system

Abstract

We provide the system. [Solution] A means of storing information received from the user, A means of receiving communication records and performing emotional analysis, A means of collecting publicly available information and analyzing the interests of the target audience, A means for generating personalized advice based on the analyzed data, Means for communicating the generated advice to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times when online communication has become routine, the number of individuals who feel anxious about face-to-face communication is increasing. Particularly, in situations where people seek new encounters through matching apps, the problem of failing to build a good relationship due to ineffective conversation has become prominent. There is a demand for a personalized communication strategy that takes into account an individual's characteristics and the interests of the conversation partner, which is insufficient in conventional dialogue support tools and manuals. Also, in order to solve these problems, flexible advice not confined to templates based on individual conversation patterns and preferences is required.

Means for Solving the Problems

[0005] This invention comprises a communication record analysis means for accumulating and analyzing user input information and analyzing interactions in matching apps, etc., and a means for collecting and analyzing publicly available information of a target person. Using this information, a generative artificial intelligence model is implemented to generate conversational advice optimized for the user's needs, and this advice is provided to the user in real time. Furthermore, by receiving and accumulating user feedback, the system aims for continuous improvement and optimization, providing a system that enhances individual communication skills.

[0006] "Means of storing information received from users" refers to the functions of technical devices or software for electronically recording and managing personal information and profile information entered or provided by users.

[0007] "Means for receiving communication records and performing sentiment analysis" refers to a technical system for acquiring conversation data through matching apps and messaging services and analyzing its content using sentiment analysis algorithms.

[0008] "Methods for collecting publicly available information and analyzing the interests of the target person" refers to methods for collecting publicly available information such as the target person's social media accounts and blogs, and identifying their interests and concerns using a specific algorithm.

[0009] "Means for generating personalized advice" refers to the functionality of a device or software that includes generative AI technology to generate specialized instructions and suggestions based on the situation and characteristics of each user, using analyzed data.

[0010] "Means for communicating generated advice to the user" refers to communication technology that sends advice created by the generation AI to the user's terminal via a network, and notifies and displays it to the target user.

[0011] "Means for receiving and storing user feedback" refers to the function of a device or program that allows users to send their opinions and results regarding advice to a system, and to electronically record and store them.

[0012] "Generative artificial intelligence" is an AI technology that generates and infers information based on large amounts of data, and provides specific instructions or answers. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention is an interactive support system for improving users' interpersonal communication skills. This system generates and provides personalized advice to the user in real time based on information provided by the user. The following describes the implementation of this system with specific examples.

[0035] System Configuration

[0036] The system consists of a server, user terminals, and multiple interfaces interconnected via a network. Users input information into the system via their terminals, and the server performs data analysis and generates advice based on that information. The resulting advice is then transmitted to the user terminals via the network.

[0037] Information input and collection

[0038] Users enter their profile information (name, age, interests, goals, etc.) using their device. This information is stored in a database by the server and serves as a reference database for subsequent processes.

[0039] Data Analysis

[0040] Conversation data acquired from users (e.g., interactions on dating apps or messaging platforms) is received by a server. The server then runs a sentiment analysis algorithm to analyze the emotional tone and keywords of the messages contained in this data. Sentiment analysis quantifies the emotions and intentions behind each message, and the analysis results are stored in a database.

[0041] Utilization of external data

[0042] The server also collects the target person's public profile and social media posts, and analyzes this data to understand their interests and tendencies. This helps determine how users should approach the target person.

[0043] Generating advice

[0044] The server uses artificial intelligence to analyze the results and generate personalized advice. The generated advice is sent to the user's terminal, from which the user can obtain information to use in subsequent communications.

[0045] Specific example

[0046] For example, if User A has a date planned, the server analyzes User A's past conversation data and extracts their interests from the other person's social media posts. It then advises User A to include music in the conversation during the date. It also makes specific suggestions, such as inviting them to a particular restaurant or event. User A can review this advice through their device and create an effective communication plan.

[0047] In this way, the present invention functions as a system that enables personalized support in a digital environment and contributes to improving users' interpersonal communication skills.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] Users register with the system using a terminal and enter profile information, interests, and goals. The terminal sends the user's input information to the server. The server stores the received information in a database and builds the user profile.

[0051] Step 2:

[0052] Users upload conversation data from dating apps and messaging platforms to a server via their devices. The devices use secure communication protocols to send the data to the server. The server places the received conversation data into a pipeline for analysis.

[0053] Step 3:

[0054] The server uses an emotion analysis algorithm to extract emotional tones from conversation data. The server then quantitatively evaluates the type and intensity of emotions and stores the results in a database.

[0055] Step 4:

[0056] The server collects publicly available information from the target individual, such as their social media accounts and blogs, via the network. The collected data is then processed by a topic modeling algorithm within the server to identify the target individual's interests and concerns.

[0057] Step 5:

[0058] The server compiles emotional data and the subject's interests obtained from the previous analysis and generates personalized advice optimized for the user. Using generative artificial intelligence technology, detailed advice is created, including conversation points and approach methods.

[0059] Step 6:

[0060] The server sends the generated advice to the user's terminal. The terminal notifies the user of the advice and displays it on the interface. The user reviews the provided advice and prepares to use it in their next communication.

[0061] Step 7:

[0062] After a date or after putting the advice into practice, users send feedback to the server using their device. The server stores the feedback in a database and uses it to improve the accuracy of the analysis data and enhance the system.

[0063] (Example 1)

[0064] 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."

[0065] In modern society, improving interpersonal communication skills is becoming increasingly important. However, methods for providing real-time support tailored to individual situations and backgrounds are limited, making it difficult to realize systems that support effective communication. In particular, analyzing diverse data and providing advice that meets the individual needs of users is a challenge.

[0066] 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.

[0067] In this invention, the server includes means for storing profile information received from the user, means for acquiring communication data and performing analysis using an emotion analysis algorithm, and means for determining the interests of the target person based on data collected from publicly available information sources. This enables the generation of personalized advice for each user in real time, and facilitates effective communication support.

[0068] "Profile information" refers to information that includes the user's basic personal attributes, such as name, age, hobbies, and goals.

[0069] "Communication data" refers to digital data that includes messages and conversations exchanged between users.

[0070] An "emotion analysis algorithm" is a computational method that extracts emotional tones and keywords from communication data and quantifies the emotions and intentions behind them.

[0071] "Publicly available information sources" refer to information media that are accessible to anyone, such as social networking services on the internet or public profiles.

[0072] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate the optimal output, i.e., personalized advice, from a specific input.

[0073] A "prompt" is a text-based instruction given to a generative AI model to elicit a specific output.

[0074] "Personalized advice" is information generated based on each user's situation and data analysis results, providing specific advice on actions and communication that the user should take.

[0075] A "user terminal" refers to a digital device used by a user to input information or receive advice.

[0076] "Evaluation" refers to user feedback, which is information used to measure the effectiveness and applicability of the advice provided.

[0077] This invention is an interactive support system for improving users' interpersonal communication skills. This system generates and provides personalized advice to the user in real time based on information provided by the user.

[0078] The system consists of a server, user terminals, and multiple interfaces interconnected via a network. The main processes are as follows:

[0079] The server stores profile information entered by users via their devices in a database. This information includes name, age, hobbies, and goals. The server also uses sentiment analysis algorithms to analyze communication data from dating apps and messaging platforms. This analysis uses generative AI models to quantify the emotional tone and intent of messages, understanding the meaning of each message. In addition, the server collects data from publicly available information sources on the internet, such as social media and public profiles, to determine the interests and tendencies of the target audience. This information is used to determine how users can effectively connect with these individuals.

[0080] The generated advice is provided by constructing prompt sentences using a generative AI model. An example of a prompt sentence is, "Based on the user's profile, advise them to talk about music in the next conversation." The server inputs this prompt sentence into the generative AI model, which then derives specific advice tailored to the user's situation.

[0081] The generated advice is sent to the user's terminal via the network, where the user can view it and use it to improve their communication. For example, if a user is preparing for a date, the server will suggest topics to discuss and places to visit based on the analyzed data. The user can then use this advice to achieve more effective interpersonal communication.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Users enter profile information (name, age, hobbies, goals, etc.) via their device. This information serves as basic data to personalize the user's communication style. The entered information is sent from the device to the server and stored in a database. Specifically, the user enters information into the application's form and sends the data by pressing the "Submit" button.

[0085] Step 2:

[0086] The server retrieves communication data from the matching apps and messaging platforms used by the user. This data is a log of conversations and is stored in text format. The server uses sentiment analysis algorithms to analyze the emotional tone and keywords from this data. It receives conversation data as input and outputs the emotional value and intent of each message. The server stores these values ​​in a database.

[0087] Step 3:

[0088] The server collects data from publicly available sources (SNS and public profiles) of the target individual. The collected data is retrieved using crawling techniques and API access. This data is then processed through analytical algorithms to identify the target individual's hobbies and interests. Publicly available data of the target individual is taken as input, and a list of hobbies and interests is generated as output.

[0089] Step 4:

[0090] The server uses a generative AI model to construct prompt sentences and generates personalized advice based on the analysis results. The prompt sentences are in the format of, "Based on the user's profile, please advise on topics to discuss in the next conversation." Analysis results and collected data are used as input, and specific advice is generated as output.

[0091] Step 5:

[0092] The server sends the generated advice to the user terminal via the network. The terminal displays the received advice on its user interface. The user reviews the terminal screen and develops a strategy for the next communication. Specifically, the user reads the advice displayed on the terminal and uses it to plan the conversation.

[0093] (Application Example 1)

[0094] 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."

[0095] In conventional customer interactions, store employees often find it difficult to provide effective and personalized service to customers, resulting in decreased customer satisfaction and sales efficiency. The present invention aims to provide a means to optimize customer interactions and deliver a more personalized experience.

[0096] 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.

[0097] In this invention, the server includes means for storing information received from users, means for receiving communication records and performing sentiment analysis, means for collecting publicly available information and analyzing the interests of the subject, and means for analyzing customer transaction history and past dialogue records and generating suggestions optimized for commercial facilities. This enables store employees to make optimized suggestions to customers in real time.

[0098] A "device for storing information received from users" is a device for storing data such as personal information, interests, and purchase history provided by users in a storage device.

[0099] A "device that receives communication records and performs sentiment analysis" refers to hardware or software that collects user messages and conversation data and analyzes the emotions and intentions contained within them.

[0100] A "device that collects publicly available information and analyzes the interests of the target person" is a device that collects information such as publicly available profiles and posts on the internet and evaluates the interests and behavioral patterns of the target person from that information.

[0101] A "device that generates personalized recommendations" is a device that automatically creates recommendations for products and services optimized for each user based on analyzed data.

[0102] A "device that communicates with a terminal device" is a device that transmits generated information and advice via a communication network for use by the user and displays it on the user's device.

[0103] A "device that analyzes customer transaction history and past dialogue records to generate proposals optimized for commercial facilities" is a device that analyzes a customer's purchase history and the content of previous communications to propose commercial facilities and services that meet the customer's needs.

[0104] This system is interconnected via a network, comprising servers, user terminals, and a network, to optimize customer interaction. The servers store user information and analyze received communication records. Specifically, they collect data from users' devices, such as smartphones, and use natural language processing and sentiment analysis algorithms to analyze it. The Python environment utilizes NLTK and Transformer libraries to extract specific emotional tones and keywords.

[0105] The server then analyzes publicly available social media posts and other data to evaluate the target audience's interests and tendencies. This includes data acquisition through web scraping techniques and APIs. The collected data is stored in a database and then used with generative AI models (such as OpenAI's GPT-3) to generate personalized suggestions.

[0106] The generated suggestions are sent to the user's device in real time, allowing the user to communicate effectively with customers based on this information. As a concrete example, imagine a scenario where a store employee, based on the customer's past purchase history and interests on social media, suggests products to recommend for their next visit in real time.

