning results and the user's current emotional state. When generating this prompt, it selects words and expressions that alleviate user stress. The output is an automatically generated response message.

[0347] Step 6:

[0348] The server organizes all communications received by the user during their vacation and generates a task list based on priority. It receives messages received during the vacation as input and sorts them according to importance and emotional burden. The final output is a task list that serves as a guide for work upon returning to work and is displayed on the terminal. The specific operation involves a process of visually organizing the listed tasks and presenting them to the user in an easy-to-understand format.

[0349] (Application Example 2)

[0350] 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."

[0351] In recent years, with the advancement of information and communication technology, the content of data communications has diversified, and users now receive a large volume of messages daily. This has made it difficult to adequately address the mental burden caused by information overload and security risks such as phishing emails. In particular, uniform notifications and responses that do not take into account the user's emotional state are problematic because they prevent the user from taking the optimal action in an emergency. Therefore, there is a need to provide more personalized response systems that take into account the user's emotions and the urgency of the situation.

[0352] 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.

[0353] In this invention, the server includes means for analyzing the user's personal data communication history and learning response content, means for evaluating incoming new data communication and determining its urgency, and means for analyzing the user's emotional state and evaluating security risks. This enables personalized notifications and responses according to the user's emotional state and appropriate responses according to urgency. Furthermore, by providing users with warnings that address security risks, the burden of information overload is reduced, allowing users to use communication with peace of mind.

[0354] "User's personal data communication history" refers to all communication history, such as emails and chats, that a user has made in the past. This data serves as foundational information for analyzing the user's communication patterns and emotional changes.

[0355] "Analysis means" refers to technologies that analyze the content of user communications using natural language processing and sentiment analysis algorithms based on data communication history, and generate response models.

[0356] "Means for determining urgency" refers to technology that analyzes the content of received data communications to determine how important and urgent that content is to the user.

[0357] "Means for generating automated responses" refers to a mechanism for automatically creating response messages in an appropriate format based on the user's communication history and the assessment of urgency.

[0358] "Means for analyzing emotional states" refers to analytical techniques that identify a user's emotions based on past and received data, and then respond appropriately to that emotional state.

[0359] "Means of assessing security risks" refers to evaluation methods for determining whether received data is potentially harmful to the user and for issuing warnings accordingly.

[0360] "Means for generating warnings" refers to technologies that create appropriate warning messages and notify users when security risks are identified.

[0361] The system of this invention enables communication management that takes user emotions into consideration, and includes means for analyzing the user's personal data communication history and learning response content. The system consists of a server, terminals, and multiple interconnected components for effectively managing each user's data.

[0362] The server is implemented in a programming language such as Python and utilizes natural language processing (NLP) libraries (e.g., NLTK, spaCy) and sentiment analysis libraries (e.g., TextBlob, IBM Watson). The server first receives the user's past communication data and analyzes it using natural language processing techniques. This analyzes the user's unique response tendencies and emotional fluctuations, and generates a response model. Using this response model, the server evaluates the urgency of newly received data communications and generates an automated response as needed.

[0363] Furthermore, the server can analyze the user's emotional state in real time and assess security risks. This allows for the identification of potential risks, such as phishing emails, and enables the user to receive appropriate warnings. These warnings take into account the stress and emotional reactions the user might face, and are designed to support them in responding calmly.

[0364] The device displays notifications and warnings sent from the server to the user in real time. This allows the user to immediately understand information regarding the importance and risks of received communications and take swift and appropriate action. As part of this system, smartphones and other mobile devices play a crucial role and are designed to ensure that the immediacy and convenience of communications are not compromised.

[0365] For example, if a fraudulent email disguised as coming from a financial institution arrives, the server analyzes the email and, based on the user's sentiment analysis, sends a warning message stating, "This email is dangerous. Do not open any links or attachments." This allows users to detect risks in advance and prevent damage.

[0366] An example of a prompt message is: "Analyze the user's sentiment and analyze the risk based on the email content and changes in sentiment. Create an appropriate warning message as needed." This provides a foundation for effectively utilizing generative AI models to make the user experience safer and more comfortable.

[0367] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0368] Step 1:

[0369] The server collects the user's past data communication history. This input data includes email and chat history. The server uses natural language processing techniques to analyze this history and identify the user's unique response tendencies and changes in emotion. The output of this process is a model of the user's emotions and responses.

[0370] Step 2:

[0371] The server detects newly received data communications. These communications become input data, and the server applies an algorithm to determine their urgency. In this process, context and keywords are analyzed, the importance of the communications is evaluated, and the results are output.

[0372] Step 3:

[0373] The server analyzes received communication data using a sentiment analysis library to re-evaluate the user's current emotional state. The input data is the content of the new communication, and the output is the result of the emotional state evaluation. Based on this evaluation, an automated response is generated as needed.

[0374] Step 4:

[0375] The server generates security alerts as needed based on the analysis results and evaluation. When an alert is necessary, such as in the case of a potentially phishing email, it creates an alert message composed of specific yet gentle language. The input is sentiment and security evaluation, and the output is the alert message.

[0376] Step 5:

[0377] The terminal displays notifications and warnings sent from the server to the user. In this process, the notification content (input) is displayed on the screen (output), making it immediately available to the user. This allows the user to take a quick and appropriate action.

[0378] 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.

[0379] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0380] 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.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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".

[0394] This invention provides a system that allows users to manage their work-related communications more efficiently and relax with peace of mind while on vacation. This system uses an AI agent that learns from the user's past communication history and provides a function to automatically respond to emails and chats on the user's behalf.

[0395] First, when a user starts using the system, they provide their past email and chat history. This allows the system to learn the user's communication patterns and model individual response styles. Next, the server generates an AI model based on this data and prepares for automated responses.

[0396] When a user takes leave, the device sends newly received emails and chat messages to the server in real time. The server evaluates these messages and determines whether an automated response is necessary. An algorithm is built in to assess the urgency of messages; for example, if a message contains keywords such as "urgent" or "immediate," the server prioritizes notifying the user of that message.

[0397] Furthermore, if an automated reply is appropriate but not urgent, the server will select an appropriate response and automatically send it through the device. For example, if a simple email requesting a meeting schedule arrives, the AI ​​will generate and send a response such as, "We have received your request, Mr. / Ms. XX. We will confirm by [date]."

[0398] Furthermore, to assist users returning to work after their holidays, the server organizes all communications during the holiday period and generates a task list based on priority. This list is sent to the terminal and serves as a guide for what users should do upon their return.

[0399] Thus, the present invention is a system that utilizes AI to enable users to efficiently manage their communications, thereby allowing them to spend their vacations without worry and to smoothly resume work after returning to the office.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] To begin using the system, users provide their past email and chat history data. This allows the system to obtain the basic data needed to learn the user's communication patterns.

[0403] Step 2:

[0404] The server analyzes the provided historical data and builds an AI model that learns user response patterns using natural language processing techniques. This model is used to generate responses based on specific vocabulary and context.

[0405] Step 3:

[0406] The device detects newly received emails and chat messages and sends that data to the server in real time. The received data is temporarily stored on the server in preparation for subsequent processing.

