system

Abstract

We provide the system. [Solution] A means of inputting the task in natural language, A means of analyzing the input problem and extracting relevant technical requirements, A means of selecting and combining multiple specialized artificial intelligence systems located throughout the country based on extracted technical requirements, Means to complement the necessary functions for the selected artificial intelligence, A means of automatically generating applications using complementary artificial intelligence, A means of distributing the generated application to user terminals, 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 persona chatbot control method performed by at least one processor, the method 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] With the emergence of various region-specific AIs, business-specific AIs, and application-specific AIs, there is a problem that users do not immediately know which AI to select and combine, and how to customize it to solve their specific problems. Due to this problem, users may waste time and may not be able to find an optimal solution.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides an interface that allows users to input problems in natural language, and a natural language processing means that analyzes the input text and extracts relevant technical requirements. Furthermore, the system has the function to select multiple specialized AIs located throughout the country based on the extracted technical requirements and combine them. It also provides means to supplement the selected AIs with essential functions, and by automatically generating an application using the results and distributing it to the user's terminal, it can provide a rapid and optimal solution to the user's problem.

[0006] "Means for inputting issues in natural language" refers to an interface that allows users to communicate their problems and requests to the system using everyday language.

[0007] "Means for analyzing a problem and extracting relevant technical requirements" refers to the process of analyzing input natural language text and identifying the technical elements necessary for solving the problem based on that analysis.

[0008] "A method for selecting and combining multiple specialized artificial intelligence systems located throughout the country" refers to a method of selecting multiple AI systems specialized for specific regions, applications, or tasks based on analysis results, and integrating them to function as a single solution.

[0009] "Means to complement necessary functions" refers to a mechanism for adding new functions to compensate for the lack of features or performance in existing AI solutions.

[0010] "Methods for automatically generating applications" refers to the process of generating program code based on necessary AI solutions and complementary functions, and then preparing it in a form that can be used by users.

[0011] "Means of distribution to user terminals" refers to methods of sending the generated application to an electronic device owned by the user and making it available for use. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the 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.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system for providing AI solutions that optimally combine specialized artificial intelligence necessary for users to solve specific problems. This system consists of a user, a terminal, and a server, each performing a specific role.

[0034] User interface

[0035] Users utilize an on-device interface for inputting tasks in natural language. This interface is intuitive, allowing users to access the system and communicate their requests without requiring specialized knowledge.

[0036] Server Processing

[0037] When the server receives a natural language task submitted by a user, it performs an analysis using natural language processing (NLP) technology internally. Technical requirements are extracted from the analysis results, and based on this information, the server searches for multiple specialized AIs nationwide and selects the optimal combination. After selection, the server adds components and programs that complement the functions of the selected AI as needed. This creates an AI solution that completely solves the user's problem.

[0038] Application generation and distribution

[0039] The server automatically generates applications based on the combined AI solutions. These applications are customized according to the user's terminal environment and quickly prepared for execution. The generated applications are sent from the server to the user's terminal, where they are installed and initially configured.

[0040] Specific example

[0041] As an example, consider a case where a user inputs a problem such as "I want to predict sales for local retail stores and suggest efficient product placement." In this case, the server selects a "sales forecasting AI" and a "product placement optimization AI," combines them, and adds a function to collect data tailored to the purchasing trends and climate conditions of the specific region. By distributing the application generated in this way to the user's terminal and making it available for real-time use on-site, the user can improve the efficiency of store operations.

[0042] This system allows users to access quick and appropriate AI solutions, significantly reducing the time and effort required to select and combine individual, specialized AIs.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uses the terminal's input interface to enter the problem they want to solve in natural language. For example, they might enter a specific problem such as "I want to improve logistics efficiency."

[0046] Step 2:

[0047] The terminal sends the task entered by the user to the server. During this process, the task data is transmitted using a secure and efficient communication protocol.

[0048] Step 3:

[0049] The server analyzes the received problem data using a natural language processing (NLP) engine. This analysis extracts keywords such as "logistics optimization" and "efficiency improvement" as technical requirements necessary to solve the problem.

[0050] Step 4:

[0051] Based on the extracted technical requirements, the server queries a database of specialized AIs located throughout the country and lists relevant AIs. For example, "Delivery Route Optimization AI" and "Demand Forecasting AI" might be selected.

[0052] Step 5:

[0053] The server applies combination patterns to the selected specialized AI. During this process, it may customize the functionality to meet the specific needs of a particular region or client, thereby enhancing the completeness of the solution.

[0054] Step 6:

[0055] The server automatically generates platform-specific executable applications based on these AI solutions. Code generation and build processes are performed internally to create the desired application.

[0056] Step 7:

[0057] The server sends the generated application to the terminal. The terminal receives the application and starts the installation process. After the installation is complete, the user is presented with the application's initial setup screen.

[0058] Step 8:

[0059] The user enters the necessary information on the initial setup screen displayed on the device, making the application ready for use. This allows the user to quickly utilize the AI ​​solutions provided by the system.

[0060] (Example 1)

[0061] 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."

[0062] The challenge lies in rapidly providing individually optimized artificial intelligence solutions for the diverse challenges of today, particularly the need to ensure that users can effectively utilize these solutions without requiring specialized knowledge. Furthermore, there is a demand for combining artificial intelligence technologies from different domains to provide comprehensive solutions to complex problems.

[0063] 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.

[0064] In this invention, the server includes means for inputting a problem in natural language, means for analyzing the input problem and extracting relevant technical requirements, and means for selecting and combining multiple specialized artificial intelligences based on the extracted technical requirements. This makes it possible for users to quickly utilize the optimal solution using specialized artificial intelligence, even without specialized knowledge.

[0065] The term "issue" refers to a specific problem or requirement that needs to be solved.

[0066] "Natural language" refers to the language that humans use on a daily basis, and is in contrast to computer program code, for example.

[0067] "Natural language processing technology" is the technology that enables computers to understand and process human language.

[0068] "Technical requirements" refer to the technical conditions and specifications necessary to solve a particular problem.

