system

Abstract

We provide the system. [Solution] A means of analyzing natural language information entered by the user to identify their requests, A method for selecting the appropriate agent from among multiple specialized artificial intelligences based on the analysis results, A means of coordinating selected agents to provide solutions to users, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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] Currently, while artificial intelligence agents specialized in various fields are being developed, it is difficult for users to effectively combine and utilize them. For this reason, when users seek an AI solution optimal for their own desires, there are problems that require a lot of time and effort in the selection and integration processes. Furthermore, when an appropriate agent cannot be selected, problems such as the inability to achieve problem-solving that could originally be achieved also occur. The purpose of the present invention is to quickly and effectively solve such problems faced by users.

Means for Solving the Problems

[0005] This invention provides means for analyzing natural language requests input by users, and for selecting an appropriate agent from among multiple specialized artificial intelligence agents based on the analyzed requests. Furthermore, it enables rapid problem solving by automatically providing an AI solution to the user by coordinating the selected agents. If necessary, it provides an algorithm for optimally combining multiple agents and means for supplementing with additional agents, thereby providing a highly effective AI solution tailored to specific challenges.

[0006] "Natural language information" refers to text data entered by users using human language.

[0007] "Means for identifying requests" refers to the process and function of analyzing important keywords and intentions from input natural language information to clarify the user's requests.

[0008] "Specialized artificial intelligence" refers to artificial intelligence programs that have been trained specifically for a particular domain or application, and are used to solve problems and challenges in that field.

[0009] "Methods for selecting agents" refer to the process and function of selecting the appropriate agent from among multiple artificial intelligence agents based on the analysis results.

[0010] "Means for coordinating agents" refers to the processes and functions that enable multiple selected artificial intelligence agents to work together to provide a unified solution.

[0011] An "AI solution" is a collection of means and methods built to solve specific problems for users by utilizing artificial intelligence technology.

[0012] An "algorithm" is a set of computational procedures or a computational model designed to solve a specific problem or challenge.

[0013] "Means of supplementing agents" refers to the process and functionality of incorporating additional artificial intelligence agents to enhance the capabilities of existing agents. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that utilizes diverse specialized artificial intelligence agents to quickly provide solutions that users require. The system consists of a user terminal, a server, and a group of connected artificial intelligence agents. The user inputs their requests in natural language using the terminal. This information is transmitted to the server in real time. The server analyzes this information using advanced natural language processing techniques. The purpose of this analysis is to identify the intent and necessary functions contained in the user's requests.

[0036] Once the analysis is complete, the server selects an appropriate artificial intelligence agent based on the analysis results. This selection is made by referring to a database of agents maintained within the system. The database stores the characteristics and usage history of each agent and is used as a criterion for selecting the optimal agent. The selected agents work together to generate solutions that meet the user's needs.

[0037] The AI ​​solution generated in this way is delivered to the user's device. Through this solution, the user can quickly obtain a means to solve problems. For example, if there is a request to streamline inventory management, the server selects and integrates an AI agent for logistics analysis and an AI agent dedicated to inventory management. This combination allows the user to obtain inventory movements and optimal order quantities in real time and perform appropriate inventory management. This process significantly reduces the user's workload and enables more intuitive and effective problem solving.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user uses their device to input the problem they want to solve or the features they want in natural language. This input is sent to the server in real time.

[0041] Step 2:

[0042] The server analyzes the received user input using a natural language processing engine. The analysis extracts keywords from the text and constructs data to understand the user's intent.

[0043] Step 3:

[0044] The server searches the agent database within the system based on the analysis results. It refers to the characteristics and past usage history of each agent and lists the agent that best suits the user's needs.

[0045] Step 4:

[0046] The server determines the optimal combination of agents. Here, an AI model is used to optimize the combination, taking into account past performance data and conditions based on the user's current situation.

[0047] Step 5:

[0048] If necessary, the server determines which additional agents should be added. This enhances the performance of the selected agents and adjusts them to provide a more effective solution for the user.

[0049] Step 6:

[0050] The server establishes coordination between selected agents and sets up workflows for executing AI solutions. It also manages data flow and interfaces between agents.

[0051] Step 7:

[0052] The server delivers the built AI solution to the user's device. Users can then evaluate and use this solution to receive support in resolving their problems.

[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] Providing efficient and rapid solutions that meet the diverse needs of users presents a significant challenge. In particular, there is a need for a system that allows users without specialized knowledge to seamlessly utilize various advanced artificial intelligence technologies tailored to their specific requirements.

[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 receiving and analyzing natural language information input by a user in real time, means for selecting multiple specialized artificial intelligences from a database based on the requests identified by the analysis, and means for generating a solution that satisfies the user's requests by coordinating the selected artificial intelligences with each other. As a result, users can quickly receive the optimal solution that meets their needs, even without specialized knowledge.

[0058] A "user" is an entity that uses a system to input its own requests.

[0059] "Natural language information" refers to information and instructions written in the language that users use on a daily basis.

[0060] "Methods for receiving and analyzing in real time" refers to technologies that instantly receive natural language information sent by users and analyze it to understand its meaning and intent.

[0061] "Requests identified through analysis" refers to the results of extracting the specific needs and objectives that users actually desire during the natural language processing process.

[0062] A "database" is a collection of information structured in a format that allows for efficient storage and management, and quick retrieval of information as needed.

[0063] "Specialized artificial intelligence" refers to artificial intelligence algorithms and models that are designed and trained specifically for a particular field or application.

[0064] "A means of generating solutions through mutual cooperation" refers to a system in which multiple selected artificial intelligences work together to produce specific solutions that meet the user's needs.

[0065] "User terminal" refers to computer devices or equipment used by users to access the system and check the results.

[0066] The system of this invention is built to analyze user requests in real time and provide accurate solutions. Users input their requests in natural language via a terminal, and this data is immediately sent to the server. In this process, the terminal performs text input via a standard input device.

[0067] The server analyzes the received natural language information using generative AI models such as TENSORFLOW® and PyTorch. This allows for a deep understanding of the user's requests and the extraction of necessary information. After information analysis, the server refers to a database of artificial intelligence agents registered in the system and selects the agent best suited to the request. For example, if there is a request for inventory management, it will select an artificial intelligence for logistics analysis and an artificial intelligence specifically for inventory management.

[0068] The selected artificial intelligence agents process data within their respective areas of expertise and generate the optimal solution. The resulting solution is then sent to the user's device in real time, allowing the user to quickly take appropriate action.

[0069] For example, if a user enters the prompt "I want to streamline inventory management," the server will select the appropriate agent and perform inventory data analysis and calculate the optimal order quantity. This functionality allows users to prevent inventory shortages and surpluses, enabling efficient management.

[0070] This system enables effective problem-solving in various business scenarios by dynamically combining diverse, specialized artificial intelligences according to user needs.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] Users enter their requests in natural language using their device. This input is done via the device's text input field and collected in the form of prompts. For example, a request such as "I want to streamline inventory management" might be entered. The entered data is immediately sent to the server.

[0074] Step 2:

[0075] The server analyzes the received natural language information using a generative AI model. Here, the server utilizes natural language processing techniques such as BERT and GPT. The input at this stage is a prompt sentence from the user, and the output identifies the intent and purpose necessary to fulfill the request. During this analysis process, sentence structure understanding and keyword extraction are performed.

[0076] Step 3:

[0077] Based on the analysis results, the server selects an appropriate artificial intelligence agent by referring to the agent database within the system. The input is the analyzed intent, and the output is a list of selected agents. In this process, the server identifies the optimal combination by considering the characteristics and history of each agent.

[0078] Step 4:

[0079] The selected artificial intelligence agents work together to generate solutions that meet the user's needs. The server facilitates information exchange between these agents and applies data analysis and predictive algorithms. The input consists of the selected agents and the analyzed needs, while the output is a specific solution. For example, this might involve a logistics analysis AI analyzing delivery patterns and an inventory management AI calculating order quantities.

[0080] Step 5:

[0081] The server sends the generated solution to the user's terminal. The user can then view the solution on their terminal and take the necessary actions. The input is the solution, and the output is the information displayed to the user. As a result, the user can take immediate action.

[0082] (Application Example 1)

[0083] 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."

[0084] In today's world, where information processing systems are needed to respond immediately to diverse user needs, there is a lack of mechanisms to provide solutions quickly and accurately. This invention aims to provide an information processing system that accurately processes user requests and efficiently combines various specialized machine learning modules to quickly present solutions.

[0085] 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.

[0086] In this invention, the server includes means for analyzing natural language information input by the user and identifying the user's request; means for selecting an appropriate processor from among multiple specialized machine learning modules based on the analysis results; means for coordinating the selected processors to provide a solution to the user; and means for dynamically selecting a processor according to the user's request and quickly providing the results of the operation. This makes it possible to quickly analyze the information the user requests and solve the problem with the optimal combination.

[0087] "Natural language information" refers to data entered by users in the form of language they use in everyday life, which is then analyzed for interpretation and processing by computer systems.

[0088] A "specialized machine learning module" is a machine learning execution unit designed specifically for a particular field or purpose, and is used for advanced data analysis and decision-making.

[0089] A "processor" is a computing device that is responsible for processing data and performing calculations and control, and it has a wide range of functions, including specialized machine learning modules.

[0090] "Collaboration" refers to a mechanism designed to enable multiple systems or modules to cooperate with each other and function efficiently.

[0091] "Operational results" refer to the output obtained after a processor or module performs analysis and calculations based on input data, and include the solutions requested by the user.

[0092] An "information processing system" is a combination of hardware and software that receives input data, processes and analyzes it in an appropriate manner, and provides useful information to the user.

[0093] The system for realizing this invention consists of a user terminal, a server, and a connected specialized machine learning module. The user inputs a request in natural language using the terminal. This natural language information is transmitted to the server via the network. The server analyzes the input data using natural language processing software to accurately identify the user's request. A general-purpose natural language processing library is used for the analysis.

[0094] The server then selects highly relevant, specialized machine learning modules based on the analysis results. These modules are chosen based on their past usage history and functions stored in the database, and they work together in conjunction with each other. The selected modules utilize the processor to quickly generate the information the user requests. The final output is sent to the user's terminal, allowing the user to obtain the solution they were looking for.

[0095] As a concrete example, consider a usage scenario where a user is using an electronic payment application. When the user enters "I want to check this month's expenses" as a prompt, the server selects and integrates the expense analysis module and budget management module, and presents a detailed expense report and savings management on the terminal. This allows the user to obtain the desired information quickly and effectively.

[0096] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0097] Step 1:

[0098] The user enters the request in natural language using a terminal. This entered prompt text is treated as the system's initial data.

[0099] Step 2:

[0100] The terminal sends natural language data entered by the user to the server. Here, input is the prompt text, and output is the data transfer to the server.

[0101] Step 3:

[0102] The server analyzes the received prompt message using natural language processing techniques. The input is the forwarded prompt message, and the output is data that identifies the user's request. Specifically, the analysis algorithm extracts the user's intent and categorizes the request.

