system

Abstract

We provide the system. [Solution] A means for designating a virtual subject based on operation information received from at least one information processing device, A means for generating a solution that matches the characteristics of a specified virtual entity by referring to a database of past knowledge, A means for transmitting the generated answer to an information processing device and displaying it, A means for obtaining evaluation information from users and updating the aforementioned knowledge database, A means of generating answers in natural language using a generative AI model, A means of presenting the generated answer using a real-world support device, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is to solve the problem that it has been difficult to provide an environment in which managers can receive prompt and individualized advice for various management issues they face. Conventional methods only present general solutions and lack flexible guidance for specific situations. As a result, the improvement of managers' capabilities and the promotion of work efficiency have been hindered.

Means for Solving the Problems

[0005] This invention provides a system that selects a virtual entity in response to user input and uses artificial intelligence to generate individually tailored answers based on a database of past success stories. This allows users to receive advice tailored to their specific concerns, based on the characteristics of the selected virtual entity. Furthermore, by acquiring and accumulating feedback, the database can be continuously updated, improving the quality of the advice provided.

[0006] A "user device" is an electronic device used by a user to operate and input or receive data.

[0007] A "virtual entity" is a software entity that operates on a computer, possesses specific characteristics and display formats, and interacts with the user.

[0008] A "success story database" is a collection of information recording the actions and decisions of past managers, and is a database used as a reference for effective solutions to specific situations.

[0009] "Natural language processing technology" is a technique that analyzes everyday human language, converts it into a format that computers can easily understand, and generates appropriate responses and information.

[0010] "Evaluation information" refers to data on feedback that users provide regarding the advice they receive, indicating the effectiveness and satisfaction level of that advice. [Brief explanation of the drawing]

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

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be 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."

[0019] [First Embodiment]

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

[0021] 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.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] The system of this invention operates with a server, terminal, and user working in coordination with each other. The user first accesses the system using a user device and can select a virtual entity according to their preferences and requirements. The selected virtual entities possess different character traits, such as friendliness or logical thinking.

[0033] Next, the user enters their management concerns into the terminal. This information is sent to the server and analyzed using natural language processing technology. Key keywords and themes are extracted and compared with a database of past success stories. The server searches this database for highly relevant solutions and generates advice tailored to the characteristics of the selected virtual entity.

[0034] The generated advice is displayed on the terminal and presented in a format that users can use immediately. Users can provide feedback to the system as they receive and implement this advice. This feedback is collected by the server and used to improve the accuracy of future advice. For example, if a project leader is struggling with how to lead a new team, the system will provide specific advice such as "Focus on clarifying the team structure and defining roles." This allows managers to know specific and actionable steps and improve work efficiency.

[0035] By having the server, terminals, and users work together in this way, it is possible to build a system that can provide assistance tailored to the individual needs of each manager in real time.

[0036] The following describes the processing flow.

[0037] Step 1:

[0038] The user accesses the system through their device and logs in by entering their login information. The server receives this information, performs authentication, and if successful, displays the user dashboard on the device.

[0039] Step 2:

[0040] The user selects a virtual entity from the dashboard. The available character types include, for example, those based on friendliness or logic. The server receives this selection and saves it to the user's profile.

[0041] Step 3:

[0042] The user enters their specific management concerns into the terminal. Once this information is entered, the terminal transfers the data to the server.

[0043] Step 4:

[0044] The server passes the received data to a natural language processing engine for analysis. It extracts important keywords and themes related to the problem and constructs search queries based on them.

[0045] Step 5:

[0046] The server searches a database of success stories and lists multiple success stories related to the extracted keywords. This allows for the selection of potential solutions based on real-world examples.

[0047] Step 6:

[0048] The server adjusts the style and expression of the advice to suit the selected virtual character, and then generates the final advice.

[0049] Step 7:

[0050] The generated advice is sent to the device and displayed to the user. The user can review it and use it to help them put it into practice.

[0051] Step 8:

[0052] Users provide feedback on the usefulness of the advice and send this feedback from their device to the server. The server receives this feedback, updates the learning algorithm in the database, and uses it to generate advice in the future.

[0053] (Example 1)

[0054] 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."

[0055] In today's work environment, individual managers face a variety of management concerns and challenges, but there is a lack of systems that provide effective solutions in real time. Furthermore, there is a need for systems that can flexibly adapt to different situations and individual characteristics. Additionally, methods for improving service quality based on user feedback are also required.

[0056] 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.

[0057] In this invention, the server includes means for selecting a virtual entity based on request information received from at least one input device, means for analyzing the received information using natural language processing and extracting important information, and means for referring to a database of past success records and searching for strategies related to the extracted information. This makes it possible to provide effective advice and strategies tailored to the needs of each user.

[0058] An "input device" is a device that receives information from the user, and this information is used for analysis and processing within the system.

[0059] "Request information" refers to information about the tasks or problems that the system will analyze, provided by the user through an input device.

[0060] A "virtual entity" is a virtual agent with different characteristics and personality, designed to interact with users.

[0061] "Natural language processing" is a technology that enables computers to understand and process human language, and it is the process of extracting intent and important information from text data.

[0062] "Important information" refers to elements or data that are deemed particularly noteworthy after analyzing the request information entered by the user.

[0063] A "success story database" is a database containing past problem-solving cases and strategies, and serves as a source of information to find solutions to current problems.

[0064] "Strategy" refers to specific proposals and methods offered as solutions to problems faced by users.

[0065] A "display device" is a device used to visually present information and strategies transmitted from a server to the user.

[0066] "Feedback" refers to information provided by users regarding their evaluation of the strategies provided by the system and the results of their implementation.

[0067] The system of the present invention supports effective problem solving through the cooperation of a server, a terminal, and a user. The user first accesses the system using a terminal. The terminal is equipped with an interface for inputting request information from the user, which includes the challenges and needs the user is facing. The terminal used here is assumed to be a general-purpose computer or smartphone.

[0068] When a user enters request information into their terminal, this information is transmitted to the server. The server analyzes the received request information using natural language processing technology. This analysis uses a text analysis engine to extract important information and keywords from the text.

[0069] Next, the server uses the extracted information to refer to a database of past success stories. This database contains a collection of successful problem-solving cases accumulated in the past, and the server searches for and selects the most relevant strategies. A typical relational database is assumed to be the database management system used here.

[0070] Subsequently, the server utilizes a generative AI model to generate the identified strategy, optimized for the characteristics of the virtual entity selected by the user. Because this virtual entity has different representations and characteristics based on user instructions, the generated strategy can also be customized to the user's needs. The generated strategy is then transmitted from the server to the terminal and presented to the user in real time.

[0071] Users receive the strategies presented and report the results and feedback obtained when applying them to their actual work to the system via their terminals. This feedback is collected on the server and used to update the database, thereby improving the accuracy of the advice provided by the system.

[0072] As a concrete example, a prompt might say, "I need advice on how to form a new team." The server then generates relevant strategies and displays them on the terminal. This allows the user to obtain specific and effective solutions.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user accesses the system using a terminal and selects a virtual entity on the initial screen. The terminal receives the identification information of the selected virtual entity as input and sends it to the server. In this step, the system is personalized based on the user's preferences.

[0076] Step 2:

[0077] The user enters management-related request information into the terminal. Specifically, this is done as text input in natural language. The terminal sends this request information to the server as digital data. The input data is raw text data.

[0078] Step 3:

[0079] The server analyzes the received request information. Using natural language processing techniques, it extracts important information and keywords from the input text data. This data processing process utilizes a text analysis engine, and the extracted information is output as structured data.

[0080] Step 4:

[0081] The server uses the extracted information to refer to a database of past success stories. Database query techniques are used to search for highly relevant strategies. The extracted keywords are used as input for the search, and the output is a list of relevant strategies.

[0082] Step 5:

[0083] The server utilizes a generative AI model to generate searched strategies optimized for the characteristics of the virtual entity. This includes writing style and expressions tailored to the virtual entity's personality. The input is the strategy list obtained in step 4, and the output is a customized strategy suggestion for the user.

[0084] Step 6:

[0085] The server sends the generated strategy to the terminal, which then displays it to the user. The user views the presented strategy and uses it to solve specific problems. The input is customized strategy data, and the output is the user's display screen.

[0086] Step 7:

[0087] The user inputs the results and feedback from using the strategy into a terminal. The terminal sends this as digital data to the server. The input is the feedback information, and the output is the feedback data sent to the server.

[0088] Step 8:

[0089] The server collects the feedback received and updates the database. Data storage technology is used here to improve the effectiveness and accuracy of future strategy generation. The output is the updated database.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] There is a need to provide immediate and appropriate support for people's decision-making and management challenges in their daily lives. However, conventional systems can only offer mechanical and general advice, and have difficulty providing flexible support tailored to the individual characteristics and needs of users. Therefore, a system is needed that is user-friendly and provides assistance that matches individual needs.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for specifying a virtual subject based on operation information received from at least one information processing device, means for referencing a database of past knowledge and generating an answer that is suitable for the characteristics of the specified virtual subject, and means for transmitting and displaying the generated answer to the information processing device. This enables assistance optimized based on the individual needs of the user.

[0095] An "information processing device" refers to any device capable of collecting, processing, and outputting information, and also serves as a means of directly interfacing with the user.

[0096] A "virtual entity" refers to a personality or character set up to interact with users in a digital space, and is an entity that has the role of providing various kinds of information.

[0097] A "knowledge database" refers to a collection of information that has accumulated past success stories and knowledge, and is used for problem-solving.

[0098] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to generate answers in natural language.

[0099] An "information interface" refers to a means by which users can interact with digital systems in a two-way manner, providing information visually or audibly.

[0100] "Real-world support devices" refer to equipment and devices that provide information to users or perform dialogue in the real world.

[0101] To implement this invention, a system is constructed that uses an information processing device, a generative AI model, and a knowledge database. The server designates a virtual entity based on operation information received from the user through the information processing device. This virtual entity is a digital character that reflects the user's characteristics and needs.

[0102] The server then consults a knowledge database and generates an answer that fits the specified virtual subject. Using a generative AI model, the natural language answer is automatically constructed. This allows for user-friendly and accurate information delivery.

[0103] The generated answers are transmitted to the information processing device via an information interface and presented to the user visually or audibly. This approach enables interactive and human-centered dialogue, supporting everyday decision-making.

[0104] For example, if a user asks a virtual entity a question such as, "How can I manage my tasks more efficiently today?", the server will generate advice based on past success stories, such as, "Classify tasks by priority and improve time management." An example of a prompt sentence input to the generating AI model would be, "User's concern: I want to know how to improve my time management." From this prompt sentence, a variety of advice is generated, which the user can receive through real-world assistive devices.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] A user operates an information processing device and accesses the system. The terminal receives the user's operation information. Here, the user selects a specific virtual entity. This selection information is sent to the server as input data. The terminal converts the selection information into a data format that the server can understand.

[0108] Step 2:

[0109] The server identifies a virtual subject based on the received selection information. It analyzes the input selection information and retrieves the corresponding virtual subject's profile from the knowledge database. This profile records the virtual subject's characteristics and conversational style. The server prepares this as the base data for the next processing step.

[0110] Step 3:

[0111] The user inputs their concerns or questions into the terminal. The terminal sends this information to the server. The information is formatted as a prompt and becomes input data for the generating AI model. The terminal processes the input data as a string and provides it to the server.

[0112] Step 4:

[0113] The server inputs a prompt sentence into a generative AI model to generate a natural language response. The generative AI model interprets the prompt sentence and analyzes its relevance to the knowledge database to generate an appropriate response. This response is then returned to the server as output.