[0069] "Specialized artificial intelligence" refers to artificial intelligence optimized for specific tasks, exhibiting higher performance in particular areas than general tasks.

[0070] A "data collection function" is a function that collects information necessary to solve a problem.

[0071] An "application" is a computer program developed to provide a specific function.

[0072] A "secure connection" is a connection method that ensures data security during communication.

[0073] "Geographical characteristics" refer to the physical and cultural features unique to a given region.

[0074] "Market trends" refer to the general trends in consumer behavior and demand within a particular market.

[0075] This invention is a system designed to allow users to easily utilize the latest artificial intelligence technology to solve specific problems. Users can input problems in natural language via an interface on their terminal, and this interface is designed to be intuitive to use. This system is implemented using a cloud server and user terminals, and is responsible for the data processing that is crucial for providing artificial intelligence solutions.

[0076] When the server receives a task submitted by a user, it analyzes its content using natural language processing (NLP) technology. Specifically, it uses open-source NLP libraries and a dedicated AI platform to accurately extract relevant technical requirements from the task. This clarifies the type and functionality of the required artificial intelligence, laying the foundation for selecting a specialized AI in the next step.

[0077] The server searches a nationwide database for the most suitable specialized artificial intelligence based on the analysis results and determines the optimal combination. After selection, it builds an AI solution that fully addresses the user's challenges by adding data collection functions and complementary programs as needed.

[0078] The generated AI solution is transformed into a customized application adapted to the user's device environment. The server then sends this application to the user's device using a secure communication method, and installation and initial setup are performed automatically or according to user instructions.

[0079] As a concrete example, consider a scenario where a user inputs a prompt into the system such as, "I want to predict sales for local retail stores and suggest efficient product placement." In this case, the server selects an AI specialized in sales forecasting and another AI for optimizing product placement, combines them, and automatically generates an application by adding functions to collect region-specific purchasing trends and weather data. Through this process, the user can utilize a highly customized AI solution to manage their store operations more efficiently.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user uses an interface on their device to input the problem they want to solve in natural language. Specifically, the user sends the problem as text data to the system using a keyboard or voice input device. This input serves as the basic data for the server to perform natural language processing.

[0083] Step 2:

[0084] The server analyzes text data received from the user using natural language processing (NLP) techniques. It receives a problem text as input and extracts technical requirements through analysis. Specifically, an NLP engine is used to extract keywords and analyze their structure, generating a list of technical requirements as output.

[0085] Step 3:

[0086] The server selects multiple specialized artificial intelligences based on the extracted technical requirements. It receives a list of technical requirements as input and searches the database for the most suitable AI module. This process selects and combines AI modules that match the conditions, determining the optimal combination of artificial intelligences as output.

[0087] Step 4:

[0088] The server adds components and programs to the selected specialized AI modules to complement their necessary functions. It receives a combination of AI modules as input and programs additional functions to address specific challenges. The output is a program with the functionality of a complete AI solution.

[0089] Step 5:

[0090] The server automatically generates customized applications based on the AI ​​solution it has built, tailored to the user's terminal environment. It receives a complete AI program as input, transforms the program while considering the terminal's specifications and constraints, and creates an executable application package as output.

[0091] Step 6:

[0092] The server sends the generated application to the user's terminal via a secure connection for installation and initial setup. The server takes the application package as input and sends it using a communication protocol. The terminal receives this data, runs the installation script to configure the environment, and builds a usable application as output.

[0093] Step 7:

[0094] Users operate the application on their device and solve problems based on real-time data. They provide user instructions and additional data as input, which the application processes to output optimal suggestions and predictions. Using these results, users can make efficient decisions.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern society, retail store and brick-and-mortar store operators face challenges related to sales forecasting and optimizing product placement. Solving these challenges requires significant time and specialized knowledge, making it difficult to obtain quick and appropriate solutions. This invention aims to provide a method for efficiently and automatically resolving these operational challenges.

[0098] 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.

[0099] In this invention, the server includes means for inputting a problem in natural language, means for analyzing the input problem and extracting relevant technical requirements, means for selecting and combining multiple specialized information processing devices located in the region based on the extracted technical requirements, means for supplementing the selected information processing devices with necessary functions, means for automatically generating user software using the supplemented information processing devices, and means for distributing the generated user software to user devices and providing support functions for predicting trends and optimizing product placement in the regional sales network. As a result, store managers can quickly perform sales forecasting and optimize product placement without requiring specialized knowledge.

[0100] "Natural language" refers to the language that humans use in everyday life, and is the format necessary for computers to understand and analyze it.

[0101] "Technical requirements" are the technical conditions and specifications necessary to solve a problem.

[0102] A "specialized information processing system" is a data processing system optimized for specific functions or tasks.

[0103] "Software used" refers to a group of programs installed on a computer to perform specific actions or functions.

[0104] "User equipment" refers to all electronic devices that users use on a daily basis, including smartphones and personal computers.

[0105] A "sales network" is a collection of routes and channels used in the process of getting a product from the manufacturer to the consumer.

[0106] "Trend forecasting" refers to making predictions in advance about future events or market movements.

[0107] "Optimizing product placement" is a technique aimed at improving sales and customer satisfaction by improving how products are displayed within a store.

[0108] The system for realizing this invention consists of a user terminal, a server, and a specialized information processing device. The user inputs the task in natural language using a terminal such as a smartphone. For example, a prompt such as, "Tell me which product is predicted to sell the most during the next sale period." This information is transmitted to the server via the internet.

[0109] The server utilizes NLP (Neuro-Language Programming) on ​​a high-performance computer to analyze the received natural language processing tasks. This analysis uses machine learning libraries such as TENSORFLOW® and PyTorch to extract technical requirements. Based on these extracted technical requirements, cloud-based computing resources are used to select and combine the most suitable specialized AI according to regional characteristics.

[0110] The server builds programs that complement the functions of the selected information processing devices as needed, and generates the software to be used. This software is intended to optimize trend forecasting and product placement within the regional sales network.