[0107] Example of a prompt:

[0108] "The customer's name is [Customer Name]. In the past, they have preferred casual fashion and shown interest in new arrivals. Could you please advise on what products I should recommend to them during this visit?"

[0109] The operation of this system will enable store employees to provide more personalized service to customers, and is expected to contribute to improved customer satisfaction.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user uses their device to enter their profile information (e.g., name, age, interests, past purchase history, etc.). This information becomes input data and is sent from the device to the server. The server completes the acquisition of basic information about the user by saving the received information to its database.

[0113] Step 2:

[0114] The server receives communication records and performs sentiment analysis. Specifically, messaging and conversation data between users and customers are input to the server, and this data is processed by sentiment analysis algorithms (e.g., NLTK or Transformer Library) to analyze the sentiment and keywords of the messages. The results of this analysis become output data and are stored in the database.

[0115] Step 3:

[0116] The server collects publicly available information from the internet (e.g., customer social media posts and public profiles) and analyzes it as input data. Information is obtained using web scraping techniques and APIs to analyze customer interests and trends. The results of this analysis, including the interests and behavioral patterns of the target individuals, are output data and stored.

[0117] Step 4:

[0118] The server generates personalized suggestions using a generative AI model (e.g., OpenAI's GPT-3) based on the stored information. This process uses the subject's interests and conversation sentiment analysis results as input data to produce the suggested content. The generated suggestions are then sent to the user's device.

[0119] Step 5:

[0120] The device displays the received suggestions to the user. The user uses these suggestions to decide how to proceed with the conversation with the customer, providing a more personalized service. Prompts are used here to provide guidance on which products should be introduced.

[0121] 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.

[0122] This invention relates to a system that recognizes a user's emotions and provides personalized advice based on those emotions. This system processes diverse information provided by the user and performs analysis using an emotion engine on the server, thereby improving the user's communication skills. The following describes the implementation of this system with specific examples.

[0123] System Configuration

[0124] The system consists of a server, a user terminal, and an analysis module utilizing an emotion engine. Users input profile information, communication records, and feedback information via the terminal. The server stores this information and performs analysis using the emotion engine. The resulting analysis is then used to generate personalized advice tailored to each user, leveraging generative artificial intelligence technology.

[0125] Information input and emotion recognition

[0126] Users input their profile and past communication records into the system using their devices. Publicly available information about the target individual is also collected by the server. Based on the data transmitted from the user's device, the emotion engine recognizes changes and trends in emotions in real time. Through this process, detailed and dynamic information about the user's emotions is stored in the database.

[0127] Data analysis and advice generation

[0128] The server combines emotional data obtained by the emotion engine with the user's preferences collected from publicly available information to generate advice tailored to the user's needs. This generation process utilizes generative artificial intelligence technology, and the generated advice is adjusted according to the user's emotional state. As a result, personalized advice is provided in the most effective format for the user.

[0129] Specific example

[0130] Consider a scenario where User B is preparing for a date with a specific person. User B uploads their profile information to the system along with recent conversation data. The server uses an emotion engine to recognize User B's potential anxieties and generates advice to help the date go smoothly. This advice includes topics the person might like and effective communication techniques. User B receives this advice and can begin communicating with confidence.

[0131] Through this process, this system provides personalized support to users, contributing to improved communication skills and the building of individual relationships.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user logs into the system using their device. The user enters or uploads their profile information and existing communication records with the target person. The device then transmits this data to the server.

[0135] Step 2:

[0136] The server stores user profile information and communication records received from the terminal in a database. This stored information is used in subsequent processing steps.

[0137] Step 3:

[0138] The server runs an emotion engine and performs sentiment analysis based on communication records received from the user. The emotion engine detects emotional tones from the communication records and analyzes changes in the user's emotions. The analysis results are recorded in a database on the server.

[0139] Step 4:

[0140] The server automatically collects publicly available information about the target individual via the network. This information includes social media posts and public blog data. The server uses this information to identify the target individual's current interests and concerns.

[0141] Step 5:

[0142] The server uses generative artificial intelligence technology to generate personalized advice based on the analysis results from the emotion engine and collected public information. This advice is customized to the user's emotional state and the target audience's interests.

[0143] Step 6:

[0144] The server sends the generated advice to the user's terminal. The terminal notifies the user of the advice and displays it in the user interface. The user uses this advice to select specific actions to improve communication with the person in question.

[0145] Step 7:

[0146] After a designated date or interaction, the user sends feedback to the server via their device. The server receives the feedback and records it in a database for service improvement and to learn from the analysis data.

[0147] (Example 2)

[0148] 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".

[0149] In modern communication, providing personalized advice tailored to each user's emotional state and preferences is crucial. However, existing systems struggle to accurately grasp users' dynamic emotional changes and generate detailed, tailored advice. This also leads to the problem of failing to improve users' communication skills.

[0150] 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.

[0151] In this invention, the server includes means for storing information received from the user, means for receiving communication history and analyzing emotions, and means for collecting publicly available information and analyzing the target's interests. This makes it possible to generate precise advice tailored to the user's emotions and preferences.

[0152] A "user" refers to an entity that utilizes the system and receives personalized advice.

[0153] "Information" refers to all data, including user profiles, communication history, and feedback.

[0154] "Communication history" refers to records of past communications made by a user.

[0155] "Emotions" are data that represent a user's subjective state and can be classified into categories such as positive, negative, and neutral.

[0156] "Analysis" refers to the process of deriving specific patterns or characteristics based on collected information.

[0157] "Publicly available information" refers to information obtained from publicly accessible data sources that pertains to the subject's activities and preferences.

[0158] "Target" refers to other people or events that the user is interested in.

[0159] "Interest" refers to data that indicates the subject's level of interest.

[0160] "Advice" refers to guidelines provided to users to help them take action or make decisions.

[0161] "Generation" refers to the process of creating new content or data.

[0162] "Generative artificial intelligence technology" refers to technology that enables machines to think and make decisions in a manner similar to that of humans.

[0163] "Emotional state" refers to the user's current emotional state.

[0164] "Preferences" refer to the individual preferences or tendencies of a user or subject.

[0165] "Collaboration" refers to the process where different data or systems are linked and work together.

[0166] This invention is an advanced system for providing personalized advice based on the user's emotional state and individual preferences. The system operates through the cooperation of a user terminal, a server, and an emotion analysis engine.

[0167] The server is equipped with a database for storing and managing information received from users. This database system typically uses "MySQL®" or "PostgreSQL." This allows for the efficient storage and management of user-provided profile information, communication history, and feedback.

[0168] Users can input data into the system via their own devices. The data transmitted from the devices is collected on a server and analyzed by an emotion engine. The emotion engine uses "natural language processing technology" to analyze emotions from the user's messages and opinions. This is done, for example, through "natural language recognition software."

[0169] The server then uses generative AI models to generate personalized advice. Technologies used as "generative AI models" in this process include "OpenAI GPT-4 (registered trademark)" and "BERT," which generate accurate advice tailored to the emotional state and preferences of the user or target.

[0170] As an example, consider a user's first day at a new workplace. The user inputs past work information and communication history, and the server performs sentiment analysis based on this information. Then, a generative AI model is used to generate advice tailored to the user for building relationships at work, and this advice is provided to the user via the terminal. An example of a prompt might be, "Please give me advice on how to make my first day at a new workplace a success. Based on my past experience and communication history with team members, please tell me what points I should focus on."

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] Users input personal information, past communication history, and feedback information into the system using their own devices. The devices then send this information to the server as input data. The input data includes the user's profile and past conversation content, which forms the basis for subsequent sentiment analysis.

[0174] Step 2:

[0175] The server stores the data received from the terminal in a database. A "database management system" is used for this data storage, often such as "MySQL" or "PostgreSQL." The entered data is efficiently stored for subsequent use within the system and made readily accessible at any time.

[0176] Step 3:

[0177] The server then uses the stored data to begin analysis in the emotion engine. The emotion engine uses natural language processing techniques to analyze the input data and extract the user's emotional state. This analysis involves examining keywords and context in the text to determine whether the user's current emotion is positive, negative, or neutral. The analysis results are then used to generate advice in the next step.

[0178] Step 4:

[0179] The server generates personalized advice using a generative AI model based on the sentiment analysis results. This process utilizes generative AI models such as OpenAI GPT-4 and BERT. Using the sentiment analysis results and the user's past history as input, the output generates advice tailored to the user. This advice includes practical suggestions that reflect the user's emotional state, preferences, and past communication patterns.

[0180] Step 5:

[0181] The server provides the user with generated advice. The user receives this output and develops a communication strategy tailored to their own situation. Through this process, the user can receive helpful advice that is relevant to their emotional state and put it into practice.

[0182] (Application Example 2)

[0183] 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 device 14 will be referred to as the "terminal."

[0184] A challenge lies in understanding users' emotions and areas where their customer service skills are lacking, and providing appropriate advice in real time. In particular, there is a need for an effective system that enables flexible responses to changes in customer emotions and improves customer satisfaction.

[0185] 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.

[0186] In this invention, the server includes means for storing data received from the user, means for acquiring communication history and performing sentiment analysis, and means for collecting external information and evaluating the interests of relevant objects. This enables accurate understanding of the user's emotions and the provision of appropriate advice in real time.

[0187] A "user" is an individual or legal entity that uses a service or system.

[0188] "Means of data retention" refers to hardware and software systems for recording and storing information received from users.

[0189] "Communication history" refers to a record of a user's communication activities, including information such as the content of those communications and the recipients.

[0190] "Means of analyzing emotions" refers to technologies or algorithms that analyze received data and determine the user's emotions.

[0191] "External information" refers to data collected from sources other than user information, and includes publicly available information about the person or event in question.

[0192] "Means for evaluating the interests of relevant subjects" refers to methods for analyzing the interests and directions of a particular person or event based on collected external information.

[0193] "Personalized support" refers to customized advice and suggestions created to suit the user's specific circumstances and emotions.

[0194] A "generative algorithm" is a set of computational procedures or algorithms used to create new data or proposals based on received information.

[0195] "Smart hardware that provides suggestions in real time based on emotion recognition results" refers to a smart device that evaluates the user's emotional state and immediately presents suggestions and advice based on the results.

[0196] The system that implements this application example is built around a server that communicates with the user's smart hardware (e.g., smart glasses). The server has a database to store profile information and communication history obtained from the user. The server also uses an emotion analysis engine to analyze emotions. For example, it utilizes Microsoft® Azure® Cognitive Services or Google® Cloud Speech-to-Text API to estimate emotions from the user's facial expressions and conversation.

[0197] The server collects external information and evaluates the user's interests in the relevant subjects. This process utilizes algorithms that analyze publicly available data on the internet to identify specific topics and trends. This allows for the efficient identification of the user's interests in specific subjects and activities.

[0198] Based on the user's emotion recognition results, a generative algorithm creates personalized support for the user. This generated support is displayed in real time on the user's smart glasses. This process utilizes advanced generative AI models, such as OpenAI's GPT model.

[0199] As a concrete example, store staff can use prompt messages through smart glasses during conversations with customers, such as, "Please tell me the appropriate way to interact with a customer when they show interest in a product. Please also include examples of related product suggestions." Based on the assistance received, they can then provide appropriate customer service. In this way, the system provides support to enable users to effectively interact with customers and make optimal suggestions.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] Users upload their profile information and communication history to the server using their device. The input is data provided by the user, and the server records this in a database. This allows for the retention of user information.

[0203] Step 2:

[0204] The server activates its emotion analysis engine and analyzes the uploaded communication history. The input is the communication history, and the output is data indicating the user's emotional state. The server uses the emotion analysis engine to analyze facial expressions and tone of voice to estimate the user's emotions. Natural language processing techniques are used in this process.