[0114] Step 5:

[0115] The server receives the generated answer and sends it to the terminal via an information interface. The terminal converts the data into a format suitable for visual or auditory input and presents it to the user. In doing so, the terminal adjusts the presentation to be easily understood by the user.

[0116] Step 6:

[0117] The system receives the answers provided by the user and uses them in the actual activity. The terminal collects feedback information from the user and sends it to the server. The feedback obtained here is used to improve the system.

[0118] Step 7:

[0119] The server analyzes the collected feedback information and updates the knowledge database in real time. This step improves the accuracy and user-friendliness of future answers. The server optimizes data analysis and updates so that the data can be used more effectively in subsequent processes.

[0120] 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.

[0121] The system of the present invention comprises a user device, a server, and a plurality of functional modules. The user accesses the system and logs in via the user device. On the initial screen, the user selects a desired virtual entity. This virtual entity can respond with different personalities and tones depending on the user's emotions.

[0122] Next, the user inputs their administrative problems or concerns in text or voice. Once input is made, the terminal sends the input data to the server. The server then passes the received data to a natural language processing engine and an emotion engine. The natural language processing engine analyzes the text and extracts relevant keywords. Meanwhile, the emotion engine analyzes the user's emotional state from the input voice or text. This allows the server to understand what emotions the user is currently experiencing.

[0123] The server then searches a database of successful case studies based on the extracted keywords and selects a solution that matches the user's emotional state. The selected solution is then generated as advice in a tone that aligns with the characteristics of the virtual entity and the user's emotions. The generated advice is sent to the terminal and displayed on the screen. Furthermore, because it is displayed in an interactive format, the user can receive the advice in a more personal way.

[0124] For example, if a user is struggling with the progress of a project and feeling stressed, the device might suggest advice such as "breaking down the specific steps into smaller steps." In this case, utilizing the emotion engine, the virtual entity can also add encouraging words in a friendly tone, such as "Let's take it one step at a time."

[0125] Finally, the user inputs and submits feedback on the advice provided via their device. The server receives this feedback and incorporates it into its database, aiming to improve the quality of future advice. In this way, incorporating an emotion engine makes it possible to provide more personalized support.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] Users access the system through their user device, enter their login information, and log in. The server receives this information, compares it with the user information in the database, and performs authentication.

[0129] Step 2:

[0130] Once the server completes authentication, a dashboard will appear on the user's device. On this screen, the user can choose their preferred virtual entity. The selected virtual entity will have a different personality and way of speaking.

[0131] Step 3:

[0132] Users enter their concerns and problems as managers into an input field on their device and send the content to the server. Input can be in text or voice.

[0133] Step 4:

[0134] The server passes the received input data to a natural language processing engine, which analyzes the text content and extracts important keywords. In parallel, it passes the data to an emotion engine, which analyzes the user's emotional state.

[0135] Step 5:

[0136] The emotion engine identifies emotional states such as joy, sadness, and anger from voice and text data and returns the results to the server. The server then uses these results to understand the user's current emotions.

[0137] Step 6:

[0138] The server uses the extracted keywords and sentiment information to search a database of success stories. Here, it selects multiple solutions that are appropriate to the user's sentiment and chooses the most suitable and balanced solution.

[0139] Step 7:

[0140] Based on the selected solution, the server generates advice in a tone that matches the characteristics of the virtual entity and the user's emotions. The generated advice is delivered in a user-friendly format.

[0141] Step 8:

[0142] The generated advice is sent to the device and displayed on the user's screen. The user can review this advice and take action if necessary.

[0143] Step 9:

[0144] Users provide feedback on the usefulness of the advice given and send that feedback from their device to the server.

[0145] Step 10:

[0146] The server receives feedback and records it in a database, which is then used to train the algorithm and improve future advice.

[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 will be referred to as the "terminal."

[0149] This invention relates to a system for providing personalized information tailored to the emotional state of a user. Conventional technologies have struggled to generate flexible responses based on user emotions, thus failing to improve user satisfaction. Therefore, there is a need for a system that analyzes the user's emotional state in real time and presents appropriate solutions that align with those emotions.

[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 specifying a virtual entity based on information received from a terminal, means for generating a response that matches the characteristics of the specified virtual entity by referring to information on past successful cases, and analysis means for analyzing the input information and identifying the user's emotional state. This enables the provision of personalized, interactive information that responds to the user's emotions.

[0152] A "terminal" is a device used by users to input information and interact with a system.

[0153] A "server" is a central information processing device that receives and processes information and returns the results to a terminal.

[0154] The term "virtual entity" refers to a personality or character created to provide responses to user input.

[0155] A "success story" is a collection of information that summarizes examples and insights that have proven effective in solving problems for past users.

[0156] "Natural language processing" is a technology that analyzes input text data and extracts or structures the necessary information.

[0157] "Sentiment analysis" is a technology used to identify a user's emotional state from text or audio data.

[0158] "Feedback information" refers to information that includes evaluations and comments from users regarding the solutions and advice provided.

[0159] The present invention will now describe embodiments for carrying it out. The system of this invention includes a terminal, a server, and a natural language processing engine and an emotion analysis engine. This makes it possible to provide users with personalized advice that is sensitive to their emotions.

[0160] First, the user uses a terminal to access the system and log in. After logging in, the user selects a virtual entity, and a dialogue based on that entity's profile begins. The terminal here is a typical computer or smart device, used to provide the user interface.

[0161] When a user enters administrative problems or concerns in text or voice, the terminal sends this data to the server. The server receives the data and uses a natural language processing engine (e.g., NLTK or spaCy) to analyze the user's words and extract important keywords. In addition, the server uses an emotion engine (e.g., IBM's tone analyzer) to identify the user's emotional state and determine what emotions are present.

[0162] Based on these analysis results, the server consults a database of successful cases. This database contains past examples, and the appropriate solution is selected based on the user's situation.

[0163] Furthermore, the server uses a generative AI model (e.g., ChatGPT® from OpenAI®) to generate solutions as advice in a tone that matches the characteristics of the virtual entity. The generated advice is displayed to the user through the terminal. At this time, an example of a prompt message might be, "Please suggest mitigation measures in a friendly tone to a project manager who is feeling stressed."

[0164] This process allows users to receive more accurate advice tailored to their emotions, creating a system that supports problem-solving.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user logs into the system using a terminal. At this time, the terminal receives the user's authentication information as input and displays a login screen. After verifying the authentication information, the terminal displays an initial screen to the user and provides an interface for selecting a virtual entity.

[0168] Step 2:

[0169] The user selects their desired virtual entity on the initial screen. This selection is entered into the terminal as a setting that will affect the user's interaction. The terminal outputs the selection information and sends it to the server, preparing for the next process.

[0170] Step 3:

[0171] Users input administrative problems or concerns into a terminal via text or voice. The terminal sends this input data to the server. The input data is then passed to the server as the content of the user's concerns.

[0172] Step 4:

[0173] The server sends the received input data to the natural language processing engine. The input data consists of text and audio. The natural language processing engine analyzes the data, extracts relevant keywords and phrases, and outputs them.

[0174] Step 5:

[0175] The server simultaneously sends the input data to the emotion engine. The emotion engine analyzes the emotional elements in the input data to identify the user's emotional state. This results in the output of the user's emotion label.

[0176] Step 6:

[0177] The server searches a success story database based on the output from its natural language processing engine and sentiment engine. The extracted keywords and sentiment labels are used as input to reference the database, and an appropriate solution is selected. This solution is output and prepared for the server's next processing step.

[0178] Step 7:

[0179] The server uses a generative AI model to generate advice based on the selected solution, in a tone that matches the virtual entity's characteristics. The inputs to this generation process are the selected solution and prompt sentences. The generated advice is then output.

[0180] Step 8:

[0181] The server sends the generated advice to the terminal. The terminal displays the advice on the screen and provides it to the user. The user receives this advice as input and uses it to solve the problem.

[0182] Step 9:

[0183] The user enters feedback on the advice provided into the terminal. The terminal sends this feedback to the server. The server receives the feedback as input, updates its success story database, and improves future responses.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] In modern society, there is an increasing need for emotional support in the home and in daily life. However, conventional systems have struggled to accurately grasp users' emotions and provide personalized advice. Furthermore, technologies for natural dialogue and responses and expressions as approachable characters are not yet sufficiently developed. Therefore, there is a need to provide an environment where users can receive support in a more familiar way.

[0187] 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.

[0188] In this invention, the server includes means for acquiring evaluation data from users and updating the success case record, means for estimating the user's psychological state using an emotion analysis engine, and means for generating a response corresponding to the estimated psychological state. This enables flexible and natural responses that are in line with the user's emotions and needs.

[0189] An "input / output device" is a device used for inputting and outputting information, and plays a role in providing or receiving information such as voice, text, and images.

[0190] "Operational information" refers to information based on user actions and instructions, and is data used to determine how the system should respond.

[0191] A "virtual intelligence" is an electronically generated entity that uses various information processing technologies to perform human-like responses and dialogues, whether as software or a system.

[0192] A "success story record" is a database that compiles the results of past problem-solving and dialogues, and is used as a resource to optimize advice and responses to users.

[0193] An "emotion analysis engine" is a technology or algorithm that analyzes a user's voice or text to infer their emotional state.

[0194] The "voice output function" is a feature that converts information generated by the system into voice and makes it audible to the user, thereby improving interactivity and user-friendliness.

[0195] A "character" is a virtual representation of a virtual intelligence that possesses different personalities and characteristics, and is used to add individuality to interactions with the user.

[0196] This invention is implemented in a form that allows users to receive emotionally responsive support through a home assistant system. Users use an input / output device equipped with voice input functionality to input various household problems and everyday troubles via voice or text.

[0197] The server analyzes the received input using a natural language processing engine and extracts relevant keywords. In the case of voice input, speech recognition technology is used. In addition, an emotion analysis engine analyzes the user's psychological state based on the input, and generates an appropriate response based on the results.

[0198] The generated advice and solutions are personalized based on past success stories. Specifically, the virtual intelligence uses different characters and tones to produce voice output according to the user's emotional state. A speaker is used for voice output.

[0199] For example, if a user is struggling with getting ready every morning, the server will select the advice "Preparing the night before can be helpful" and present it with an encouraging message like "Let's do our best together." This advice process utilizes a generative AI model to generate appropriate prompts.

[0200] Examples of prompt messages include the following:

[0201] "When a user is wondering what's most effective for their morning routine, think about how to generate specific and encouraging advice."

[0202] In this way, the system provides flexible and friendly responses that meet the user's needs and emotions, thereby reducing the stress of daily life and offering beneficial support to the user.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] Users input questions or concerns via voice or text through the input device of the home assistant system. This input data is then transmitted to the system.

[0206] Step 2:

[0207] When using voice input, the device converts the voice data into text using a speech recognition system. This converted text is then passed to a natural language processing engine, where relevant keywords are extracted. The input is voice data, and the output is text data.

[0208] Step 3:

[0209] The server uses an emotion analysis engine to assess the user's emotional state based on the text they input. This assessment process analyzes the text data and outputs the current emotional state as a numerical value or category.

[0210] Step 4:

[0211] The server inputs keywords extracted by the natural language processing engine and emotional states estimated by the sentiment analysis engine into a dedicated success story record to search for the optimal solution. The selected solution is then retrieved from the database.

[0212] Step 5:

[0213] The server uses a generative AI model to generate appropriate prompts based on user input and emotional state, and then, following those prompts, generates individually customized advice. Input consists of keywords and emotional data, while output is personalized advice.

[0214] Step 6:

[0215] The terminal provides the generated advice to the user via its voice output function. At this time, the virtual intelligence presents the advice in either an audible or visual form, according to the selected character and tone.

[0216] Step 7:

[0217] Users provide feedback on the advice, and this feedback data is sent to the server. The server enters this feedback into the success story record and updates the database to improve the quality of future advice.