[0103] Step 4:

[0104] The server selects the appropriate processor from several possible specialized machine learning modules based on the analysis results. The input is the analysis data, and the output is a list of the selected processors. The selection criteria operate based on the module's past usage history and capabilities.

[0105] Step 5:

[0106] The selected processors work together on the server to process data and generate solutions based on user requirements. The input is the instructions for the processor, and the output is the generated solution. The processor performs data calculations to produce accurate output.

[0107] Step 6:

[0108] The generated solution is sent from the server to the terminal and presented to the user. Here, the input is the generated solution, and the output is the information displayed on the terminal. Specific actions include the display of information in visual graphs and list formats.

[0109] 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.

[0110] This invention is a user-centered artificial intelligence system that combines an emotion engine to analyze the user's emotional state and provide appropriate AI solutions. The system consists of the user's terminal, a server, and a group of specialized artificial intelligence agents equipped with the emotion engine.

[0111] Users input their requests and inquiries in natural language via their terminals. The server receives this input information in real time and analyzes it using a natural language processing engine. This analysis identifies the user's main requests, and the emotion engine identifies the user's underlying emotional state. The emotion engine evaluates the emotional nuances of the input text and extracts information such as whether the user is stressed or satisfied.

[0112] The server selects the most suitable specialized artificial intelligence agent from the database based on the analyzed user's requests and emotions. During the selection process, it considers the cooperation between agents to form a group of agents best suited to the user's situation. If necessary, it utilizes the results of the emotion engine to incorporate additional supplementary agents, providing effective solutions that address the user's emotional state.

[0113] For example, if a user enters a request such as "I want to know how to reduce stress caused by project delays," the server analyzes this request and uses an emotion engine to identify that the user is experiencing stress. Then, it selects and integrates an AI agent specializing in stress management and another AI agent specializing in project management. This combination allows the user to receive effective stress reduction measures and project management techniques in real time, enabling smoother problem-solving.

[0114] Thus, the present invention improves convenience and effectiveness by providing an optimal AI solution based on user requests and emotions.

[0115] The following describes the processing flow.

[0116] Step 1:

[0117] The user inputs the problem they want to solve and the type of support they require using natural language via their device. This input data is immediately sent to the server.

[0118] Step 2:

[0119] The server analyzes the received user input using a natural language processing engine. The purpose of this analysis is to identify the user's main requests and extract relevant keywords and phrases.

[0120] Step 3:

[0121] Based on the extracted information, the server uses an emotion engine to identify the user's emotional state. This process involves analyzing emotional nuances from the vocabulary and context in the text to identify emotions such as "anxiety" or "satisfaction."

[0122] Step 4:

[0123] Based on the analysis results, the server selects the appropriate specialized artificial intelligence agent from the database. Here, the agent best suited to the user's needs and emotional state is chosen.

[0124] Step 5:

[0125] The server utilizes the results of the emotion engine as needed to form the optimal combination of agents. If additional supplementary agents are required, they will also be selected.

[0126] Step 6:

[0127] The server establishes coordination between selected agents and generates AI solutions while taking user emotions into consideration. This process involves data sharing and task coordination among agents.

[0128] Step 7:

[0129] The server delivers the built AI solution to the user's device. Through the device, the user can utilize this solution to obtain information and support to solve their own problems.

[0130] (Example 2)

[0131] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0132] Conventional artificial intelligence systems have the limitation of only being able to provide simple responses to user requests and being unable to solve complex problems that take into account the user's emotional state. Furthermore, there was a lack of effective means to select and integrate appropriate specialized information processing devices. As a result, it was difficult to provide users with the solutions they truly needed quickly and appropriately.

[0133] 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.

[0134] In this invention, the server includes means for analyzing natural language information input by the user and identifying requests; means for sentiment analysis to identify the user's emotional state from the analyzed information; and means for selecting an appropriate process from among multiple specialized information processing devices based on the sentiment analysis and request analysis results. This makes it possible to provide a comprehensive solution that takes the user's emotional state into consideration.

[0135] A "user" refers to an individual or organization that inputs natural language information into the system.

[0136] "Natural language information" refers to data and text expressed in human language.

[0137] "Analysis" refers to the process of interpreting input information and identifying its meaning and intent.

[0138] A "request" refers to a specific demand or wish that a user is seeking to resolve.

[0139] "Emotional analysis" refers to a technology that identifies a user's emotional state from the information they input.

[0140] "Emotional state" refers to the type and degree of emotions a user is experiencing.

[0141] A "specialized information processing device" refers to an information processing device that performs specialized processing in a specific field.

[0142] "Process selection" refers to the act of choosing the optimal information processing process based on the analysis results.

[0143] "Cooperation" refers to the collaborative operation of selected information processing devices.

[0144] "Solution" refers to the specific methods and support provided to address user requests or problems.

[0145] This invention relates to a system that analyzes a user's emotional state and provides an optimal AI solution based on that analysis. The system consists of a user's terminal, a server, and a specialized information processing unit equipped with emotion analysis capabilities.

[0146] Users input requests and inquiries in natural language using their devices. The input information is sent to a server, which analyzes the information using natural language processing engines such as Google Cloud Natural Language and IBM Watson to identify the user's requests. In addition to this analysis, sentiment analysis technology is used to evaluate the user's emotional nuances from the input text and identify their emotional state.

[0147] Based on the analyzed requests and emotional states, the server selects the most suitable specialized information processing device from the database. During this process, optimization is performed to ensure that multiple information processing devices can work together efficiently. The selected information processing device then provides the user with practical solutions, such as stress management or project management.

[0148] For example, if a user enters a request such as "I want to know how to reduce stress caused by project delays," the server analyzes this request and identifies that the user is experiencing stress. Then, it selects an information processing device specialized in stress management and another specialized in project management, and provides the user with effective stress reduction measures and project management methods.

[0149] Furthermore, an example of a prompt to be input to the generating AI model could be, "If a user is experiencing stress due to project delays, what combination of information processing devices would be effective?"

[0150] This system enables the provision of flexible and effective solutions tailored to user needs, which is expected to reduce stress and improve work efficiency.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] Users input their requests and inquiries in natural language using their terminals. This input may include specific details, such as wanting to know how to reduce stress caused by project delays. The input data is sent from the terminal to the server in string format.

[0154] Step 2:

[0155] The server receives natural language text data sent by the user. To parse the received input data, the server runs a natural language processing engine such as Google Cloud Natural Language. At this stage, it identifies key keywords in the input and extracts the user's main requests. The output is a data object representing the requests.

[0156] Step 3:

[0157] The server uses a sentiment analysis engine to identify the user's emotional state based on the results of natural language processing. Data processing involves analyzing the emotional nuances of words and phrases in the text and outputting emotional indicators such as stress and satisfaction. The output is a data object representing the user's emotional state.

[0158] Step 4:

[0159] The server selects the most suitable specialized information processing device from the database based on the extracted request and emotional state data. During the selection process, it coordinates information processing devices related to the request and those appropriate for the emotional state. The output is a list of the selected information processing devices.

[0160] Step 5:

[0161] The server coordinates selected information processing devices and works together to provide users with appropriate solutions. Specifically, it generates concrete advice and methods regarding stress management and project management, and sends them to the user's terminal. The output is the content of the solutions provided to the user.

[0162] Step 6:

[0163] Users receive the solutions provided on their devices and provide feedback as needed. This feedback is sent to the server and used for further analysis and improvement. The output is feedback data.

[0164] (Application Example 2)

[0165] 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".

[0166] In modern urban life, individual citizens face a wide range of problems, and solving them requires an optimal approach based on emotions. However, conventional information systems do not take into account the emotional state of users, making it difficult to suggest the most appropriate services and facilities for them.

[0167] 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.

[0168] In this invention, the server includes means for analyzing natural language information input by the user and identifying the user's request; means for selecting an appropriate agent from among multiple specialized artificial intelligences based on the analysis results; means for coordinating the selected agents to provide a solution to the user; and means for suggesting public facilities and services that correspond to the user's emotional state. This enables the provision of optimal information and service suggestions based on the user's emotional state.

[0169] "Natural language information" refers to the everyday language used by users, and includes information such as the expressions of language used when humans communicate.

[0170] "Specialized artificial intelligence" refers to artificial intelligence technology that possesses functions specialized for a particular field or purpose, and is designed to efficiently solve problems in that field.

[0171] An "agent" is a unit of software that has a specific task or objective and acts autonomously to accomplish it.

[0172] "Emotional state" refers to the user's mental and psychological state, and includes indicators such as stress and satisfaction analyzed by the emotion engine.

[0173] "Public facilities and services" include infrastructure and service operations such as roads, public transportation, public libraries, and parks that are accessible to users.

[0174] To realize the system of this invention, a user terminal, a server, and a group of specialized artificial intelligence agents equipped with an emotion engine are required. First, the user inputs complaints and worries in natural language through a terminal such as a smartphone. This natural language information is sent to the server.

[0175] The server receives the input natural language information and performs analysis using natural language processing software, including an emotion engine (such as Google Cloud Natural Language API or IBM Watson). This identifies the user's main requests and emotional state. The emotion engine evaluates the user's emotional nuances and extracts emotional indicators such as stress and satisfaction.

[0176] Subsequently, the server selects the most suitable specialized artificial intelligence agent from the database based on the analysis results. These agents may specialize in areas such as traffic information or public facilities, and they work together to provide the optimal solution according to the user's situation. For example, if a user is stressed by city noise, the server will collaborate with an agent that suggests a quieter route to achieve this.

[0177] Ultimately, appropriate information about public facilities and services tailored to the user's emotional state is fed back to the user's device. This enables users to solve problems in their daily lives in an optimal way based on their emotions.

[0178] For example, when a user inputs "City noise has been stressing me out while working recently" into the app, the system analyzes this information and suggests solutions such as a quiet cafe or an online white noise service. An example of a prompt to the generating AI model would be: "Please analyze the user's input, 'City noise has been stressing me out while working recently.' Please suggest corresponding stress reduction measures. Please also consider available city facilities and services."

[0179] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0180] Step 1:

[0181] Users input their complaints and concerns in natural language through their device. This input data is captured by the application on the device and sent to the server. The input consists of text information including the user's worries, and the output is the transmission of data to the server.

[0182] Step 2:

[0183] The server receives the natural language information and analyzes this text using a natural language processing engine. Specifically, it extracts emotional indicators (e.g., stress and satisfaction) from the text using an emotion engine. The input is natural language text, and the output is data indicating the user's requests and emotional state.

[0184] Step 3:

[0185] The server selects the most suitable specialized artificial intelligence agent from the database based on the analysis results. This selection process includes expanding the range of agents with knowledge related to emotional states and requests. The input is the analyzed emotional state and user request data, and the output is a list of appropriate agents.

[0186] Step 4:

[0187] The server coordinates the selected agents to generate the optimal solution for the user. In this process, multiple agents work together to form the solution. The input is the selected group of agents, and the output is the solution provided to the user.