[0111] The generated software is delivered to the user's terminal via the internet and installed on the terminal. Using this software, users can instantly obtain information to support data analysis and decision-making in store operations. As a concrete example of how the generated software can suggest optimized in-store layouts and actually increase sales, one bookstore can input "Please tell me the best-selling book genre next weekend" and receive product placement suggestions optimized for that region and time.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] Users open the application on a device such as a smartphone or tablet and input a question using natural language. An example of an input question is, "Tell me what type of product will sell the most in the next campaign." This input is immediately sent to the server.

[0115] Step 2:

[0116] The server analyzes the received natural language task using NLP (Neuro-Language Programming) techniques. This analysis is performed using frameworks such as TensorFlow, and extracts technical requirements from the task. The input is the user's task statement, and the output of the analysis is a list of technical requirements. For example, the need for sales forecasting and placement suggestions might be extracted.

[0117] Step 3:

[0118] The server selects a specialized AI model tailored to the regional characteristics based on the extracted technical requirements. This involves selecting multiple models, such as predictive AI and optimization AI. The input for this step is a list of technical requirements, and the output is the selected AI models.

[0119] Step 4:

[0120] The server adds necessary complementary functions to the selected AI models and generates customized software for use. Examples of complementary functions include a more accurate prediction module using local sales data. The input is the selected AI models, and the output is the final software module.

[0121] Step 5:

[0122] The server delivers the generated software to the user's terminal, making it available for real-time use. The input for this step is customized software, and the output is an application that is installed and ready to run on the user's terminal.

[0123] Step 6:

[0124] Users can use the delivered software to receive real-time sales forecasts and product placement suggestions, enabling them to make decisions to improve store operations. For example, it can predict best-selling book genres over the weekend and incorporate that information into actual sales strategies.

[0125] 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.

[0126] This invention is a system that combines specialized AI to solve problems entered by the user, and further recognizes the user's emotional state to select the appropriate AI and customize the application. The user, terminal, and server each perform specific functions, thereby providing a solution that meets the user's needs.

[0127] User Interface and Emotion Recognition

[0128] Users input their challenges in natural language using an interface provided on their device. This interface incorporates an emotion engine to detect the user's emotions and analyze the emotional elements included in the input. For example, if a user says, "I urgently need to improve logistics efficiency," the emotion engine recognizes from the keyword "urgent" that the user is feeling anxious.

[0129] Server processing and AI selection

[0130] Upon receiving user issues and emotional information, the server uses natural language processing (NLP) techniques to analyze the issues and extract technical requirements. Based on the emotional information, it adjusts the priority of selected AIs and selects relevant specialized AIs, taking into account factors such as urgency. In this case, "logistics optimization AI" and "real-time monitoring AI" may be selected.

[0131] AI combinations and functional complementarity

[0132] When combining selected specialized AIs, the server complements the necessary functions based on the user's emotional state. For example, for users who are feeling anxious, it is designed to reduce stress by adding rich dashboards and timely alert functions.

[0133] Application generation and customization

[0134] The server automatically generates an executable application based on the final AI solution. This application's interface and functionality are customized based on information obtained through the emotion engine. For example, it might include relaxing color tones and audio feedback for the user.

[0135] Distribution and Initial Setup

[0136] The generated application is sent from the server to the user's terminal and then installed on the terminal. The user can then perform initial setup and begin using the application optimized for their emotional state.

[0137] In this way, the present invention provides an AI solution that takes into account the user's technical requirements and emotions, enabling a more personalized user experience.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user inputs their task in natural language through the device's interface. At the same time, an emotion engine operates, analyzing the user's facial expressions and tone of voice to detect their emotional state.

[0141] Step 2:

[0142] The device sends the entered task and detected emotion data to the server. This includes text data and emotion information obtained through speech recognition and image analysis.

[0143] Step 3:

[0144] The server passes the received data to a natural language processing (NLP) engine, which analyzes the content of the problem and extracts the technical requirements. In doing so, it takes emotional information into consideration to determine which requirements should be prioritized.

[0145] Step 4:

[0146] The server references a nationwide database of specialized AIs based on technical requirements and selects the most relevant AI. Using emotional information, for example, if urgency is indicated, it prioritizes selecting an AI capable of responding quickly.

[0147] Step 5:

[0148] The server adds necessary supplementary functions based on the selected AI. For example, if the user is feeling anxious, it adds infographics or explanatory content to reassure them.

[0149] Step 6:

[0150] The server automatically generates applications based on the completed AI solution, tailored to the user's emotional state. These applications feature customized interfaces and notification methods based on the user's emotions.

[0151] Step 7:

[0152] The generated application is sent from the server to the terminal and automatically installed on the terminal. After installation, an interface opens, guiding the user through the initial setup process.

[0153] Step 8:

[0154] The user enters the necessary information by following the setup prompts and begins using the application. Application operation is supported by continuously monitoring the user's emotional state and adaptively optimizing the interface.

[0155] (Example 2)

[0156] 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".

[0157] In today's complex social environment, users face a wide range of challenges, and their urgency and importance depend heavily on their emotional state. However, conventional systems have struggled to provide efficient solutions that take users' emotional states into account. In particular, generating personalized application programs that reflect emotional information has been technically complex.

[0158] 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.

[0159] In this invention, the server includes means for inputting a problem in descriptive format, means for analyzing the input problem and extracting relevant requirements, and means for analyzing emotional elements included in the input from individual devices. This enables the provision of rapid and personalized solutions that take into account the user's emotional state.

[0160] A "problem" refers to a specific issue or request that a user needs to resolve.

[0161] "Descriptive format" refers to a text-based input method using natural language.

[0162] "Requirements" refer to the abstract or specific conditions or specifications necessary to solve a problem.

[0163] An "intelligent machine" refers to a system or tool that possesses artificial intelligence technology designed to address a specific problem.

[0164] "Individual device" refers to an independent device used directly by a specific user.

[0165] "Emotional elements" refer to the expression of emotions and feelings included in the user's input.