[0205] Step 3:

[0206] The server collects external information and identifies specific topics and interests. The input is external data, and the output is interest assessment data regarding relevant subjects. The server aggregates publicly available information via the internet and runs an algorithm to evaluate relevant trends. This algorithm reveals the subject's interests.

[0207] Step 4:

[0208] The server uses a generative AI model to create personalized support based on emotion recognition results and the target's interest data. The input is emotion data and interest evaluations, and the output is customized support for the user. The generative AI model generates optimal suggestions tailored to the user's situation.

[0209] Step 5:

[0210] The server communicates the generated assistance data to the user's smart glasses in real time. The input is the generated assistance data, and the output is the assistance content displayed on the smart glasses. Based on the received information, the user appropriately handles customer interactions. This process enhances the user's communication skills.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention is an interactive support system for improving users' interpersonal communication skills. This system generates and provides personalized advice to the user in real time based on information provided by the user. The following describes the implementation of this system with specific examples.

[0228] System Configuration

[0229] The system consists of a server, user terminals, and multiple interfaces interconnected via a network. Users input information into the system via their terminals, and the server performs data analysis and generates advice based on that information. The resulting advice is then transmitted to the user terminals via the network.

[0230] Information input and collection

[0231] Users enter their profile information (name, age, interests, goals, etc.) using their device. This information is stored in a database by the server and serves as a reference database for subsequent processes.

[0232] Data Analysis

[0233] Conversation data acquired from users (e.g., interactions on dating apps or messaging platforms) is received by a server. The server then runs a sentiment analysis algorithm to analyze the emotional tone and keywords of the messages contained in this data. Sentiment analysis quantifies the emotions and intentions behind each message, and the analysis results are stored in a database.

[0234] Utilization of external data

[0235] The server also collects the target person's public profile and social media posts, and analyzes this data to understand their interests and tendencies. This helps determine how users should approach the target person.

[0236] Generating advice

[0237] The server uses artificial intelligence to analyze the results and generate personalized advice. The generated advice is sent to the user's terminal, from which the user can obtain information to use in subsequent communications.

[0238] Specific example

[0239] For example, if User A has a date planned, the server analyzes User A's past conversation data and extracts their interests from the other person's social media posts. It then advises User A to include music in the conversation during the date. It also makes specific suggestions, such as inviting them to a particular restaurant or event. User A can review this advice through their device and create an effective communication plan.

[0240] In this way, the present invention functions as a system that enables personalized support in a digital environment and contributes to improving users' interpersonal communication skills.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] Users register with the system using a terminal and enter profile information, interests, and goals. The terminal sends the user's input information to the server. The server stores the received information in a database and builds the user profile.

[0244] Step 2:

[0245] Users upload conversation data from dating apps and messaging platforms to a server via their devices. The devices use secure communication protocols to send the data to the server. The server places the received conversation data into a pipeline for analysis.

[0246] Step 3:

[0247] The server uses an emotion analysis algorithm to extract emotional tones from conversation data. The server then quantitatively evaluates the type and intensity of emotions and stores the results in a database.

[0248] Step 4:

[0249] The server collects publicly available information from the target individual, such as their social media accounts and blogs, via the network. The collected data is then processed by a topic modeling algorithm within the server to identify the target individual's interests and concerns.

[0250] Step 5:

[0251] The server compiles emotional data and the subject's interests obtained from the previous analysis and generates personalized advice optimized for the user. Using generative artificial intelligence technology, detailed advice is created, including conversation points and approach methods.

[0252] Step 6:

[0253] The server sends the generated advice to the user's terminal. The terminal notifies the user of the advice and displays it on the interface. The user reviews the provided advice and prepares to use it in their next communication.

[0254] Step 7:

[0255] After a date or after putting the advice into practice, users send feedback to the server using their device. The server stores the feedback in a database and uses it to improve the accuracy of the analysis data and enhance the system.

[0256] (Example 1)

[0257] 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."

[0258] In modern society, improving interpersonal communication skills is becoming increasingly important. However, methods for providing real-time support tailored to individual situations and backgrounds are limited, making it difficult to realize systems that support effective communication. In particular, analyzing diverse data and providing advice that meets the individual needs of users is a challenge.

[0259] 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.

[0260] In this invention, the server includes means for storing profile information received from the user, means for acquiring communication data and performing analysis using an emotion analysis algorithm, and means for determining the interests of the target person based on data collected from publicly available information sources. This enables the generation of personalized advice for each user in real time, and facilitates effective communication support.

[0261] "Profile information" refers to information that includes the user's basic personal attributes, such as name, age, hobbies, and goals.

[0262] "Communication data" refers to digital data that includes messages and conversations exchanged between users.

[0263] An "emotion analysis algorithm" is a computational method that extracts emotional tones and keywords from communication data and quantifies the emotions and intentions behind them.

[0264] "Publicly available information sources" refer to information media that are accessible to anyone, such as social networking services on the internet or public profiles.

[0265] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate the optimal output, i.e., personalized advice, from a specific input.

[0266] A "prompt" is a text-based instruction given to a generative AI model to elicit a specific output.

[0267] "Personalized advice" is information generated based on each user's situation and data analysis results, providing specific advice on actions and communication that the user should take.

[0268] A "user terminal" refers to a digital device used by a user to input information or receive advice.

[0269] "Evaluation" refers to user feedback, which is information used to measure the effectiveness and applicability of the advice provided.

[0270] This invention is an interactive support system for improving users' interpersonal communication skills. This system generates and provides personalized advice to the user in real time based on information provided by the user.

[0271] The system consists of a server, user terminals, and multiple interfaces interconnected via a network. The main processes are as follows:

[0272] The server stores profile information entered by users via their devices in a database. This information includes name, age, hobbies, and goals. The server also uses sentiment analysis algorithms to analyze communication data from dating apps and messaging platforms. This analysis uses generative AI models to quantify the emotional tone and intent of messages, understanding the meaning of each message. In addition, the server collects data from publicly available information sources on the internet, such as social media and public profiles, to determine the interests and tendencies of the target audience. This information is used to determine how users can effectively connect with these individuals.

[0273] The generated advice is provided by constructing prompt sentences using a generative AI model. An example of a prompt sentence is, "Based on the user's profile, advise them to talk about music in the next conversation." The server inputs this prompt sentence into the generative AI model, which then derives specific advice tailored to the user's situation.

[0274] The generated advice is sent to the user's terminal via the network, where the user can view it and use it to improve their communication. For example, if a user is preparing for a date, the server will suggest topics to discuss and places to visit based on the analyzed data. The user can then use this advice to achieve more effective interpersonal communication.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] Users enter profile information (name, age, hobbies, goals, etc.) via their device. This information serves as basic data to personalize the user's communication style. The entered information is sent from the device to the server and stored in a database. Specifically, the user enters information into the application's form and sends the data by pressing the "Submit" button.

[0278] Step 2:

[0279] The server retrieves communication data from the matching apps and messaging platforms used by the user. This data is a log of conversations and is stored in text format. The server uses sentiment analysis algorithms to analyze the emotional tone and keywords from this data. It receives conversation data as input and outputs the emotional value and intent of each message. The server stores these values ​​in a database.

[0280] Step 3:

[0281] The server collects data from public information sources (SNS and public profiles) of the person targeted by the user. The collected data is obtained using crawling technology and API access. This data is processed through analysis algorithms to identify the hobbies and interests of the target person. The public data of the target person is obtained as input, and a list of hobbies and interests is generated as the output.

[0282] Step 4:

[0283] The server constructs a prompt sentence using the generative AI model and generates personalized advice based on the analysis results. The prompt sentence is in the form of "Please advise on the topics to be discussed in the next conversation based on the user's profile." The analysis results and the collected data are used as input, and specific advice is generated as the output.

[0284] Step 5:

[0285] The server transmits the generated advice to the user terminal via the network. The terminal displays the received advice on the user interface. The user checks the screen of the terminal and formulates a strategy for the next communication. As a specific action, the user reads the advice displayed on the terminal and constructs a conversation plan based on it.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In conventional customer conversations, it is difficult for store employees to provide effective and personalized responses to customers, resulting in problems such as a decline in customer satisfaction and sales efficiency. The purpose of the present invention is to provide a means for optimizing conversations with customers and providing a more personalized experience.

[0289] 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.

[0290] In this invention, the server includes means for storing information received from users, means for receiving communication records and performing sentiment analysis, means for collecting publicly available information and analyzing the interests of the subject, and means for analyzing customer transaction history and past dialogue records and generating suggestions optimized for commercial facilities. This enables store employees to make optimized suggestions to customers in real time.

[0291] A "device for storing information received from users" is a device for storing data such as personal information, interests, and purchase history provided by users in a storage device.

[0292] A "device that receives communication records and performs sentiment analysis" refers to hardware or software that collects user messages and conversation data and analyzes the emotions and intentions contained within them.

[0293] A "device that collects publicly available information and analyzes the interests of the target person" is a device that collects information such as publicly available profiles and posts on the internet and evaluates the interests and behavioral patterns of the target person from that information.

[0294] A "device that generates personalized recommendations" is a device that automatically creates recommendations for products and services optimized for each user based on analyzed data.

[0295] A "device that communicates with a terminal device" is a device that transmits generated information and advice via a communication network for use by the user and displays it on the user's device.

[0296] A "device that analyzes customer transaction history and past dialogue records to generate proposals optimized for commercial facilities" is a device that analyzes a customer's purchase history and the content of previous communications to propose commercial facilities and services that meet the customer's needs.

[0297] This system is interconnected via a network, comprising servers, user terminals, and a network, to optimize customer interaction. The servers store user information and analyze received communication records. Specifically, they collect data from users' devices, such as smartphones, and use natural language processing and sentiment analysis algorithms to analyze it. The Python environment utilizes NLTK and Transformer libraries to extract specific emotional tones and keywords.

[0298] The server then analyzes publicly available social media posts and other data to evaluate the target audience's interests and tendencies. This includes data acquisition through web scraping techniques and APIs. The collected data is stored in a database and then used with generative AI models (such as OpenAI's GPT-3) to generate personalized suggestions.

[0299] The generated suggestions are sent to the user's device in real time, allowing the user to communicate effectively with customers based on this information. As a concrete example, imagine a scenario where a store employee, based on the customer's past purchase history and interests on social media, suggests products to recommend for their next visit in real time.

[0300] Example of a prompt:

[0301] "The customer's name is [Customer Name]. In the past, they have preferred casual fashion and shown interest in new arrivals. Could you please advise on what products I should recommend to them during this visit?"

[0302] The operation of this system will enable store employees to provide more personalized service to customers, and is expected to contribute to improved customer satisfaction.

[0303] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0304] Step 1:

[0305] The user uses the terminal to input their profile information (e.g., name, age, interests, past purchase history, etc.). This information becomes the input data and is sent from the terminal to the server. The server completes the acquisition of basic information about the user by storing the received information in the database.

[0306] Step 2:

[0307] The server receives the communication records and performs sentiment analysis. Specifically, user-customer messaging and conversation data are input into the server, and this data is applied to sentiment analysis algorithms (e.g., NLTK or transformer libraries) to analyze the sentiment and keywords of the messages. The analysis results become the output data and are stored in the database.

[0308] Step 3:

[0309] The server collects public information on the Internet (e.g., customers' SNS posts and public profiles) and analyzes this as input data. Information is obtained using web scraping techniques and APIs, and the customers' interests and trends are analyzed. As a result of this analysis, the interests and behavior patterns of the target person become the output data and are saved.

[0310] Step 4:

[0311] The server uses a generative AI model (e.g., GPT-3 from OpenAI) to generate personalized proposals based on the stored information. In this process, the interests of the target person and the sentiment analysis results of the conversation are used as input data, and the proposal content is output. The generated proposals are sent to the user's terminal.