[0218] 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.

[0219] 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 (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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] The system of this invention operates with a server, terminal, and user working in coordination with each other. The user first accesses the system using a user device and can select a virtual entity according to their preferences and requirements. The selected virtual entities possess different character traits, such as friendliness or logical thinking.

[0235] Next, the user enters their management concerns into the terminal. This information is sent to the server and analyzed using natural language processing technology. Key keywords and themes are extracted and compared with a database of past success stories. The server searches this database for highly relevant solutions and generates advice tailored to the characteristics of the selected virtual entity.

[0236] The generated advice is displayed on the terminal and presented in a format that users can use immediately. Users can provide feedback to the system as they receive and implement this advice. This feedback is collected by the server and used to improve the accuracy of future advice. For example, if a project leader is struggling with how to lead a new team, the system will provide specific advice such as "Focus on clarifying the team structure and defining roles." This allows managers to know specific and actionable steps and improve work efficiency.

[0237] By having the server, terminals, and users work together in this way, it is possible to build a system that can provide assistance tailored to the individual needs of each manager in real time.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user accesses the system through their device and logs in by entering their login information. The server receives this information, performs authentication, and if successful, displays the user dashboard on the device.

[0241] Step 2:

[0242] The user selects a virtual entity from the dashboard. The available character types include, for example, those based on friendliness or logic. The server receives this selection and saves it to the user's profile.

[0243] Step 3:

[0244] The user enters their specific management concerns into the terminal. Once this information is entered, the terminal transfers the data to the server.

[0245] Step 4:

[0246] The server passes the received data to a natural language processing engine for analysis. It extracts important keywords and themes related to the problem and constructs search queries based on them.

[0247] Step 5:

[0248] The server searches a database of success stories and lists multiple success stories related to the extracted keywords. This allows for the selection of potential solutions based on real-world examples.

[0249] Step 6:

[0250] The server adjusts the style and expression of the advice to suit the selected virtual character, and then generates the final advice.

[0251] Step 7:

[0252] The generated advice is sent to the device and displayed to the user. The user can review it and use it to help them put it into practice.

[0253] Step 8:

[0254] Users provide feedback on the usefulness of the advice and send this feedback from their device to the server. The server receives this feedback, updates the learning algorithm in the database, and uses it to generate advice in the future.

[0255] (Example 1)

[0256] 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."

[0257] In today's work environment, individual managers face a variety of management concerns and challenges, but there is a lack of systems that provide effective solutions in real time. Furthermore, there is a need for systems that can flexibly adapt to different situations and individual characteristics. Additionally, methods for improving service quality based on user feedback are also required.

[0258] 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.

[0259] In this invention, the server includes means for selecting a virtual entity based on request information received from at least one input device, means for analyzing the received information using natural language processing and extracting important information, and means for referring to a database of past success records and searching for strategies related to the extracted information. This makes it possible to provide effective advice and strategies tailored to the needs of each user.

[0260] An "input device" is a device that receives information from the user, and this information is used for analysis and processing within the system.

[0261] "Request information" refers to information about the tasks or problems that the system will analyze, provided by the user through an input device.

[0262] A "virtual entity" is a virtual agent with different characteristics and personality, designed to interact with users.

[0263] "Natural language processing" is a technology that enables computers to understand and process human language, and it is the process of extracting intent and important information from text data.

[0264] "Important information" refers to elements or data that are deemed particularly noteworthy after analyzing the request information entered by the user.

[0265] A "success story database" is a database containing past problem-solving cases and strategies, and serves as a source of information to find solutions to current problems.

[0266] "Strategy" refers to specific proposals and methods offered as solutions to problems faced by users.

[0267] A "display device" is a device used to visually present information and strategies transmitted from a server to the user.

[0268] "Feedback" refers to information provided by users regarding their evaluation of the strategies provided by the system and the results of their implementation.

[0269] The system of the present invention supports effective problem solving through the cooperation of a server, a terminal, and a user. The user first accesses the system using a terminal. The terminal is equipped with an interface for inputting request information from the user, which includes the challenges and needs the user is facing. The terminal used here is assumed to be a general-purpose computer or smartphone.

[0270] When a user enters request information into their terminal, this information is transmitted to the server. The server analyzes the received request information using natural language processing technology. This analysis uses a text analysis engine to extract important information and keywords from the text.

[0271] Next, the server uses the extracted information to refer to a database of past success stories. This database contains a collection of successful problem-solving cases accumulated in the past, and the server searches for and selects the most relevant strategies. A typical relational database is assumed to be the database management system used here.

[0272] Subsequently, the server utilizes a generative AI model to generate the identified strategy, optimized for the characteristics of the virtual entity selected by the user. Because this virtual entity has different representations and characteristics based on user instructions, the generated strategy can also be customized to the user's needs. The generated strategy is then transmitted from the server to the terminal and presented to the user in real time.

[0273] Users receive the strategies presented and report the results and feedback obtained when applying them to their actual work to the system via their terminals. This feedback is collected on the server and used to update the database, thereby improving the accuracy of the advice provided by the system.

[0274] As a concrete example, a prompt might say, "I need advice on how to form a new team." The server then generates relevant strategies and displays them on the terminal. This allows the user to obtain specific and effective solutions.

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

[0276] Step 1:

[0277] The user accesses the system using a terminal and selects a virtual entity on the initial screen. The terminal receives the identification information of the selected virtual entity as input and sends it to the server. In this step, the system is personalized based on the user's preferences.

[0278] Step 2:

[0279] The user enters management-related request information into the terminal. Specifically, this is done as text input in natural language. The terminal sends this request information to the server as digital data. The input data is raw text data.

[0280] Step 3:

[0281] The server analyzes the received request information. Using natural language processing technology, it extracts important information and keywords from the input text data. In this data processing process, a text analysis engine is utilized, and the extracted information is output as structured data.

[0282] Step 4:

[0283] The server uses the extracted information to refer to the past successful record database. Using database query technology, it searches for highly relevant strategies. The extracted keywords are used as the input for the search, and a list of relevant strategies is obtained as the output.

[0284] Step 5:

[0285] The server utilizes the generative AI model to generate the searched strategies in an optimized form for the characteristics of the virtual entity. This includes the style and expression tailored to the character of the virtual entity. The input is the list of strategies obtained in Step 4, and the output is a customized strategy proposal for the user.

[0286] Step 6:

[0287] The server sends the generated strategies to the terminal, and the terminal displays them to the user. The user browses the presented strategies and uses them to solve specific problems. The input is the customized strategy data, and the output is the user's display screen.

[0288] Step 7:

[0289] The user inputs the results and feedback of using the strategy into the terminal. The terminal sends this as digital data to the server. The input is the feedback information, and the output is the feedback data sent to the server.

[0290] Step 8:

[0291] The server collects the feedback received and updates the database. Data storage technology is used here to improve the effectiveness and accuracy of future strategy generation. The output is the updated database.

[0292] (Application Example 1)

[0293] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0294] There is a need to provide immediate and appropriate support for people's decision-making and management challenges in their daily lives. However, conventional systems can only offer mechanical and general advice, and have difficulty providing flexible support tailored to the individual characteristics and needs of users. Therefore, a system is needed that is user-friendly and provides assistance that matches individual needs.

[0295] 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.

[0296] In this invention, the server includes means for specifying a virtual subject based on operation information received from at least one information processing device, means for referencing a database of past knowledge and generating an answer that is suitable for the characteristics of the specified virtual subject, and means for transmitting and displaying the generated answer to the information processing device. This enables assistance optimized based on the individual needs of the user.

[0297] An "information processing device" refers to any device capable of collecting, processing, and outputting information, and also serves as a means of directly interfacing with the user.

[0298] A "virtual entity" refers to a personality or character set up to interact with users in a digital space, and is an entity that has the role of providing various kinds of information.

[0299] A "knowledge database" refers to a collection of information that has accumulated past success stories and knowledge, and is used for problem-solving.

[0300] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to generate answers in natural language.

[0301] An "information interface" refers to a means by which users can interact with digital systems in a two-way manner, providing information visually or audibly.

[0302] "Real-world support devices" refer to equipment and devices that provide information to users or perform dialogue in the real world.

[0303] To implement this invention, a system is constructed that uses an information processing device, a generative AI model, and a knowledge database. The server designates a virtual entity based on operation information received from the user through the information processing device. This virtual entity is a digital character that reflects the user's characteristics and needs.

[0304] The server then consults a knowledge database and generates an answer that fits the specified virtual subject. Using a generative AI model, the natural language answer is automatically constructed. This allows for user-friendly and accurate information delivery.

[0305] The generated answers are transmitted to the information processing device via an information interface and presented to the user visually or audibly. This approach enables interactive and human-centered dialogue, supporting everyday decision-making.

[0306] As a specific example, when a user asks a virtual entity a question such as "How can I manage today's tasks efficiently?", the server generates advice like "Classify tasks by priority and improve time management" based on past success cases. An example of the prompt text input into the generation AI model is in the form of "User's concern: Want to know how to improve time management". Various advices are generated from this prompt text, and the user can receive them through a real-world support device.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The user operates an information processing device to access the system. The terminal receives the user's operation information. Here, the user selects a specific virtual entity. The selection information is sent to the server as input data. The terminal converts the data format so that the server can understand the selection information.

[0310] Step 2:

[0311] Based on the received selection information, the server designates a virtual entity. It analyzes the input selection information and retrieves the profile of the corresponding virtual entity from the knowledge database. This profile records the characteristics and conversation style of the virtual entity. The server prepares this as the data for the next processing step.

[0312] Step 3:

[0313] The user inputs concerns or questions into the terminal. The terminal sends this information to the server. The information is formatted as a prompt text and becomes the input data for the generation AI model. The terminal processes the input data as a character string and provides it to the server.

[0314] Step 4:

[0315] The server inputs a prompt sentence into a generative AI model to generate a natural language response. The generative AI model interprets the prompt sentence and analyzes its relevance to the knowledge database to generate an appropriate response. This response is then returned to the server as output.

[0316] Step 5:

[0317] The server receives the generated answer and sends it to the terminal via an information interface. The terminal converts the data into a format suitable for visual or auditory input and presents it to the user. In doing so, the terminal adjusts the presentation to be easily understood by the user.

[0318] Step 6:

[0319] The system receives the answers provided by the user and uses them in the actual activity. The terminal collects feedback information from the user and sends it to the server. The feedback obtained here is used to improve the system.

[0320] Step 7:

[0321] The server analyzes the collected feedback information and updates the knowledge database in real time. This step improves the accuracy and user-friendliness of future answers. The server optimizes data analysis and updates so that the data can be used more effectively in subsequent processes.

[0322] 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.

[0323] The system of the present invention comprises a user device, a server, and a plurality of functional modules. The user accesses the system and logs in via the user device. On the initial screen, the user selects a desired virtual entity. This virtual entity can respond with different personalities and tones depending on the user's emotions.

[0324] Next, the user inputs their administrative problems or concerns in text or voice. Once input is made, the terminal sends the input data to the server. The server then passes the received data to a natural language processing engine and an emotion engine. The natural language processing engine analyzes the text and extracts relevant keywords. Meanwhile, the emotion engine analyzes the user's emotional state from the input voice or text. This allows the server to understand what emotions the user is currently experiencing.

[0325] The server then searches a database of successful case studies based on the extracted keywords and selects a solution that matches the user's emotional state. The selected solution is then generated as advice in a tone that aligns with the characteristics of the virtual entity and the user's emotions. The generated advice is sent to the terminal and displayed on the screen. Furthermore, because it is displayed in an interactive format, the user can receive the advice in a more personal way.