[0166] An "application program" refers to software that has a specific function or role and can be executed by a user.

[0167] "Emotional information" refers to data and analysis results related to users' emotions and feelings.

[0168] "Personalization" refers to a state that is customized according to the specific needs and emotional state of each individual user.

[0169] The present invention provides an optimal artificial intelligence solution to a problem that a user wishes to solve. The user inputs the problem in natural language using a terminal. The terminal is equipped with an emotion engine that analyzes emotional elements from the input.

[0170] Task and emotional information transmitted from the terminal are received by the server. The server analyzes the input task using natural language processing techniques and extracts relevant requirements. Common natural language processing tools and libraries are used for this process. The analyzed information is used to select the most suitable intelligent machine from among multiple intelligent machines stored in the server's database.

[0171] In selecting intelligent machines, priority is given to those that match the extracted requirements, and the priority is adjusted based on the user's emotional information. For example, if a user enters the prompt "I want to increase sales by 20% right now," the server can prioritize selecting intelligent machines such as "Sales Forecasting AI" or "Marketing Optimization AI."

[0172] Furthermore, the server combines selected intelligent machines, complements functions according to the user's emotional state, and automatically generates application programs. These application programs are distributed to individual devices, and users can utilize customized programs tailored to their needs by performing initial setup. Throughout this entire process, emotional information-based color tones and interface designs are reflected in the applications, resulting in a more personalized experience.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The user inputs a task into the device. The input is in natural language, and the device's built-in emotion engine analyzes this input in real time. The input describes the specific problem or request the user is facing. The emotion engine extracts emotional elements from the text and outputs the user's emotional state as numerical data. This result is used in subsequent processing.

[0176] Step 2:

[0177] The terminal sends the entered task and sentiment data to the server. The server receives this data and analyzes the task using natural language processing technology. It tokenizes the text data received as input and extracts keywords and phrases. This outputs the technical requirements, and the processing priorities are determined accordingly.

[0178] Step 3:

[0179] The server selects the most suitable intelligent machine from multiple specific intelligent machines in the database based on extracted technical requirements and sentiment data. It uses technical requirements and sentiment data as input and ranks the multiple intelligent machines by evaluation scores. The output is a list of the selected intelligent machines, which optimizes the necessary functions and characteristics.

[0180] Step 4:

[0181] The server combines selected intelligent machines to generate application programs that complement the user's emotional state. The inputs used are a list of selected intelligent machines and the user's emotional information. The generating AI model designs the program and outputs it as an application. This application includes a user-friendly interface.

[0182] Step 5:

[0183] The server sends the generated application program to the terminal. The application is then installed on the terminal, and the user performs the initial setup. The input is program data from the server, and the output is the user's customized application ready to begin using. The application deployed on the terminal includes color tones and audio feedback that respond to the user's emotional state.

[0184] (Application Example 2)

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

[0186] Autonomous vehicles are required to appropriately adjust the driving style and in-vehicle environment according to the emotional state of the user, providing a comfortable and safe travel experience. However, conventional systems lack sufficient technology to effectively recognize passengers' emotions and automatically adjust the environment accordingly, resulting in a challenge in meeting the diverse needs of users.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes means for inputting a task in natural language, means for analyzing the input task and extracting relevant technical requirements, means for selecting and combining multiple specialized artificial intelligences based on the extracted technical requirements, means for supplementing the selected artificial intelligence with necessary functions, means for automatically generating an application using the supplemented artificial intelligence, means for distributing the generated application to the user terminal, and means for recognizing passenger emotions and adjusting the driving style and spatial environment. This makes it possible to operate an autonomous vehicle that accurately reflects the emotional state of the user.

[0189] "Means for inputting tasks in natural language" refers to an interface configured to allow users to input tasks into the system using everyday language.

[0190] "Means for analyzing input issues and extracting relevant technical requirements" refers to a mechanism that uses natural language processing technology to identify necessary technical requirements from the content of issues entered by the user.

[0191] "Methods for selecting and combining multiple specialized artificial intelligences" refers to methods for selecting appropriate artificial intelligences based on extracted technical requirements and enabling them to work together.

[0192] "Means of supplementing the necessary functions for the selected artificial intelligence" refers to steps taken to ensure that the selected artificial intelligence possesses all the functions necessary to solve the problem.

[0193] "Methods for automatically generating applications using complementary artificial intelligence" refers to the process of automatically constructing executable software programs based on the capabilities of complementary artificial intelligence.

[0194] "Means of distributing the generated application to the user's terminal" refers to a mechanism that sends the completed application to the user's device and makes it usable.

[0195] "Means of recognizing passenger emotions and adjusting driving style and spatial environment" refers to technology that analyzes emotions from the user's facial expressions and voice, and dynamically changes the settings of the autonomous vehicle based on that analysis.

[0196] In this system, to improve passenger comfort inside autonomous vehicles, users input challenges they face in natural language via a terminal. The terminal uses hardware such as a camera and microphone to recognize the user's emotions and sends the collected emotional data to a server. Based on this, the server uses natural language processing technology to extract technical requirements and selects and combines appropriate specialized artificial intelligence. The selected artificial intelligence is then integrated into the system with the necessary functions complemented.

[0197] The server leverages the capabilities of specialized artificial intelligence suitable for embedded systems to automatically generate applications for autonomous vehicles. These applications are distributed to user terminals, enabling adjustments to the driving style and in-vehicle environment settings based on passenger emotions.

[0198] For example, if a business person wants to concentrate on work during their commute, the system can recognize this emotion from their facial expressions and voice, and then lower the volume in the car and adjust the seat angle to provide a comfortable environment.

[0199] Here, the following prompt can be used as input to the generative AI model: "If the passenger's mood is detected as relaxed, suggest an AI combination that reclines the seats in the car, plays relaxing music, and smooths the drive."

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

[0201] Step 1:

[0202] The user inputs the task in natural language using a device. The device acquires the input task as text data and activates the emotion engine. The emotion engine uses the camera and microphone to detect the user's emotional state from their facial expressions and voice. The input is the task text and emotion data, and the output is a set of the task and emotion to be sent to the server.