[0312] Step 5:

[0313] The device displays the received suggestions to the user. The user uses these suggestions to decide how to proceed with the conversation with the customer, providing a more personalized service. Prompts are used here to provide guidance on which products should be introduced.

[0314] 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.

[0315] This invention relates to a system that recognizes a user's emotions and provides personalized advice based on those emotions. This system processes diverse information provided by the user and performs analysis using an emotion engine on the server, thereby improving the user's communication skills. The following describes the implementation of this system with specific examples.

[0316] System Configuration

[0317] The system consists of a server, a user terminal, and an analysis module utilizing an emotion engine. Users input profile information, communication records, and feedback information via the terminal. The server stores this information and performs analysis using the emotion engine. The resulting analysis is then used to generate personalized advice tailored to each user, leveraging generative artificial intelligence technology.

[0318] Information input and emotion recognition

[0319] Users input their profile and past communication records into the system using their devices. Publicly available information about the target individual is also collected by the server. Based on the data transmitted from the user's device, the emotion engine recognizes changes and trends in emotions in real time. Through this process, detailed and dynamic information about the user's emotions is stored in the database.

[0320] Data analysis and advice generation

[0321] The server combines emotional data obtained by the emotion engine with the user's preferences collected from publicly available information to generate advice tailored to the user's needs. This generation process utilizes generative artificial intelligence technology, and the generated advice is adjusted according to the user's emotional state. As a result, personalized advice is provided in the most effective format for the user.

[0322] Specific example

[0323] Consider a scenario where User B is preparing for a date with a specific person. User B uploads their profile information to the system along with recent conversation data. The server uses an emotion engine to recognize User B's potential anxieties and generates advice to help the date go smoothly. This advice includes topics the person might like and effective communication techniques. User B receives this advice and can begin communicating with confidence.

[0324] Through this process, this system provides personalized support to users, contributing to improved communication skills and the building of individual relationships.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The user logs into the system using their device. The user enters or uploads their profile information and existing communication records with the target person. The device then transmits this data to the server.

[0328] Step 2:

[0329] The server stores user profile information and communication records received from the terminal in a database. This stored information is used in subsequent processing steps.

[0330] Step 3:

[0331] The server runs an emotion engine and performs sentiment analysis based on communication records received from the user. The emotion engine detects emotional tones from the communication records and analyzes changes in the user's emotions. The analysis results are recorded in a database on the server.

[0332] Step 4:

[0333] The server automatically collects publicly available information about the target individual via the network. This information includes social media posts and public blog data. The server uses this information to identify the target individual's current interests and concerns.

[0334] Step 5:

[0335] The server uses generative artificial intelligence technology to generate personalized advice based on the analysis results from the emotion engine and collected public information. This advice is customized to the user's emotional state and the target audience's interests.

[0336] Step 6:

[0337] The server sends the generated advice to the user's terminal. The terminal notifies the user of the advice and displays it in the user interface. The user uses this advice to select specific actions to improve communication with the person in question.

[0338] Step 7:

[0339] After a designated date or interaction, the user sends feedback to the server via their device. The server receives the feedback and records it in a database for service improvement and to learn from the analysis data.

[0340] (Example 2)

[0341] 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".

[0342] In modern communication, providing personalized advice tailored to each user's emotional state and preferences is crucial. However, existing systems struggle to accurately grasp users' dynamic emotional changes and generate detailed, tailored advice. This also leads to the problem of failing to improve users' communication skills.

[0343] 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.

[0344] In this invention, the server includes means for storing information received from the user, means for receiving communication history and analyzing emotions, and means for collecting publicly available information and analyzing the target's interests. This makes it possible to generate precise advice tailored to the user's emotions and preferences.

[0345] A "user" refers to an entity that utilizes the system and receives personalized advice.

[0346] "Information" refers to all data, including user profiles, communication history, and feedback.

[0347] "Communication history" refers to records of past communications made by a user.

[0348] "Emotions" are data that represent a user's subjective state and can be classified into categories such as positive, negative, and neutral.

[0349] "Analysis" refers to the process of deriving specific patterns or characteristics based on collected information.

[0350] "Publicly available information" refers to information obtained from publicly accessible data sources that pertains to the subject's activities and preferences.

[0351] "Target" refers to other people or events that the user is interested in.

[0352] "Interest" refers to data that indicates the subject's level of interest.

[0353] "Advice" refers to guidelines provided to users to help them take action or make decisions.

[0354] "Generation" refers to the process of creating new content or data.

[0355] "Generative artificial intelligence technology" refers to technology that enables machines to think and make decisions in a manner similar to that of humans.

[0356] "Emotional state" refers to the user's current emotional state.

[0357] "Preferences" refer to the individual preferences or tendencies of a user or subject.

[0358] "Collaboration" refers to the process where different data or systems are linked and work together.

[0359] This invention is an advanced system for providing personalized advice based on the user's emotional state and individual preferences. The system operates through the cooperation of a user terminal, a server, and an emotion analysis engine.

[0360] The server is equipped with a database for storing and managing information received from users. This database system typically uses MySQL or PostgreSQL. This allows for the efficient storage and management of user-provided profile information, communication history, and feedback.

[0361] Users can input data into the system via their own devices. The data transmitted from the devices is collected on a server and analyzed by an emotion engine. The emotion engine uses "natural language processing technology" to analyze emotions from the user's messages and opinions. This is done, for example, through "natural language recognition software."

[0362] The server then uses generative AI models to generate personalized advice. Technologies used as "generative AI models" in this process include "OpenAI GPT-4" and "BERT," which generate accurate advice tailored to the emotional state and preferences of the user or target.

[0363] As an example, consider a user's first day at a new workplace. The user inputs past work information and communication history, and the server performs sentiment analysis based on this information. Then, a generative AI model is used to generate advice tailored to the user for building relationships at work, and this advice is provided to the user via the terminal. An example of a prompt might be, "Please give me advice on how to make my first day at a new workplace a success. Based on my past experience and communication history with team members, please tell me what points I should focus on."

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] Users input personal information, past communication history, and feedback information into the system using their own devices. The devices then send this information to the server as input data. The input data includes the user's profile and past conversation content, which forms the basis for subsequent sentiment analysis.

[0367] Step 2:

[0368] The server stores the data received from the terminal in a database. A "database management system" is used for this data storage, often such as "MySQL" or "PostgreSQL." The entered data is efficiently stored for subsequent use within the system and made readily accessible at any time.

[0369] Step 3:

[0370] The server then uses the stored data to begin analysis in the emotion engine. The emotion engine uses natural language processing techniques to analyze the input data and extract the user's emotional state. This analysis involves examining keywords and context in the text to determine whether the user's current emotion is positive, negative, or neutral. The analysis results are then used to generate advice in the next step.

[0371] Step 4:

[0372] The server generates personalized advice using a generative AI model based on the sentiment analysis results. This process utilizes generative AI models such as OpenAI GPT-4 and BERT. Using the sentiment analysis results and the user's past history as input, the output generates advice tailored to the user. This advice includes practical suggestions that reflect the user's emotional state, preferences, and past communication patterns.

[0373] Step 5:

[0374] The server provides the user with generated advice. The user receives this output and develops a communication strategy tailored to their own situation. Through this process, the user can receive helpful advice that is relevant to their emotional state and put it into practice.

[0375] (Application Example 2)

[0376] 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."

[0377] A challenge lies in understanding users' emotions and areas where their customer service skills are lacking, and providing appropriate advice in real time. In particular, there is a need for an effective system that enables flexible responses to changes in customer emotions and improves customer satisfaction.

[0378] 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.

[0379] In this invention, the server includes means for storing data received from the user, means for acquiring communication history and performing sentiment analysis, and means for collecting external information and evaluating the interests of relevant objects. This enables accurate understanding of the user's emotions and the provision of appropriate advice in real time.

[0380] A "user" is an individual or legal entity that uses a service or system.

[0381] "Means of data retention" refers to hardware and software systems for recording and storing information received from users.

[0382] "Communication history" refers to a record of a user's communication activities, including information such as the content of those communications and the recipients.

[0383] "Means of analyzing emotions" refers to technologies or algorithms that analyze received data and determine the user's emotions.

[0384] "External information" refers to data collected from sources other than user information, and includes publicly available information about the person or event in question.

[0385] "Means for evaluating the interests of relevant subjects" refers to methods for analyzing the interests and directions of a particular person or event based on collected external information.

[0386] "Personalized support" refers to customized advice and suggestions created to suit the user's specific circumstances and emotions.

[0387] A "generative algorithm" is a set of computational procedures or algorithms used to create new data or proposals based on received information.

[0388] "Smart hardware that provides suggestions in real time based on emotion recognition results" refers to a smart device that evaluates the user's emotional state and immediately presents suggestions and advice based on the results.

[0389] The system that implements this application example is built around a server that communicates with the user's smart hardware (e.g., smart glasses). The server has a database to store profile information and communication history obtained from the user. The server also uses an emotion analysis engine to analyze emotions. For example, it might utilize Microsoft's Azure Cognitive Services or Google Cloud Speech-to-Text API to estimate emotions from the user's facial expressions and conversation.

[0390] The server collects external information and evaluates the user's interests in the relevant subjects. This process utilizes algorithms that analyze publicly available data on the internet to identify specific topics and trends. This allows for the efficient identification of the user's interests in specific subjects and activities.

[0391] Based on the user's emotion recognition results, a generative algorithm creates personalized support for the user. This generated support is displayed in real time on the user's smart glasses. This process utilizes advanced generative AI models, such as OpenAI's GPT model.

[0392] As a concrete example, store staff can use prompt messages through smart glasses during conversations with customers, such as, "Please tell me the appropriate way to interact with a customer when they show interest in a product. Please also include examples of related product suggestions." Based on the assistance received, they can then provide appropriate customer service. In this way, the system provides support to enable users to effectively interact with customers and make optimal suggestions.

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] Users upload their profile information and communication history to the server using their device. The input is data provided by the user, and the server records this in a database. This allows for the retention of user information.

[0396] Step 2:

[0397] The server activates its emotion analysis engine and analyzes the uploaded communication history. The input is the communication history, and the output is data indicating the user's emotional state. The server uses the emotion analysis engine to analyze facial expressions and tone of voice to estimate the user's emotions. Natural language processing techniques are used in this process.

[0398] Step 3:

[0399] The server collects external information and identifies specific topics and interests. The input is external data, and the output is interest assessment data regarding relevant subjects. The server aggregates publicly available information via the internet and runs an algorithm to evaluate relevant trends. This algorithm reveals the subject's interests.

[0400] Step 4:

[0401] The server uses a generative AI model to create personalized support based on emotion recognition results and the target's interest data. The input is emotion data and interest evaluations, and the output is customized support for the user. The generative AI model generates optimal suggestions tailored to the user's situation.

[0402] Step 5:

[0403] The server communicates the generated assistance data to the user's smart glasses in real time. The input is the generated assistance data, and the output is the assistance content displayed on the smart glasses. Based on the received information, the user appropriately handles customer interactions. This process enhances the user's communication skills.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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".

[0420] This invention is an interactive support system for improving users' interpersonal communication skills. This system generates and provides personalized advice to the user in real time based on information provided by the user. The following describes the implementation of this system with specific examples.

[0421] System Configuration

[0422] The system consists of a server, user terminals, and multiple interfaces interconnected via a network. Users input information into the system via their terminals, and the server performs data analysis and generates advice based on that information. The resulting advice is then transmitted to the user terminals via the network.

[0423] Information input and collection

[0424] Users enter their profile information (name, age, interests, goals, etc.) using their device. This information is stored in a database by the server and serves as a reference database for subsequent processes.