[0407] Step 4:

[0408] The server evaluates the urgency of incoming messages. This is done based on keyword analysis and sender identification, and if it determines that the message is highly urgent, it instructs the terminal to immediately notify the user.

[0409] Step 5:

[0410] For low-urgency messages, the server uses an AI model to generate an appropriate automated response. For example, for routine meeting request emails, the system constructs a response based on response styles it has learned.

[0411] Step 6:

[0412] The terminal follows instructions from the server and sends a generated automated response to the recipient. This history of automated replies is saved as a log so that the user can review it as needed.

[0413] Step 7:

[0414] After communication processing during the holiday period is complete, the server organizes all messages and generates a prioritized task list based on their content. This includes categorization by importance and deadline.

[0415] Step 8:

[0416] Upon user return, the terminal displays a task list received from the server to assist in resuming work. Users can then use this list to efficiently process tasks.

[0417] (Example 1)

[0418] 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."

[0419] In today's information and communication environment, users need to process a large volume of electronic communications on a daily basis, and this task can affect work efficiency and mental burden. In particular, during vacations, users temporarily disconnect from work communications, making it difficult to manage communications during that time. In addition, misjudging the urgency of communications can lead to missing important information. Therefore, there is a need to provide an environment where users can relax during their vacations and quickly resume work upon their return.

[0420] 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.

[0421] In this invention, the server includes means for analyzing the user's electronic communication history and learning the content of responses, means for evaluating newly received electronic communications and determining their urgency, and means for sending emergency notifications to the user's computer. This enables the automatic processing of electronic communications even when the user is absent, allowing access to important information at the appropriate time.

[0422] A "user" refers to an individual or legal entity that uses this system to manage electronic communications.

[0423] "Electronic communication history" refers to all digital communication data, such as emails and chat messages, that a user has sent or received to date.

[0424] "Analysis" refers to the process of using computers to evaluate data in detail and extract specific information or patterns.

[0425] "Response content" refers to the information or message generated as a response to communication from the user.

[0426] "Learning" is the process of using AI technology to acquire specific behavioral patterns from data and update the model.

[0427] "Urgency" is a measure used to evaluate the importance and priority of a communication to a user.

[0428] "Automatic response" refers to a reply message that is generated and sent by the system without any manual action from the user.

[0429] "Notifications" refer to system functions that include messages and warnings to immediately convey information to the user.

[0430] The term "computer" refers to a broad range of devices, including all electronic equipment that performs data processing, and especially computers.

[0431] This invention provides a system that frees users from manual communication handling. The system mainly consists of three elements: a server, a terminal, and a user.

[0432] To begin using the system, users must provide their past electronic communication history to the server. This is done by importing data using email clients or chat applications. Specifically, dedicated client software can be used to transfer history data from major communication platforms to the server.

[0433] The server analyzes the received data and executes a learning process using a generative AI model. During this process, natural language processing techniques are utilized to identify patterns in user responses. By employing deep learning algorithms using libraries such as TensorFlow and PyTorch, a response model optimized for each individual user is generated.

[0434] When a user sets a vacation, the device is configured to send newly received electronic communications to the server. The device assumes a device running a currently dominant OS (e.g., iOS, Android, Windows, etc.) and transfers data using a secure protocol.

[0435] The server evaluates newly received messages and determines their urgency. If the message is highly urgent, it immediately sends a notification to the user's computer. This determination is made using algorithms that perform keyword analysis of the message content. If an automated response is deemed appropriate, the server generates a reply based on the AI ​​model and sends it through the terminal. As a concrete example of a prompt, telling the server something like, "When a new email arrives, please use the AI ​​model to analyze its content and determine if an automated response is possible," will immediately generate a response.

[0436] This system allows users to respond to communications even when they are away, and provides an environment where communication processing can be carried out smoothly according to priority upon their return.

[0437] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0438] Step 1:

[0439] The user provides their past electronic communication history to the server. The user imports past message data into the system using an email client or chat application. The input includes data from the user's chosen communication platform, which the server receives and converts into a parseable format. This conversion includes the process of converting the file to a standard data format (e.g., a CSV file). The output is the user's communication data, ready for analysis, input into the server.

[0440] Step 2:

[0441] The server analyzes the received communication data using a specific algorithm. Specifically, it uses natural language processing techniques to extract patterns and frequently occurring phrases within the messages. As input, the server is provided with the user's communication history data, which is then analyzed. As part of the data processing, text analysis is performed to identify keywords and response styles that the user has used in the past. Based on this, a user-specific response model is generated as output and stored on the server.

[0442] Step 3:

[0443] The server uses the generated response model to assess the urgency of newly received communications. New messages received from terminals are forwarded to the server, and analysis begins. The input consists of user communication data and newly received messages, and the AI ​​model is used to determine the urgency. The output is the determined urgency level of the message, and the user is notified if necessary.

[0444] Step 4:

[0445] The terminal instantly notifies the user of emergency message notifications sent from the server. If the message is deemed highly urgent, the terminal displays an alert according to the user's default notification settings. The input is emergency message information provided by the server, and the output is a notification displayed on the user's device. This allows the user to respond immediately to particularly important communications.

[0446] Step 5:

[0447] The server uses AI to generate automated responses for non-urgent messages. As input, the server receives new messages requiring processing and user response models, and sends the generated response content via the terminal. As output, a response message tailored to the user's style is generated and sent to the recipient. An example of this is a response to a scheduling email.

[0448] Step 6:

[0449] When a user returns from vacation, the server generates a task list with priority based on the processed communication data. The input includes all communication data received during the vacation and its evaluation results, which are used to formulate action plans. The output is a task list necessary for resuming work upon returning to the office, which is sent to the user's terminal. This creates an environment where users can efficiently resume their work.

[0450] (Application Example 1)

[0451] 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."

[0452] Conventional communication management systems have difficulty providing appropriate emergency responses when users are absent, and in particular, real-time management of mechanical equipment is insufficient in production sites. Furthermore, there is a lack of means to effectively organize communications that occur while users are on vacation and to respond quickly upon their return to work.

[0453] 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.

[0454] In this invention, the server includes means for analyzing the user's personal data communication history and learning the content of responses; means for evaluating newly received data communications and determining their urgency; means for generating and transmitting an automated response based on the determined urgency; and means for delivering instructions in real time to mechanical devices in the production environment and applying the automated response. This enables appropriate emergency response at the production site even when the user is absent, and allows for efficient response based on the organization and prioritization of communications.

[0455] "Personal data communication history" refers to emails, messages, and other communication information that a user has sent in the past, and is used to analyze specific trends and patterns.

[0456] "Urgency" is an indicator used to determine whether received data communications require immediate attention, and is usually determined by keywords and content.

[0457] An "automated response" is a reply message generated by an AI model, acting on behalf of the user, and is sent with appropriate content pre-formed.

[0458] "Mechanical equipment in the production environment" refers to machinery, robots, and other equipment operating in factories and production sites, and is subject to management and operation.

[0459] "Real-time instruction delivery" refers to a process where, as soon as information is received, the appropriate instruction is immediately sent to the target device, with the aim of ensuring a response without delay.