[0203] Step 2:

[0204] The server analyzes the set of tasks and emotions received from the terminal. It uses natural language processing techniques to break down the tasks and extract technical requirements. Specifically, it tokenizes the input task text, identifies keywords, and searches the database for corresponding technical requirements. The input is a set of tasks and emotions, and the output is a list of technical requirements.

[0205] Step 3:

[0206] The server selects the appropriate specialized artificial intelligence based on the extracted technical requirements and emotional information. The selected AI combinations are then prioritized based on the emotional information. The input is a list of technical requirements and emotional data, and the output is a list of specialized AIs.

[0207] Step 4:

[0208] The server complements the functions required by the selected specialized artificial intelligence and integrates them into a system. To complement it, it adds additional modules to existing AI functions, improving overall performance. The input is a list of specialized AIs, and the output is the AI ​​system with complemented functions.

[0209] Step 5:

[0210] The server generates applications for autonomous vehicles using complementary artificial intelligence. The generated applications are designed while confirming operating conditions using prompt statements. Specific operating procedures are programmed into the generated applications. The input is the complementary AI system, and the output is an executable application file.

[0211] Step 6:

[0212] The server distributes the generated application to the user's terminal. The terminal properly installs the received application and configures its operation settings based on the vehicle environment. The input is an executable application file, and the output is the application running on the terminal.

[0213] Step 7:

[0214] The device continuously recognizes passengers' emotions in real time and dynamically adjusts the in-car environment and driving style. For example, if a passenger is relaxed, it will recline the seat and change the background music. The input is passenger emotion data, and the output is the adjusted in-car settings.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] This invention is a system for providing AI solutions that optimally combine specialized artificial intelligence necessary for users to solve specific problems. This system consists of a user, a terminal, and a server, each performing a specific role.

[0232] User interface

[0233] Users utilize an on-device interface for inputting tasks in natural language. This interface is intuitive, allowing users to access the system and communicate their requests without requiring specialized knowledge.

[0234] Server Processing

[0235] When the server receives a natural language task submitted by a user, it performs an analysis using natural language processing (NLP) technology internally. Technical requirements are extracted from the analysis results, and based on this information, the server searches for multiple specialized AIs nationwide and selects the optimal combination. After selection, the server adds components and programs that complement the functions of the selected AI as needed. This creates an AI solution that completely solves the user's problem.

[0236] Application generation and distribution

[0237] The server automatically generates applications based on the combined AI solutions. These applications are customized according to the user's terminal environment and quickly prepared for execution. The generated applications are sent from the server to the user's terminal, where they are installed and initially configured.

[0238] Specific example

[0239] As an example, consider a case where a user inputs a problem such as "I want to predict sales for local retail stores and suggest efficient product placement." In this case, the server selects a "sales forecasting AI" and a "product placement optimization AI," combines them, and adds a function to collect data tailored to the purchasing trends and climate conditions of the specific region. By distributing the application generated in this way to the user's terminal and making it available for real-time use on-site, the user can improve the efficiency of store operations.

[0240] This system allows users to access quick and appropriate AI solutions, significantly reducing the time and effort required to select and combine individual, specialized AIs.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The user uses the terminal's input interface to enter the problem they want to solve in natural language. For example, they might enter a specific problem such as "I want to improve logistics efficiency."

[0244] Step 2:

[0245] The terminal sends the task entered by the user to the server. During this process, the task data is transmitted using a secure and efficient communication protocol.

[0246] Step 3:

[0247] The server analyzes the received problem data using a natural language processing (NLP) engine. This analysis extracts keywords such as "logistics optimization" and "efficiency improvement" as technical requirements necessary to solve the problem.

[0248] Step 4:

[0249] Based on the extracted technical requirements, the server queries a database of specialized AIs located throughout the country and lists relevant AIs. For example, "Delivery Route Optimization AI" and "Demand Forecasting AI" might be selected.

[0250] Step 5:

[0251] The server applies combination patterns to the selected specialized AI. During this process, it may customize the functionality to meet the specific needs of a particular region or client, thereby enhancing the completeness of the solution.

[0252] Step 6:

[0253] The server automatically generates platform-specific executable applications based on these AI solutions. Code generation and build processes are performed internally to create the desired application.

[0254] Step 7:

[0255] The server sends the generated application to the terminal. The terminal receives the application and starts the installation process. After the installation is complete, the user is presented with the application's initial setup screen.

[0256] Step 8:

[0257] The user enters the necessary information on the initial setup screen displayed on the device, making the application ready for use. This allows the user to quickly utilize the AI ​​solutions provided by the system.

[0258] (Example 1)

[0259] 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."

[0260] The challenge lies in rapidly providing individually optimized artificial intelligence solutions for the diverse challenges of today, particularly the need to ensure that users can effectively utilize these solutions without requiring specialized knowledge. Furthermore, there is a demand for combining artificial intelligence technologies from different domains to provide comprehensive solutions to complex problems.

[0261] 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.

[0262] In this invention, the server includes means for inputting a problem in natural language, means for analyzing the input problem and extracting relevant technical requirements, and means for selecting and combining multiple specialized artificial intelligences based on the extracted technical requirements. This makes it possible for users to quickly utilize the optimal solution using specialized artificial intelligence, even without specialized knowledge.

[0263] The term "issue" refers to a specific problem or requirement that needs to be solved.

[0264] "Natural language" refers to the language that humans use on a daily basis, and is in contrast to computer program code, for example.

[0265] "Natural language processing technology" is the technology that enables computers to understand and process human language.

[0266] "Technical requirements" refer to the technical conditions and specifications necessary to solve a particular problem.

[0267] "Specialized artificial intelligence" refers to artificial intelligence optimized for specific tasks, exhibiting higher performance in particular areas than general tasks.

[0268] A "data collection function" is a function that collects information necessary to solve a problem.

[0269] An "application" is a computer program developed to provide a specific function.

[0270] A "secure connection" is a connection method that ensures data security during communication.