[0425] Data Analysis

[0426] Conversation data acquired from users (e.g., interactions on dating apps or messaging platforms) is received by a server. The server then runs a sentiment analysis algorithm to analyze the emotional tone and keywords of the messages contained in this data. Sentiment analysis quantifies the emotions and intentions behind each message, and the analysis results are stored in a database.

[0427] Utilization of external data

[0428] The server also collects the target person's public profile and social media posts, and analyzes this data to understand their interests and tendencies. This helps determine how users should approach the target person.

[0429] Generating advice

[0430] The server uses artificial intelligence to analyze the results and generate personalized advice. The generated advice is sent to the user's terminal, from which the user can obtain information to use in subsequent communications.

[0431] Specific example

[0432] For example, if User A has a date planned, the server analyzes User A's past conversation data and extracts their interests from the other person's social media posts. It then advises User A to include music in the conversation during the date. It also makes specific suggestions, such as inviting them to a particular restaurant or event. User A can review this advice through their device and create an effective communication plan.

[0433] In this way, the present invention functions as a system that enables personalized support in a digital environment and contributes to improving users' interpersonal communication skills.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] Users register with the system using a terminal and enter profile information, interests, and goals. The terminal sends the user's input information to the server. The server stores the received information in a database and builds the user profile.

[0437] Step 2:

[0438] Users upload conversation data from dating apps and messaging platforms to a server via their devices. The devices use secure communication protocols to send the data to the server. The server places the received conversation data into a pipeline for analysis.

[0439] Step 3:

[0440] The server uses an emotion analysis algorithm to extract emotional tones from conversation data. The server then quantitatively evaluates the type and intensity of emotions and stores the results in a database.

[0441] Step 4:

[0442] The server collects publicly available information from the target individual, such as their social media accounts and blogs, via the network. The collected data is then processed by a topic modeling algorithm within the server to identify the target individual's interests and concerns.

[0443] Step 5:

[0444] The server compiles emotional data and the subject's interests obtained from the previous analysis and generates personalized advice optimized for the user. Using generative artificial intelligence technology, detailed advice is created, including conversation points and approach methods.

[0445] Step 6:

[0446] The server sends the generated advice to the user's terminal. The terminal notifies the user of the advice and displays it on the interface. The user reviews the provided advice and prepares to use it in their next communication.

[0447] Step 7:

[0448] After a date or after putting the advice into practice, users send feedback to the server using their device. The server stores the feedback in a database and uses it to improve the accuracy of the analysis data and enhance the system.

[0449] (Example 1)

[0450] 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."

[0451] In modern society, improving interpersonal communication skills is becoming increasingly important. However, methods for providing real-time support tailored to individual situations and backgrounds are limited, making it difficult to realize systems that support effective communication. In particular, analyzing diverse data and providing advice that meets the individual needs of users is a challenge.

[0452] 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.

[0453] In this invention, the server includes means for storing profile information received from the user, means for acquiring communication data and performing analysis using an emotion analysis algorithm, and means for determining the interests of the target person based on data collected from publicly available information sources. This enables the generation of personalized advice for each user in real time, and facilitates effective communication support.

[0454] "Profile information" refers to information that includes the user's basic personal attributes, such as name, age, hobbies, and goals.

[0455] "Communication data" refers to digital data that includes messages and conversations exchanged between users.

[0456] An "emotion analysis algorithm" is a computational method that extracts emotional tones and keywords from communication data and quantifies the emotions and intentions behind them.

[0457] "Publicly available information sources" refer to information media that are accessible to anyone, such as social networking services on the internet or public profiles.

[0458] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate the optimal output, i.e., personalized advice, from a specific input.

[0459] A "prompt" is a text-based instruction given to a generative AI model to elicit a specific output.

[0460] "Personalized advice" is information generated based on each user's situation and data analysis results, providing specific advice on actions and communication that the user should take.

[0461] A "user terminal" refers to a digital device used by a user to input information or receive advice.

[0462] "Evaluation" refers to user feedback, which is information used to measure the effectiveness and applicability of the advice provided.

[0463] This invention is an interactive support system for improving users' interpersonal communication skills. This system generates and provides personalized advice to the user in real time based on information provided by the user.

[0464] The system consists of a server, user terminals, and multiple interfaces interconnected via a network. The main processes are as follows:

[0465] The server stores profile information entered by users via their devices in a database. This information includes name, age, hobbies, and goals. The server also uses sentiment analysis algorithms to analyze communication data from dating apps and messaging platforms. This analysis uses generative AI models to quantify the emotional tone and intent of messages, understanding the meaning of each message. In addition, the server collects data from publicly available information sources on the internet, such as social media and public profiles, to determine the interests and tendencies of the target audience. This information is used to determine how users can effectively connect with these individuals.

[0466] The generated advice is provided by constructing prompt sentences using a generative AI model. An example of a prompt sentence is, "Based on the user's profile, advise them to talk about music in the next conversation." The server inputs this prompt sentence into the generative AI model, which then derives specific advice tailored to the user's situation.

[0467] The generated advice is sent to the user's terminal via the network, where the user can view it and use it to improve their communication. For example, if a user is preparing for a date, the server will suggest topics to discuss and places to visit based on the analyzed data. The user can then use this advice to achieve more effective interpersonal communication.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] Users enter profile information (name, age, hobbies, goals, etc.) via their device. This information serves as basic data to personalize the user's communication style. The entered information is sent from the device to the server and stored in a database. Specifically, the user enters information into the application's form and sends the data by pressing the "Submit" button.

[0471] Step 2:

[0472] The server retrieves communication data from the matching apps and messaging platforms used by the user. This data is a log of conversations and is stored in text format. The server uses sentiment analysis algorithms to analyze the emotional tone and keywords from this data. It receives conversation data as input and outputs the emotional value and intent of each message. The server stores these values ​​in a database.

[0473] Step 3:

[0474] The server collects data from publicly available sources (SNS and public profiles) of the target individual. The collected data is retrieved using crawling techniques and API access. This data is then processed through analytical algorithms to identify the target individual's hobbies and interests. Publicly available data of the target individual is taken as input, and a list of hobbies and interests is generated as output.

[0475] Step 4:

[0476] The server uses a generative AI model to construct prompt sentences and generates personalized advice based on the analysis results. The prompt sentences are in the format of, "Based on the user's profile, please advise on topics to discuss in the next conversation." Analysis results and collected data are used as input, and specific advice is generated as output.

[0477] Step 5:

[0478] The server sends the generated advice to the user terminal via the network. The terminal displays the received advice on its user interface. The user reviews the terminal screen and develops a strategy for the next communication. Specifically, the user reads the advice displayed on the terminal and uses it to plan the conversation.

[0479] (Application Example 1)

[0480] 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."

[0481] In conventional customer interactions, store employees often find it difficult to provide effective and personalized service to customers, resulting in decreased customer satisfaction and sales efficiency. The present invention aims to provide a means to optimize customer interactions and deliver a more personalized experience.

[0482] 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.

[0483] In this invention, the server includes means for storing information received from users, means for receiving communication records and performing sentiment analysis, means for collecting publicly available information and analyzing the interests of the subject, and means for analyzing customer transaction history and past dialogue records and generating suggestions optimized for commercial facilities. This enables store employees to make optimized suggestions to customers in real time.

[0484] A "device for storing information received from users" is a device for storing data such as personal information, interests, and purchase history provided by users in a storage device.

[0485] A "device that receives communication records and performs sentiment analysis" refers to hardware or software that collects user messages and conversation data and analyzes the emotions and intentions contained within them.

[0486] A "device that collects publicly available information and analyzes the interests of the target person" is a device that collects information such as publicly available profiles and posts on the internet and evaluates the interests and behavioral patterns of the target person from that information.

[0487] A "device that generates personalized recommendations" is a device that automatically creates recommendations for products and services optimized for each user based on analyzed data.

[0488] A "device that communicates with a terminal device" is a device that transmits generated information and advice via a communication network for use by the user and displays it on the user's device.

[0489] A "device that analyzes customer transaction history and past dialogue records to generate proposals optimized for commercial facilities" is a device that analyzes a customer's purchase history and the content of previous communications to propose commercial facilities and services that meet the customer's needs.

[0490] This system is interconnected via a network, comprising servers, user terminals, and a network, to optimize customer interaction. The servers store user information and analyze received communication records. Specifically, they collect data from users' devices, such as smartphones, and use natural language processing and sentiment analysis algorithms to analyze it. The Python environment utilizes NLTK and Transformer libraries to extract specific emotional tones and keywords.

[0491] The server then analyzes publicly available social media posts and other data to evaluate the target audience's interests and tendencies. This includes data acquisition through web scraping techniques and APIs. The collected data is stored in a database and then used with generative AI models (such as OpenAI's GPT-3) to generate personalized suggestions.

[0492] The generated suggestions are sent to the user's device in real time, allowing the user to communicate effectively with customers based on this information. As a concrete example, imagine a scenario where a store employee, based on the customer's past purchase history and interests on social media, suggests products to recommend for their next visit in real time.

[0493] Example of a prompt:

[0494] "The customer's name is [Customer Name]. In the past, they have preferred casual fashion and shown interest in new arrivals. Could you please advise on what products I should recommend to them during this visit?"

[0495] The operation of this system will enable store employees to provide more personalized service to customers, and is expected to contribute to improved customer satisfaction.

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The user uses their device to enter their profile information (e.g., name, age, interests, past purchase history, etc.). This information becomes input data and is sent from the device to the server. The server completes the acquisition of basic information about the user by saving the received information to its database.

[0499] Step 2:

[0500] The server receives communication records and performs sentiment analysis. Specifically, messaging and conversation data between users and customers are input to the server, and this data is processed by sentiment analysis algorithms (e.g., NLTK or Transformer Library) to analyze the sentiment and keywords of the messages. The results of this analysis become output data and are stored in the database.

[0501] Step 3:

[0502] The server collects publicly available information from the internet (e.g., customer social media posts and public profiles) and analyzes it as input data. Information is obtained using web scraping techniques and APIs to analyze customer interests and trends. The results of this analysis, including the interests and behavioral patterns of the target individuals, are output data and stored.

[0503] Step 4:

[0504] The server generates personalized suggestions using a generative AI model (e.g., OpenAI's GPT-3) based on the stored information. This process uses the subject's interests and conversation sentiment analysis results as input data to produce the suggested content. The generated suggestions are then sent to the user's device.

[0505] Step 5:

[0506] The device displays the received suggestions to the user. The user uses these suggestions to decide how to proceed with the conversation with the customer, providing a more personalized service. Prompts are used here to provide guidance on which products should be introduced.

[0507] 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.

[0508] This invention relates to a system that recognizes a user's emotions and provides personalized advice based on those emotions. This system processes diverse information provided by the user and performs analysis using an emotion engine on the server, thereby improving the user's communication skills. The following describes the implementation of this system with specific examples.

[0509] System Configuration

[0510] The system consists of a server, a user terminal, and an analysis module utilizing an emotion engine. Users input profile information, communication records, and feedback information via the terminal. The server stores this information and performs analysis using the emotion engine. The resulting analysis is then used to generate personalized advice tailored to each user, leveraging generative artificial intelligence technology.

[0511] Information input and emotion recognition

[0512] Users input their profile and past communication records into the system using their devices. Publicly available information about the target individual is also collected by the server. Based on the data transmitted from the user's device, the emotion engine recognizes changes and trends in emotions in real time. Through this process, detailed and dynamic information about the user's emotions is stored in the database.

[0513] Data analysis and advice generation

[0514] The server combines emotional data obtained by the emotion engine with the user's preferences collected from publicly available information to generate advice tailored to the user's needs. This generation process utilizes generative artificial intelligence technology, and the generated advice is adjusted according to the user's emotional state. As a result, personalized advice is provided in the most effective format for the user.