[0188] Step 5:

[0189] The server selects appropriate public facility and service information based on the user's emotional state and sends it to the terminal. This step includes filtering the information and evaluating its relevance. The input is the formed solution and related information, and the output is the customized information displayed on the user's terminal.

[0190] Step 6:

[0191] The user views the information received on their device and initiates the next action. For example, this might involve visiting a suggested quiet cafe, which helps the user relieve stress. The input for this step is the received solution information, and the output is the user's next action.

[0192] 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.

[0193] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0194] 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.

[0195] [Second Embodiment]

[0196] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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".

[0208] This invention is a system that utilizes diverse specialized artificial intelligence agents to quickly provide solutions that users require. The system consists of a user terminal, a server, and a group of connected artificial intelligence agents. The user inputs their requests in natural language using the terminal. This information is transmitted to the server in real time. The server analyzes this information using advanced natural language processing techniques. The purpose of this analysis is to identify the intent and necessary functions contained in the user's requests.

[0209] Once the analysis is complete, the server selects an appropriate artificial intelligence agent based on the analysis results. This selection is made by referring to a database of agents maintained within the system. The database stores the characteristics and usage history of each agent and is used as a criterion for selecting the optimal agent. The selected agents work together to generate solutions that meet the user's needs.

[0210] The AI ​​solution generated in this way is delivered to the user's device. Through this solution, the user can quickly obtain a means to solve problems. For example, if there is a request to streamline inventory management, the server selects and integrates an AI agent for logistics analysis and an AI agent dedicated to inventory management. This combination allows the user to obtain inventory movements and optimal order quantities in real time and perform appropriate inventory management. This process significantly reduces the user's workload and enables more intuitive and effective problem solving.

[0211] The following describes the processing flow.

[0212] Step 1:

[0213] The user uses their device to input the problem they want to solve or the features they want in natural language. This input is sent to the server in real time.

[0214] Step 2:

[0215] The server analyzes the received user input using a natural language processing engine. The analysis extracts keywords from the text and constructs data to understand the user's intent.

[0216] Step 3:

[0217] The server searches the agent database within the system based on the analysis results. It refers to the characteristics and past usage history of each agent and lists the agent that best suits the user's needs.

[0218] Step 4:

[0219] The server determines the optimal combination of agents. Here, an AI model is used to perform optimization, taking into account past performance data and conditions based on the user's current situation.

[0220] Step 5:

[0221] If necessary, the server determines which additional agents should be added. This enhances the performance of the selected agents and adjusts them to provide a more effective solution for the user.

[0222] Step 6:

[0223] The server establishes coordination between selected agents and sets up workflows for executing AI solutions. It also manages data flow and interfaces between agents.

[0224] Step 7:

[0225] The server delivers the built AI solution to the user's device. Users can then evaluate and use this solution to receive support in resolving their problems.

[0226] (Example 1)

[0227] 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."

[0228] Providing efficient and rapid solutions that meet the diverse needs of users presents a significant challenge. In particular, there is a need for a system that allows users without specialized knowledge to seamlessly utilize various advanced artificial intelligence technologies tailored to their specific requirements.

[0229] 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.

[0230] In this invention, the server includes means for receiving and analyzing natural language information input by a user in real time, means for selecting multiple specialized artificial intelligences from a database based on the requests identified by the analysis, and means for generating a solution that satisfies the user's requests by coordinating the selected artificial intelligences with each other. As a result, users can quickly receive the optimal solution that meets their needs, even without specialized knowledge.

[0231] A "user" is an entity that uses a system to input its own requests.

[0232] "Natural language information" refers to information and instructions written in the language that users use on a daily basis.

[0233] "Methods for receiving and analyzing in real time" refers to technologies that instantly receive natural language information sent by users and analyze it to understand its meaning and intent.

[0234] "Requests identified through analysis" refers to the results of extracting the specific needs and objectives that users actually desire during the natural language processing process.

[0235] A "database" is a collection of information structured in a format that allows for efficient storage and management, and quick retrieval of information as needed.

[0236] "Specialized artificial intelligence" refers to artificial intelligence algorithms and models that are designed and trained specifically for a particular field or application.

[0237] "A means of generating solutions through mutual cooperation" refers to a system in which multiple selected artificial intelligences work together to produce specific solutions that meet the user's needs.

[0238] "User terminal" refers to computer devices or equipment used by users to access the system and check the results.

[0239] The system of this invention is built to analyze user requests in real time and provide accurate solutions. Users input their requests in natural language via a terminal, and this data is immediately sent to the server. In this process, the terminal performs text input via a standard input device.

[0240] The server analyzes the received natural language information using generative AI models such as TensorFlow and PyTorch. This allows for a deep understanding of the user's requests and the extraction of necessary information. After information analysis, the server refers to a database of artificial intelligence agents registered in the system and selects the agent that best suits the request. For example, if there is a request for inventory management, it will select an AI for logistics analysis and an AI specifically for inventory management.

[0241] The selected artificial intelligence agents process data within their respective areas of expertise and generate the optimal solution. The resulting solution is then sent to the user's device in real time, allowing the user to quickly take appropriate action.

[0242] For example, if a user enters the prompt "I want to streamline inventory management," the server will select the appropriate agent and perform inventory data analysis and calculate the optimal order quantity. This functionality allows users to prevent inventory shortages and surpluses, enabling efficient management.

[0243] This system enables effective problem-solving in various business scenarios by dynamically combining diverse specialized artificial intelligences according to user needs.

[0244] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0245] Step 1:

[0246] Users enter their requests in natural language using their device. This input is done via the device's text input field and collected in the form of prompts. For example, a request such as "I want to streamline inventory management" might be entered. The entered data is immediately sent to the server.

[0247] Step 2:

[0248] The server analyzes the received natural language information using a generative AI model. Here, the server utilizes natural language processing techniques such as BERT and GPT. The input at this stage is a prompt sentence from the user, and the output identifies the intent and purpose necessary to fulfill the request. During this analysis process, sentence structure understanding and keyword extraction are performed.

[0249] Step 3:

[0250] Based on the analysis results, the server selects an appropriate artificial intelligence agent by referring to the agent database within the system. The input is the analyzed intent, and the output is a list of selected agents. In this process, the server identifies the optimal combination by considering the characteristics and history of each agent.

[0251] Step 4:

[0252] The selected artificial intelligence agents work together to generate solutions that meet the user's needs. The server facilitates information exchange between these agents and applies data analysis and predictive algorithms. The input consists of the selected agents and the analyzed needs, while the output is a specific solution. For example, this might involve a logistics analysis AI analyzing delivery patterns and an inventory management AI calculating order quantities.

[0253] Step 5:

[0254] The server sends the generated solution to the user's terminal. The user can then view the solution on their terminal and take the necessary actions. The input is the solution, and the output is the information displayed to the user. As a result, the user can take immediate action.

[0255] (Application Example 1)

[0256] 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."

[0257] In today's world, where information processing systems are needed to respond immediately to diverse user needs, there is a lack of mechanisms to provide solutions quickly and accurately. This invention aims to provide an information processing system that accurately processes user requests and efficiently combines various specialized machine learning modules to quickly present solutions.

[0258] 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.

[0259] In this invention, the server includes means for analyzing natural language information input by the user and identifying the user's request; means for selecting an appropriate processor from among multiple specialized machine learning modules based on the analysis results; means for coordinating the selected processors to provide a solution to the user; and means for dynamically selecting a processor according to the user's request and quickly providing the results of the operation. This makes it possible to quickly analyze the information the user requests and solve the problem with the optimal combination.

[0260] "Natural language information" refers to data entered by users in the form of language they use in everyday life, which is then analyzed for interpretation and processing by computer systems.

[0261] A "specialized machine learning module" is a machine learning execution unit designed specifically for a particular field or purpose, and is used for advanced data analysis and decision-making.

[0262] A "processor" is a computing device that is responsible for processing data and performing calculations and control, and it has a wide range of functions, including specialized machine learning modules.

[0263] "Collaboration" refers to a mechanism designed to enable multiple systems or modules to cooperate with each other and function efficiently.

[0264] "Operational results" refer to the output obtained after a processor or module performs analysis and calculations based on input data, and include the solutions requested by the user.

[0265] An "information processing system" is a combination of hardware and software that receives input data, processes and analyzes it in an appropriate manner, and provides useful information to the user.

[0266] The system for realizing this invention consists of a user terminal, a server, and a connected specialized machine learning module. The user inputs a request in natural language using the terminal. This natural language information is transmitted to the server via the network. The server analyzes the input data using natural language processing software to accurately identify the user's request. A general-purpose natural language processing library is used for the analysis.

[0267] The server then selects highly relevant, specialized machine learning modules based on the analysis results. These modules are chosen based on their past usage history and functions stored in the database, and they work together in conjunction with each other. The selected modules utilize the processor to quickly generate the information the user requests. The final output is sent to the user's terminal, allowing the user to obtain the solution they were looking for.

[0268] As a concrete example, consider a usage scenario where a user is using an electronic payment application. When the user enters "I want to check this month's expenses" as a prompt, the server selects and integrates the expense analysis module and budget management module, and presents a detailed expense report and savings management on the terminal. This allows the user to obtain the desired information quickly and effectively.

[0269] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0270] Step 1:

[0271] The user enters the request in natural language using a terminal. This entered prompt text is treated as the system's initial data.

[0272] Step 2:

[0273] The terminal sends natural language data entered by the user to the server. Here, input is the prompt text, and output is the data transfer to the server.

[0274] Step 3:

[0275] The server analyzes the received prompt message using natural language processing techniques. The input is the forwarded prompt message, and the output is data that identifies the user's request. Specifically, the analysis algorithm extracts the user's intent and categorizes the request.

[0276] Step 4:

[0277] The server selects the appropriate processor from several possible specialized machine learning modules based on the analysis results. The input is the analysis data, and the output is a list of the selected processors. The selection criteria operate based on the module's past usage history and capabilities.

[0278] Step 5:

[0279] The selected processors work together on the server to process data and generate solutions based on user requirements. The input is the instructions for the processor, and the output is the generated solution. The processor performs data calculations to produce accurate output.

[0280] Step 6:

[0281] The generated solution is sent from the server to the terminal and presented to the user. Here, the input is the generated solution, and the output is the information displayed on the terminal. Specific actions include the display of information in visual graphs and list formats.

[0282] 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.

[0283] The present invention is a user-centered artificial intelligence system combined with an emotion engine, which can analyze the user's emotional state and provide appropriate AI solutions. The system is composed of the user's terminal, server, and a group of specialized artificial intelligence agents equipped with an emotion engine.

[0284] The user inputs their desires and inquiries in natural language via the terminal. The server receives these input information in real-time and analyzes them using a natural language processing engine. Through this analysis, in addition to the user's main requirements, the emotion engine identifies the user's potential emotional state. The emotion engine evaluates the emotional nuances of the input text and extracts information such as whether the user is feeling stressed or satisfied.