[0271] "Geographical characteristics" refer to the physical and cultural features unique to a given region.

[0272] "Market trends" refer to the general trends in consumer behavior and demand within a particular market.

[0273] This invention is a system designed to allow users to easily utilize the latest artificial intelligence technology to solve specific problems. Users can input problems in natural language via an interface on their terminal, and this interface is designed to be intuitive to use. This system is implemented using a cloud server and user terminals, and is responsible for the data processing that is crucial for providing artificial intelligence solutions.

[0274] When the server receives a task submitted by a user, it analyzes its content using natural language processing (NLP) technology. Specifically, it uses open-source NLP libraries and a dedicated AI platform to accurately extract relevant technical requirements from the task. This clarifies the type and functionality of the required artificial intelligence, laying the foundation for selecting a specialized AI in the next step.

[0275] The server searches a nationwide database for the most suitable specialized artificial intelligence based on the analysis results and determines the optimal combination. After selection, it builds an AI solution that fully addresses the user's challenges by adding data collection functions and complementary programs as needed.

[0276] The generated AI solution is transformed into a customized application adapted to the user's device environment. The server then sends this application to the user's device using a secure communication method, and installation and initial setup are performed automatically or according to user instructions.

[0277] As a concrete example, consider a scenario where a user inputs a prompt into the system such as, "I want to predict sales for local retail stores and suggest efficient product placement." In this case, the server selects an AI specialized in sales forecasting and another AI for optimizing product placement, combines them, and automatically generates an application by adding functions to collect region-specific purchasing trends and weather data. Through this process, the user can utilize a highly customized AI solution to manage their store operations more efficiently.

[0278] The flow of the specific process in Example 1 will be described using FIG. 11.

[0279] Step 1:

[0280] The user uses the interface on the terminal to input the problem to be solved in natural language. As a specific operation, the user uses a keyboard or a voice input device to send the problem as text data to the system. This input becomes the basic data for the server to perform natural language processing.

[0281] Step 2:

[0282] s The server analyzes the text data received from the user using natural language processing technology. It performs an operation of receiving the problem text as input and extracting technical requirements through analysis. Specifically, an NLP engine is used to extract keywords and analyze their structure, and a list of technical requirements is generated as output.

[0283] Step 3:

[0284] The server selects a plurality of specialized artificial intelligences based on the extracted technical requirements. It receives a list of technical requirements as input and searches the database for the most suitable AI modules. In this process, AI modules that meet the conditions are selected and combined to determine the optimal combination of artificial intelligence as output.

[0285] Step 4:

[0286] The server adds components and programs that complement the necessary functions to the selected specialized AI modules. It receives the combination of AI modules as input, and a process of programming additional functions to handle specific problems is performed. The output is a program with the functions of a complete AI solution.

[0287] Step 5:

[0288] The server automatically generates customized applications based on the AI ​​solution it has built, tailored to the user's terminal environment. It receives a complete AI program as input, transforms the program while considering the terminal's specifications and constraints, and creates an executable application package as output.

[0289] Step 6:

[0290] The server sends the generated application to the user's terminal via a secure connection for installation and initial setup. The server takes the application package as input and sends it using a communication protocol. The terminal receives this data, runs the installation script to configure the environment, and builds a usable application as output.

[0291] Step 7:

[0292] Users operate the application on their device and solve problems based on real-time data. They provide user instructions and additional data as input, which the application processes to output optimal suggestions and predictions. Using these results, users can make efficient decisions.

[0293] (Application Example 1)

[0294] 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."

[0295] In modern society, retail store and brick-and-mortar store operators face challenges related to sales forecasting and optimizing product placement. Solving these challenges requires significant time and specialized knowledge, making it difficult to obtain quick and appropriate solutions. This invention aims to provide a method for efficiently and automatically resolving these operational challenges.

[0296] 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.

[0297] In this invention, the server includes means for inputting a problem in natural language, means for analyzing the input problem and extracting relevant technical requirements, means for selecting and combining multiple specialized information processing devices located in the region based on the extracted technical requirements, means for supplementing the selected information processing devices with necessary functions, means for automatically generating user software using the supplemented information processing devices, and means for distributing the generated user software to user devices and providing support functions for predicting trends and optimizing product placement in the regional sales network. As a result, store managers can quickly perform sales forecasting and optimize product placement without requiring specialized knowledge.

[0298] "Natural language" refers to the language that humans use in everyday life, and is the format necessary for computers to understand and analyze it.

[0299] "Technical requirements" are the technical conditions and specifications necessary to solve a problem.

[0300] A "specialized information processing system" is a data processing system optimized for specific functions or tasks.

[0301] "Software used" refers to a group of programs installed on a computer to perform specific actions or functions.

[0302] "User equipment" refers to all electronic devices that users use on a daily basis, including smartphones and personal computers.

[0303] A "sales network" is a collection of routes and channels used in the process of getting a product from the manufacturer to the consumer.

[0304] "Trend forecasting" refers to making predictions in advance about future events or market movements.

[0305] "Optimization of product placement" is a method aimed at improving the display method of products in a store and enhancing sales and customer satisfaction.

[0306] The system for realizing this invention consists of a user terminal, a server, and a specialized information processing device. The user uses a terminal such as a smartphone to input problems in natural language. For example, it is a prompt sentence like "Tell me the products that are predicted to sell the most during the next sales period." This information is transmitted to the server via the Internet.

[0307] The server utilizes NLP installed on a high-performance computer to analyze the received natural language problems. In this analysis, machine learning libraries such as TensorFlow and PyTorch are used to extract technical requirements. Based on the extracted technical requirements, the optimal specialized AI is selected and combined according to regional characteristics by leveraging cloud-based computing resources.

[0308] The server constructs a program to complement the functions of the selected information processing device as needed and generates usage software. This software is for optimizing trend prediction and product placement in the regional sales network.