[0460] To implement this system, the server receives the user's personal data communication history and analyzes its contents. The server uses Python and the AI ​​framework TensorFlow to generate an AI model that learns the user's response tendencies. This makes it possible to model the user's individual communication style and determine the urgency of newly received data communications.

[0461] The server evaluates the data and analyzes the urgency of new messages in real time as they arrive. It uses a message queuing service like AWS SQS to process the necessary data and delivers appropriate instructions to mechanical equipment in the production environment in real time, supporting the continuous operation of the production line.

[0462] As a concrete example, if a production machine malfunctions in a factory, the automated response system can immediately detect the problem, generate an alternative procedure, and send it to the machine, even if the operator is on break. This minimizes production delays.

[0463] Furthermore, the server has a mechanism to organize all communications received during vacation and provide users with a priority-based task list upon their return. This allows them to smoothly resume work after returning to the office.

[0464] An example of a prompt for the generating AI model is: "If an unexpected problem occurs while the operator is on break, suggest how to deal with it and generate a method for handling it immediately, safely, and efficiently." Based on this prompt, the AI ​​forms an appropriate response and applies automated instructions to the device.

[0465] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0466] Step 1:

[0467] The server receives past data communication history from the user. This input data includes records of sending and receiving emails and messages. The server analyzes this data to extract the user's communication trends. As part of data processing, a natural language processing library is used to recognize patterns in message content, and the output is a generative AI model that models the user's unique response style.

[0468] Step 2:

[0469] When a terminal receives new data communication, it immediately sends the content of that communication to the server. This input message includes the date, sender, and content. The server performs data calculations using a pre-configured keyword detection algorithm to determine the urgency of the received message. As output, if the urgency is high, it generates a notification flag indicating that it should be processed with priority.

[0470] Step 3:

[0471] The server generates an automated response based on the determined urgency level. Inputs include a generation AI model, the content of the received message, and an urgency flag. The server uses the model to generate an appropriate response and sends it to the terminal via the message queue service. The output is the automated response message displayed on the terminal.

[0472] Step 4:

[0473] Mechanical devices installed in the production environment receive instructions from terminals. Based on the response messages output from the server, the devices perform the specified actions. This input contains instructions on what specific operations the machine should perform. As a result of the execution, the planned production activities are maintained, and this becomes the output.

[0474] Step 5:

[0475] When a user returns from vacation, the server organizes all communications received during their vacation. Input data includes received messages, their urgency, and processing history. The server generates a task list based on priority and sends it to the user's terminal, providing guidance on actions to take upon their return. This output is the task list displayed on the user's screen.

[0476] 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.

[0477] This invention is a communication management system that takes user emotions into consideration, and by incorporating an emotion engine that analyzes the user's past and present emotions, it provides more personalized responses. This system aims to improve communication methods that respond to the user's emotional state, in addition to automated responses to emails and chats.

[0478] First, the user provides past email and chat data. This allows the system to learn not only the user's response patterns but also the emotional changes in past communications. The server analyzes this data and applies natural language processing and sentiment analysis algorithms to generate a user response model. This process helps to understand the situations in which specific emotions were triggered and the user's reactions to them.

[0479] The device detects newly received data communication and sends it to the server. At this time, the emotion engine is activated and evaluates the user's emotions that the sent message is expected to evoke. For example, if the message contains content that would cause stress, the emotion engine will identify that.

[0480] The server generates an automated response based on the urgency of the message, including emotionally sensitive content when sending it. For example, if the content might cause offense to the recipient, it selects a response that incorporates apologies and mitigating expressions.

[0481] Furthermore, the server organizes communications during vacation based on the user's emotional changes and generates a task list based on priorities that take emotional stress into account. This list, displayed on the terminal, serves as a guide for the user to smoothly resume work and helps reduce mental burden.

[0482] By incorporating the emotion engine of this invention, users can not only receive appropriate communication responses according to their emotional state, but also reduce emotional stress during vacation and improve work efficiency upon returning to work.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] Users provide the system with past email and chat data. This gives the system a foundation to learn not only individual reply patterns but also emotional responses.

[0486] Step 2:

[0487] The server analyzes the provided data and uses natural language processing techniques and sentiment analysis algorithms to build a response model that learns the user's reply style and emotional changes. This model serves as the basis for generating response content.

[0488] Step 3:

[0489] The device sends newly received emails and chat messages to the server in real time. This provides the server with the data necessary for sentiment analysis at the time of reception.

[0490] Step 4:

[0491] The server analyzes incoming messages and uses a sentiment engine to evaluate how the message might affect the user's emotions. For example, it can detect potentially stressful situations from the sender's past message history.

[0492] Step 5:

[0493] If the message is urgent or a standard automated response is appropriate, the server uses an AI model to generate a response. This response is then refined to reflect emotional assessments, such as expressing gratitude or using polite language.

[0494] Step 6:

[0495] The terminal sends a generated response to the recipient based on instructions from the server. This response is sent on behalf of the user and is emotionally sensitive.

[0496] Step 7:

[0497] After communications during the holiday period have been processed, the server organizes the received messages, prioritizes them based on their content and the emotional impact on the users, and generates a task list. This task list is created taking emotional load into consideration.

[0498] Step 8:

[0499] The terminal displays a task list and action plan received from the server upon the user's return from vacation, assisting them in resuming work. Based on these guidelines, users can efficiently carry out their tasks while reducing mental stress.

[0500] (Example 2)

[0501] 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."

[0502] Modern information and communication are constantly increasing, particularly in the workplace, leading to rising psychological burdens. In particular, accurately understanding the priority of various types of information and providing appropriate responses immediately is difficult, and users are often overwhelmed by the sheer volume of data. Furthermore, automated responses that do not consider the user's emotional state can cause additional stress. Therefore, there is a need for communication management systems that provide efficient and appropriate responses while taking user emotions into consideration.

[0503] 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.

[0504] In this invention, the server includes means for analyzing the user's information history and learning response content, means for evaluating newly received information and determining its importance, and means for analyzing the user's emotions and generating responses that take their emotional state into consideration. This enables accurate responses that take the user's emotions into account and information organization based on priority.

[0505] "Information history" refers to records of various communications that a user has experienced in the past, including exchanges such as emails and chats.

[0506] "Means of learning response content" refers to the process by which the system extracts patterns from past information history to understand how users respond.

[0507] "Means for determining importance" refers to criteria or algorithms used to identify which newly received information should be processed with priority.

[0508] "Means for analyzing emotions and generating responses that take emotional state into consideration" refers to a process that evaluates the user's emotions from past and present communications and automatically generates appropriate responses based on that evaluation.

[0509] "Priority-based information organization" refers to a method of classifying received information according to certain criteria or conditions and setting a processing order based on its importance and urgency.

[0510] This invention is a system that analyzes a user's information history and manages communication while taking the user's emotions into consideration. The following describes a specific form for implementing this system.

[0511] Users provide the system with their past email and chat history. This creates foundational data for learning the user's communication style and emotional changes. First, this information is sent to the server.