[0515] Specific example

[0516] Consider a scenario where User B is preparing for a date with a specific person. User B uploads their profile information to the system along with recent conversation data. The server uses an emotion engine to recognize User B's potential anxieties and generates advice to help the date go smoothly. This advice includes topics the person might like and effective communication techniques. User B receives this advice and can begin communicating with confidence.

[0517] Through this process, this system provides personalized support to users, contributing to improved communication skills and the building of individual relationships.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The user logs into the system using their device. The user enters or uploads their profile information and existing communication records with the target person. The device then transmits this data to the server.

[0521] Step 2:

[0522] The server stores user profile information and communication records received from the terminal in a database. This stored information is used in subsequent processing steps.

[0523] Step 3:

[0524] The server runs an emotion engine and performs sentiment analysis based on communication records received from the user. The emotion engine detects emotional tones from the communication records and analyzes changes in the user's emotions. The analysis results are recorded in a database on the server.

[0525] Step 4:

[0526] The server automatically collects publicly available information about the target individual via the network. This information includes social media posts and public blog data. The server uses this information to identify the target individual's current interests and concerns.

[0527] Step 5:

[0528] The server uses generative artificial intelligence technology to generate personalized advice based on the analysis results from the emotion engine and collected public information. This advice is customized to the user's emotional state and the target audience's interests.

[0529] Step 6:

[0530] The server sends the generated advice to the user's terminal. The terminal notifies the user of the advice and displays it in the user interface. The user uses this advice to select specific actions to improve communication with the person in question.

[0531] Step 7:

[0532] After a designated date or interaction, the user sends feedback to the server via their device. The server receives the feedback and records it in a database for service improvement and to learn from the analysis data.

[0533] (Example 2)

[0534] 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."

[0535] In modern communication, providing personalized advice tailored to each user's emotional state and preferences is crucial. However, existing systems struggle to accurately grasp users' dynamic emotional changes and generate detailed, tailored advice. This also leads to the problem of failing to improve users' communication skills.

[0536] 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.

[0537] In this invention, the server includes means for storing information received from the user, means for receiving communication history and analyzing emotions, and means for collecting publicly available information and analyzing the target's interests. This makes it possible to generate precise advice tailored to the user's emotions and preferences.

[0538] A "user" refers to an entity that utilizes the system and receives personalized advice.

[0539] "Information" refers to all data, including user profiles, communication history, and feedback.

[0540] "Communication history" refers to records of past communications made by a user.

[0541] "Emotions" are data that represent a user's subjective state and can be classified into categories such as positive, negative, and neutral.

[0542] "Analysis" refers to the process of deriving specific patterns or characteristics based on collected information.

[0543] "Publicly available information" refers to information obtained from publicly accessible data sources that pertains to the subject's activities and preferences.

[0544] "Target" refers to other people or events that the user is interested in.

[0545] "Interest" refers to data that indicates the subject's level of interest.

[0546] "Advice" refers to guidelines provided to users to help them take action or make decisions.

[0547] "Generation" refers to the process of creating new content or data.

[0548] "Generative artificial intelligence technology" refers to technology that enables machines to think and make decisions in a manner similar to that of humans.

[0549] "Emotional state" refers to the user's current emotional state.

[0550] "Preferences" refer to the individual preferences or tendencies of a user or subject.

[0551] "Collaboration" refers to the process where different data or systems are linked and work together.

[0552] This invention is an advanced system for providing personalized advice based on the user's emotional state and individual preferences. The system operates through the cooperation of a user terminal, a server, and an emotion analysis engine.

[0553] The server is equipped with a database for storing and managing information received from users. This database system typically uses MySQL or PostgreSQL. This allows for the efficient storage and management of user-provided profile information, communication history, and feedback.

[0554] Users can input data into the system via their own devices. The data transmitted from the devices is collected on a server and analyzed by an emotion engine. The emotion engine uses "natural language processing technology" to analyze emotions from the user's messages and opinions. This is done, for example, through "natural language recognition software."

[0555] The server then uses generative AI models to generate personalized advice. Technologies used as "generative AI models" in this process include "OpenAI GPT-4" and "BERT," which generate accurate advice tailored to the emotional state and preferences of the user or target.

[0556] As an example, consider a user's first day at a new workplace. The user inputs past work information and communication history, and the server performs sentiment analysis based on this information. Then, a generative AI model is used to generate advice tailored to the user for building relationships at work, and this advice is provided to the user via the terminal. An example of a prompt might be, "Please give me advice on how to make my first day at a new workplace a success. Based on my past experience and communication history with team members, please tell me what points I should focus on."

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] Users input personal information, past communication history, and feedback information into the system using their own devices. The devices then send this information to the server as input data. The input data includes the user's profile and past conversation content, which forms the basis for subsequent sentiment analysis.

[0560] Step 2:

[0561] The server stores the data received from the terminal in a database. A "database management system" is used for this data storage, often such as "MySQL" or "PostgreSQL." The entered data is efficiently stored for subsequent use within the system and made readily accessible at any time.

[0562] Step 3:

[0563] The server then uses the stored data to begin analysis in the emotion engine. The emotion engine uses natural language processing techniques to analyze the input data and extract the user's emotional state. This analysis involves examining keywords and context in the text to determine whether the user's current emotion is positive, negative, or neutral. The analysis results are then used to generate advice in the next step.

[0564] Step 4:

[0565] The server generates personalized advice using a generative AI model based on the sentiment analysis results. This process utilizes generative AI models such as OpenAI GPT-4 and BERT. Using the sentiment analysis results and the user's past history as input, the output generates advice tailored to the user. This advice includes practical suggestions that reflect the user's emotional state, preferences, and past communication patterns.

[0566] Step 5:

[0567] The server provides the user with generated advice. The user receives this output and develops a communication strategy tailored to their own situation. Through this process, the user can receive helpful advice that is relevant to their emotional state and put it into practice.

[0568] (Application Example 2)

[0569] 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."

[0570] A challenge lies in understanding users' emotions and areas where their customer service skills are lacking, and providing appropriate advice in real time. In particular, there is a need for an effective system that enables flexible responses to changes in customer emotions and improves customer satisfaction.

[0571] 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.

[0572] In this invention, the server includes means for storing data received from the user, means for acquiring communication history and performing sentiment analysis, and means for collecting external information and evaluating the interests of relevant objects. This enables accurate understanding of the user's emotions and the provision of appropriate advice in real time.

[0573] A "user" is an individual or legal entity that uses a service or system.

[0574] "Means of data retention" refers to hardware and software systems for recording and storing information received from users.

[0575] "Communication history" refers to a record of a user's communication activities, including information such as the content of those communications and the recipients.

[0576] "Means of analyzing emotions" refers to technologies or algorithms that analyze received data and determine the user's emotions.

[0577] "External information" refers to data collected from sources other than user information, and includes publicly available information about the person or event in question.

[0578] "Means for evaluating the interests of relevant subjects" refers to methods for analyzing the interests and directions of a particular person or event based on collected external information.

[0579] "Personalized support" refers to customized advice and suggestions created to suit the user's specific circumstances and emotions.

[0580] A "generative algorithm" is a set of computational procedures or algorithms used to create new data or proposals based on received information.

[0581] "Smart hardware that provides suggestions in real time based on emotion recognition results" refers to a smart device that evaluates the user's emotional state and immediately presents suggestions and advice based on the results.

[0582] The system that implements this application example is built around a server that communicates with the user's smart hardware (e.g., smart glasses). The server has a database to store profile information and communication history obtained from the user. The server also uses an emotion analysis engine to analyze emotions. For example, it might utilize Microsoft's Azure Cognitive Services or Google Cloud Speech-to-Text API to estimate emotions from the user's facial expressions and conversation.

[0583] The server collects external information and evaluates the user's interests in the relevant subjects. This process utilizes algorithms that analyze publicly available data on the internet to identify specific topics and trends. This allows for the efficient identification of the user's interests in specific subjects and activities.

[0584] Based on the user's emotion recognition results, a generative algorithm creates personalized support for the user. This generated support is displayed in real time on the user's smart glasses. This process utilizes advanced generative AI models, such as OpenAI's GPT model.

[0585] As a concrete example, store staff can use prompt messages through smart glasses during conversations with customers, such as, "Please tell me the appropriate way to interact with a customer when they show interest in a product. Please also include examples of related product suggestions." Based on the assistance received, they can then provide appropriate customer service. In this way, the system provides support to enable users to effectively interact with customers and make optimal suggestions.

[0586] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0587] Step 1:

[0588] Users upload their profile information and communication history to the server using their device. The input is data provided by the user, and the server records this in a database. This allows for the retention of user information.

[0589] Step 2:

[0590] The server activates its emotion analysis engine and analyzes the uploaded communication history. The input is the communication history, and the output is data indicating the user's emotional state. The server uses the emotion analysis engine to analyze facial expressions and tone of voice to estimate the user's emotions. Natural language processing techniques are used in this process.

[0591] Step 3:

[0592] The server collects external information and identifies specific topics and interests. The input is external data, and the output is interest assessment data regarding relevant subjects. The server aggregates publicly available information via the internet and runs an algorithm to evaluate relevant trends. This algorithm reveals the subject's interests.

[0593] Step 4:

[0594] The server uses a generative AI model to create personalized support based on emotion recognition results and the target's interest data. The input is emotion data and interest evaluations, and the output is customized support for the user. The generative AI model generates optimal suggestions tailored to the user's situation.

[0595] Step 5:

[0596] The server communicates the generated assistance data to the user's smart glasses in real time. The input is the generated assistance data, and the output is the assistance content displayed on the smart glasses. Based on the received information, the user appropriately handles customer interactions. This process enhances the user's communication skills.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] [Fourth Embodiment]

[0601] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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".

[0614] This invention is an interactive support system for improving users' interpersonal communication skills. This system generates and provides personalized advice to the user in real time based on information provided by the user. The following describes the implementation of this system with specific examples.

[0615] System Configuration

[0616] The system consists of a server, user terminals, and multiple interfaces interconnected via a network. Users input information into the system via their terminals, and the server performs data analysis and generates advice based on that information. The resulting advice is then transmitted to the user terminals via the network.

[0617] Information input and collection

[0618] Users enter their profile information (name, age, interests, goals, etc.) using their device. This information is stored in a database by the server and serves as a reference database for subsequent processes.

[0619] Data Analysis

[0620] Conversation data acquired from users (e.g., interactions on dating apps or messaging platforms) is received by a server. The server then runs a sentiment analysis algorithm to analyze the emotional tone and keywords of the messages contained in this data. Sentiment analysis quantifies the emotions and intentions behind each message, and the analysis results are stored in a database.

[0621] Utilization of external data

[0622] The server also collects the target person's public profile and social media posts, and analyzes this data to understand their interests and tendencies. This helps determine how users should approach the target person.

[0623] Generating advice

[0624] The server uses artificial intelligence to analyze the results and generate personalized advice. The generated advice is sent to the user's terminal, from which the user can obtain information to use in subsequent communications.

[0625] Specific example

[0626] For example, if User A has a date planned, the server analyzes User A's past conversation data and extracts their interests from the other person's social media posts. It then advises User A to include music in the conversation during the date. It also makes specific suggestions, such as inviting them to a particular restaurant or event. User A can review this advice through their device and create an effective communication plan.

[0627] In this way, the present invention functions as a system that enables personalized support in a digital environment and contributes to improving users' interpersonal communication skills.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] Users register with the system using a terminal and enter profile information, interests, and goals. The terminal sends the user's input information to the server. The server stores the received information in a database and builds the user profile.

[0631] Step 2:

[0632] Users upload conversation data from dating apps and messaging platforms to a server via their devices. The devices use secure communication protocols to send the data to the server. The server places the received conversation data into a pipeline for analysis.