[0309] The generated software is distributed to the user terminal through the Internet and installed on the terminal. The user can immediately obtain information to support data analysis and decision-making in store operations using this usage software. As a specific example of proposing in-store placement optimization and achieving actual sales improvement, in a certain bookstore, by inputting "Please tell me the genre of the books that will sell the most next weekend," there is an advantage that a product placement proposal optimized for that region and time can be received.

[0310] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0311] Step 1:

[0312] Users open the application on a device such as a smartphone or tablet and input a question using natural language. An example of an input question is, "Tell me what type of product will sell the most in the next campaign." This input is immediately sent to the server.

[0313] Step 2:

[0314] The server analyzes the received natural language task using NLP (Neuro-Language Programming) techniques. This analysis is performed using frameworks such as TensorFlow, and extracts technical requirements from the task. The input is the user's task statement, and the output of the analysis is a list of technical requirements. For example, the need for sales forecasting and placement suggestions might be extracted.

[0315] Step 3:

[0316] The server selects a specialized AI model tailored to the regional characteristics based on the extracted technical requirements. This involves selecting multiple models, such as predictive AI and optimization AI. The input for this step is a list of technical requirements, and the output is the selected AI models.

[0317] Step 4:

[0318] The server adds necessary complementary functions to the selected AI models and generates customized software for use. Examples of complementary functions include a more accurate prediction module using local sales data. The input is the selected AI models, and the output is the final software module.

[0319] Step 5:

[0320] The server delivers the generated software to the user's terminal, making it available for real-time use. The input for this step is customized software, and the output is an application that is installed and ready to run on the user's terminal.

[0321] Step 6:

[0322] Users can use the delivered software to receive real-time sales forecasts and product placement suggestions, enabling them to make decisions to improve store operations. For example, it can predict best-selling book genres over the weekend and incorporate that information into actual sales strategies.

[0323] 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.

[0324] This invention is a system that combines specialized AI to solve problems entered by the user, and further recognizes the user's emotional state to select the appropriate AI and customize the application. The user, terminal, and server each perform specific functions, thereby providing a solution that meets the user's needs.

[0325] User Interface and Emotion Recognition

[0326] Users input their challenges in natural language using an interface provided on their device. This interface incorporates an emotion engine to detect the user's emotions and analyze the emotional elements included in the input. For example, if a user says, "I urgently need to improve logistics efficiency," the emotion engine recognizes from the keyword "urgent" that the user is feeling anxious.

[0327] Server processing and AI selection

[0328] Upon receiving user issues and emotional information, the server uses natural language processing (NLP) techniques to analyze the issues and extract technical requirements. Based on the emotional information, it adjusts the priority of selected AIs and selects relevant specialized AIs, taking into account factors such as urgency. In this case, "logistics optimization AI" and "real-time monitoring AI" may be selected.

[0329] AI combinations and functional complementarity

[0330] When combining selected specialized AIs, the server complements the necessary functions based on the user's emotional state. For example, for users who are feeling anxious, it is designed to reduce stress by adding rich dashboards and timely alert functions.

[0331] Application generation and customization

[0332] The server automatically generates an executable application based on the final AI solution. This application's interface and functionality are customized based on information obtained through the emotion engine. For example, it might include relaxing color tones and audio feedback for the user.

[0333] Distribution and Initial Setup

[0334] The generated application is sent from the server to the user's terminal and then installed on the terminal. The user can then perform initial setup and begin using the application optimized for their emotional state.

[0335] In this way, the present invention provides an AI solution that takes into account the user's technical requirements and emotions, enabling a more personalized user experience.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user inputs their task in natural language through the device's interface. At the same time, an emotion engine operates, analyzing the user's facial expressions and tone of voice to detect their emotional state.

[0339] Step 2:

[0340] The device sends the entered task and detected emotion data to the server. This includes text data and emotion information obtained through speech recognition and image analysis.

[0341] Step 3:

[0342] The server passes the received data to a natural language processing (NLP) engine, which analyzes the content of the problem and extracts the technical requirements. In doing so, it takes emotional information into consideration to determine which requirements should be prioritized.

[0343] Step 4:

[0344] The server references a nationwide database of specialized AIs based on technical requirements and selects the most relevant AI. Using emotional information, for example, if urgency is indicated, it prioritizes selecting an AI capable of responding quickly.

[0345] Step 5:

[0346] The server adds necessary supplementary functions based on the selected AI. For example, if the user is feeling anxious, it adds infographics or explanatory content to reassure them.

[0347] Step 6:

[0348] The server automatically generates applications based on the completed AI solution, tailored to the user's emotional state. These applications feature customized interfaces and notification methods based on the user's emotions.

[0349] Step 7:

[0350] The generated application is sent from the server to the terminal and automatically installed on the terminal. After installation, an interface opens, guiding the user through the initial setup process.

[0351] Step 8:

[0352] The user enters the necessary information by following the setup prompts and begins using the application. Application operation is supported by continuously monitoring the user's emotional state and adaptively optimizing the interface.

[0353] (Example 2)

[0354] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0355] In today's complex social environment, users face a wide range of challenges, and their urgency and importance depend heavily on their emotional state. However, conventional systems have struggled to provide efficient solutions that take users' emotional states into account. In particular, generating personalized application programs that reflect emotional information has been technically complex.

[0356] 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.

[0357] In this invention, the server includes means for inputting a problem in descriptive format, means for analyzing the input problem and extracting relevant requirements, and means for analyzing emotional elements included in the input from individual devices. This enables the provision of rapid and personalized solutions that take into account the user's emotional state.

[0358] A "problem" refers to a specific issue or request that a user needs to resolve.

[0359] "Descriptive format" refers to a text-based input method using natural language.

[0360] "Requirements" refer to the abstract or specific conditions or specifications necessary to solve a problem.

[0361] An "intelligent machine" refers to a system or tool that possesses artificial intelligence technology designed to address a specific problem.

[0362] "Individual device" refers to an independent device used directly by a specific user.

[0363] "Emotional elements" refer to the expression of emotions and feelings included in the user's input.

[0364] An "application program" refers to software that has a specific function or role and can be executed by a user.