[0512] The server uses natural language processing toolkits (e.g., NLTK, spaCy) and sentiment analysis software (e.g., VADER, TextBlob) to analyze the received historical data. These tools analyze sentiment from text and learn user response patterns. This analysis is important for understanding what emotions were evoked in specific situations and for grasping the user's response to those emotions.

[0513] The device instantly detects the arrival of new emails or chat messages. This detected information is then sent back to the server. The server uses an emotion engine to evaluate how the sent message will affect the user's emotions.

[0514] Based on this assessment, the server generates an automated response. This response is designed with a particular focus on reducing stress, and includes mitigating expressions and apologies for content that the user might find offensive. This system allows users to receive communications that are appropriate to their emotional state.

[0515] For example, if a user receives an important message while on vacation, the system organizes the information and generates a task list based on priority. This task list serves as a guide for quickly resuming work upon returning to the office.

[0516] Another example of a prompt is: "What words soothe you when you're feeling stressed? Please tell me some specific phrases or content." Based on such prompts, responses tailored to the user's needs are constructed.

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] Users provide the system with their past email and chat data. This input data serves as foundational data for analyzing communication patterns and emotional changes. Specifically, users upload data through a dedicated interface, which is then received by the server.

[0520] Step 2:

[0521] The server analyzes the provided data using natural language processing toolkits (e.g., NLTK, spaCy). It receives text data as input and extracts user response patterns through morphological analysis and sentiment scoring. The output provides characteristic user sentiment patterns and response tendencies. This process evaluates whether specific words or expressions evoke positive or negative emotions.

[0522] Step 3:

[0523] The device detects when a new message (email or chat) arrives. This becomes new input data and is sent to the server. This process happens automatically when the received message arrives in the user's inbox.

[0524] Step 4:

[0525] The server uses an emotion engine to analyze newly received messages. Based on the messages received as input data, it evaluates the impact on the user's emotions from the tone and content of the messages. The output is an emotion score and a predicted emotional state. Specifically, the process includes keyword extraction and the assignment of corresponding emotion labels.

[0526] Step 5:

[0527] The server generates an automated response that takes emotions into consideration, based on the results of sentiment analysis. Using a generative AI model, it creates the optimal reply based on past learning results and the user's current emotional state. When generating this prompt, it selects words and expressions that alleviate user stress. The output is an automatically generated response message.

[0528] Step 6:

[0529] The server organizes all communications received by the user during their vacation and generates a task list based on priority. It receives messages received during the vacation as input and sorts them according to importance and emotional burden. The final output is a task list that serves as a guide for work upon returning to work and is displayed on the terminal. The specific operation involves a process of visually organizing the listed tasks and presenting them to the user in an easy-to-understand format.

[0530] (Application Example 2)

[0531] 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."

[0532] In recent years, with the advancement of information and communication technology, the content of data communications has diversified, and users now receive a large volume of messages daily. This has made it difficult to adequately address the mental burden caused by information overload and security risks such as phishing emails. In particular, uniform notifications and responses that do not take into account the user's emotional state are problematic because they prevent the user from taking the optimal action in an emergency. Therefore, there is a need to provide more personalized response systems that take into account the user's emotions and the urgency of the situation.

[0533] 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.

[0534] In this invention, the server includes means for analyzing the user's personal data communication history and learning response content, means for evaluating incoming new data communication and determining its urgency, and means for analyzing the user's emotional state and evaluating security risks. This enables personalized notifications and responses according to the user's emotional state and appropriate responses according to urgency. Furthermore, by providing users with warnings that address security risks, the burden of information overload is reduced, allowing users to use communication with peace of mind.

[0535] "User's personal data communication history" refers to all communication history, such as emails and chats, that a user has made in the past. This data serves as foundational information for analyzing the user's communication patterns and emotional changes.

[0536] "Analysis means" refers to technologies that analyze the content of user communications using natural language processing and sentiment analysis algorithms based on data communication history, and generate response models.

[0537] "Means for determining urgency" refers to technology that analyzes the content of received data communications to determine how important and urgent that content is to the user.

[0538] "Means for generating automated responses" refers to a mechanism for automatically creating response messages in an appropriate format based on the user's communication history and the assessment of urgency.

[0539] "Means for analyzing emotional states" refers to analytical techniques that identify a user's emotions based on past and received data, and then respond appropriately to that emotional state.

[0540] "Means of assessing security risks" refers to evaluation methods for determining whether received data is potentially harmful to the user and for issuing warnings accordingly.

[0541] "Means for generating warnings" refers to technologies that create appropriate warning messages and notify users when security risks are identified.

[0542] The system of this invention enables communication management that takes user emotions into consideration, and includes means for analyzing the user's personal data communication history and learning response content. The system consists of a server, terminals, and multiple interconnected components for effectively managing each user's data.

[0543] The server is implemented in a programming language such as Python and utilizes natural language processing (NLP) libraries (e.g., NLTK, spaCy) and sentiment analysis libraries (e.g., TextBlob, IBM Watson). The server first receives the user's past communication data and analyzes it using natural language processing techniques. This analyzes the user's unique response tendencies and emotional fluctuations, and generates a response model. Using this response model, the server evaluates the urgency of newly received data communications and generates an automated response as needed.

[0544] Furthermore, the server can analyze the user's emotional state in real time and assess security risks. This allows for the identification of potential risks, such as phishing emails, and enables the user to receive appropriate warnings. These warnings take into account the stress and emotional reactions the user might face, and are designed to support them in responding calmly.

[0545] The device displays notifications and warnings sent from the server to the user in real time. This allows the user to immediately understand information regarding the importance and risks of received communications and take swift and appropriate action. As part of this system, smartphones and other mobile devices play a crucial role and are designed to ensure that the immediacy and convenience of communications are not compromised.

[0546] For example, if a fraudulent email disguised as coming from a financial institution arrives, the server analyzes the email and, based on the user's sentiment analysis, sends a warning message stating, "This email is dangerous. Do not open any links or attachments." This allows users to detect risks in advance and prevent damage.

[0547] An example of a prompt message is: "Analyze the user's sentiment and analyze the risk based on the email content and changes in sentiment. Create an appropriate warning message as needed." This provides a foundation for effectively utilizing generative AI models to make the user experience safer and more comfortable.

[0548] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0549] Step 1:

[0550] The server collects the user's past data communication history. This input data includes email and chat history. The server uses natural language processing techniques to analyze this history and identify the user's unique response tendencies and changes in emotion. The output of this process is a model of the user's emotions and responses.

[0551] Step 2:

[0552] The server detects newly received data communications. These communications become input data, and the server applies an algorithm to determine their urgency. In this process, context and keywords are analyzed, the importance of the communications is evaluated, and the results are output.

[0553] Step 3:

[0554] The server analyzes received communication data using a sentiment analysis library to re-evaluate the user's current emotional state. The input data is the content of the new communication, and the output is the result of the emotional state evaluation. Based on this evaluation, an automated response is generated as needed.

[0555] Step 4:

[0556] The server generates security alerts as needed based on the analysis results and evaluation. When an alert is necessary, such as in the case of a potentially phishing email, it creates an alert message composed of specific yet gentle language. The input is sentiment and security evaluation, and the output is the alert message.