[0285] Based on the analyzed user desires and emotions, the server selects the optimal specialized artificial intelligence agent from the database. In the selection process, the cooperation between agents is considered to form a group of agents most suitable for the user's situation. If necessary, the results of the emotion engine are utilized to incorporate additional supplementary agents to provide an effective solution corresponding to the user's emotional state.

[0286] As a specific example, when the user inputs a desire such as "want to know how to relieve stress caused by project delays", the server analyzes this desire and uses the emotion engine to identify that the user is feeling stressed. Subsequently, an AI agent specialized in stress management and an AI agent specialized in project management are selected and coordinated. Through this combination, the user can be provided with effective stress relief measures and project management methods in real-time, enabling smooth progress in problem-solving.

[0287] In this way, the present invention provides an optimal AI solution based on the user's desires and emotions, improving convenience and effectiveness.

[0288] The following describes the processing flow.

[0289] Step 1:

[0290] The user inputs the problem they want to solve and the type of support they require using natural language via their device. This input data is immediately sent to the server.

[0291] Step 2:

[0292] The server analyzes the received user input using a natural language processing engine. The purpose of this analysis is to identify the user's main requests and extract relevant keywords and phrases.

[0293] Step 3:

[0294] Based on the extracted information, the server uses an emotion engine to identify the user's emotional state. This process involves analyzing emotional nuances from the vocabulary and context in the text to identify emotions such as "anxiety" or "satisfaction."

[0295] Step 4:

[0296] Based on the analysis results, the server selects the appropriate specialized artificial intelligence agent from the database. Here, the agent best suited to the user's needs and emotional state is chosen.

[0297] Step 5:

[0298] The server utilizes the results of the emotion engine as needed to form the optimal combination of agents. If additional supplementary agents are required, they will also be selected.

[0299] Step 6:

[0300] The server establishes coordination between selected agents and generates AI solutions while taking user emotions into consideration. This process involves data sharing and task coordination among agents.

[0301] Step 7:

[0302] The server distributes the constructed AI solution to the user's terminal. The user can utilize this solution through the terminal to obtain information and support for solving their own problems.

[0303] (Example 2)

[0304] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0305] In conventional artificial intelligence systems, there was a problem that they could only provide simple responses to user requests and could not solve complex problems considering the user's emotional state. Also, there was a lack of effective means for selecting and coordinating appropriate specialized information processing devices. As a result, it was difficult to quickly and appropriately provide the solutions that the user really needed.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0307] In this invention, the server includes means for analyzing the natural language information input from the user to identify the request, means for performing sentiment analysis to identify the user's emotional state from the analyzed information, and means for selecting an appropriate process from among a plurality of specialized information processing devices based on the results of the sentiment analysis and the analysis of the request. This enables the provision of a composite solution considering the user's emotional state.

[0308] The "user" refers to an individual or group that inputs natural language information into the system.

[0309] The "natural language information" refers to data and text expressed in human language.

[0310] "Analysis" refers to the process of interpreting input information and identifying its meaning and intent.

[0311] A "request" refers to a specific demand or wish that a user is seeking to resolve.

[0312] "Emotional analysis" refers to a technology that identifies a user's emotional state from the information they input.

[0313] "Emotional state" refers to the type and degree of emotions a user is experiencing.

[0314] A "specialized information processing device" refers to an information processing device that performs specialized processing in a specific field.

[0315] "Process selection" refers to the act of choosing the optimal information processing process based on the analysis results.

[0316] "Cooperation" refers to the collaborative operation of selected information processing devices.

[0317] "Solution" refers to the specific methods and support provided to address user requests or problems.

[0318] This invention relates to a system that analyzes a user's emotional state and provides an optimal AI solution based on that analysis. The system consists of a user's terminal, a server, and a specialized information processing unit equipped with emotion analysis capabilities.

[0319] Users input requests and inquiries in natural language using their devices. The input information is sent to a server, which analyzes the information using natural language processing engines such as Google Cloud Natural Language and IBM Watson to identify the user's requests. In addition to this analysis, sentiment analysis technology is used to evaluate the user's emotional nuances from the input text and identify their emotional state.

[0320] Based on the analyzed requests and emotional states, the server selects the most suitable specialized information processing device from the database. During this process, optimization is performed to ensure that multiple information processing devices can work together efficiently. The selected information processing device then provides the user with practical solutions, such as stress management or project management.

[0321] For example, if a user enters a request such as "I want to know how to reduce stress caused by project delays," the server analyzes this request and identifies that the user is experiencing stress. Then, it selects an information processing device specialized in stress management and another specialized in project management, and provides the user with effective stress reduction measures and project management methods.

[0322] Furthermore, an example of a prompt to be input to the generating AI model could be, "If a user is experiencing stress due to project delays, what combination of information processing devices would be effective?"

[0323] This system enables the provision of flexible and effective solutions tailored to user needs, which is expected to reduce stress and improve work efficiency.

[0324] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0325] Step 1:

[0326] Users input their requests and inquiries in natural language using their terminals. This input may include specific details, such as wanting to know how to reduce stress caused by project delays. The input data is sent from the terminal to the server in string format.

[0327] Step 2:

[0328] The server receives natural language text data sent by the user. To parse the received input data, the server runs a natural language processing engine such as Google Cloud Natural Language. At this stage, it identifies key keywords in the input and extracts the user's main requests. The output is a data object representing the requests.

[0329] Step 3:

[0330] The server uses a sentiment analysis engine to identify the user's emotional state based on the results of natural language processing. Data processing involves analyzing the emotional nuances of words and phrases in the text and outputting emotional indicators such as stress and satisfaction. The output is a data object representing the user's emotional state.

[0331] Step 4:

[0332] The server selects the most suitable specialized information processing device from the database based on the extracted request and emotional state data. During the selection process, it coordinates information processing devices related to the request and those appropriate for the emotional state. The output is a list of the selected information processing devices.

[0333] Step 5:

[0334] The server coordinates selected information processing devices and works together to provide users with appropriate solutions. Specifically, it generates concrete advice and methods regarding stress management and project management, and sends them to the user's terminal. The output is the content of the solutions provided to the user.

[0335] Step 6:

[0336] Users receive the solutions provided on their devices and provide feedback as needed. This feedback is sent to the server and used for further analysis and improvement. The output is feedback data.

[0337] (Application Example 2)

[0338] 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."

[0339] In modern urban life, individual citizens face a wide range of problems, and solving them requires an optimal approach based on emotions. However, conventional information systems do not take into account the emotional state of users, making it difficult to suggest the most appropriate services and facilities for them.

[0340] 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.

[0341] In this invention, the server includes means for analyzing natural language information input by the user and identifying the user's request; means for selecting an appropriate agent from among multiple specialized artificial intelligences based on the analysis results; means for coordinating the selected agents to provide a solution to the user; and means for suggesting public facilities and services that correspond to the user's emotional state. This enables the provision of optimal information and service suggestions based on the user's emotional state.

[0342] "Natural language information" refers to the everyday language used by users, and includes information such as the expressions of language used when humans communicate.

[0343] "Specialized artificial intelligence" refers to artificial intelligence technology that possesses functions specialized for a particular field or purpose, and is designed to efficiently solve problems in that field.

[0344] An "agent" is a unit of software that has a specific task or objective and acts autonomously to accomplish it.

[0345] "Emotional state" refers to the user's mental and psychological state, and includes indicators such as stress and satisfaction analyzed by the emotion engine.

[0346] "Public facilities and services" include infrastructure and service operations such as roads, public transportation, public libraries, and parks that are accessible to users.

[0347] To realize the system of this invention, a user terminal, a server, and a group of specialized artificial intelligence agents equipped with an emotion engine are required. First, the user inputs complaints and worries in natural language through a terminal such as a smartphone. This natural language information is sent to the server.

[0348] The server receives the input natural language information and performs analysis using natural language processing software, including an emotion engine (such as Google Cloud Natural Language API or IBM Watson). This identifies the user's main requests and emotional state. The emotion engine evaluates the user's emotional nuances and extracts emotional indicators such as stress and satisfaction.

[0349] Subsequently, the server selects the most suitable specialized artificial intelligence agent from the database based on the analysis results. These agents may specialize in areas such as traffic information or public facilities, and they work together to provide the optimal solution according to the user's situation. For example, if a user is stressed by city noise, the server will collaborate with an agent that suggests a quieter route to achieve this.

[0350] Ultimately, appropriate information about public facilities and services tailored to the user's emotional state is fed back to the user's device. This enables users to solve problems in their daily lives in an optimal way based on their emotions.

[0351] For example, when a user inputs "City noise has been stressing me out while working recently" into the app, the system analyzes this information and suggests solutions such as a quiet cafe or an online white noise service. An example of a prompt to the generating AI model would be: "Please analyze the user's input, 'City noise has been stressing me out while working recently.' Please suggest corresponding stress reduction measures. Please also consider available city facilities and services."

[0352] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0353] Step 1:

[0354] Users input their complaints and concerns in natural language through their device. This input data is captured by the application on the device and sent to the server. The input consists of text information including the user's worries, and the output is the transmission of data to the server.

[0355] Step 2:

[0356] The server receives the natural language information and analyzes this text using a natural language processing engine. Specifically, it extracts emotional indicators (e.g., stress and satisfaction) from the text using an emotion engine. The input is natural language text, and the output is data indicating the user's requests and emotional state.

[0357] Step 3:

[0358] The server selects the most suitable specialized artificial intelligence agent from the database based on the analysis results. This selection process includes expanding the range of agents with knowledge related to emotional states and requests. The input is the analyzed emotional state and user request data, and the output is a list of appropriate agents.

[0359] Step 4:

[0360] The server coordinates the selected agents to generate the optimal solution for the user. In this process, multiple agents work together to form the solution. The input is the selected group of agents, and the output is the solution provided to the user.

[0361] Step 5:

[0362] The server selects appropriate public facility and service information based on the user's emotional state and sends it to the terminal. This step includes filtering the information and evaluating its relevance. The input is the formed solution and related information, and the output is the customized information displayed on the user's terminal.

[0363] Step 6:

[0364] The user views the information received on their device and initiates the next action. For example, this might involve visiting a suggested quiet cafe, which helps the user relieve stress. The input for this step is the received solution information, and the output is the user's next action.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] [Third Embodiment]

[0369] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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".

[0381] This invention is a system that utilizes diverse specialized artificial intelligence agents to quickly provide solutions that users require. The system consists of a user terminal, a server, and a group of connected artificial intelligence agents. The user inputs their requests in natural language using the terminal. This information is transmitted to the server in real time. The server analyzes this information using advanced natural language processing techniques. The purpose of this analysis is to identify the intent and necessary functions contained in the user's requests.

[0382] Once the analysis is complete, the server selects an appropriate artificial intelligence agent based on the analysis results. This selection is made by referring to a database of agents maintained within the system. The database stores the characteristics and usage history of each agent and is used as a criterion for selecting the optimal agent. The selected agents work together to generate solutions that meet the user's needs.