[0326] For example, if a user is struggling with the progress of a project and feeling stressed, the device might suggest advice such as "breaking down the specific steps into smaller steps." In this case, utilizing the emotion engine, the virtual entity can also add encouraging words in a friendly tone, such as "Let's take it one step at a time."

[0327] Finally, the user inputs and submits feedback on the advice provided via their device. The server receives this feedback and incorporates it into its database, aiming to improve the quality of future advice. In this way, incorporating an emotion engine makes it possible to provide more personalized support.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] Users access the system through their user device, enter their login information, and log in. The server receives this information, compares it with the user information in the database, and performs authentication.

[0331] Step 2:

[0332] Once the server completes authentication, a dashboard will appear on the user's device. On this screen, the user can choose their preferred virtual entity. The selected virtual entity will have a different personality and way of speaking.

[0333] Step 3:

[0334] Users enter their concerns and problems as managers into an input field on their device and send the content to the server. Input can be in text or voice.

[0335] Step 4:

[0336] The server passes the received input data to a natural language processing engine, which analyzes the text content and extracts important keywords. In parallel, it passes the data to an emotion engine, which analyzes the user's emotional state.

[0337] Step 5:

[0338] The emotion engine identifies emotional states such as joy, sadness, and anger from voice and text data and returns the results to the server. The server then uses these results to understand the user's current emotions.

[0339] Step 6:

[0340] The server uses the extracted keywords and sentiment information to search a database of success stories. Here, it selects multiple solutions that are appropriate to the user's sentiment and chooses the most suitable and balanced solution.

[0341] Step 7:

[0342] Based on the selected solution, the server generates advice in a tone that matches the characteristics of the virtual entity and the user's emotions. The generated advice is delivered in a user-friendly format.

[0343] Step 8:

[0344] The generated advice is sent to the device and displayed on the user's screen. The user can review this advice and take action if necessary.

[0345] Step 9:

[0346] Users provide feedback on the usefulness of the advice given and send that feedback from their device to the server.

[0347] Step 10:

[0348] The server receives feedback and records it in a database, which is then used to train the algorithm and improve future advice.

[0349] (Example 2)

[0350] 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".

[0351] This invention relates to a system for providing personalized information tailored to the emotional state of a user. Conventional technologies have struggled to generate flexible responses based on user emotions, thus failing to improve user satisfaction. Therefore, there is a need for a system that analyzes the user's emotional state in real time and presents appropriate solutions that align with those emotions.

[0352] 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.

[0353] In this invention, the server includes means for specifying a virtual entity based on information received from a terminal, means for generating a response that matches the characteristics of the specified virtual entity by referring to information on past successful cases, and analysis means for analyzing the input information and identifying the user's emotional state. This enables the provision of personalized, interactive information that responds to the user's emotions.

[0354] A "terminal" is a device used by users to input information and interact with a system.

[0355] A "server" is a central information processing device that receives and processes information and returns the results to a terminal.

[0356] The term "virtual entity" refers to a personality or character created to provide responses to user input.

[0357] A "success story" is a collection of information that summarizes examples and insights that have proven effective in solving problems for past users.

[0358] "Natural language processing" is a technology that analyzes input text data and extracts or structures the necessary information.

[0359] "Sentiment analysis" is a technology used to identify a user's emotional state from text or audio data.

[0360] "Feedback information" refers to information that includes evaluations and comments from users regarding the solutions and advice provided.

[0361] The present invention will now describe embodiments for carrying it out. The system of this invention includes a terminal, a server, and a natural language processing engine and an emotion analysis engine. This makes it possible to provide users with personalized advice that is sensitive to their emotions.

[0362] First, the user uses a terminal to access the system and log in. After logging in, the user selects a virtual entity, and a dialogue based on that entity's profile begins. The terminal here is a typical computer or smart device, used to provide the user interface.

[0363] When a user enters administrative problems or concerns in text or voice, the terminal sends this data to the server. The server receives the data and uses a natural language processing engine (e.g., NLTK or spaCy) to analyze the user's words and extract important keywords. In addition, the server uses an emotion engine (e.g., IBM's tone analyzer) to identify the user's emotional state and determine what emotions are present.

[0364] Based on these analysis results, the server consults a database of successful cases. This database contains past examples, and the appropriate solution is selected based on the user's situation.

[0365] Furthermore, the server uses a generative AI model (e.g., OpenAI's ChatGPT) to generate solutions as advice in a tone that matches the characteristics of the virtual entity. The generated advice is displayed to the user through the terminal. At this time, an example of a prompt message might be, "Please suggest mitigation measures in a friendly tone to a project manager who is feeling stressed."

[0366] This process allows users to receive more accurate advice tailored to their emotions, creating a system that supports problem-solving.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] The user logs into the system using a terminal. At this time, the terminal receives the user's authentication information as input and displays a login screen. After verifying the authentication information, the terminal displays an initial screen to the user and provides an interface for selecting a virtual entity.

[0370] Step 2:

[0371] The user selects their desired virtual entity on the initial screen. This selection is entered into the terminal as a setting that will affect the user's interaction. The terminal outputs the selection information and sends it to the server, preparing for the next process.

[0372] Step 3:

[0373] Users input administrative problems or concerns into a terminal via text or voice. The terminal sends this input data to the server. The input data is then passed to the server as the content of the user's concerns.

[0374] Step 4:

[0375] The server sends the received input data to the natural language processing engine. The input data consists of text and audio. The natural language processing engine analyzes the data, extracts relevant keywords and phrases, and outputs them.

[0376] Step 5:

[0377] The server simultaneously sends the input data to the emotion engine. The emotion engine analyzes the emotional elements in the input data to identify the user's emotional state. This results in the output of the user's emotion label.

[0378] Step 6:

[0379] The server searches a success story database based on the output from its natural language processing engine and sentiment engine. The extracted keywords and sentiment labels are used as input to reference the database, and an appropriate solution is selected. This solution is output and prepared for the server's next processing step.

[0380] Step 7:

[0381] The server uses a generative AI model to generate advice based on the selected solution, in a tone that matches the virtual entity's characteristics. The inputs to this generation process are the selected solution and prompt sentences. The generated advice is then output.

[0382] Step 8:

[0383] The server sends the generated advice to the terminal. The terminal displays the advice on the screen and provides it to the user. The user receives this advice as input and uses it to solve the problem.

[0384] Step 9:

[0385] The user enters feedback on the advice provided into the terminal. The terminal sends this feedback to the server. The server receives the feedback as input, updates its success story database, and improves future responses.

[0386] (Application Example 2)

[0387] 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."

[0388] In modern society, there is an increasing need for emotional support in the home and in daily life. However, conventional systems have struggled to accurately grasp users' emotions and provide personalized advice. Furthermore, technologies for natural dialogue and responses and expressions as approachable characters are not yet sufficiently developed. Therefore, there is a need to provide an environment where users can receive support in a more familiar way.

[0389] 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.

[0390] In this invention, the server includes means for acquiring evaluation data from users and updating the success case record, means for estimating the user's psychological state using an emotion analysis engine, and means for generating a response corresponding to the estimated psychological state. This enables flexible and natural responses that are in line with the user's emotions and needs.

[0391] An "input / output device" is a device used for inputting and outputting information, and plays a role in providing or receiving information such as voice, text, and images.

[0392] "Operational information" refers to information based on user actions and instructions, and is data used to determine how the system should respond.

[0393] A "virtual intelligence" is an electronically generated entity that uses various information processing technologies to perform human-like responses and dialogues, whether as software or a system.

[0394] A "success story record" is a database that compiles the results of past problem-solving and dialogues, and is used as a resource to optimize advice and responses to users.

[0395] An "emotion analysis engine" is a technology or algorithm that analyzes a user's voice or text to infer their emotional state.

[0396] The "voice output function" is a feature that converts information generated by the system into voice and makes it audible to the user, thereby improving interactivity and user-friendliness.

[0397] A "character" is a virtual representation of a virtual intelligence that possesses different personalities and characteristics, and is used to add individuality to interactions with the user.

[0398] This invention is implemented in a form that allows users to receive emotionally responsive support through a home assistant system. Users use an input / output device equipped with voice input functionality to input various household problems and everyday troubles via voice or text.

[0399] The server analyzes the received input using a natural language processing engine and extracts relevant keywords. In the case of voice input, speech recognition technology is used. In addition, an emotion analysis engine analyzes the user's psychological state based on the input, and generates an appropriate response based on the results.

[0400] The generated advice and solutions are personalized based on past success stories. Specifically, the virtual intelligence uses different characters and tones to produce voice output according to the user's emotional state. A speaker is used for voice output.

[0401] For example, if a user is struggling with getting ready every morning, the server will select the advice "Preparing the night before can be helpful" and present it with an encouraging message like "Let's do our best together." This advice process utilizes a generative AI model to generate appropriate prompts.

[0402] Examples of prompt messages include the following:

[0403] "When a user is wondering what's most effective for their morning routine, think about how to generate specific and encouraging advice."

[0404] In this way, the system provides flexible and friendly responses that meet the user's needs and emotions, thereby reducing the stress of daily life and offering beneficial support to the user.

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] Users input questions or concerns via voice or text through the input device of the home assistant system. This input data is then transmitted to the system.

[0408] Step 2:

[0409] When using voice input, the device converts the voice data into text using a speech recognition system. This converted text is then passed to a natural language processing engine, where relevant keywords are extracted. The input is voice data, and the output is text data.

[0410] Step 3:

[0411] The server uses an emotion analysis engine to assess the user's emotional state based on the text they input. This assessment process analyzes the text data and outputs the current emotional state as a numerical value or category.

[0412] Step 4:

[0413] The server inputs keywords extracted by the natural language processing engine and emotional states estimated by the sentiment analysis engine into a dedicated success story record to search for the optimal solution. The selected solution is then retrieved from the database.

[0414] Step 5:

[0415] The server uses a generative AI model to generate appropriate prompts based on user input and emotional state, and then, following those prompts, generates individually customized advice. Input consists of keywords and emotional data, while output is personalized advice.

[0416] Step 6:

[0417] The terminal provides the generated advice to the user via its voice output function. At this time, the virtual intelligence presents the advice in either an audible or visual form, according to the selected character and tone.

[0418] Step 7:

[0419] Users provide feedback on the advice, and this feedback data is sent to the server. The server enters this feedback into the success story record and updates the database to improve the quality of future advice.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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".

[0436] The system of this invention operates with a server, terminal, and user working in coordination with each other. The user first accesses the system using a user device and can select a virtual entity according to their preferences and requirements. The selected virtual entities possess different character traits, such as friendliness or logical thinking.

[0437] Next, the user enters their management concerns into the terminal. This information is sent to the server and analyzed using natural language processing technology. Key keywords and themes are extracted and compared with a database of past success stories. The server searches this database for highly relevant solutions and generates advice tailored to the characteristics of the selected virtual entity.

[0438] The generated advice is displayed on the terminal and presented in a format that users can use immediately. Users can provide feedback to the system as they receive and implement this advice. This feedback is collected by the server and used to improve the accuracy of future advice. For example, if a project leader is struggling with how to lead a new team, the system will provide specific advice such as "Focus on clarifying the team structure and defining roles." This allows managers to know specific and actionable steps and improve work efficiency.

[0439] By having the server, terminals, and users work together in this way, it is possible to build a system that can provide assistance tailored to the individual needs of each manager in real time.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The user accesses the system through their device and logs in by entering their login information. The server receives this information, performs authentication, and if successful, displays the user dashboard on the device.

[0443] Step 2:

[0444] The user selects a virtual entity from the dashboard. The available character types include, for example, those based on friendliness or logic. The server receives this selection and saves it to the user's profile.

[0445] Step 3:

[0446] The user enters their specific management concerns into the terminal. Once this information is entered, the terminal transfers the data to the server.