[0557] Step 5:

[0558] The terminal displays notifications and warnings sent from the server to the user. In this process, the notification content (input) is displayed on the screen (output), making it immediately available to the user. This allows the user to take a quick and appropriate action.

[0559] 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.

[0560] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0561] 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.

[0562] [Fourth Embodiment]

[0563] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0564] 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.

[0565] 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).

[0566] 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.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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".

[0576] This invention provides a system that allows users to manage their work-related communications more efficiently and relax with peace of mind while on vacation. This system uses an AI agent that learns from the user's past communication history and provides a function to automatically respond to emails and chats on the user's behalf.

[0577] First, when a user starts using the system, they provide their past email and chat history. This allows the system to learn the user's communication patterns and model individual response styles. Next, the server generates an AI model based on this data and prepares for automated responses.

[0578] When a user takes leave, the device sends newly received emails and chat messages to the server in real time. The server evaluates these messages and determines whether an automated response is necessary. An algorithm is built in to assess the urgency of messages; for example, if a message contains keywords such as "urgent" or "immediate," the server prioritizes notifying the user of that message.

[0579] Furthermore, if an automated reply is appropriate but not urgent, the server will select an appropriate response and automatically send it through the device. For example, if a simple email requesting a meeting schedule arrives, the AI ​​will generate and send a response such as, "We have received your request, Mr. / Ms. XX. We will confirm by [date]."

[0580] Furthermore, to assist users returning to work after their holidays, the server organizes all communications during the holiday period and generates a task list based on priority. This list is sent to the terminal and serves as a guide for what users should do upon their return.

[0581] Thus, the present invention is a system that utilizes AI to enable users to efficiently manage their communications, thereby allowing them to spend their vacations without worry and to smoothly resume work after returning to the office.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] To begin using the system, users provide their past email and chat history data. This allows the system to obtain the basic data needed to learn the user's communication patterns.

[0585] Step 2:

[0586] The server analyzes the provided historical data and builds an AI model that learns user response patterns using natural language processing techniques. This model is used to generate responses based on specific vocabulary and context.

[0587] Step 3:

[0588] The device detects newly received emails and chat messages and sends that data to the server in real time. The received data is temporarily stored on the server in preparation for subsequent processing.

[0589] Step 4:

[0590] The server evaluates the urgency of incoming messages. This is done based on keyword analysis and sender identification, and if it determines that the message is highly urgent, it instructs the terminal to immediately notify the user.

[0591] Step 5:

[0592] For low-urgency messages, the server uses an AI model to generate an appropriate automated response. For example, for routine meeting request emails, the system constructs a response based on response styles it has learned.

[0593] Step 6:

[0594] The terminal follows instructions from the server and sends a generated automated response to the recipient. This history of automated replies is saved as a log so that the user can review it as needed.

[0595] Step 7:

[0596] After communication processing during the holiday period is complete, the server organizes all messages and generates a prioritized task list based on their content. This includes categorization by importance and deadline.

[0597] Step 8:

[0598] Upon user return, the terminal displays a task list received from the server to assist in resuming work. Users can then use this list to efficiently process tasks.

[0599] (Example 1)

[0600] 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".

[0601] In today's information and communication environment, users need to process a large volume of electronic communications on a daily basis, and this task can affect work efficiency and mental burden. In particular, during vacations, users temporarily disconnect from work communications, making it difficult to manage communications during that time. In addition, misjudging the urgency of communications can lead to missing important information. Therefore, there is a need to provide an environment where users can relax during their vacations and quickly resume work upon their return.

[0602] 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.

[0603] In this invention, the server includes means for analyzing the user's electronic communication history and learning the content of responses, means for evaluating newly received electronic communications and determining their urgency, and means for sending emergency notifications to the user's computer. This enables the automatic processing of electronic communications even when the user is absent, allowing access to important information at the appropriate time.

[0604] A "user" refers to an individual or legal entity that uses this system to manage electronic communications.

[0605] "Electronic communication history" refers to all digital communication data, such as emails and chat messages, that a user has sent or received to date.

[0606] "Analysis" refers to the process of using computers to evaluate data in detail and extract specific information or patterns.

[0607] "Response content" refers to the information or message generated as a response to communication from the user.

[0608] "Learning" is the process of using AI technology to acquire specific behavioral patterns from data and update the model.

[0609] "Urgency" is a measure used to evaluate the importance and priority of a communication to a user.

[0610] "Automatic response" refers to a reply message that is generated and sent by the system without any manual action from the user.

[0611] "Notifications" refer to system functions that include messages and warnings to immediately convey information to the user.

[0612] The term "computer" refers to a broad range of devices, including all electronic equipment that performs data processing, and especially computers.

[0613] This invention provides a system that frees users from manual communication handling. The system mainly consists of three elements: a server, a terminal, and a user.

[0614] To begin using the system, users must provide their past electronic communication history to the server. This is done by importing data using email clients or chat applications. Specifically, dedicated client software can be used to transfer history data from major communication platforms to the server.

[0615] The server analyzes the received data and executes a learning process using a generative AI model. During this process, natural language processing techniques are utilized to identify patterns in user responses. By employing deep learning algorithms using libraries such as TensorFlow and PyTorch, a response model optimized for each individual user is generated.

[0616] When a user sets a vacation, the device is configured to send newly received electronic communications to the server. The device assumes a device running a currently dominant OS (e.g., iOS, Android, Windows, etc.) and transfers data using a secure protocol.

[0617] The server evaluates newly received messages and determines their urgency. If the message is highly urgent, it immediately sends a notification to the user's computer. This determination is made using algorithms that perform keyword analysis of the message content. If an automated response is deemed appropriate, the server generates a reply based on the AI ​​model and sends it through the terminal. As a concrete example of a prompt, telling the server something like, "When a new email arrives, please use the AI ​​model to analyze its content and determine if an automated response is possible," will immediately generate a response.

[0618] This system allows users to respond to communications even when they are away, and provides an environment where communication processing can be carried out smoothly according to priority upon their return.

[0619] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0620] Step 1:

[0621] The user provides their past electronic communication history to the server. The user imports past message data into the system using an email client or chat application. The input includes data from the user's chosen communication platform, which the server receives and converts into a parseable format. This conversion includes the process of converting the file to a standard data format (e.g., a CSV file). The output is the user's communication data, ready for analysis, input into the server.

[0622] Step 2:

[0623] The server analyzes the received communication data using a specific algorithm. Specifically, it uses natural language processing techniques to extract patterns and frequently occurring phrases within the messages. As input, the server is provided with the user's communication history data, which is then analyzed. As part of the data processing, text analysis is performed to identify keywords and response styles that the user has used in the past. Based on this, a user-specific response model is generated as output and stored on the server.

[0624] Step 3:

[0625] The server uses the generated response model to assess the urgency of newly received communications. New messages received from terminals are forwarded to the server, and analysis begins. The input consists of user communication data and newly received messages, and the AI ​​model is used to determine the urgency. The output is the determined urgency level of the message, and the user is notified if necessary.