[0383] The AI ​​solution generated in this way is delivered to the user's device. Through this solution, the user can quickly obtain a means to solve problems. For example, if there is a request to streamline inventory management, the server selects and integrates an AI agent for logistics analysis and an AI agent dedicated to inventory management. This combination allows the user to obtain inventory movements and optimal order quantities in real time and perform appropriate inventory management. This process significantly reduces the user's workload and enables more intuitive and effective problem solving.

[0384] The following describes the processing flow.

[0385] Step 1:

[0386] The user uses their device to input the problem they want to solve or the features they want in natural language. This input is sent to the server in real time.

[0387] Step 2:

[0388] The server analyzes the received user input using a natural language processing engine. The analysis extracts keywords from the text and constructs data to understand the user's intent.

[0389] Step 3:

[0390] The server searches the agent database within the system based on the analysis results. It refers to the characteristics and past usage history of each agent and lists the agent that best suits the user's needs.

[0391] Step 4:

[0392] The server determines the optimal combination of agents. Here, an AI model is used to perform optimization, taking into account past performance data and conditions based on the user's current situation.

[0393] Step 5:

[0394] If necessary, the server determines which additional agents should be added. This enhances the performance of the selected agents and adjusts them to provide a more effective solution for the user.

[0395] Step 6:

[0396] The server establishes coordination between selected agents and sets up workflows for executing AI solutions. It also manages data flow and interfaces between agents.

[0397] Step 7:

[0398] The server delivers the built AI solution to the user's device. Users can then evaluate and use this solution to receive support in resolving their problems.

[0399] (Example 1)

[0400] 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."

[0401] Providing efficient and rapid solutions that meet the diverse needs of users presents a significant challenge. In particular, there is a need for a system that allows users without specialized knowledge to seamlessly utilize various advanced artificial intelligence technologies tailored to their specific requirements.

[0402] 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.

[0403] In this invention, the server includes means for receiving and analyzing natural language information input by a user in real time, means for selecting multiple specialized artificial intelligences from a database based on the requests identified by the analysis, and means for generating a solution that satisfies the user's requests by coordinating the selected artificial intelligences with each other. As a result, users can quickly receive the optimal solution that meets their needs, even without specialized knowledge.

[0404] A "user" is an entity that uses a system to input its own requests.

[0405] "Natural language information" refers to information and instructions written in the language that users use on a daily basis.

[0406] "Methods for receiving and analyzing in real time" refers to technologies that instantly receive natural language information sent by users and analyze it to understand its meaning and intent.

[0407] "Requests identified through analysis" refers to the results of extracting the specific needs and objectives that users actually desire during the natural language processing process.

[0408] A "database" is a collection of information structured in a format that allows for efficient storage and management, and quick retrieval of information as needed.

[0409] "Specialized artificial intelligence" refers to artificial intelligence algorithms and models that are designed and trained specifically for a particular field or application.

[0410] "A means of generating solutions through mutual cooperation" refers to a system in which multiple selected artificial intelligences work together to produce specific solutions that meet the user's needs.

[0411] "User terminal" refers to computer devices or equipment used by users to access the system and check the results.

[0412] The system of this invention is built to analyze user requests in real time and provide accurate solutions. Users input their requests in natural language via a terminal, and this data is immediately sent to the server. In this process, the terminal performs text input via a standard input device.

[0413] The server analyzes the received natural language information using generative AI models such as TensorFlow and PyTorch. This allows for a deep understanding of the user's requests and the extraction of necessary information. After information analysis, the server refers to a database of artificial intelligence agents registered in the system and selects the agent that best suits the request. For example, if there is a request for inventory management, it will select an AI for logistics analysis and an AI specifically for inventory management.

[0414] The selected artificial intelligence agents process data within their respective areas of expertise and generate the optimal solution. The resulting solution is then sent to the user's device in real time, allowing the user to quickly take appropriate action.

[0415] For example, if a user enters the prompt "I want to streamline inventory management," the server will select the appropriate agent and perform inventory data analysis and calculate the optimal order quantity. This functionality allows users to prevent inventory shortages and surpluses, enabling efficient management.

[0416] This system enables effective problem-solving in various business scenarios by dynamically combining diverse specialized artificial intelligences according to user needs.

[0417] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0418] Step 1:

[0419] Users enter their requests in natural language using their device. This input is done via the device's text input field and collected in the form of prompts. For example, a request such as "I want to streamline inventory management" might be entered. The entered data is immediately sent to the server.

[0420] Step 2:

[0421] The server analyzes the received natural language information using a generative AI model. Here, the server utilizes natural language processing techniques such as BERT and GPT. The input at this stage is a prompt sentence from the user, and the output identifies the intent and purpose necessary to fulfill the request. During this analysis process, sentence structure understanding and keyword extraction are performed.

[0422] Step 3:

[0423] Based on the analysis results, the server selects an appropriate artificial intelligence agent by referring to the agent database within the system. The input is the analyzed intent, and the output is a list of selected agents. In this process, the server identifies the optimal combination by considering the characteristics and history of each agent.

[0424] Step 4:

[0425] The selected artificial intelligence agents work together to generate solutions that meet the user's needs. The server facilitates information exchange between these agents and applies data analysis and predictive algorithms. The input consists of the selected agents and the analyzed needs, while the output is a specific solution. For example, this might involve a logistics analysis AI analyzing delivery patterns and an inventory management AI calculating order quantities.

[0426] Step 5:

[0427] The server sends the generated solution to the user's terminal. The user can then view the solution on their terminal and take the necessary actions. The input is the solution, and the output is the information displayed to the user. As a result, the user can take immediate action.

[0428] (Application Example 1)

[0429] 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."

[0430] In today's world, where information processing systems are needed to respond immediately to diverse user needs, there is a lack of mechanisms to provide solutions quickly and accurately. This invention aims to provide an information processing system that accurately processes user requests and efficiently combines various specialized machine learning modules to quickly present solutions.

[0431] 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.

[0432] In this invention, the server includes means for analyzing natural language information input by the user and identifying the user's request; means for selecting an appropriate processor from among multiple specialized machine learning modules based on the analysis results; means for coordinating the selected processors to provide a solution to the user; and means for dynamically selecting a processor according to the user's request and quickly providing the results of the operation. This makes it possible to quickly analyze the information the user requests and solve the problem with the optimal combination.

[0433] "Natural language information" refers to data entered by users in the form of language they use in everyday life, which is then analyzed for interpretation and processing by computer systems.

[0434] A "specialized machine learning module" is a machine learning execution unit designed specifically for a particular field or purpose, and is used for advanced data analysis and decision-making.

[0435] A "processor" is a computing device that is responsible for processing data and performing calculations and control, and it has a wide range of functions, including specialized machine learning modules.

[0436] "Collaboration" refers to a mechanism designed to enable multiple systems or modules to cooperate with each other and function efficiently.

[0437] "Operational results" refer to the output obtained after a processor or module performs analysis and calculations based on input data, and include the solutions requested by the user.

[0438] An "information processing system" is a combination of hardware and software that receives input data, processes and analyzes it in an appropriate manner, and provides useful information to the user.

[0439] The system for realizing this invention consists of a user terminal, a server, and a connected specialized machine learning module. The user inputs a request in natural language using the terminal. This natural language information is transmitted to the server via the network. The server analyzes the input data using natural language processing software to accurately identify the user's request. A general-purpose natural language processing library is used for the analysis.

[0440] The server then selects highly relevant, specialized machine learning modules based on the analysis results. These modules are chosen based on their past usage history and functions stored in the database, and they work together in conjunction with each other. The selected modules utilize the processor to quickly generate the information the user requests. The final output is sent to the user's terminal, allowing the user to obtain the solution they were looking for.

[0441] As a concrete example, consider a usage scenario where a user is using an electronic payment application. When the user enters "I want to check this month's expenses" as a prompt, the server selects and integrates the expense analysis module and budget management module, and presents a detailed expense report and savings management on the terminal. This allows the user to obtain the desired information quickly and effectively.

[0442] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0443] Step 1:

[0444] The user enters the request in natural language using a terminal. This entered prompt text is treated as the system's initial data.

[0445] Step 2:

[0446] The terminal sends natural language data entered by the user to the server. Here, input is the prompt text, and output is the data transfer to the server.

[0447] Step 3:

[0448] The server analyzes the received prompt message using natural language processing techniques. The input is the forwarded prompt message, and the output is data that identifies the user's request. Specifically, the analysis algorithm extracts the user's intent and categorizes the request.

[0449] Step 4:

[0450] The server selects the appropriate processor from several possible specialized machine learning modules based on the analysis results. The input is the analysis data, and the output is a list of the selected processors. The selection criteria operate based on the module's past usage history and capabilities.

[0451] Step 5:

[0452] The selected processors work together on the server to process data and generate solutions based on user requirements. The input is the instructions for the processor, and the output is the generated solution. The processor performs data calculations to produce accurate output.

[0453] Step 6:

[0454] The generated solution is sent from the server to the terminal and presented to the user. Here, the input is the generated solution, and the output is the information displayed on the terminal. Specific actions include the display of information in visual graphs and list formats.

[0455] 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.

[0456] This invention is a user-centered artificial intelligence system that combines an emotion engine to analyze the user's emotional state and provide appropriate AI solutions. The system consists of the user's terminal, a server, and a group of specialized artificial intelligence agents equipped with the emotion engine.

[0457] Users input their requests and inquiries in natural language via their terminals. The server receives this input information in real time and analyzes it using a natural language processing engine. This analysis identifies the user's main requests, and the emotion engine identifies the user's underlying emotional state. The emotion engine evaluates the emotional nuances of the input text and extracts information such as whether the user is stressed or satisfied.

[0458] The server selects the most suitable specialized artificial intelligence agent from the database based on the analyzed user's requests and emotions. During the selection process, it considers the cooperation between agents to form a group of agents best suited to the user's situation. If necessary, it utilizes the results of the emotion engine to incorporate additional supplementary agents, providing effective solutions that address the user's emotional state.

[0459] For example, if a user enters a request such as "I want to know how to reduce stress caused by project delays," the server analyzes this request and uses an emotion engine to identify that the user is experiencing stress. Then, it selects and integrates an AI agent specializing in stress management and another AI agent specializing in project management. This combination allows the user to receive effective stress reduction measures and project management techniques in real time, enabling smoother problem-solving.

[0460] Thus, the present invention improves convenience and effectiveness by providing an optimal AI solution based on user requests and emotions.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] The user inputs the problem they want to solve and the type of support they require using natural language via their device. This input data is immediately sent to the server.

[0464] Step 2:

[0465] The server analyzes the received user input using a natural language processing engine. The purpose of this analysis is to identify the user's main requests and extract relevant keywords and phrases.

[0466] Step 3:

[0467] Based on the extracted information, the server uses an emotion engine to identify the user's emotional state. This process involves analyzing emotional nuances from the vocabulary and context in the text to identify emotions such as "anxiety" or "satisfaction."

[0468] Step 4:

[0469] Based on the analysis results, the server selects the appropriate specialized artificial intelligence agent from the database. Here, the agent best suited to the user's needs and emotional state is chosen.