[0447] Step 4:

[0448] The server passes the received data to a natural language processing engine for analysis. It extracts important keywords and themes related to the problem and constructs search queries based on them.

[0449] Step 5:

[0450] The server searches a database of success stories and lists multiple success stories related to the extracted keywords. This allows for the selection of potential solutions based on real-world examples.

[0451] Step 6:

[0452] The server adjusts the style and expression of the advice to suit the selected virtual character, and then generates the final advice.

[0453] Step 7:

[0454] The generated advice is sent to the device and displayed to the user. The user can review it and use it to help them put it into practice.

[0455] Step 8:

[0456] Users provide feedback on the usefulness of the advice and send this feedback from their device to the server. The server receives this feedback, updates the learning algorithm in the database, and uses it to generate advice in the future.

[0457] (Example 1)

[0458] 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."

[0459] In today's work environment, individual managers face a variety of management concerns and challenges, but there is a lack of systems that provide effective solutions in real time. Furthermore, there is a need for systems that can flexibly adapt to different situations and individual characteristics. Additionally, methods for improving service quality based on user feedback are also required.

[0460] 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.

[0461] In this invention, the server includes means for selecting a virtual entity based on request information received from at least one input device, means for analyzing the received information using natural language processing and extracting important information, and means for referring to a database of past success records and searching for strategies related to the extracted information. This makes it possible to provide effective advice and strategies tailored to the needs of each user.

[0462] An "input device" is a device that receives information from the user, and this information is used for analysis and processing within the system.

[0463] "Request information" refers to information about the tasks or problems that the system will analyze, provided by the user through an input device.

[0464] A "virtual entity" is a virtual agent with different characteristics and personality, designed to interact with users.

[0465] "Natural language processing" is a technology that enables computers to understand and process human language, and it is the process of extracting intent and important information from text data.

[0466] "Important information" refers to elements or data that are deemed particularly noteworthy after analyzing the request information entered by the user.

[0467] A "success story database" is a database containing past problem-solving cases and strategies, and serves as a source of information to find solutions to current problems.

[0468] "Strategy" refers to specific proposals and methods offered as solutions to problems faced by users.

[0469] A "display device" is a device used to visually present information and strategies transmitted from a server to the user.

[0470] "Feedback" refers to information provided by users regarding their evaluation of the strategies provided by the system and the results of their implementation.

[0471] The system of the present invention supports effective problem solving through the cooperation of a server, a terminal, and a user. The user first accesses the system using a terminal. The terminal is equipped with an interface for inputting request information from the user, which includes the challenges and needs the user is facing. The terminal used here is assumed to be a general-purpose computer or smartphone.

[0472] When a user enters request information into their terminal, this information is transmitted to the server. The server analyzes the received request information using natural language processing technology. This analysis uses a text analysis engine to extract important information and keywords from the text.

[0473] Next, the server uses the extracted information to refer to a database of past success stories. This database contains a collection of successful problem-solving cases accumulated in the past, and the server searches for and selects the most relevant strategies. A typical relational database is assumed to be the database management system used here.

[0474] Subsequently, the server utilizes a generative AI model to generate the identified strategy, optimized for the characteristics of the virtual entity selected by the user. Because this virtual entity has different representations and characteristics based on user instructions, the generated strategy can also be customized to the user's needs. The generated strategy is then transmitted from the server to the terminal and presented to the user in real time.

[0475] Users receive the strategies presented and report the results and feedback obtained when applying them to their actual work to the system via their terminals. This feedback is collected on the server and used to update the database, thereby improving the accuracy of the advice provided by the system.

[0476] As a concrete example, a prompt might say, "I need advice on how to form a new team." The server then generates relevant strategies and displays them on the terminal. This allows the user to obtain specific and effective solutions.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user accesses the system using a terminal and selects a virtual entity on the initial screen. The terminal receives the identification information of the selected virtual entity as input and sends it to the server. In this step, the system is personalized based on the user's preferences.

[0480] Step 2:

[0481] The user enters management-related request information into the terminal. Specifically, this is done as text input in natural language. The terminal sends this request information to the server as digital data. The input data is raw text data.

[0482] Step 3:

[0483] The server analyzes the received request information. Using natural language processing techniques, it extracts important information and keywords from the input text data. This data processing process utilizes a text analysis engine, and the extracted information is output as structured data.

[0484] Step 4:

[0485] The server uses the extracted information to refer to a database of past success stories. Database query techniques are used to search for highly relevant strategies. The extracted keywords are used as input for the search, and the output is a list of relevant strategies.

[0486] Step 5:

[0487] The server utilizes a generative AI model to generate searched strategies optimized for the characteristics of the virtual entity. This includes writing style and expressions tailored to the virtual entity's personality. The input is the strategy list obtained in step 4, and the output is a customized strategy suggestion for the user.

[0488] Step 6:

[0489] The server sends the generated strategy to the terminal, which then displays it to the user. The user views the presented strategy and uses it to solve specific problems. The input is customized strategy data, and the output is the user's display screen.

[0490] Step 7:

[0491] The user inputs the results and feedback from using the strategy into a terminal. The terminal sends this as digital data to the server. The input is the feedback information, and the output is the feedback data sent to the server.

[0492] Step 8:

[0493] The server collects the feedback received and updates the database. Data storage technology is used here to improve the effectiveness and accuracy of future strategy generation. The output is the updated database.

[0494] (Application Example 1)

[0495] 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."

[0496] There is a need to provide immediate and appropriate support for people's decision-making and management challenges in their daily lives. However, conventional systems can only offer mechanical and general advice, and have difficulty providing flexible support tailored to the individual characteristics and needs of users. Therefore, a system is needed that is user-friendly and provides assistance that matches individual needs.

[0497] 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.

[0498] In this invention, the server includes means for specifying a virtual subject based on operation information received from at least one information processing device, means for referencing a database of past knowledge and generating an answer that is suitable for the characteristics of the specified virtual subject, and means for transmitting and displaying the generated answer to the information processing device. This enables assistance optimized based on the individual needs of the user.

[0499] An "information processing device" refers to any device capable of collecting, processing, and outputting information, and also serves as a means of directly interfacing with the user.

[0500] A "virtual entity" refers to a personality or character set up to interact with users in a digital space, and is an entity that has the role of providing various kinds of information.

[0501] A "knowledge database" refers to a collection of information that has accumulated past success stories and knowledge, and is used for problem-solving.

[0502] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to generate answers in natural language.

[0503] An "information interface" refers to a means by which users can interact with digital systems in a two-way manner, providing information visually or audibly.

[0504] "Real-world support devices" refer to equipment and devices that provide information to users or perform dialogue in the real world.

[0505] To implement this invention, a system is constructed that uses an information processing device, a generative AI model, and a knowledge database. The server designates a virtual entity based on operation information received from the user through the information processing device. This virtual entity is a digital character that reflects the user's characteristics and needs.

[0506] The server then consults a knowledge database and generates an answer that fits the specified virtual subject. Using a generative AI model, the natural language answer is automatically constructed. This allows for user-friendly and accurate information delivery.

[0507] The generated answers are transmitted to the information processing device via an information interface and presented to the user visually or audibly. This approach enables interactive and human-centered dialogue, supporting everyday decision-making.

[0508] For example, if a user asks a virtual entity a question such as, "How can I manage my tasks more efficiently today?", the server will generate advice based on past success stories, such as, "Classify tasks by priority and improve time management." An example of a prompt sentence input to the generating AI model would be, "User's concern: I want to know how to improve my time management." From this prompt sentence, a variety of advice is generated, which the user can receive through real-world assistive devices.

[0509] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0510] Step 1:

[0511] A user operates an information processing device and accesses the system. The terminal receives the user's operation information. Here, the user selects a specific virtual entity. This selection information is sent to the server as input data. The terminal converts the selection information into a data format that the server can understand.

[0512] Step 2:

[0513] The server identifies a virtual subject based on the received selection information. It analyzes the input selection information and retrieves the corresponding virtual subject's profile from the knowledge database. This profile records the virtual subject's characteristics and conversational style. The server prepares this as the base data for the next processing step.

[0514] Step 3:

[0515] The user inputs their concerns or questions into the terminal. The terminal sends this information to the server. The information is formatted as a prompt and becomes input data for the generating AI model. The terminal processes the input data as a string and provides it to the server.

[0516] Step 4:

[0517] The server inputs a prompt sentence into a generative AI model to generate a natural language response. The generative AI model interprets the prompt sentence and analyzes its relevance to the knowledge database to generate an appropriate response. This response is then returned to the server as output.

[0518] Step 5:

[0519] The server receives the generated answer and sends it to the terminal via an information interface. The terminal converts the data into a format suitable for visual or auditory input and presents it to the user. In doing so, the terminal adjusts the presentation to be easily understood by the user.

[0520] Step 6:

[0521] The system receives the answers provided by the user and uses them in the actual activity. The terminal collects feedback information from the user and sends it to the server. The feedback obtained here is used to improve the system.

[0522] Step 7:

[0523] The server analyzes the collected feedback information and updates the knowledge database in real time. This step improves the accuracy and user-friendliness of future answers. The server optimizes data analysis and updates so that the data can be used more effectively in subsequent processes.

[0524] 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.

[0525] The system of the present invention comprises a user device, a server, and a plurality of functional modules. The user accesses the system and logs in via the user device. On the initial screen, the user selects a desired virtual entity. This virtual entity can respond with different personalities and tones depending on the user's emotions.

[0526] Next, the user inputs their administrative problems or concerns in text or voice. Once input is made, the terminal sends the input data to the server. The server then passes the received data to a natural language processing engine and an emotion engine. The natural language processing engine analyzes the text and extracts relevant keywords. Meanwhile, the emotion engine analyzes the user's emotional state from the input voice or text. This allows the server to understand what emotions the user is currently experiencing.

[0527] The server then searches a database of successful case studies based on the extracted keywords and selects a solution that matches the user's emotional state. The selected solution is then generated as advice in a tone that aligns with the characteristics of the virtual entity and the user's emotions. The generated advice is sent to the terminal and displayed on the screen. Furthermore, because it is displayed in an interactive format, the user can receive the advice in a more personal way.

[0528] For example, if a user is struggling with the progress of a project and feeling stressed, the device might suggest advice such as "breaking down the specific steps into smaller steps." In this case, utilizing the emotion engine, the virtual entity can also add encouraging words in a friendly tone, such as "Let's take it one step at a time."

[0529] Finally, the user inputs and submits feedback on the advice provided via their device. The server receives this feedback and incorporates it into its database, aiming to improve the quality of future advice. In this way, incorporating an emotion engine makes it possible to provide more personalized support.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] Users access the system through their user device, enter their login information, and log in. The server receives this information, compares it with the user information in the database, and performs authentication.

[0533] Step 2:

[0534] Once the server completes authentication, a dashboard will appear on the user's device. On this screen, the user can choose their preferred virtual entity. The selected virtual entity will have a different personality and way of speaking.

[0535] Step 3:

[0536] Users enter their concerns and problems as managers into an input field on their device and send the content to the server. Input can be in text or voice.

[0537] Step 4:

[0538] The server passes the received input data to a natural language processing engine, which analyzes the text content and extracts important keywords. In parallel, it passes the data to an emotion engine, which analyzes the user's emotional state.

[0539] Step 5:

[0540] The emotion engine identifies emotional states such as joy, sadness, and anger from voice and text data and returns the results to the server. The server then uses these results to understand the user's current emotions.

[0541] Step 6:

[0542] The server uses the extracted keywords and sentiment information to search a database of success stories. Here, it selects multiple solutions that are appropriate to the user's sentiment and chooses the most suitable and balanced solution.