[0626] Step 4:

[0627] The terminal instantly notifies the user of emergency message notifications sent from the server. If the message is deemed highly urgent, the terminal displays an alert according to the user's default notification settings. The input is emergency message information provided by the server, and the output is a notification displayed on the user's device. This allows the user to respond immediately to particularly important communications.

[0628] Step 5:

[0629] The server uses AI to generate automated responses for non-urgent messages. As input, the server receives new messages requiring processing and user response models, and sends the generated response content via the terminal. As output, a response message tailored to the user's style is generated and sent to the recipient. An example of this is a response to a scheduling email.

[0630] Step 6:

[0631] When a user returns from vacation, the server generates a task list with priority based on the processed communication data. The input includes all communication data received during the vacation and its evaluation results, which are used to formulate action plans. The output is a task list necessary for resuming work upon returning to the office, which is sent to the user's terminal. This creates an environment where users can efficiently resume their work.

[0632] (Application Example 1)

[0633] 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".

[0634] Conventional communication management systems have difficulty providing appropriate emergency responses when users are absent, and in particular, real-time management of mechanical equipment is insufficient in production sites. Furthermore, there is a lack of means to effectively organize communications that occur while users are on vacation and to respond quickly upon their return to work.

[0635] 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.

[0636] In this invention, the server includes means for analyzing the user's personal data communication history and learning the content of responses; means for evaluating newly received data communications and determining their urgency; means for generating and transmitting an automated response based on the determined urgency; and means for delivering instructions in real time to mechanical devices in the production environment and applying the automated response. This enables appropriate emergency response at the production site even when the user is absent, and allows for efficient response based on the organization and prioritization of communications.

[0637] "Personal data communication history" refers to emails, messages, and other communication information that a user has sent in the past, and is used to analyze specific trends and patterns.

[0638] "Urgency" is an indicator used to determine whether received data communications require immediate attention, and is usually determined by keywords and content.

[0639] An "automated response" is a reply message generated by an AI model, acting on behalf of the user, and is sent with appropriate content pre-formed.

[0640] "Mechanical equipment in the production environment" refers to machinery, robots, and other equipment operating in factories and production sites, and is subject to management and operation.

[0641] "Real-time instruction delivery" refers to a process where, as soon as information is received, the appropriate instruction is immediately sent to the target device, with the aim of ensuring a response without delay.

[0642] To implement this system, the server receives the user's personal data communication history and analyzes its contents. The server uses Python and the AI ​​framework TensorFlow to generate an AI model that learns the user's response tendencies. This makes it possible to model the user's individual communication style and determine the urgency of newly received data communications.

[0643] The server evaluates the data and analyzes the urgency of new messages in real time as they arrive. It uses a message queuing service like AWS SQS to process the necessary data and delivers appropriate instructions to mechanical equipment in the production environment in real time, supporting the continuous operation of the production line.

[0644] As a concrete example, if a production machine malfunctions in a factory, the automated response system can immediately detect the problem, generate an alternative procedure, and send it to the machine, even if the operator is on break. This minimizes production delays.

[0645] Furthermore, the server has a mechanism to organize all communications received during vacation and provide users with a priority-based task list upon their return. This allows them to smoothly resume work after returning to the office.

[0646] An example of a prompt for the generating AI model is: "If an unexpected problem occurs while the operator is on break, suggest how to deal with it and generate a method for handling it immediately, safely, and efficiently." Based on this prompt, the AI ​​forms an appropriate response and applies automated instructions to the device.

[0647] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0648] Step 1:

[0649] The server receives past data communication history from the user. This input data includes records of sending and receiving emails and messages. The server analyzes this data to extract the user's communication trends. As part of data processing, a natural language processing library is used to recognize patterns in message content, and the output is a generative AI model that models the user's unique response style.

[0650] Step 2:

[0651] When a terminal receives new data communication, it immediately sends the content of that communication to the server. This input message includes the date, sender, and content. The server performs data calculations using a pre-configured keyword detection algorithm to determine the urgency of the received message. As output, if the urgency is high, it generates a notification flag indicating that it should be processed with priority.

[0652] Step 3:

[0653] The server generates an automated response based on the determined urgency level. Inputs include a generation AI model, the content of the received message, and an urgency flag. The server uses the model to generate an appropriate response and sends it to the terminal via the message queue service. The output is the automated response message displayed on the terminal.

[0654] Step 4:

[0655] Mechanical devices installed in the production environment receive instructions from terminals. Based on the response messages output from the server, the devices perform the specified actions. This input contains instructions on what specific operations the machine should perform. As a result of the execution, the planned production activities are maintained, and this becomes the output.

[0656] Step 5:

[0657] When a user returns from vacation, the server organizes all communications received during their vacation. Input data includes received messages, their urgency, and processing history. The server generates a task list based on priority and sends it to the user's terminal, providing guidance on actions to take upon their return. This output is the task list displayed on the user's screen.

[0658] 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.

[0659] This invention is a communication management system that takes user emotions into consideration, and by incorporating an emotion engine that analyzes the user's past and present emotions, it provides more personalized responses. This system aims to improve communication methods that respond to the user's emotional state, in addition to automated responses to emails and chats.

[0660] First, the user provides past email and chat data. This allows the system to learn not only the user's response patterns but also the emotional changes in past communications. The server analyzes this data and applies natural language processing and sentiment analysis algorithms to generate a user response model. This process helps to understand the situations in which specific emotions were triggered and the user's reactions to them.

[0661] The device detects newly received data communication and sends it to the server. At this time, the emotion engine is activated and evaluates the user's emotions that the sent message is expected to evoke. For example, if the message contains content that would cause stress, the emotion engine will identify that.

[0662] The server generates an automated response based on the urgency of the message, including emotionally sensitive content when sending it. For example, if the content might cause offense to the recipient, it selects a response that incorporates apologies and mitigating expressions.

[0663] Furthermore, the server organizes communications during vacation based on the user's emotional changes and generates a task list based on priorities that take emotional stress into account. This list, displayed on the terminal, serves as a guide for the user to smoothly resume work and helps reduce mental burden.

[0664] By incorporating the emotion engine of this invention, users can not only receive appropriate communication responses according to their emotional state, but also reduce emotional stress during vacation and improve work efficiency upon returning to work.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] Users provide the system with past email and chat data. This gives the system a foundation to learn not only individual reply patterns but also emotional responses.

[0668] Step 2:

[0669] The server analyzes the provided data and uses natural language processing techniques and sentiment analysis algorithms to build a response model that learns the user's reply style and emotional changes. This model serves as the basis for generating response content.

[0670] Step 3:

[0671] The device sends newly received emails and chat messages to the server in real time. This provides the server with the data necessary for sentiment analysis at the time of reception.

[0672] Step 4:

[0673] The server analyzes incoming messages and uses a sentiment engine to evaluate how the message might affect the user's emotions. For example, it can detect potentially stressful situations from the sender's past message history.

[0674] Step 5:

[0675] If the message is urgent or a standard automated response is appropriate, the server uses an AI model to generate a response. This response is then refined to reflect emotional assessments, such as expressing gratitude or using polite language.