[0470] Step 5:

[0471] The server utilizes the results of the emotion engine as needed to form the optimal combination of agents. If additional supplementary agents are required, they will also be selected.

[0472] Step 6:

[0473] The server establishes coordination between selected agents and generates AI solutions while taking user emotions into consideration. This process involves data sharing and task coordination among agents.

[0474] Step 7:

[0475] The server delivers the constructed AI solution to the user's device. Through the device, the user can utilize this solution to obtain information and support to solve their own problems.

[0476] (Example 2)

[0477] 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."

[0478] Conventional artificial intelligence systems have the limitation of only being able to provide simple responses to user requests and being unable to solve complex problems that take into account the user's emotional state. Furthermore, there was a lack of effective means to select and integrate appropriate specialized information processing devices. As a result, it was difficult to provide users with the solutions they truly needed quickly and appropriately.

[0479] 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.

[0480] In this invention, the server includes means for analyzing natural language information input by the user and identifying requests; means for sentiment analysis to identify the user's emotional state from the analyzed information; and means for selecting an appropriate process from among multiple specialized information processing devices based on the sentiment analysis and request analysis results. This makes it possible to provide a comprehensive solution that takes the user's emotional state into consideration.

[0481] A "user" refers to an individual or organization that inputs natural language information into the system.

[0482] "Natural language information" refers to data and text expressed in human language.

[0483] "Analysis" refers to the process of interpreting input information and identifying its meaning and intent.

[0484] A "request" refers to a specific demand or wish that a user is seeking to resolve.

[0485] "Emotional analysis" refers to a technology that identifies a user's emotional state from the information they input.

[0486] "Emotional state" refers to the type and degree of emotions a user is experiencing.

[0487] A "specialized information processing device" refers to an information processing device that performs specialized processing in a specific field.

[0488] "Process selection" refers to the act of choosing the optimal information processing process based on the analysis results.

[0489] "Cooperation" refers to the collaborative operation of selected information processing devices.

[0490] "Solution" refers to the specific methods and support provided to address user requests or problems.

[0491] This invention relates to a system that analyzes a user's emotional state and provides an optimal AI solution based on that analysis. The system consists of a user's terminal, a server, and a specialized information processing unit equipped with emotion analysis capabilities.

[0492] Users input requests and inquiries in natural language using their devices. The input information is sent to a server, which analyzes the information using natural language processing engines such as Google Cloud Natural Language and IBM Watson to identify the user's requests. In addition to this analysis, sentiment analysis technology is used to evaluate the user's emotional nuances from the input text and identify their emotional state.

[0493] Based on the analyzed requests and emotional states, the server selects the most suitable specialized information processing device from the database. During this process, optimization is performed to ensure that multiple information processing devices can work together efficiently. The selected information processing device then provides the user with practical solutions, such as stress management or project management.

[0494] For example, if a user enters a request such as "I want to know how to reduce stress caused by project delays," the server analyzes this request and identifies that the user is experiencing stress. Then, it selects an information processing device specialized in stress management and another specialized in project management, and provides the user with effective stress reduction measures and project management methods.

[0495] Furthermore, an example of a prompt to be input to the generating AI model could be, "If a user is experiencing stress due to project delays, what combination of information processing devices would be effective?"

[0496] This system enables the provision of flexible and effective solutions tailored to user needs, which is expected to reduce stress and improve work efficiency.

[0497] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0498] Step 1:

[0499] Users input their requests and inquiries in natural language using their terminals. This input may include specific details, such as wanting to know how to reduce stress caused by project delays. The input data is sent from the terminal to the server in string format.

[0500] Step 2:

[0501] The server receives natural language text data sent by the user. To parse the received input data, the server runs a natural language processing engine such as Google Cloud Natural Language. At this stage, it identifies key keywords in the input and extracts the user's main requests. The output is a data object representing the requests.

[0502] Step 3:

[0503] The server uses a sentiment analysis engine to identify the user's emotional state based on the results of natural language processing. Data processing involves analyzing the emotional nuances of words and phrases in the text and outputting emotional indicators such as stress and satisfaction. The output is a data object representing the user's emotional state.

[0504] Step 4:

[0505] The server selects the most suitable specialized information processing device from the database based on the extracted request and emotional state data. During the selection process, it coordinates information processing devices related to the request and those appropriate for the emotional state. The output is a list of the selected information processing devices.

[0506] Step 5:

[0507] The server coordinates selected information processing devices and works together to provide users with appropriate solutions. Specifically, it generates concrete advice and methods regarding stress management and project management, and sends them to the user's terminal. The output is the content of the solutions provided to the user.

[0508] Step 6:

[0509] Users receive the solutions provided on their devices and provide feedback as needed. This feedback is sent to the server and used for further analysis and improvement. The output is feedback data.

[0510] (Application Example 2)

[0511] 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."

[0512] In modern urban life, individual citizens face a wide range of problems, and solving them requires an optimal approach based on emotions. However, conventional information systems do not take into account the emotional state of users, making it difficult to suggest the most appropriate services and facilities for them.

[0513] 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.

[0514] In this invention, the server includes means for analyzing natural language information input by the user and identifying the user's request; means for selecting an appropriate agent from among multiple specialized artificial intelligences based on the analysis results; means for coordinating the selected agents to provide a solution to the user; and means for suggesting public facilities and services that correspond to the user's emotional state. This enables the provision of optimal information and service suggestions based on the user's emotional state.

[0515] "Natural language information" refers to the everyday language used by users, and includes information such as the expressions of language used when humans communicate.

[0516] "Specialized artificial intelligence" refers to artificial intelligence technology that possesses functions specialized for a particular field or purpose, and is designed to efficiently solve problems in that field.

[0517] An "agent" is a unit of software that has a specific task or objective and acts autonomously to accomplish it.

[0518] "Emotional state" refers to the user's mental and psychological state, and includes indicators such as stress and satisfaction analyzed by the emotion engine.

[0519] "Public facilities and services" include infrastructure and service operations such as roads, public transportation, public libraries, and parks that are accessible to users.

[0520] To realize the system of this invention, a user terminal, a server, and a group of specialized artificial intelligence agents equipped with an emotion engine are required. First, the user inputs their complaints and worries in natural language through a terminal such as a smartphone. This natural language information is sent to the server.

[0521] The server receives the input natural language information and performs analysis using natural language processing software, including an emotion engine (such as Google Cloud Natural Language API or IBM Watson). This identifies the user's main requests and emotional state. The emotion engine evaluates the user's emotional nuances and extracts emotional indicators such as stress and satisfaction.

[0522] Subsequently, the server selects the most suitable specialized artificial intelligence agent from the database based on the analysis results. These agents may specialize in areas such as traffic information or public facilities, and they work together to provide the optimal solution according to the user's situation. For example, if a user is stressed by city noise, the server will collaborate with an agent that suggests a quieter route to achieve this.

[0523] Ultimately, appropriate information about public facilities and services tailored to the user's emotional state is fed back to the user's device. This enables users to solve problems in their daily lives in an optimal way based on their emotions.

[0524] For example, when a user inputs "City noise has been stressing me out while working recently" into the app, the system analyzes this information and suggests solutions such as a quiet cafe or an online white noise service. An example of a prompt to the generating AI model would be: "Please analyze the user's input, 'City noise has been stressing me out while working recently.' Please suggest corresponding stress reduction measures. Please also consider available city facilities and services."

[0525] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0526] Step 1:

[0527] Users input their complaints and concerns in natural language through their device. This input data is captured by the application on the device and sent to the server. The input consists of text information including the user's worries, and the output is the transmission of data to the server.

[0528] Step 2:

[0529] The server receives the natural language information and analyzes this text using a natural language processing engine. Specifically, it extracts emotional indicators (e.g., stress and satisfaction) from the text using an emotion engine. The input is natural language text, and the output is data indicating the user's requests and emotional state.

[0530] Step 3:

[0531] The server selects the most suitable specialized artificial intelligence agent from the database based on the analysis results. This selection process includes expanding the range of agents with knowledge related to emotional states and requests. The input is the analyzed emotional state and user request data, and the output is a list of appropriate agents.

[0532] Step 4:

[0533] The server coordinates the selected agents to generate the optimal solution for the user. In this process, multiple agents work together to form the solution. The input is the selected group of agents, and the output is the solution provided to the user.

[0534] Step 5:

[0535] The server selects appropriate public facility and service information based on the user's emotional state and sends it to the terminal. This step includes filtering the information and evaluating its relevance. The input is the formed solution and related information, and the output is the customized information displayed on the user's terminal.

[0536] Step 6:

[0537] The user views the information received on their device and initiates the next action. For example, this might involve visiting a suggested quiet cafe, which helps the user relieve stress. The input for this step is the received solution information, and the output is the user's next action.

[0538] 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.

[0539] 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.

[0540] 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.

[0541] [Fourth Embodiment]

[0542] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0543] 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.

[0544] 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).

[0545] 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.

[0546] 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.

[0547] 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).

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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.

[0554] 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".

[0555] This invention is a system that utilizes diverse specialized artificial intelligence agents to quickly provide solutions that users require. The system consists of a user terminal, a server, and a group of connected artificial intelligence agents. The user inputs their requests in natural language using the terminal. This information is transmitted to the server in real time. The server analyzes this information using advanced natural language processing techniques. The purpose of this analysis is to identify the intent and necessary functions contained in the user's requests.

[0556] Once the analysis is complete, the server selects an appropriate artificial intelligence agent based on the analysis results. This selection is made by referring to a database of agents maintained within the system. The database stores the characteristics and usage history of each agent and is used as a criterion for selecting the optimal agent. The selected agents work together to generate solutions that meet the user's needs.

[0557] The AI ​​solution generated in this way is delivered to the user's device. Through this solution, the user can quickly obtain a means to solve problems. For example, if there is a request to streamline inventory management, the server selects and integrates an AI agent for logistics analysis and an AI agent dedicated to inventory management. This combination allows the user to obtain inventory movements and optimal order quantities in real time and perform appropriate inventory management. This process significantly reduces the user's workload and enables more intuitive and effective problem solving.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The user uses their device to input the problem they want to solve or the features they want in natural language. This input is sent to the server in real time.

[0561] Step 2:

[0562] The server analyzes the received user input using a natural language processing engine. The analysis extracts keywords from the text and constructs data to understand the user's intent.

[0563] Step 3:

[0564] The server searches the agent database within the system based on the analysis results. It refers to the characteristics and past usage history of each agent and lists the agent that best suits the user's needs.

[0565] Step 4:

[0566] The server determines the optimal combination of agents. Here, an AI model is used to perform optimization, taking into account past performance data and conditions based on the user's current situation.

[0567] Step 5:

[0568] If necessary, the server determines which additional agents should be added. This enhances the performance of the selected agents and adjusts them to provide a more effective solution for the user.

[0569] Step 6:

[0570] The server establishes coordination between selected agents and sets up workflows for executing AI solutions. It also manages data flow and interfaces between agents.

[0571] Step 7:

[0572] The server delivers the built AI solution to the user's device. Users can then evaluate and use this solution to receive support in resolving their problems.