[0543] Step 7:

[0544] Based on the selected solution, the server generates advice in a tone that matches the characteristics of the virtual entity and the user's emotions. The generated advice is delivered in a user-friendly format.

[0545] Step 8:

[0546] The generated advice is sent to the device and displayed on the user's screen. The user can review this advice and take action if necessary.

[0547] Step 9:

[0548] Users provide feedback on the usefulness of the advice given and send that feedback from their device to the server.

[0549] Step 10:

[0550] The server receives feedback and records it in a database, which is then used to train the algorithm and improve future advice.

[0551] (Example 2)

[0552] 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."

[0553] This invention relates to a system for providing personalized information tailored to the emotional state of a user. Conventional technologies have struggled to generate flexible responses based on user emotions, thus failing to improve user satisfaction. Therefore, there is a need for a system that analyzes the user's emotional state in real time and presents appropriate solutions that align with those emotions.

[0554] 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.

[0555] In this invention, the server includes means for specifying a virtual entity based on information received from a terminal, means for generating a response that matches the characteristics of the specified virtual entity by referring to information on past successful cases, and analysis means for analyzing the input information and identifying the user's emotional state. This enables the provision of personalized, interactive information that responds to the user's emotions.

[0556] A "terminal" is a device used by users to input information and interact with a system.

[0557] A "server" is a central information processing device that receives and processes information and returns the results to a terminal.

[0558] The term "virtual entity" refers to a personality or character created to provide responses to user input.

[0559] A "success story" is a collection of information that summarizes examples and insights that have proven effective in solving problems for past users.

[0560] "Natural language processing" is a technology that analyzes input text data and extracts or structures the necessary information.

[0561] "Sentiment analysis" is a technology used to identify a user's emotional state from text or audio data.

[0562] "Feedback information" refers to information that includes evaluations and comments from users regarding the solutions and advice provided.

[0563] The present invention will now describe embodiments for carrying it out. The system of this invention includes a terminal, a server, and a natural language processing engine and an emotion analysis engine. This makes it possible to provide users with personalized advice that is sensitive to their emotions.

[0564] First, the user uses a terminal to access the system and log in. After logging in, the user selects a virtual entity, and a dialogue based on that entity's profile begins. The terminal here is a typical computer or smart device, used to provide the user interface.

[0565] When a user enters administrative problems or concerns in text or voice, the terminal sends this data to the server. The server receives the data and uses a natural language processing engine (e.g., NLTK or spaCy) to analyze the user's words and extract important keywords. In addition, the server uses an emotion engine (e.g., IBM's tone analyzer) to identify the user's emotional state and determine what emotions are present.

[0566] Based on these analysis results, the server consults a database of successful cases. This database contains past examples, and the appropriate solution is selected based on the user's situation.

[0567] Furthermore, the server uses a generative AI model (e.g., OpenAI's ChatGPT) to generate solutions as advice in a tone that matches the characteristics of the virtual entity. The generated advice is displayed to the user through the terminal. At this time, an example of a prompt message might be, "Please suggest mitigation measures in a friendly tone to a project manager who is feeling stressed."

[0568] This process allows users to receive more accurate advice tailored to their emotions, creating a system that supports problem-solving.

[0569] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0570] Step 1:

[0571] The user logs into the system using a terminal. At this time, the terminal receives the user's authentication information as input and displays a login screen. After verifying the authentication information, the terminal displays an initial screen to the user and provides an interface for selecting a virtual entity.

[0572] Step 2:

[0573] The user selects their desired virtual entity on the initial screen. This selection is entered into the terminal as a setting that will affect the user's interaction. The terminal outputs the selection information and sends it to the server, preparing for the next process.

[0574] Step 3:

[0575] Users input administrative problems or concerns into a terminal via text or voice. The terminal sends this input data to the server. The input data is then passed to the server as the content of the user's concerns.

[0576] Step 4:

[0577] The server sends the received input data to the natural language processing engine. The input data consists of text and audio. The natural language processing engine analyzes the data, extracts relevant keywords and phrases, and outputs them.

[0578] Step 5:

[0579] The server simultaneously sends the input data to the emotion engine. The emotion engine analyzes the emotional elements in the input data to identify the user's emotional state. This results in the output of the user's emotion label.

[0580] Step 6:

[0581] The server searches a success story database based on the output from its natural language processing engine and sentiment engine. The extracted keywords and sentiment labels are used as input to reference the database, and an appropriate solution is selected. This solution is output and prepared for the server's next processing step.

[0582] Step 7:

[0583] The server uses a generative AI model to generate advice based on the selected solution, in a tone that matches the virtual entity's characteristics. The inputs to this generation process are the selected solution and prompt sentences. The generated advice is then output.

[0584] Step 8:

[0585] The server sends the generated advice to the terminal. The terminal displays the advice on the screen and provides it to the user. The user receives this advice as input and uses it to solve the problem.

[0586] Step 9:

[0587] The user enters feedback on the advice provided into the terminal. The terminal sends this feedback to the server. The server receives the feedback as input, updates its success story database, and improves future responses.

[0588] (Application Example 2)

[0589] 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."

[0590] In modern society, there is an increasing need for emotional support in the home and in daily life. However, conventional systems have struggled to accurately grasp users' emotions and provide personalized advice. Furthermore, technologies for natural dialogue and responses and expressions as approachable characters are not yet sufficiently developed. Therefore, there is a need to provide an environment where users can receive support in a more familiar way.

[0591] 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.

[0592] In this invention, the server includes means for acquiring evaluation data from users and updating the success case record, means for estimating the user's psychological state using an emotion analysis engine, and means for generating a response corresponding to the estimated psychological state. This enables flexible and natural responses that are in line with the user's emotions and needs.

[0593] An "input / output device" is a device used for inputting and outputting information, and plays a role in providing or receiving information such as voice, text, and images.

[0594] "Operational information" refers to information based on user actions and instructions, and is data used to determine how the system should respond.

[0595] A "virtual intelligence" is an electronically generated entity that uses various information processing technologies to perform human-like responses and dialogues, whether as software or a system.

[0596] A "success story record" is a database that compiles the results of past problem-solving and dialogues, and is used as a resource to optimize advice and responses to users.

[0597] An "emotion analysis engine" is a technology or algorithm that analyzes a user's voice or text to infer their emotional state.

[0598] The "voice output function" is a feature that converts information generated by the system into voice and makes it audible to the user, thereby improving interactivity and user-friendliness.

[0599] A "character" is a virtual representation of a virtual intelligence that possesses different personalities and characteristics, and is used to add individuality to interactions with the user.

[0600] This invention is implemented in a form that allows users to receive emotionally responsive support through a home assistant system. Users use an input / output device equipped with voice input functionality to input various household problems and everyday troubles via voice or text.

[0601] The server analyzes the received input using a natural language processing engine and extracts relevant keywords. In the case of voice input, speech recognition technology is used. In addition, an emotion analysis engine analyzes the user's psychological state based on the input, and generates an appropriate response based on the results.

[0602] The generated advice and solutions are personalized based on past success stories. Specifically, the virtual intelligence uses different characters and tones to produce voice output according to the user's emotional state. A speaker is used for voice output.

[0603] For example, if a user is struggling with getting ready every morning, the server will select the advice "Preparing the night before can be helpful" and present it with an encouraging message like "Let's do our best together." This advice process utilizes a generative AI model to generate appropriate prompts.

[0604] Examples of prompt messages include the following:

[0605] "When a user is wondering what's most effective for their morning routine, think about how to generate specific and encouraging advice."

[0606] In this way, the system provides flexible and friendly responses that meet the user's needs and emotions, thereby reducing the stress of daily life and offering beneficial support to the user.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] Users input questions or concerns via voice or text through the input device of the home assistant system. This input data is then transmitted to the system.

[0610] Step 2:

[0611] When using voice input, the device converts the voice data into text using a speech recognition system. This converted text is then passed to a natural language processing engine, where relevant keywords are extracted. The input is voice data, and the output is text data.

[0612] Step 3:

[0613] The server uses an emotion analysis engine to assess the user's emotional state based on the text they input. This assessment process analyzes the text data and outputs the current emotional state as a numerical value or category.

[0614] Step 4:

[0615] The server inputs keywords extracted by the natural language processing engine and emotional states estimated by the sentiment analysis engine into a dedicated success story record to search for the optimal solution. The selected solution is then retrieved from the database.

[0616] Step 5:

[0617] The server uses a generative AI model to generate appropriate prompts based on user input and emotional state, and then, following those prompts, generates individually customized advice. Input consists of keywords and emotional data, while output is personalized advice.

[0618] Step 6:

[0619] The terminal provides the generated advice to the user via its voice output function. At this time, the virtual intelligence presents the advice in either an audible or visual form, according to the selected character and tone.

[0620] Step 7:

[0621] Users provide feedback on the advice, and this feedback data is sent to the server. The server enters this feedback into the success story record and updates the database to improve the quality of future advice.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] [Fourth Embodiment]

[0626] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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).

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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".

[0639] The system of this invention operates with a server, terminal, and user working in coordination with each other. The user first accesses the system using a user device and can select a virtual entity according to their preferences and requirements. The selected virtual entities possess different character traits, such as friendliness or logical thinking.

[0640] Next, the user enters their management concerns into the terminal. This information is sent to the server and analyzed using natural language processing technology. Key keywords and themes are extracted and compared with a database of past success stories. The server searches this database for highly relevant solutions and generates advice tailored to the characteristics of the selected virtual entity.

[0641] The generated advice is displayed on the terminal and presented in a format that users can use immediately. Users can provide feedback to the system as they receive and implement this advice. This feedback is collected by the server and used to improve the accuracy of future advice. For example, if a project leader is struggling with how to lead a new team, the system will provide specific advice such as "Focus on clarifying the team structure and defining roles." This allows managers to know specific and actionable steps and improve work efficiency.

[0642] By having the server, terminals, and users work together in this way, it is possible to build a system that can provide assistance tailored to the individual needs of each manager in real time.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The user accesses the system through their device and logs in by entering their login information. The server receives this information, performs authentication, and if successful, displays the user dashboard on the device.

[0646] Step 2:

[0647] The user selects a virtual entity from the dashboard. The available character types include, for example, those based on friendliness or logic. The server receives this selection and saves it to the user's profile.

[0648] Step 3:

[0649] The user enters their specific management concerns into the terminal. Once this information is entered, the terminal transfers the data to the server.

[0650] Step 4:

[0651] The server passes the received data to a natural language processing engine for analysis. It extracts important keywords and themes related to the problem and constructs search queries based on them.

[0652] Step 5:

[0653] The server searches a database of success stories and lists multiple success stories related to the extracted keywords. This allows for the selection of potential solutions based on real-world examples.

[0654] Step 6:

[0655] The server adjusts the style and expression of the advice to suit the selected virtual character, and then generates the final advice.

[0656] Step 7:

[0657] The generated advice is sent to the device and displayed to the user. The user can review it and use it to help them put it into practice.

[0658] Step 8:

[0659] Users provide feedback on the usefulness of the advice and send this feedback from their device to the server. The server receives this feedback, updates the learning algorithm in the database, and uses it to generate advice in the future.

[0660] (Example 1)

[0661] 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".

[0662] In today's work environment, individual managers face a variety of management concerns and challenges, but there is a lack of systems that provide effective solutions in real time. Furthermore, there is a need for systems that can flexibly adapt to different situations and individual characteristics. Additionally, methods for improving service quality based on user feedback are also required.

[0663] 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.

[0664] In this invention, the server includes means for selecting a virtual entity based on request information received from at least one input device, means for analyzing the received information using natural language processing and extracting important information, and means for referring to a database of past success records and searching for strategies related to the extracted information. This makes it possible to provide effective advice and strategies tailored to the needs of each user.