[0676] Step 6:

[0677] The terminal sends a generated response to the recipient based on instructions from the server. This response is sent on behalf of the user and is emotionally sensitive.

[0678] Step 7:

[0679] After communications during the holiday period have been processed, the server organizes the received messages, prioritizes them based on their content and the emotional impact on the users, and generates a task list. This task list is created taking emotional load into consideration.

[0680] Step 8:

[0681] The terminal displays a task list and action plan received from the server upon the user's return from vacation, assisting them in resuming work. Based on these guidelines, users can efficiently carry out their tasks while reducing mental stress.

[0682] (Example 2)

[0683] 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".

[0684] Modern information and communication are constantly increasing, particularly in the workplace, leading to rising psychological burdens. In particular, accurately understanding the priority of various types of information and providing appropriate responses immediately is difficult, and users are often overwhelmed by the sheer volume of data. Furthermore, automated responses that do not consider the user's emotional state can cause additional stress. Therefore, there is a need for communication management systems that provide efficient and appropriate responses while taking user emotions into consideration.

[0685] 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.

[0686] In this invention, the server includes means for analyzing the user's information history and learning response content, means for evaluating newly received information and determining its importance, and means for analyzing the user's emotions and generating responses that take their emotional state into consideration. This enables accurate responses that take the user's emotions into account and information organization based on priority.

[0687] "Information history" refers to records of various communications that a user has experienced in the past, including exchanges such as emails and chats.

[0688] "Means of learning response content" refers to the process by which the system extracts patterns from past information history to understand how users respond.

[0689] "Means for determining importance" refers to criteria or algorithms used to identify which newly received information should be processed with priority.

[0690] "Means for analyzing emotions and generating responses that take emotional state into consideration" refers to a process that evaluates the user's emotions from past and present communications and automatically generates appropriate responses based on that evaluation.

[0691] "Priority-based information organization" refers to a method of classifying received information according to certain criteria or conditions and setting a processing order based on its importance and urgency.

[0692] This invention is a system that analyzes a user's information history and manages communication while taking the user's emotions into consideration. The following describes a specific form for implementing this system.

[0693] Users provide the system with their past email and chat history. This creates foundational data for learning the user's communication style and emotional changes. First, this information is sent to the server.

[0694] The server uses natural language processing toolkits (e.g., NLTK, spaCy) and sentiment analysis software (e.g., VADER, TextBlob) to analyze the received historical data. These tools analyze sentiment from text and learn user response patterns. This analysis is important for understanding what emotions were evoked in specific situations and for grasping the user's response to those emotions.

[0695] The device instantly detects the arrival of new emails or chat messages. This detected information is then sent back to the server. The server uses an emotion engine to evaluate how the sent message will affect the user's emotions.

[0696] Based on this assessment, the server generates an automated response. This response is designed with a particular focus on reducing stress, and includes mitigating expressions and apologies for content that the user might find offensive. This system allows users to receive communications that are appropriate to their emotional state.

[0697] For example, if a user receives an important message while on vacation, the system organizes the information and generates a task list based on priority. This task list serves as a guide for quickly resuming work upon returning to the office.

[0698] Another example of a prompt is: "What words soothe you when you're feeling stressed? Please tell me some specific phrases or content." Based on such prompts, responses tailored to the user's needs are constructed.

[0699] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0700] Step 1:

[0701] Users provide the system with their past email and chat data. This input data serves as foundational data for analyzing communication patterns and emotional changes. Specifically, users upload data through a dedicated interface, which is then received by the server.

[0702] Step 2:

[0703] The server analyzes the provided data using natural language processing toolkits (e.g., NLTK, spaCy). It receives text data as input and extracts user response patterns through morphological analysis and sentiment scoring. The output provides characteristic user sentiment patterns and response tendencies. This process evaluates whether specific words or expressions evoke positive or negative emotions.

[0704] Step 3:

[0705] The device detects when a new message (email or chat) arrives. This becomes new input data and is sent to the server. This process happens automatically when the received message arrives in the user's inbox.

[0706] Step 4:

[0707] The server uses an emotion engine to analyze newly received messages. Based on the messages received as input data, it evaluates the impact on the user's emotions from the tone and content of the messages. The output is an emotion score and a predicted emotional state. Specifically, the process includes keyword extraction and the assignment of corresponding emotion labels.

[0708] Step 5:

[0709] The server generates an automated response that takes emotions into consideration, based on the results of sentiment analysis. Using a generative AI model, it creates the optimal reply based on past learning results and the user's current emotional state. When generating this prompt, it selects words and expressions that alleviate user stress. The output is an automatically generated response message.

[0710] Step 6:

[0711] The server organizes all communications received by the user during their vacation and generates a task list based on priority. It receives messages received during the vacation as input and sorts them according to importance and emotional burden. The final output is a task list that serves as a guide for work upon returning to work and is displayed on the terminal. The specific operation involves a process of visually organizing the listed tasks and presenting them to the user in an easy-to-understand format.

[0712] (Application Example 2)

[0713] 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".

[0714] In recent years, with the advancement of information and communication technology, the content of data communications has diversified, and users now receive a large volume of messages daily. This has made it difficult to adequately address the mental burden caused by information overload and security risks such as phishing emails. In particular, uniform notifications and responses that do not take into account the user's emotional state are problematic because they prevent the user from taking the optimal action in an emergency. Therefore, there is a need to provide more personalized response systems that take into account the user's emotions and the urgency of the situation.

[0715] 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.

[0716] In this invention, the server includes means for analyzing the user's personal data communication history and learning response content, means for evaluating incoming new data communication and determining its urgency, and means for analyzing the user's emotional state and evaluating security risks. This enables personalized notifications and responses according to the user's emotional state and appropriate responses according to urgency. Furthermore, by providing users with warnings that address security risks, the burden of information overload is reduced, allowing users to use communication with peace of mind.

[0717] "User's personal data communication history" refers to all communication history, such as emails and chats, that a user has made in the past. This data serves as foundational information for analyzing the user's communication patterns and emotional changes.

[0718] "Analysis means" refers to technologies that analyze the content of user communications using natural language processing and sentiment analysis algorithms based on data communication history, and generate response models.

[0719] "Means for determining urgency" refers to technology that analyzes the content of received data communications to determine how important and urgent that content is to the user.

[0720] "Means for generating automated responses" refers to a mechanism for automatically creating response messages in an appropriate format based on the user's communication history and the assessment of urgency.

[0721] "Means for analyzing emotional states" refers to analytical techniques that identify a user's emotions based on past and received data, and then respond appropriately to that emotional state.

[0722] "Means of assessing security risks" refers to evaluation methods for determining whether received data is potentially harmful to the user and for issuing warnings accordingly.

[0723] "Means for generating warnings" refers to technologies that create appropriate warning messages and notify users when security risks are identified.

[0724] The system of this invention enables communication management that takes user emotions into consideration, and includes means for analyzing the user's personal data communication history and learning response content. The system consists of a server, terminals, and multiple interconnected components for effectively managing each user's data.