[0633] Step 3:

[0634] The server uses an emotion analysis algorithm to extract emotional tones from conversation data. The server then quantitatively evaluates the type and intensity of emotions and stores the results in a database.

[0635] Step 4:

[0636] The server collects publicly available information from the target individual, such as their social media accounts and blogs, via the network. The collected data is then processed by a topic modeling algorithm within the server to identify the target individual's interests and concerns.

[0637] Step 5:

[0638] The server compiles emotional data and the subject's interests obtained from the previous analysis and generates personalized advice optimized for the user. Using generative artificial intelligence technology, detailed advice is created, including conversation points and approach methods.

[0639] Step 6:

[0640] The server sends the generated advice to the user's terminal. The terminal notifies the user of the advice and displays it on the interface. The user reviews the provided advice and prepares to use it in their next communication.

[0641] Step 7:

[0642] After a date or after putting the advice into practice, users send feedback to the server using their device. The server stores the feedback in a database and uses it to improve the accuracy of the analysis data and enhance the system.

[0643] (Example 1)

[0644] 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".

[0645] In modern society, improving interpersonal communication skills is becoming increasingly important. However, methods for providing real-time support tailored to individual situations and backgrounds are limited, making it difficult to realize systems that support effective communication. In particular, analyzing diverse data and providing advice that meets the individual needs of users is a challenge.

[0646] 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.

[0647] In this invention, the server includes means for storing profile information received from the user, means for acquiring communication data and performing analysis using an emotion analysis algorithm, and means for determining the interests of the target person based on data collected from publicly available information sources. This enables the generation of personalized advice for each user in real time, and facilitates effective communication support.

[0648] "Profile information" refers to information that includes the user's basic personal attributes, such as name, age, hobbies, and goals.

[0649] "Communication data" refers to digital data that includes messages and conversations exchanged between users.

[0650] An "emotion analysis algorithm" is a computational method that extracts emotional tones and keywords from communication data and quantifies the emotions and intentions behind them.

[0651] "Publicly available information sources" refer to information media that are accessible to anyone, such as social networking services on the internet or public profiles.

[0652] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate the optimal output, i.e., personalized advice, from a specific input.

[0653] A "prompt" is a text-based instruction given to a generative AI model to elicit a specific output.

[0654] "Personalized advice" is information generated based on each user's situation and data analysis results, providing specific advice on actions and communication that the user should take.

[0655] A "user terminal" refers to a digital device used by a user to input information or receive advice.

[0656] "Evaluation" refers to user feedback, which is information used to measure the effectiveness and applicability of the advice provided.

[0657] This invention is an interactive support system for improving users' interpersonal communication skills. This system generates and provides personalized advice to the user in real time based on information provided by the user.

[0658] The system consists of a server, user terminals, and multiple interfaces interconnected via a network. The main processes are as follows:

[0659] The server stores profile information entered by users via their devices in a database. This information includes name, age, hobbies, and goals. The server also uses sentiment analysis algorithms to analyze communication data from dating apps and messaging platforms. This analysis uses generative AI models to quantify the emotional tone and intent of messages, understanding the meaning of each message. In addition, the server collects data from publicly available information sources on the internet, such as social media and public profiles, to determine the interests and tendencies of the target audience. This information is used to determine how users can effectively connect with these individuals.

[0660] The generated advice is provided by constructing prompt sentences using a generative AI model. An example of a prompt sentence is, "Based on the user's profile, advise them to talk about music in the next conversation." The server inputs this prompt sentence into the generative AI model, which then derives specific advice tailored to the user's situation.

[0661] The generated advice is sent to the user's terminal via the network, where the user can view it and use it to improve their communication. For example, if a user is preparing for a date, the server will suggest topics to discuss and places to visit based on the analyzed data. The user can then use this advice to achieve more effective interpersonal communication.

[0662] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0663] Step 1:

[0664] Users enter profile information (name, age, hobbies, goals, etc.) via their device. This information serves as basic data to personalize the user's communication style. The entered information is sent from the device to the server and stored in a database. Specifically, the user enters information into the application's form and sends the data by pressing the "Submit" button.

[0665] Step 2:

[0666] The server retrieves communication data from the matching apps and messaging platforms used by the user. This data is a log of conversations and is stored in text format. The server uses sentiment analysis algorithms to analyze the emotional tone and keywords from this data. It receives conversation data as input and outputs the emotional value and intent of each message. The server stores these values ​​in a database.

[0667] Step 3:

[0668] The server collects data from publicly available sources (SNS and public profiles) of the target individual. The collected data is retrieved using crawling techniques and API access. This data is then processed through analytical algorithms to identify the target individual's hobbies and interests. Publicly available data of the target individual is taken as input, and a list of hobbies and interests is generated as output.

[0669] Step 4:

[0670] The server uses a generative AI model to construct prompt sentences and generates personalized advice based on the analysis results. The prompt sentences are in the format of, "Based on the user's profile, please advise on topics to discuss in the next conversation." Analysis results and collected data are used as input, and specific advice is generated as output.

[0671] Step 5:

[0672] The server sends the generated advice to the user terminal via the network. The terminal displays the received advice on its user interface. The user reviews the terminal screen and develops a strategy for the next communication. Specifically, the user reads the advice displayed on the terminal and uses it to plan the conversation.

[0673] (Application Example 1)

[0674] 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".

[0675] In conventional customer interactions, store employees often find it difficult to provide effective and personalized service to customers, resulting in decreased customer satisfaction and sales efficiency. The present invention aims to provide a means to optimize customer interactions and deliver a more personalized experience.

[0676] 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.

[0677] In this invention, the server includes means for storing information received from users, means for receiving communication records and performing sentiment analysis, means for collecting publicly available information and analyzing the interests of the subject, and means for analyzing customer transaction history and past dialogue records and generating suggestions optimized for commercial facilities. This enables store employees to make optimized suggestions to customers in real time.

[0678] A "device for storing information received from users" is a device for storing data such as personal information, interests, and purchase history provided by users in a storage device.

[0679] A "device that receives communication records and performs sentiment analysis" refers to hardware or software that collects user messages and conversation data and analyzes the emotions and intentions contained within them.

[0680] A "device that collects publicly available information and analyzes the interests of the target person" is a device that collects information such as publicly available profiles and posts on the internet and evaluates the interests and behavioral patterns of the target person from that information.

[0681] A "device that generates personalized recommendations" is a device that automatically creates recommendations for products and services optimized for each user based on analyzed data.

[0682] A "device that communicates with a terminal device" is a device that transmits generated information and advice via a communication network for use by the user and displays it on the user's device.

[0683] A "device that analyzes customer transaction history and past dialogue records to generate proposals optimized for commercial facilities" is a device that analyzes a customer's purchase history and the content of previous communications to propose commercial facilities and services that meet the customer's needs.

[0684] This system is interconnected via a network, comprising servers, user terminals, and a network, to optimize customer interaction. The servers store user information and analyze received communication records. Specifically, they collect data from users' devices, such as smartphones, and use natural language processing and sentiment analysis algorithms to analyze it. The Python environment utilizes NLTK and Transformer libraries to extract specific emotional tones and keywords.

[0685] The server then analyzes publicly available social media posts and other data to evaluate the target audience's interests and tendencies. This includes data acquisition through web scraping techniques and APIs. The collected data is stored in a database and then used with generative AI models (such as OpenAI's GPT-3) to generate personalized suggestions.

[0686] The generated suggestions are sent to the user's device in real time, allowing the user to communicate effectively with customers based on this information. As a concrete example, imagine a scenario where a store employee, based on the customer's past purchase history and interests on social media, suggests products to recommend for their next visit in real time.

[0687] Example of a prompt:

[0688] "The customer's name is [Customer Name]. In the past, they have preferred casual fashion and shown interest in new arrivals. Could you please advise on what products I should recommend to them during this visit?"

[0689] The operation of this system will enable store employees to provide more personalized service to customers, and is expected to contribute to improved customer satisfaction.

[0690] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0691] Step 1:

[0692] The user uses their device to enter their profile information (e.g., name, age, interests, past purchase history, etc.). This information becomes input data and is sent from the device to the server. The server completes the acquisition of basic information about the user by saving the received information to its database.

[0693] Step 2:

[0694] The server receives communication records and performs sentiment analysis. Specifically, messaging and conversation data between users and customers are input to the server, and this data is processed by sentiment analysis algorithms (e.g., NLTK or Transformer Library) to analyze the sentiment and keywords of the messages. The results of this analysis become output data and are stored in the database.

[0695] Step 3:

[0696] The server collects publicly available information from the internet (e.g., customer social media posts and public profiles) and analyzes it as input data. Information is obtained using web scraping techniques and APIs to analyze customer interests and trends. The results of this analysis, including the interests and behavioral patterns of the target individuals, are output data and stored.

[0697] Step 4:

[0698] The server generates personalized suggestions using a generative AI model (e.g., OpenAI's GPT-3) based on the stored information. This process uses the subject's interests and conversation sentiment analysis results as input data to produce the suggested content. The generated suggestions are then sent to the user's device.

[0699] Step 5:

[0700] The device displays the received suggestions to the user. The user uses these suggestions to decide how to proceed with the conversation with the customer, providing a more personalized service. Prompts are used here to provide guidance on which products should be introduced.

[0701] 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.

[0702] This invention relates to a system that recognizes a user's emotions and provides personalized advice based on those emotions. This system processes diverse information provided by the user and performs analysis using an emotion engine on the server, thereby improving the user's communication skills. The following describes the implementation of this system with specific examples.

[0703] System Configuration

[0704] The system consists of a server, a user terminal, and an analysis module utilizing an emotion engine. Users input profile information, communication records, and feedback information via the terminal. The server stores this information and performs analysis using the emotion engine. The resulting analysis is then used to generate personalized advice tailored to each user, leveraging generative artificial intelligence technology.

[0705] Information input and emotion recognition

[0706] Users input their profile and past communication records into the system using their devices. Publicly available information about the target individual is also collected by the server. Based on the data transmitted from the user's device, the emotion engine recognizes changes and trends in emotions in real time. Through this process, detailed and dynamic information about the user's emotions is stored in the database.

[0707] Data analysis and advice generation

[0708] The server combines emotional data obtained by the emotion engine with the user's preferences collected from publicly available information to generate advice tailored to the user's needs. This generation process utilizes generative artificial intelligence technology, and the generated advice is adjusted according to the user's emotional state. As a result, personalized advice is provided in the most effective format for the user.

[0709] Specific example

[0710] Consider a scenario where User B is preparing for a date with a specific person. User B uploads their profile information to the system along with recent conversation data. The server uses an emotion engine to recognize User B's potential anxieties and generates advice to help the date go smoothly. This advice includes topics the person might like and effective communication techniques. User B receives this advice and can begin communicating with confidence.

[0711] Through this process, this system provides personalized support to users, contributing to improved communication skills and the building of individual relationships.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The user logs into the system using their device. The user enters or uploads their profile information and existing communication records with the target person. The device then transmits this data to the server.

[0715] Step 2:

[0716] The server stores user profile information and communication records received from the terminal in a database. This stored information is used in subsequent processing steps.

[0717] Step 3:

[0718] The server runs an emotion engine and performs sentiment analysis based on communication records received from the user. The emotion engine detects emotional tones from the communication records and analyzes changes in the user's emotions. The analysis results are recorded in a database on the server.

[0719] Step 4:

[0720] The server automatically collects publicly available information about the target individual via the network. This information includes social media posts and public blog data. The server uses this information to identify the target individual's current interests and concerns.

[0721] Step 5:

[0722] The server uses generative artificial intelligence technology to generate personalized advice based on the analysis results from the emotion engine and collected public information. This advice is customized to the user's emotional state and the target audience's interests.