[0665] An "input device" is a device that receives information from the user, and this information is used for analysis and processing within the system.

[0666] "Request information" refers to information about the tasks or problems that the system will analyze, provided by the user through an input device.

[0667] A "virtual entity" is a virtual agent with different characteristics and personality, designed to interact with users.

[0668] "Natural language processing" is a technology that enables computers to understand and process human language, and it is the process of extracting intent and important information from text data.

[0669] "Important information" refers to elements or data that are deemed particularly noteworthy after analyzing the request information entered by the user.

[0670] A "success story database" is a database containing past problem-solving cases and strategies, and serves as a source of information to find solutions to current problems.

[0671] "Strategy" refers to specific proposals and methods offered as solutions to problems faced by users.

[0672] A "display device" is a device used to visually present information and strategies transmitted from a server to the user.

[0673] "Feedback" refers to information provided by users regarding their evaluation of the strategies provided by the system and the results of their implementation.

[0674] The system of the present invention supports effective problem solving through the cooperation of a server, a terminal, and a user. The user first accesses the system using a terminal. The terminal is equipped with an interface for inputting request information from the user, which includes the challenges and needs the user is facing. The terminal used here is assumed to be a general-purpose computer or smartphone.

[0675] When a user enters request information into their terminal, this information is transmitted to the server. The server analyzes the received request information using natural language processing technology. This analysis uses a text analysis engine to extract important information and keywords from the text.

[0676] Next, the server uses the extracted information to refer to a database of past success stories. This database contains a collection of successful problem-solving cases accumulated in the past, and the server searches for and selects the most relevant strategies. A typical relational database is assumed to be the database management system used here.

[0677] Subsequently, the server utilizes a generative AI model to generate the identified strategy, optimized for the characteristics of the virtual entity selected by the user. Because this virtual entity has different representations and characteristics based on user instructions, the generated strategy can also be customized to the user's needs. The generated strategy is then transmitted from the server to the terminal and presented to the user in real time.

[0678] Users receive the strategies presented and report the results and feedback obtained when applying them to their actual work to the system via their terminals. This feedback is collected on the server and used to update the database, thereby improving the accuracy of the advice provided by the system.

[0679] As a concrete example, a prompt might say, "I need advice on how to form a new team." The server then generates relevant strategies and displays them on the terminal. This allows the user to obtain specific and effective solutions.

[0680] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0681] Step 1:

[0682] The user accesses the system using a terminal and selects a virtual entity on the initial screen. The terminal receives the identification information of the selected virtual entity as input and sends it to the server. In this step, the system is personalized based on the user's preferences.

[0683] Step 2:

[0684] The user enters management-related request information into the terminal. Specifically, this is done as text input in natural language. The terminal sends this request information to the server as digital data. The input data is raw text data.

[0685] Step 3:

[0686] The server analyzes the received request information. Using natural language processing techniques, it extracts important information and keywords from the input text data. This data processing process utilizes a text analysis engine, and the extracted information is output as structured data.

[0687] Step 4:

[0688] The server uses the extracted information to refer to a database of past success stories. Database query techniques are used to search for highly relevant strategies. The extracted keywords are used as input for the search, and the output is a list of relevant strategies.

[0689] Step 5:

[0690] The server utilizes a generative AI model to generate searched strategies optimized for the characteristics of the virtual entity. This includes writing style and expressions tailored to the virtual entity's personality. The input is the strategy list obtained in step 4, and the output is a customized strategy suggestion for the user.

[0691] Step 6:

[0692] The server sends the generated strategy to the terminal, which then displays it to the user. The user views the presented strategy and uses it to solve specific problems. The input is customized strategy data, and the output is the user's display screen.

[0693] Step 7:

[0694] The user inputs the results and feedback from using the strategy into a terminal. The terminal sends this as digital data to the server. The input is the feedback information, and the output is the feedback data sent to the server.

[0695] Step 8:

[0696] The server collects the feedback received and updates the database. Data storage technology is used here to improve the effectiveness and accuracy of future strategy generation. The output is the updated database.

[0697] (Application Example 1)

[0698] 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".

[0699] There is a need to provide immediate and appropriate support for people's decision-making and management challenges in their daily lives. However, conventional systems can only offer mechanical and general advice, and have difficulty providing flexible support tailored to the individual characteristics and needs of users. Therefore, a system is needed that is user-friendly and provides assistance that matches individual needs.

[0700] 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.

[0701] In this invention, the server includes means for specifying a virtual subject based on operation information received from at least one information processing device, means for referencing a database of past knowledge and generating an answer that is suitable for the characteristics of the specified virtual subject, and means for transmitting and displaying the generated answer to the information processing device. This enables assistance optimized based on the individual needs of the user.

[0702] An "information processing device" refers to any device capable of collecting, processing, and outputting information, and also serves as a means of directly interfacing with the user.

[0703] A "virtual entity" refers to a personality or character set up to interact with users in a digital space, and is an entity that has the role of providing various kinds of information.

[0704] A "knowledge database" refers to a collection of information that has accumulated past success stories and knowledge, and is used for problem-solving.

[0705] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to generate answers in natural language.

[0706] An "information interface" refers to a means by which users can interact with digital systems in a two-way manner, providing information visually or audibly.

[0707] "Real-world support devices" refer to equipment and devices that provide information to users or perform dialogue in the real world.

[0708] To implement this invention, a system is constructed that uses an information processing device, a generative AI model, and a knowledge database. The server designates a virtual entity based on operation information received from the user through the information processing device. This virtual entity is a digital character that reflects the user's characteristics and needs.

[0709] The server then consults a knowledge database and generates an answer that fits the specified virtual subject. Using a generative AI model, the natural language answer is automatically constructed. This allows for user-friendly and accurate information delivery.

[0710] The generated answers are transmitted to the information processing device via an information interface and presented to the user visually or audibly. This approach enables interactive and human-centered dialogue, supporting everyday decision-making.

[0711] For example, if a user asks a virtual entity a question such as, "How can I manage my tasks more efficiently today?", the server will generate advice based on past success stories, such as, "Classify tasks by priority and improve time management." An example of a prompt sentence input to the generating AI model would be, "User's concern: I want to know how to improve my time management." From this prompt sentence, a variety of advice is generated, which the user can receive through real-world assistive devices.

[0712] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0713] Step 1:

[0714] A user operates an information processing device and accesses the system. The terminal receives the user's operation information. Here, the user selects a specific virtual entity. This selection information is sent to the server as input data. The terminal converts the selection information into a data format that the server can understand.

[0715] Step 2:

[0716] The server identifies a virtual subject based on the received selection information. It analyzes the input selection information and retrieves the corresponding virtual subject's profile from the knowledge database. This profile records the virtual subject's characteristics and conversational style. The server prepares this as the base data for the next processing step.

[0717] Step 3:

[0718] The user inputs their concerns or questions into the terminal. The terminal sends this information to the server. The information is formatted as a prompt and becomes input data for the generating AI model. The terminal processes the input data as a string and provides it to the server.

[0719] Step 4:

[0720] The server inputs a prompt sentence into a generative AI model to generate a natural language response. The generative AI model interprets the prompt sentence and analyzes its relevance to the knowledge database to generate an appropriate response. This response is then returned to the server as output.

[0721] Step 5:

[0722] The server receives the generated answer and sends it to the terminal via an information interface. The terminal converts the data into a format suitable for visual or auditory input and presents it to the user. In doing so, the terminal adjusts the presentation to be easily understood by the user.

[0723] Step 6:

[0724] The system receives the answers provided by the user and uses them in the actual activity. The terminal collects feedback information from the user and sends it to the server. The feedback obtained here is used to improve the system.

[0725] Step 7:

[0726] The server analyzes the collected feedback information and updates the knowledge database in real time. This step improves the accuracy and user-friendliness of future answers. The server optimizes data analysis and updates so that the data can be used more effectively in subsequent processes.

[0727] 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.

[0728] The system of the present invention comprises a user device, a server, and a plurality of functional modules. The user accesses the system and logs in via the user device. On the initial screen, the user selects a desired virtual entity. This virtual entity can respond with different personalities and tones depending on the user's emotions.

[0729] Next, the user inputs their administrative problems or concerns in text or voice. Once input is made, the terminal sends the input data to the server. The server then passes the received data to a natural language processing engine and an emotion engine. The natural language processing engine analyzes the text and extracts relevant keywords. Meanwhile, the emotion engine analyzes the user's emotional state from the input voice or text. This allows the server to understand what emotions the user is currently experiencing.

[0730] The server then searches a database of successful case studies based on the extracted keywords and selects a solution that matches the user's emotional state. The selected solution is then generated as advice in a tone that aligns with the characteristics of the virtual entity and the user's emotions. The generated advice is sent to the terminal and displayed on the screen. Furthermore, because it is displayed in an interactive format, the user can receive the advice in a more personal way.

[0731] For example, if a user is struggling with the progress of a project and feeling stressed, the device might suggest advice such as "breaking down the specific steps into smaller steps." In this case, utilizing the emotion engine, the virtual entity can also add encouraging words in a friendly tone, such as "Let's take it one step at a time."

[0732] Finally, the user inputs and submits feedback on the advice provided via their device. The server receives this feedback and incorporates it into its database, aiming to improve the quality of future advice. In this way, incorporating an emotion engine makes it possible to provide more personalized support.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] Users access the system through their user device, enter their login information, and log in. The server receives this information, compares it with the user information in the database, and performs authentication.

[0736] Step 2:

[0737] Once the server completes authentication, a dashboard will appear on the user's device. On this screen, the user can choose their preferred virtual entity. The selected virtual entity will have a different personality and way of speaking.

[0738] Step 3:

[0739] Users enter their concerns and problems as managers into an input field on their device and send the content to the server. Input can be in text or voice.

[0740] Step 4:

[0741] The server passes the received input data to a natural language processing engine, which analyzes the text content and extracts important keywords. In parallel, it passes the data to an emotion engine, which analyzes the user's emotional state.

[0742] Step 5:

[0743] The emotion engine identifies emotional states such as joy, sadness, and anger from voice and text data and returns the results to the server. The server then uses these results to understand the user's current emotions.

[0744] Step 6:

[0745] The server uses the extracted keywords and sentiment information to search a database of success stories. Here, it selects multiple solutions that are appropriate to the user's sentiment and chooses the most suitable and balanced solution.

[0746] Step 7:

[0747] Based on the selected solution, the server generates advice in a tone that matches the characteristics of the virtual entity and the user's emotions. The generated advice is delivered in a user-friendly format.

[0748] Step 8:

[0749] The generated advice is sent to the device and displayed on the user's screen. The user can review this advice and take action if necessary.

[0750] Step 9:

[0751] Users provide feedback on the usefulness of the advice given and send that feedback from their device to the server.

[0752] Step 10:

[0753] The server receives feedback and records it in a database, which is then used to train the algorithm and improve future advice.

[0754] (Example 2)

[0755] 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".

[0756] This invention relates to a system for providing personalized information tailored to the emotional state of a user. Conventional technologies have struggled to generate flexible responses based on user emotions, thus failing to improve user satisfaction. Therefore, there is a need for a system that analyzes the user's emotional state in real time and presents appropriate solutions that align with those emotions.

[0757] 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.

[0758] In this invention, the server includes means for specifying a virtual entity based on information received from a terminal, means for generating a response that matches the characteristics of the specified virtual entity by referring to information on past successful cases, and analysis means for analyzing the input information and identifying the user's emotional state. This enables the provision of personalized, interactive information that responds to the user's emotions.

[0759] A "terminal" is a device used by users to input information and interact with a system.

[0760] A "server" is a central information processing device that receives and processes information and returns the results to a terminal.