[0573] (Example 1)

[0574] 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".

[0575] Providing efficient and rapid solutions that meet the diverse needs of users presents a significant challenge. In particular, there is a need for a system that allows users without specialized knowledge to seamlessly utilize various advanced artificial intelligence technologies tailored to their specific requirements.

[0576] 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.

[0577] In this invention, the server includes means for receiving and analyzing natural language information input by a user in real time, means for selecting multiple specialized artificial intelligences from a database based on the requests identified by the analysis, and means for generating a solution that satisfies the user's requests by coordinating the selected artificial intelligences with each other. As a result, users can quickly receive the optimal solution that meets their needs, even without specialized knowledge.

[0578] A "user" is an entity that uses a system to input its own requests.

[0579] "Natural language information" refers to information and instructions written in the language that users use on a daily basis.

[0580] "Methods for receiving and analyzing in real time" refers to technologies that instantly receive natural language information sent by users and analyze it to understand its meaning and intent.

[0581] "Requests identified through analysis" refers to the results of extracting the specific needs and objectives that users actually desire during the natural language processing process.

[0582] A "database" is a collection of information structured in a format that allows for efficient storage and management, and quick retrieval of information as needed.

[0583] "Specialized artificial intelligence" refers to artificial intelligence algorithms and models that are designed and trained specifically for a particular field or application.

[0584] "A means of generating solutions through mutual cooperation" refers to a system in which multiple selected artificial intelligences work together to produce specific solutions that meet the user's needs.

[0585] "User terminal" refers to computer devices or equipment used by users to access the system and check the results.

[0586] The system of this invention is built to analyze user requests in real time and provide accurate solutions. Users input their requests in natural language via a terminal, and this data is immediately sent to the server. In this process, the terminal performs text input via a standard input device.

[0587] The server analyzes the received natural language information using generative AI models such as TensorFlow and PyTorch. This allows for a deep understanding of the user's requests and the extraction of necessary information. After information analysis, the server refers to a database of artificial intelligence agents registered in the system and selects the agent that best suits the request. For example, if there is a request for inventory management, it will select an AI for logistics analysis and an AI specifically for inventory management.

[0588] The selected artificial intelligence agents process data within their respective areas of expertise and generate the optimal solution. The resulting solution is then sent to the user's device in real time, allowing the user to quickly take appropriate action.

[0589] For example, if a user enters the prompt "I want to streamline inventory management," the server will select the appropriate agent and perform inventory data analysis and calculate the optimal order quantity. This functionality allows users to prevent inventory shortages and surpluses, enabling efficient management.

[0590] This system enables effective problem-solving in various business scenarios by dynamically combining diverse specialized artificial intelligences according to user needs.

[0591] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0592] Step 1:

[0593] Users enter their requests in natural language using their device. This input is done via the device's text input field and collected in the form of prompts. For example, a request such as "I want to streamline inventory management" might be entered. The entered data is immediately sent to the server.

[0594] Step 2:

[0595] The server analyzes the received natural language information using a generative AI model. Here, the server utilizes natural language processing techniques such as BERT and GPT. The input at this stage is a prompt sentence from the user, and the output identifies the intent and purpose necessary to fulfill the request. During this analysis process, sentence structure understanding and keyword extraction are performed.

[0596] Step 3:

[0597] Based on the analysis results, the server selects an appropriate artificial intelligence agent by referring to the agent database within the system. The input is the analyzed intent, and the output is a list of selected agents. In this process, the server identifies the optimal combination by considering the characteristics and history of each agent.

[0598] Step 4:

[0599] The selected artificial intelligence agents work together to generate solutions that meet the user's needs. The server facilitates information exchange between these agents and applies data analysis and predictive algorithms. The input consists of the selected agents and the analyzed needs, while the output is a specific solution. For example, this might involve a logistics analysis AI analyzing delivery patterns and an inventory management AI calculating order quantities.

[0600] Step 5:

[0601] The server sends the generated solution to the user's terminal. The user can then view the solution on their terminal and take the necessary actions. The input is the solution, and the output is the information displayed to the user. As a result, the user can take immediate action.

[0602] (Application Example 1)

[0603] 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".

[0604] In today's world, where information processing systems are needed to respond immediately to diverse user needs, there is a lack of mechanisms to provide solutions quickly and accurately. This invention aims to provide an information processing system that accurately processes user requests and efficiently combines various specialized machine learning modules to quickly present solutions.

[0605] 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.

[0606] In this invention, the server includes means for analyzing natural language information input by the user and identifying the user's request; means for selecting an appropriate processor from among multiple specialized machine learning modules based on the analysis results; means for coordinating the selected processors to provide a solution to the user; and means for dynamically selecting a processor according to the user's request and quickly providing the results of the operation. This makes it possible to quickly analyze the information the user requests and solve the problem with the optimal combination.

[0607] "Natural language information" refers to data entered by users in the form of language they use in everyday life, which is then analyzed for interpretation and processing by computer systems.

[0608] A "specialized machine learning module" is a machine learning execution unit designed specifically for a particular field or purpose, and is used for advanced data analysis and decision-making.

[0609] A "processor" is a computing device that is responsible for processing data and performing calculations and control, and it has a wide range of functions, including specialized machine learning modules.

[0610] "Collaboration" refers to a mechanism designed to enable multiple systems or modules to cooperate with each other and function efficiently.

[0611] "Operational results" refer to the output obtained after a processor or module performs analysis and calculations based on input data, and include the solutions requested by the user.

[0612] An "information processing system" is a combination of hardware and software that receives input data, processes and analyzes it in an appropriate manner, and provides useful information to the user.

[0613] The system for realizing this invention consists of a user terminal, a server, and a connected specialized machine learning module. The user inputs a request in natural language using the terminal. This natural language information is transmitted to the server via the network. The server analyzes the input data using natural language processing software to accurately identify the user's request. A general-purpose natural language processing library is used for the analysis.

[0614] The server then selects highly relevant, specialized machine learning modules based on the analysis results. These modules are chosen based on their past usage history and functions stored in the database, and they work together in conjunction with each other. The selected modules utilize the processor to quickly generate the information the user requests. The final output is sent to the user's terminal, allowing the user to obtain the solution they were looking for.

[0615] As a concrete example, consider a usage scenario where a user is using an electronic payment application. When the user enters "I want to check this month's expenses" as a prompt, the server selects and integrates the expense analysis module and budget management module, and presents a detailed expense report and savings management on the terminal. This allows the user to obtain the desired information quickly and effectively.

[0616] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0617] Step 1:

[0618] The user enters the request in natural language using a terminal. This entered prompt text is treated as the system's initial data.

[0619] Step 2:

[0620] The terminal sends natural language data entered by the user to the server. Here, input is the prompt text, and output is the data transfer to the server.

[0621] Step 3:

[0622] The server analyzes the received prompt message using natural language processing techniques. The input is the forwarded prompt message, and the output is data that identifies the user's request. Specifically, the analysis algorithm extracts the user's intent and categorizes the request.

[0623] Step 4:

[0624] The server selects the appropriate processor from several possible specialized machine learning modules based on the analysis results. The input is the analysis data, and the output is a list of the selected processors. The selection criteria operate based on the module's past usage history and capabilities.

[0625] Step 5:

[0626] The selected processors work together on the server to process data and generate solutions based on user requirements. The input is the instructions for the processor, and the output is the generated solution. The processor performs data calculations to produce accurate output.

[0627] Step 6:

[0628] The generated solution is sent from the server to the terminal and presented to the user. Here, the input is the generated solution, and the output is the information displayed on the terminal. Specific actions include the display of information in visual graphs and list formats.

[0629] 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.

[0630] This invention is a user-centered artificial intelligence system that combines an emotion engine to analyze the user's emotional state and provide appropriate AI solutions. The system consists of the user's terminal, a server, and a group of specialized artificial intelligence agents equipped with the emotion engine.

[0631] Users input their requests and inquiries in natural language via their terminals. The server receives this input information in real time and analyzes it using a natural language processing engine. This analysis identifies the user's main requests, and the emotion engine identifies the user's underlying emotional state. The emotion engine evaluates the emotional nuances of the input text and extracts information such as whether the user is stressed or satisfied.

[0632] The server selects the most suitable specialized artificial intelligence agent from the database based on the analyzed user's requests and emotions. During the selection process, it considers the cooperation between agents to form a group of agents best suited to the user's situation. If necessary, it utilizes the results of the emotion engine to incorporate additional supplementary agents, providing effective solutions that address the user's emotional state.

[0633] For example, if a user enters a request such as "I want to know how to reduce stress caused by project delays," the server analyzes this request and uses an emotion engine to identify that the user is experiencing stress. Then, it selects and integrates an AI agent specializing in stress management and another AI agent specializing in project management. This combination allows the user to receive effective stress reduction measures and project management techniques in real time, enabling smoother problem-solving.

[0634] Thus, the present invention improves convenience and effectiveness by providing an optimal AI solution based on user requests and emotions.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The user inputs the problem they want to solve and the type of support they require using natural language via their device. This input data is immediately sent to the server.

[0638] Step 2:

[0639] The server analyzes the received user input using a natural language processing engine. The purpose of this analysis is to identify the user's main requests and extract relevant keywords and phrases.

[0640] Step 3:

[0641] Based on the extracted information, the server uses an emotion engine to identify the user's emotional state. This process involves analyzing emotional nuances from the vocabulary and context in the text to identify emotions such as "anxiety" or "satisfaction."

[0642] Step 4:

[0643] Based on the analysis results, the server selects the appropriate specialized artificial intelligence agent from the database. Here, the agent best suited to the user's needs and emotional state is chosen.

[0644] Step 5:

[0645] The server utilizes the results of the emotion engine as needed to form the optimal combination of agents. If additional supplementary agents are required, they will also be selected.

[0646] Step 6:

[0647] The server establishes coordination between selected agents and generates AI solutions while taking user emotions into consideration. This process involves data sharing and task coordination among agents.

[0648] Step 7:

[0649] The server delivers the constructed AI solution to the user's device. Through the device, the user can utilize this solution to obtain information and support to solve their own problems.

[0650] (Example 2)

[0651] 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".

[0652] Conventional artificial intelligence systems have the limitation of only being able to provide simple responses to user requests and being unable to solve complex problems that take into account the user's emotional state. Furthermore, there was a lack of effective means to select and integrate appropriate specialized information processing devices. As a result, it was difficult to provide users with the solutions they truly needed quickly and appropriately.

[0653] 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.

[0654] In this invention, the server includes means for analyzing natural language information input by the user and identifying requests; means for sentiment analysis to identify the user's emotional state from the analyzed information; and means for selecting an appropriate process from among multiple specialized information processing devices based on the sentiment analysis and request analysis results. This makes it possible to provide a comprehensive solution that takes the user's emotional state into consideration.

[0655] A "user" refers to an individual or organization that inputs natural language information into the system.

[0656] "Natural language information" refers to data and text expressed in human language.

[0657] "Analysis" refers to the process of interpreting input information and identifying its meaning and intent.