[0723] Step 6:

[0724] The server sends the generated advice to the user's terminal. The terminal notifies the user of the advice and displays it in the user interface. The user uses this advice to select specific actions to improve communication with the person in question.

[0725] Step 7:

[0726] After a designated date or interaction, the user sends feedback to the server via their device. The server receives the feedback and records it in a database for service improvement and to learn from the analysis data.

[0727] (Example 2)

[0728] 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".

[0729] In modern communication, providing personalized advice tailored to each user's emotional state and preferences is crucial. However, existing systems struggle to accurately grasp users' dynamic emotional changes and generate detailed, tailored advice. This also leads to the problem of failing to improve users' communication skills.

[0730] 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.

[0731] In this invention, the server includes means for storing information received from the user, means for receiving communication history and analyzing emotions, and means for collecting publicly available information and analyzing the target's interests. This makes it possible to generate precise advice tailored to the user's emotions and preferences.

[0732] A "user" refers to an entity that utilizes the system and receives personalized advice.

[0733] "Information" refers to all data, including user profiles, communication history, and feedback.

[0734] "Communication history" refers to records of past communications made by a user.

[0735] "Emotions" are data that represent a user's subjective state and can be classified into categories such as positive, negative, and neutral.

[0736] "Analysis" refers to the process of deriving specific patterns or characteristics based on collected information.

[0737] "Publicly available information" refers to information obtained from publicly accessible data sources that pertains to the subject's activities and preferences.

[0738] "Target" refers to other people or events that the user is interested in.

[0739] "Interest" refers to data that indicates the subject's level of interest.

[0740] "Advice" refers to guidelines provided to users to help them take action or make decisions.

[0741] "Generation" refers to the process of creating new content or data.

[0742] "Generative artificial intelligence technology" refers to technology that enables machines to think and make decisions in a manner similar to that of humans.

[0743] "Emotional state" refers to the user's current emotional state.

[0744] "Preferences" refer to the individual preferences or tendencies of a user or subject.

[0745] "Collaboration" refers to the process where different data or systems are linked and work together.

[0746] This invention is an advanced system for providing personalized advice based on the user's emotional state and individual preferences. The system operates through the cooperation of a user terminal, a server, and an emotion analysis engine.

[0747] The server is equipped with a database for storing and managing information received from users. This database system typically uses MySQL or PostgreSQL. This allows for the efficient storage and management of user-provided profile information, communication history, and feedback.

[0748] Users can input data into the system via their own devices. The data transmitted from the devices is collected on a server and analyzed by an emotion engine. The emotion engine uses "natural language processing technology" to analyze emotions from the user's messages and opinions. This is done, for example, through "natural language recognition software."

[0749] The server then uses generative AI models to generate personalized advice. Technologies used as "generative AI models" in this process include "OpenAI GPT-4" and "BERT," which generate accurate advice tailored to the emotional state and preferences of the user or target.

[0750] As an example, consider a user's first day at a new workplace. The user inputs past work information and communication history, and the server performs sentiment analysis based on this information. Then, a generative AI model is used to generate advice tailored to the user for building relationships at work, and this advice is provided to the user via the terminal. An example of a prompt might be, "Please give me advice on how to make my first day at a new workplace a success. Based on my past experience and communication history with team members, please tell me what points I should focus on."

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] Users input personal information, past communication history, and feedback information into the system using their own devices. The devices then send this information to the server as input data. The input data includes the user's profile and past conversation content, which forms the basis for subsequent sentiment analysis.

[0754] Step 2:

[0755] The server stores the data received from the terminal in a database. A "database management system" is used for this data storage, often such as "MySQL" or "PostgreSQL." The entered data is efficiently stored for subsequent use within the system and made readily accessible at any time.

[0756] Step 3:

[0757] The server then uses the stored data to begin analysis in the emotion engine. The emotion engine uses natural language processing techniques to analyze the input data and extract the user's emotional state. This analysis involves examining keywords and context in the text to determine whether the user's current emotion is positive, negative, or neutral. The analysis results are then used to generate advice in the next step.

[0758] Step 4:

[0759] The server generates personalized advice using a generative AI model based on the sentiment analysis results. This process utilizes generative AI models such as OpenAI GPT-4 and BERT. Using the sentiment analysis results and the user's past history as input, the output generates advice tailored to the user. This advice includes practical suggestions that reflect the user's emotional state, preferences, and past communication patterns.

[0760] Step 5:

[0761] The server provides the user with generated advice. The user receives this output and develops a communication strategy tailored to their own situation. Through this process, the user can receive helpful advice that is relevant to their emotional state and put it into practice.

[0762] (Application Example 2)

[0763] 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".

[0764] A challenge lies in understanding users' emotions and areas where their customer service skills are lacking, and providing appropriate advice in real time. In particular, there is a need for an effective system that enables flexible responses to changes in customer emotions and improves customer satisfaction.

[0765] 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.

[0766] In this invention, the server includes means for storing data received from the user, means for acquiring communication history and performing sentiment analysis, and means for collecting external information and evaluating the interests of relevant objects. This enables accurate understanding of the user's emotions and the provision of appropriate advice in real time.

[0767] A "user" is an individual or legal entity that uses a service or system.

[0768] "Means of data retention" refers to hardware and software systems for recording and storing information received from users.

[0769] "Communication history" refers to a record of a user's communication activities, including information such as the content of those communications and the recipients.

[0770] "Means of analyzing emotions" refers to technologies or algorithms that analyze received data and determine the user's emotions.

[0771] "External information" refers to data collected from sources other than user information, and includes publicly available information about the person or event in question.

[0772] "Means for evaluating the interests of relevant subjects" refers to methods for analyzing the interests and directions of a particular person or event based on collected external information.

[0773] "Personalized support" refers to customized advice and suggestions created to suit the user's specific circumstances and emotions.

[0774] A "generative algorithm" is a set of computational procedures or algorithms used to create new data or proposals based on received information.

[0775] "Smart hardware that provides suggestions in real time based on emotion recognition results" refers to a smart device that evaluates the user's emotional state and immediately presents suggestions and advice based on the results.

[0776] The system that implements this application example is built around a server that communicates with the user's smart hardware (e.g., smart glasses). The server has a database to store profile information and communication history obtained from the user. The server also uses an emotion analysis engine to analyze emotions. For example, it might utilize Microsoft's Azure Cognitive Services or Google Cloud Speech-to-Text API to estimate emotions from the user's facial expressions and conversation.

[0777] The server collects external information and evaluates the user's interests in the relevant subjects. This process utilizes algorithms that analyze publicly available data on the internet to identify specific topics and trends. This allows for the efficient identification of the user's interests in specific subjects and activities.

[0778] Based on the user's emotion recognition results, a generative algorithm creates personalized support for the user. This generated support is displayed in real time on the user's smart glasses. This process utilizes advanced generative AI models, such as OpenAI's GPT model.

[0779] As a concrete example, store staff can use prompt messages through smart glasses during conversations with customers, such as, "Please tell me the appropriate way to interact with a customer when they show interest in a product. Please also include examples of related product suggestions." Based on the assistance received, they can then provide appropriate customer service. In this way, the system provides support to enable users to effectively interact with customers and make optimal suggestions.

[0780] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0781] Step 1:

[0782] Users upload their profile information and communication history to the server using their device. The input is data provided by the user, and the server records this in a database. This allows for the retention of user information.

[0783] Step 2:

[0784] The server activates its emotion analysis engine and analyzes the uploaded communication history. The input is the communication history, and the output is data indicating the user's emotional state. The server uses the emotion analysis engine to analyze facial expressions and tone of voice to estimate the user's emotions. Natural language processing techniques are used in this process.

[0785] Step 3:

[0786] The server collects external information and identifies specific topics and interests. The input is external data, and the output is interest assessment data regarding relevant subjects. The server aggregates publicly available information via the internet and runs an algorithm to evaluate relevant trends. This algorithm reveals the subject's interests.

[0787] Step 4:

[0788] The server uses a generative AI model to create personalized support based on emotion recognition results and the target's interest data. The input is emotion data and interest evaluations, and the output is customized support for the user. The generative AI model generates optimal suggestions tailored to the user's situation.

[0789] Step 5:

[0790] The server communicates the generated assistance data to the user's smart glasses in real time. The input is the generated assistance data, and the output is the assistance content displayed on the smart glasses. Based on the received information, the user appropriately handles customer interactions. This process enhances the user's communication skills.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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."

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] The following is further disclosed regarding the embodiments described above.

[0813] (Claim 1)

[0814] A means of storing information received from the user,

[0815] A means of receiving communication records and performing emotional analysis,

[0816] A means of collecting publicly available information and analyzing the interests of the target audience,

[0817] A means for generating personalized advice based on the analyzed data,

[0818] Means for communicating the generated advice to the user,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, further comprising means for receiving and storing user feedback.

[0822] (Claim 3)

[0823] The system according to claim 1, wherein the personalized advice is generated using generative artificial intelligence.

[0824] "Example 1"

[0825] (Claim 1)

[0826] A means of storing profile information received from the user,

[0827] A means of acquiring communication data and performing analysis using an emotion analysis algorithm,

[0828] A means of determining the interests of the target audience based on data collected from publicly available sources,

[0829] A means of generating prompt sentences using a generative AI model based on analysis results and generating personalized advice,

[0830] A means of sending the generated advice to the user's terminal for display,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for receiving and storing evaluations provided by users.

[0834] (Claim 3)

[0835] The system according to claim 1, which generates personalized advice using an AI model.

[0836] "Application Example 1"

[0837] (Claim 1)

[0838] A device for storing information received from the user,

[0839] A device that receives communication records and performs emotional analysis,

[0840] A device that collects publicly available information and analyzes the interests of the target audience,

[0841] A device that generates personalized suggestions based on the analyzed data,

[0842] A device that communicates the generated proposal to a terminal device,

[0843] A device that analyzes customer transaction history and past dialogue records to generate proposals optimized for commercial facilities,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, further comprising a device for receiving and storing opinions provided by users.

[0847] (Claim 3)

[0848] The system according to claim 1, wherein the personalized proposals are created using a generative model.

[0849] "Example 2 of combining an emotion engine"

[0850] (Claim 1)

[0851] A means of storing information received from the user,

[0852] A means of receiving communication history and analyzing emotions,

[0853] A means of collecting publicly available information and analyzing the subject's interests,

[0854] A means for generating personalized advice based on the analyzed data,

[0855] A means for communicating the generated advice to the user,

[0856] A means of creating context-based advice using generative artificial intelligence technology,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, further comprising means for receiving and storing feedback of opinions provided by users.

[0860] (Claim 3)

[0861] The system according to claim 1, which links the emotional state and preferences of the subject when generating the personalized advice.

[0862] "Application example 2 when combining with an emotional engine"

[0863] (Claim 1)

[0864] A means of storing data received from the user,

[0865] A means of acquiring communication history and performing emotional analysis,

[0866] A means of collecting external information and evaluating the interests of the relevant subject,

[0867] A means of creating individualized support based on the analyzed information,

[0868] Means for providing the aforementioned support to the user,

[0869] A method using smart hardware that provides suggestions in real time based on emotion recognition results,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, further comprising means for obtaining and retaining opinions provided by users.

[0873] (Claim 3)

[0874] The system according to claim 1, wherein the personalized support is created using a generation algorithm. [Explanation of Symbols]

[0875] 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 storing information received from the user, A means of receiving communication records and performing emotional analysis, A means of collecting publicly available information and analyzing the interests of the target audience, A means for generating personalized advice based on the analyzed data, Means for communicating the generated advice to the user, A system that includes this.

2. The system according to claim 1, further comprising means for receiving and storing user-provided feedback.

3. The system according to claim 1, wherein the personalized advice is generated using generative artificial intelligence.

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