[0761] The term "virtual entity" refers to a personality or character created to provide responses to user input.

[0762] A "success story" is a collection of information that summarizes examples and insights that have proven effective in solving problems for past users.

[0763] "Natural language processing" is a technology that analyzes input text data and extracts or structures the necessary information.

[0764] "Sentiment analysis" is a technology used to identify a user's emotional state from text or audio data.

[0765] "Feedback information" refers to information that includes evaluations and comments from users regarding the solutions and advice provided.

[0766] The present invention will now describe embodiments for carrying it out. The system of this invention includes a terminal, a server, and a natural language processing engine and an emotion analysis engine. This makes it possible to provide users with personalized advice that is sensitive to their emotions.

[0767] First, the user uses a terminal to access the system and log in. After logging in, the user selects a virtual entity, and a dialogue based on that entity's profile begins. The terminal here is a typical computer or smart device, used to provide the user interface.

[0768] When a user enters administrative problems or concerns in text or voice, the terminal sends this data to the server. The server receives the data and uses a natural language processing engine (e.g., NLTK or spaCy) to analyze the user's words and extract important keywords. In addition, the server uses an emotion engine (e.g., IBM's tone analyzer) to identify the user's emotional state and determine what emotions are present.

[0769] Based on these analysis results, the server consults a database of successful cases. This database contains past examples, and the appropriate solution is selected based on the user's situation.

[0770] Furthermore, the server uses a generative AI model (e.g., OpenAI's ChatGPT) to generate solutions as advice in a tone that matches the characteristics of the virtual entity. The generated advice is displayed to the user through the terminal. At this time, an example of a prompt message might be, "Please suggest mitigation measures in a friendly tone to a project manager who is feeling stressed."

[0771] This process allows users to receive more accurate advice tailored to their emotions, creating a system that supports problem-solving.

[0772] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0773] Step 1:

[0774] The user logs into the system using a terminal. At this time, the terminal receives the user's authentication information as input and displays a login screen. After verifying the authentication information, the terminal displays an initial screen to the user and provides an interface for selecting a virtual entity.

[0775] Step 2:

[0776] The user selects their desired virtual entity on the initial screen. This selection is entered into the terminal as a setting that will affect the user's interaction. The terminal outputs the selection information and sends it to the server, preparing for the next process.

[0777] Step 3:

[0778] Users input administrative problems or concerns into a terminal via text or voice. The terminal sends this input data to the server. The input data is then passed to the server as the content of the user's concerns.

[0779] Step 4:

[0780] The server sends the received input data to the natural language processing engine. The input data consists of text and audio. The natural language processing engine analyzes the data, extracts relevant keywords and phrases, and outputs them.

[0781] Step 5:

[0782] The server simultaneously sends the input data to the emotion engine. The emotion engine analyzes the emotional elements in the input data to identify the user's emotional state. This results in the output of the user's emotion label.

[0783] Step 6:

[0784] The server searches a success story database based on the output from its natural language processing engine and sentiment engine. The extracted keywords and sentiment labels are used as input to reference the database, and an appropriate solution is selected. This solution is output and prepared for the server's next processing step.

[0785] Step 7:

[0786] The server uses a generative AI model to generate advice based on the selected solution, in a tone that matches the virtual entity's characteristics. The inputs to this generation process are the selected solution and prompt sentences. The generated advice is then output.

[0787] Step 8:

[0788] The server sends the generated advice to the terminal. The terminal displays the advice on the screen and provides it to the user. The user receives this advice as input and uses it to solve the problem.

[0789] Step 9:

[0790] The user enters feedback on the advice provided into the terminal. The terminal sends this feedback to the server. The server receives the feedback as input, updates its success story database, and improves future responses.

[0791] (Application Example 2)

[0792] 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".

[0793] In modern society, there is an increasing need for emotional support in the home and in daily life. However, conventional systems have struggled to accurately grasp users' emotions and provide personalized advice. Furthermore, technologies for natural dialogue and responses and expressions as approachable characters are not yet sufficiently developed. Therefore, there is a need to provide an environment where users can receive support in a more familiar way.

[0794] 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.

[0795] In this invention, the server includes means for acquiring evaluation data from users and updating the success case record, means for estimating the user's psychological state using an emotion analysis engine, and means for generating a response corresponding to the estimated psychological state. This enables flexible and natural responses that are in line with the user's emotions and needs.

[0796] An "input / output device" is a device used for inputting and outputting information, and plays a role in providing or receiving information such as voice, text, and images.

[0797] "Operational information" refers to information based on user actions and instructions, and is data used to determine how the system should respond.

[0798] A "virtual intelligence" is an electronically generated entity that uses various information processing technologies to perform human-like responses and dialogues, whether as software or a system.

[0799] A "success story record" is a database that compiles the results of past problem-solving and dialogues, and is used as a resource to optimize advice and responses to users.

[0800] An "emotion analysis engine" is a technology or algorithm that analyzes a user's voice or text to infer their emotional state.

[0801] The "voice output function" is a feature that converts information generated by the system into voice and makes it audible to the user, thereby improving interactivity and user-friendliness.

[0802] A "character" is a virtual representation of a virtual intelligence that possesses different personalities and characteristics, and is used to add individuality to interactions with the user.

[0803] This invention is implemented in a form that allows users to receive emotionally responsive support through a home assistant system. Users use an input / output device equipped with voice input functionality to input various household problems and everyday troubles via voice or text.

[0804] The server analyzes the received input using a natural language processing engine and extracts relevant keywords. In the case of voice input, speech recognition technology is used. In addition, an emotion analysis engine analyzes the user's psychological state based on the input, and generates an appropriate response based on the results.

[0805] The generated advice and solutions are personalized based on past success stories. Specifically, the virtual intelligence uses different characters and tones to produce voice output according to the user's emotional state. A speaker is used for voice output.

[0806] For example, if a user is struggling with getting ready every morning, the server will select the advice "Preparing the night before can be helpful" and present it with an encouraging message like "Let's do our best together." This advice process utilizes a generative AI model to generate appropriate prompts.

[0807] Examples of prompt messages include the following:

[0808] "When a user is wondering what's most effective for their morning routine, think about how to generate specific and encouraging advice."

[0809] In this way, the system provides flexible and friendly responses that meet the user's needs and emotions, thereby reducing the stress of daily life and offering beneficial support to the user.

[0810] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0811] Step 1:

[0812] Users input questions or concerns via voice or text through the input device of the home assistant system. This input data is then transmitted to the system.

[0813] Step 2:

[0814] When using voice input, the device converts the voice data into text using a speech recognition system. This converted text is then passed to a natural language processing engine, where relevant keywords are extracted. The input is voice data, and the output is text data.

[0815] Step 3:

[0816] The server uses an emotion analysis engine to assess the user's emotional state based on the text they input. This assessment process analyzes the text data and outputs the current emotional state as a numerical value or category.

[0817] Step 4:

[0818] The server inputs keywords extracted by the natural language processing engine and emotional states estimated by the sentiment analysis engine into a dedicated success story record to search for the optimal solution. The selected solution is then retrieved from the database.

[0819] Step 5:

[0820] The server uses a generative AI model to generate appropriate prompts based on user input and emotional state, and then, following those prompts, generates individually customized advice. Input consists of keywords and emotional data, while output is personalized advice.

[0821] Step 6:

[0822] The terminal provides the generated advice to the user via its voice output function. At this time, the virtual intelligence presents the advice in either an audible or visual form, according to the selected character and tone.

[0823] Step 7:

[0824] Users provide feedback on the advice, and this feedback data is sent to the server. The server enters this feedback into the success story record and updates the database to improve the quality of future advice.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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."

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] The following is further disclosed regarding the embodiments described above.

[0847] (Claim 1)

[0848] A means for designating a virtual entity based on operation information received from at least one user device,

[0849] A means for generating a solution that matches the characteristics of a specified virtual entity by referring to a database of past success stories,

[0850] A means for transmitting and displaying the generated answer on the user's device,

[0851] A means for obtaining user evaluation information and updating the aforementioned success story database,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, wherein the generated answer is constructed using natural language processing technology.

[0855] (Claim 3)

[0856] The system according to claim 1, wherein a virtual entity is represented as multiple personalities with different speech patterns and visual representations based on instructions from the user.

[0857] "Example 1"

[0858] (Claim 1)

[0859] A means for selecting a virtual entity based on request information received from at least one input device,

[0860] A means of analyzing received information using natural language processing and extracting important information,

[0861] A means of referencing a database of past success stories and searching for strategies related to the extracted information,

[0862] A means of generating strategies in a format that is compatible with the characteristics of a virtual entity,

[0863] A means for transmitting and presenting the generated strategy to a display device,

[0864] A means of obtaining feedback from users and updating the database,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, wherein the generated strategy is constructed using generative AI technology.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein a virtual entity is represented as multiple roles having different representation methods and visual characteristics based on user instructions.

[0870] "Application Example 1"

[0871] (Claim 1)

[0872] A means for designating a virtual subject based on operation information received from at least one information processing device,

[0873] A means for generating a solution that matches the characteristics of a specified virtual entity by referring to a database of past knowledge,

[0874] A means for transmitting the generated answer to an information processing device and displaying it,

[0875] A means for obtaining evaluation information from users and updating the aforementioned knowledge database,

[0876] A means of generating answers in natural language using a generative AI model,

[0877] A means of presenting the generated answer using a real-world support device,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, wherein the generated answers are constructed using natural language processing technology and presented through various information interfaces.

[0881] (Claim 3)

[0882] The system according to claim 1, wherein a virtual entity is represented in multiple forms having different methods of expression and visual characteristics based on user instructions.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] A means for designating a virtual entity based on information received from at least one terminal,

[0886] A means for generating a solution that matches the characteristics of a specified virtual entity by referring to information on past success stories,

[0887] An analytical means for analyzing input information and identifying the user's emotional state,

[0888] A means for adjusting the answer according to the identified emotional state and transmitting and displaying it on the user's device,

[0889] A means for obtaining user feedback information and updating the aforementioned success story database,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, wherein the generated answer is constructed using natural language processing technology and sentiment analysis technology.

[0893] (Claim 3)

[0894] The system according to claim 1, wherein a virtual entity is expressed as multiple personalities with different expressions and tones based on the user's instructions.

[0895] "Application example 2 when combining with an emotional engine"

[0896] (Claim 1)

[0897] A means for designating a virtual intelligence based on operational information received from at least one input / output device,

[0898] A means of generating a solution that is suited to the characteristics of a specified virtual intelligence, by referring to past success stories,

[0899] A means of transmitting and presenting the generated solution to an input / output device,

[0900] A means for obtaining evaluation data from users and updating the aforementioned success story record,

[0901] A means of estimating the user's psychological state using an emotion analysis engine,

[0902] Means for generating a response corresponding to an estimated psychological state,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, wherein the generated solution is constructed using natural language processing technology and has a voice output function.

[0906] (Claim 3)

[0907] The system according to claim 1, wherein a virtual intelligence is represented as multiple characters having different voice expressions and visual representations based on instructions from the user. [Explanation of symbols]

[0908] 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 for designating a virtual subject based on operation information received from at least one information processing device, A means for generating a solution that matches the characteristics of a specified virtual entity by referring to a database of past knowledge, A means for transmitting the generated answer to an information processing device and displaying it, A means for obtaining evaluation information from users and updating the aforementioned knowledge database, A means of generating answers in natural language using a generative AI model, A means of presenting the generated answer using a real-world support device, A system that includes this.

2. The system according to claim 1, wherein the generated answers are constructed using natural language processing technology and presented through various information interfaces.

3. The system according to claim 1, wherein a virtual entity is represented in multiple forms having different methods of expression and visual characteristics based on user instructions.

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