[0658] A "request" refers to a specific demand or wish that a user is seeking to resolve.

[0659] "Emotional analysis" refers to a technology that identifies a user's emotional state from the information they input.

[0660] "Emotional state" refers to the type and degree of emotions a user is experiencing.

[0661] A "specialized information processing device" refers to an information processing device that performs specialized processing in a specific field.

[0662] "Process selection" refers to the act of choosing the optimal information processing process based on the analysis results.

[0663] "Cooperation" refers to the collaborative operation of selected information processing devices.

[0664] "Solution" refers to the specific methods and support provided to address user requests or problems.

[0665] This invention relates to a system that analyzes a user's emotional state and provides an optimal AI solution based on that analysis. The system consists of a user's terminal, a server, and a specialized information processing unit equipped with emotion analysis capabilities.

[0666] Users input requests and inquiries in natural language using their devices. The input information is sent to a server, which analyzes the information using natural language processing engines such as Google Cloud Natural Language and IBM Watson to identify the user's requests. In addition to this analysis, sentiment analysis technology is used to evaluate the user's emotional nuances from the input text and identify their emotional state.

[0667] Based on the analyzed requests and emotional states, the server selects the most suitable specialized information processing device from the database. During this process, optimization is performed to ensure that multiple information processing devices can work together efficiently. The selected information processing device then provides the user with practical solutions, such as stress management or project management.

[0668] For example, if a user enters a request such as "I want to know how to reduce stress caused by project delays," the server analyzes this request and identifies that the user is experiencing stress. Then, it selects an information processing device specialized in stress management and another specialized in project management, and provides the user with effective stress reduction measures and project management methods.

[0669] Furthermore, an example of a prompt to be input to the generating AI model could be, "If a user is experiencing stress due to project delays, what combination of information processing devices would be effective?"

[0670] This system enables the provision of flexible and effective solutions tailored to user needs, which is expected to reduce stress and improve work efficiency.

[0671] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0672] Step 1:

[0673] Users input their requests and inquiries in natural language using their terminals. This input may include specific details, such as wanting to know how to reduce stress caused by project delays. The input data is sent from the terminal to the server in string format.

[0674] Step 2:

[0675] The server receives natural language text data sent by the user. To parse the received input data, the server runs a natural language processing engine such as Google Cloud Natural Language. At this stage, it identifies key keywords in the input and extracts the user's main requests. The output is a data object representing the requests.

[0676] Step 3:

[0677] The server uses a sentiment analysis engine to identify the user's emotional state based on the results of natural language processing. Data processing involves analyzing the emotional nuances of words and phrases in the text and outputting emotional indicators such as stress and satisfaction. The output is a data object representing the user's emotional state.

[0678] Step 4:

[0679] The server selects the most suitable specialized information processing device from the database based on the extracted request and emotional state data. During the selection process, it coordinates information processing devices related to the request and those appropriate for the emotional state. The output is a list of the selected information processing devices.

[0680] Step 5:

[0681] The server coordinates selected information processing devices and works together to provide users with appropriate solutions. Specifically, it generates concrete advice and methods regarding stress management and project management, and sends them to the user's terminal. The output is the content of the solutions provided to the user.

[0682] Step 6:

[0683] Users receive the solutions provided on their devices and provide feedback as needed. This feedback is sent to the server and used for further analysis and improvement. The output is feedback data.

[0684] (Application Example 2)

[0685] 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".

[0686] In modern urban life, individual citizens face a wide range of problems, and solving them requires an optimal approach based on emotions. However, conventional information systems do not take into account the emotional state of users, making it difficult to suggest the most appropriate services and facilities for them.

[0687] 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.

[0688] In this invention, the server includes means for analyzing natural language information input by the user and identifying the user's request; means for selecting an appropriate agent from among multiple specialized artificial intelligences based on the analysis results; means for coordinating the selected agents to provide a solution to the user; and means for suggesting public facilities and services that correspond to the user's emotional state. This enables the provision of optimal information and service suggestions based on the user's emotional state.

[0689] "Natural language information" refers to the everyday language used by users, and includes information such as the expressions of language used when humans communicate.

[0690] "Specialized artificial intelligence" refers to artificial intelligence technology that possesses functions specialized for a particular field or purpose, and is designed to efficiently solve problems in that field.

[0691] An "agent" is a unit of software that has a specific task or objective and acts autonomously to accomplish it.

[0692] "Emotional state" refers to the user's mental and psychological state, and includes indicators such as stress and satisfaction analyzed by the emotion engine.

[0693] "Public facilities and services" include infrastructure and service operations such as roads, public transportation, public libraries, and parks that are accessible to users.

[0694] To realize the system of this invention, a user terminal, a server, and a group of specialized artificial intelligence agents equipped with an emotion engine are required. First, the user inputs their complaints and worries in natural language through a terminal such as a smartphone. This natural language information is sent to the server.

[0695] The server receives the input natural language information and performs analysis using natural language processing software, including an emotion engine (such as Google Cloud Natural Language API or IBM Watson). This identifies the user's main requests and emotional state. The emotion engine evaluates the user's emotional nuances and extracts emotional indicators such as stress and satisfaction.

[0696] Subsequently, the server selects the most suitable specialized artificial intelligence agent from the database based on the analysis results. These agents may specialize in areas such as traffic information or public facilities, and they work together to provide the optimal solution according to the user's situation. For example, if a user is stressed by city noise, the server will collaborate with an agent that suggests a quieter route to achieve this.

[0697] Ultimately, appropriate information about public facilities and services tailored to the user's emotional state is fed back to the user's device. This enables users to solve problems in their daily lives in an optimal way based on their emotions.

[0698] For example, when a user inputs "City noise has been stressing me out while working recently" into the app, the system analyzes this information and suggests solutions such as a quiet cafe or an online white noise service. An example of a prompt to the generating AI model would be: "Please analyze the user's input, 'City noise has been stressing me out while working recently.' Please suggest corresponding stress reduction measures. Please also consider available city facilities and services."

[0699] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0700] Step 1:

[0701] Users input their complaints and concerns in natural language through their device. This input data is captured by the application on the device and sent to the server. The input consists of text information including the user's worries, and the output is the transmission of data to the server.

[0702] Step 2:

[0703] The server receives the natural language information and analyzes this text using a natural language processing engine. Specifically, it extracts emotional indicators (e.g., stress and satisfaction) from the text using an emotion engine. The input is natural language text, and the output is data indicating the user's requests and emotional state.

[0704] Step 3:

[0705] The server selects the most suitable specialized artificial intelligence agent from the database based on the analysis results. This selection process includes expanding the range of agents with knowledge related to emotional states and requests. The input is the analyzed emotional state and user request data, and the output is a list of appropriate agents.

[0706] Step 4:

[0707] The server coordinates the selected agents to generate the optimal solution for the user. In this process, multiple agents work together to form the solution. The input is the selected group of agents, and the output is the solution provided to the user.

[0708] Step 5:

[0709] The server selects appropriate public facility and service information based on the user's emotional state and sends it to the terminal. This step includes filtering the information and evaluating its relevance. The input is the formed solution and related information, and the output is the customized information displayed on the user's terminal.

[0710] Step 6:

[0711] The user views the information received on their device and initiates the next action. For example, this might involve visiting a suggested quiet cafe, which helps the user relieve stress. The input for this step is the received solution information, and the output is the user's next action.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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."

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] The following is further disclosed regarding the embodiments described above.

[0734] (Claim 1)

[0735] A means of analyzing natural language information entered by the user to identify their requests,

[0736] A method for selecting the appropriate agent from among multiple specialized artificial intelligences based on the analysis results,

[0737] A means of coordinating selected agents to provide solutions to users,

[0738] A system that includes this.

[0739] (Claim 2)

[0740] The system according to claim 1, comprising means for implementing an optimal algorithm for combining selected specialized artificial intelligences.

[0741] (Claim 3)

[0742] The system according to claim 1, comprising means for supplementing additional artificial intelligence as needed.

[0743] "Example 1"

[0744] (Claim 1)

[0745] A means of receiving and analyzing natural language information input by users in real time,

[0746] A means of selecting multiple specialized artificial intelligences from a database based on the requirements identified through analysis,

[0747] A means of generating solutions that satisfy user needs by having selected artificial intelligences work together,

[0748] A means of sending the generated solution to the user's device,

[0749] A system that includes this.

[0750] (Claim 2)

[0751] The system according to claim 1, comprising means for implementing an optimal processing algorithm for combining selected specialized artificial intelligences.

[0752] (Claim 3)

[0753] The system according to claim 1, further comprising means for supplementing additional artificial intelligence as needed and optimizing the selection process.

[0754] "Application Example 1"

[0755] (Claim 1)

[0756] A means of analyzing natural language information entered by the user to identify their requests,

[0757] A means of selecting an appropriate processor from among multiple specialized machine learning modules based on the analysis results,

[0758] A means of coordinating selected processors to provide solutions to users,

[0759] A means of dynamically selecting a processor in response to user requests and quickly providing operational results,

[0760] A system that includes this.

[0761] (Claim 2)

[0762] The system according to claim 1, comprising means for implementing an optimal procedure for combining selected specialized machine learning modules.

[0763] (Claim 3)

[0764] The system according to claim 1, comprising means for supplementing additional machine learning models as needed.

[0765] "Example 2 of combining an emotion engine"

[0766] (Claim 1)

[0767] A means of analyzing natural language information entered by the user to identify their requests,

[0768] An emotion analysis method for identifying the user's emotional state from the analyzed information,

[0769] A means for selecting an appropriate process from among multiple specialized information processing devices based on the results of emotion analysis and desire analysis,

[0770] A means of providing solutions to users by linking selected information processing devices,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, comprising means for implementing optimization processing for combining selected specialized information processing devices.

[0774] (Claim 3)

[0775] The system according to claim 1, further comprising means for supplementing additional information processing devices as needed.

[0776] "Application example 2 when combining with an emotional engine"

[0777] (Claim 1)

[0778] A means of analyzing natural language information entered by the user to identify their requests,

[0779] A method for selecting the appropriate agent from among multiple specialized artificial intelligences based on the analysis results,

[0780] A means of coordinating selected agents to provide solutions to users,

[0781] A means of proposing public facilities and services that respond to emotional states,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, comprising means for implementing an optimal algorithm for combining selected specialized artificial intelligences and means for implementing an algorithm for optimizing user support based on emotional state.

[0785] (Claim 3)

[0786] The system according to claim 1, further comprising means for supplementing additional artificial intelligence as needed and means for providing public transport information based on sentiment analysis results. [Explanation of symbols]

[0787] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing natural language information entered by the user to identify their requests, A method for selecting the appropriate agent from among multiple specialized artificial intelligences based on the analysis results, A means of coordinating selected agents to provide solutions to users, A system that includes this.

2. The system according to claim 1, comprising means for implementing an optimal algorithm for combining selected specialized artificial intelligences.

3. The system according to claim 1, further comprising means for supplementing additional artificial intelligence as needed.

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