[0725] The server is implemented in a programming language such as Python and utilizes natural language processing (NLP) libraries (e.g., NLTK, spaCy) and sentiment analysis libraries (e.g., TextBlob, IBM Watson). The server first receives the user's past communication data and analyzes it using natural language processing techniques. This analyzes the user's unique response tendencies and emotional fluctuations, and generates a response model. Using this response model, the server evaluates the urgency of newly received data communications and generates an automated response as needed.

[0726] Furthermore, the server can analyze the user's emotional state in real time and assess security risks. This allows for the identification of potential risks, such as phishing emails, and enables the user to receive appropriate warnings. These warnings take into account the stress and emotional reactions the user might face, and are designed to support them in responding calmly.

[0727] The device displays notifications and warnings sent from the server to the user in real time. This allows the user to immediately understand information regarding the importance and risks of received communications and take swift and appropriate action. As part of this system, smartphones and other mobile devices play a crucial role and are designed to ensure that the immediacy and convenience of communications are not compromised.

[0728] For example, if a fraudulent email disguised as coming from a financial institution arrives, the server analyzes the email and, based on the user's sentiment analysis, sends a warning message stating, "This email is dangerous. Do not open any links or attachments." This allows users to detect risks in advance and prevent damage.

[0729] An example of a prompt message is: "Analyze the user's sentiment and analyze the risk based on the email content and changes in sentiment. Create an appropriate warning message as needed." This provides a foundation for effectively utilizing generative AI models to make the user experience safer and more comfortable.

[0730] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0731] Step 1:

[0732] The server collects the user's past data communication history. This input data includes email and chat history. The server uses natural language processing techniques to analyze this history and identify the user's unique response tendencies and changes in emotion. The output of this process is a model of the user's emotions and responses.

[0733] Step 2:

[0734] The server detects newly received data communications. These communications become input data, and the server applies an algorithm to determine their urgency. In this process, context and keywords are analyzed, the importance of the communications is evaluated, and the results are output.

[0735] Step 3:

[0736] The server analyzes received communication data using a sentiment analysis library to re-evaluate the user's current emotional state. The input data is the content of the new communication, and the output is the result of the emotional state evaluation. Based on this evaluation, an automated response is generated as needed.

[0737] Step 4:

[0738] The server generates security alerts as needed based on the analysis results and evaluation. When an alert is necessary, such as in the case of a potentially phishing email, it creates an alert message composed of specific yet gentle language. The input is sentiment and security evaluation, and the output is the alert message.

[0739] Step 5:

[0740] The terminal displays notifications and warnings sent from the server to the user. In this process, the notification content (input) is displayed on the screen (output), making it immediately available to the user. This allows the user to take a quick and appropriate action.

[0741] 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.

[0742] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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."

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] The following is further disclosed regarding the embodiments described above.

[0763] (Claim 1)

[0764] A means of analyzing a user's personal data communication history and learning the content of their responses,

[0765] A means of evaluating incoming new data communications and determining their urgency,

[0766] A means for generating and sending an automated response based on the determined urgency,

[0767] In cases of high urgency, a means of immediately notifying the user,

[0768] A means to organize data communications received during the holiday period and provide priority-based action guidelines upon return,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, further comprising means for analyzing user-specific response trends from past data communications and generating a response model.

[0772] (Claim 3)

[0773] The system according to claim 1, further comprising means for transmitting an emergency notification to a user's electronic device, wherein the transmitting means selects notification content based on specific conditions.

[0774] "Example 1"

[0775] (Claim 1)

[0776] A means of analyzing the user's electronic communication history and learning the content of responses,

[0777] A means of evaluating newly received electronic communications and determining their urgency,

[0778] A means for generating and sending an automated response based on the determined urgency,

[0779] In cases of high urgency, a means of immediately notifying the user,

[0780] A means to organize electronic communications received during the holiday period and provide priority-based action guidelines upon return,

[0781] A means of forwarding communications received from the user's terminal to the server,

[0782] A means of generating individual responses using specific language processing techniques,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, further comprising means for analyzing user-specific reply trends from past electronic communications and generating a response model.

[0786] (Claim 3)

[0787] The system according to claim 1, further comprising means for sending an emergency notification to a user's computer, wherein the sending means selects the content of the notification based on specific conditions.

[0788] "Application Example 1"

[0789] (Claim 1)

[0790] A means of analyzing a user's personal data communication history and learning the content of their responses,

[0791] A means of evaluating incoming new data communications and determining their urgency,

[0792] A means for generating and sending an automated response based on the determined urgency,

[0793] In cases of high urgency, a means of immediately notifying the user,

[0794] A means to organize data communications received during the holiday period and provide priority-based action guidelines upon return,

[0795] A means for delivering instructions in real time to mechanical equipment in a production environment and applying automated responses,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, further comprising means for analyzing user-specific response trends from past data communications and generating a response model.

[0799] (Claim 3)

[0800] The system according to claim 1, further comprising means for transmitting an emergency notification to a user's electronic device, wherein the transmitting means selects notification content based on specific conditions.

[0801] "Example 2 of combining an emotion engine"

[0802] (Claim 1)

[0803] A means of analyzing the user's information history and learning from their responses,

[0804] A means of evaluating incoming new information and determining its importance,

[0805] A means for generating and sending an automated response based on the determined importance level,

[0806] If the importance is high, there is a means to immediately notify the user,

[0807] A means of organizing information received during the leave period and providing priority-based action guidelines upon return,

[0808] A means for analyzing user emotions and generating responses that take into account their emotional state,

[0809] A means of creating a task list to reduce the user's emotional burden based on emotion analysis,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, further comprising means for analyzing user-specific response trends from past information and generating a response model.

[0813] (Claim 3)

[0814] The system according to claim 1, further comprising means for transmitting important notices to a user's electronic device, wherein the transmitting means selects the content of the notices based on specific conditions.

[0815] "Application example 2 when combining with an emotional engine"

[0816] (Claim 1)

[0817] A means of analyzing a user's personal data communication history and learning the content of their responses,

[0818] A means of evaluating incoming new data communications and determining their urgency,

[0819] A means for generating and sending an automated response based on the determined urgency,

[0820] In cases of high urgency, a means of immediately notifying the user,

[0821] A means to organize data communications received during the holiday period and provide priority-based action guidelines upon return,

[0822] A means of analyzing the emotional state of users and evaluating security risks,

[0823] A means of generating appropriate warnings based on security risks and notifying users,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, further comprising means for analyzing user-specific response trends from past data communications and generating a response model.

[0827] (Claim 3)

[0828] The system according to claim 1, further comprising means for transmitting an emergency notification to a user's electronic device, wherein the transmitting means selects notification content based on specific conditions. [Explanation of Symbols]

[0829] 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 analyzing a user's personal data communication history and learning the content of their responses, A means of evaluating incoming new data communications and determining their urgency, A means for generating and sending an automated response based on the determined urgency, In cases of high urgency, a means of immediately notifying the user, A means to organize data communications received during the holiday period and provide priority-based action guidelines upon return, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing user-specific response trends from past data communications and generating a response model.

3. The system according to claim 1, further comprising means for transmitting an emergency notification to a user's electronic device, wherein the transmitting means selects notification content based on specific conditions.

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