[0365] "Emotional information" refers to data and analysis results related to users' emotions and feelings.

[0366] "Personalization" refers to a state that is customized according to the specific needs and emotional state of each individual user.

[0367] The present invention provides an optimal artificial intelligence solution to a problem that a user wishes to solve. The user inputs the problem in natural language using a terminal. The terminal is equipped with an emotion engine that analyzes emotional elements from the input.

[0368] Task and emotional information transmitted from the terminal are received by the server. The server analyzes the input task using natural language processing techniques and extracts relevant requirements. Common natural language processing tools and libraries are used for this process. The analyzed information is used to select the most suitable intelligent machine from among multiple intelligent machines stored in the server's database.

[0369] In selecting intelligent machines, priority is given to those that match the extracted requirements, and the priority is adjusted based on the user's emotional information. For example, if a user enters the prompt "I want to increase sales by 20% right now," the server can prioritize selecting intelligent machines such as "Sales Forecasting AI" or "Marketing Optimization AI."

[0370] Furthermore, the server combines selected intelligent machines, complements functions according to the user's emotional state, and automatically generates application programs. These application programs are distributed to individual devices, and users can utilize customized programs tailored to their needs by performing initial setup. Throughout this entire process, emotional information-based color tones and interface designs are reflected in the applications, resulting in a more personalized experience.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The user inputs a task into the device. The input is in natural language, and the device's built-in emotion engine analyzes this input in real time. The input describes the specific problem or request the user is facing. The emotion engine extracts emotional elements from the text and outputs the user's emotional state as numerical data. This result is used in subsequent processing.

[0374] Step 2:

[0375] The terminal sends the entered task and sentiment data to the server. The server receives this data and analyzes the task using natural language processing technology. It tokenizes the text data received as input and extracts keywords and phrases. This outputs the technical requirements, and the processing priorities are determined accordingly.

[0376] Step 3:

[0377] The server selects the most suitable intelligent machine from multiple specific intelligent machines in the database based on extracted technical requirements and sentiment data. It uses technical requirements and sentiment data as input and ranks the multiple intelligent machines by evaluation scores. The output is a list of the selected intelligent machines, which optimizes the necessary functions and characteristics.

[0378] Step 4:

[0379] The server combines selected intelligent machines to generate application programs that complement the user's emotional state. The inputs used are a list of selected intelligent machines and the user's emotional information. The generating AI model designs the program and outputs it as an application. This application includes a user-friendly interface.

[0380] Step 5:

[0381] The server sends the generated application program to the terminal. The application is then installed on the terminal, and the user performs the initial setup. The input is program data from the server, and the output is the user's customized application ready to begin using. The application deployed on the terminal includes color tones and audio feedback that respond to the user's emotional state.

[0382] (Application Example 2)

[0383] 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."

[0384] Autonomous vehicles are required to appropriately adjust the driving style and in-vehicle environment according to the emotional state of the user, providing a comfortable and safe travel experience. However, conventional systems lack sufficient technology to effectively recognize passengers' emotions and automatically adjust the environment accordingly, resulting in a challenge in meeting the diverse needs of users.

[0385] 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.

[0386] In this invention, the server includes means for inputting a task in natural language, means for analyzing the input task and extracting relevant technical requirements, means for selecting and combining multiple specialized artificial intelligences based on the extracted technical requirements, means for supplementing the selected artificial intelligence with necessary functions, means for automatically generating an application using the supplemented artificial intelligence, means for distributing the generated application to the user terminal, and means for recognizing passenger emotions and adjusting the driving style and spatial environment. This makes it possible to operate an autonomous vehicle that accurately reflects the emotional state of the user.

[0387] "Means for inputting tasks in natural language" refers to an interface configured to allow users to input tasks into the system using everyday language.

[0388] "Means for analyzing input issues and extracting relevant technical requirements" refers to a mechanism that uses natural language processing technology to identify necessary technical requirements from the content of issues entered by the user.

[0389] "Methods for selecting and combining multiple specialized artificial intelligences" refers to methods for selecting appropriate artificial intelligences based on extracted technical requirements and enabling them to work together.

[0390] "Means of supplementing the necessary functions for the selected artificial intelligence" refers to steps taken to ensure that the selected artificial intelligence possesses all the functions necessary to solve the problem.

[0391] "Methods for automatically generating applications using complementary artificial intelligence" refers to the process of automatically constructing executable software programs based on the capabilities of complementary artificial intelligence.

[0392] "Means of distributing the generated application to the user's terminal" refers to a mechanism that sends the completed application to the user's device and makes it usable.

[0393] "Means of recognizing passenger emotions and adjusting driving style and spatial environment" refers to technology that analyzes emotions from the user's facial expressions and voice, and dynamically changes the settings of the autonomous vehicle based on that analysis.

[0394] In this system, to improve passenger comfort inside autonomous vehicles, users input challenges they face in natural language via a terminal. The terminal uses hardware such as a camera and microphone to recognize the user's emotions and sends the collected emotional data to a server. Based on this, the server uses natural language processing technology to extract technical requirements and selects and combines appropriate specialized artificial intelligence. The selected artificial intelligence is then integrated into the system with the necessary functions complemented.

[0395] The server leverages the capabilities of specialized artificial intelligence suitable for embedded systems to automatically generate applications for autonomous vehicles. These applications are distributed to user terminals, enabling adjustments to the driving style and in-vehicle environment settings based on passenger emotions.

[0396] For example, if a business person wants to concentrate on work during their commute, the system can recognize this emotion from their facial expressions and voice, and then lower the volume in the car and adjust the seat angle to provide a comfortable environment.

[0397] Here, the following prompt can be used as input to the generative AI model: "If the passenger's mood is detected as relaxed, suggest an AI combination that reclines the seats in the car, plays relaxing music, and smooths the drive."

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The user inputs the task in natural language using a device. The device acquires the input task as text data and activates the emotion engine. The emotion engine uses the camera and microphone to detect the user's emotional state from their facial expressions and voice. The input is the task text and emotion data, and the output is a set of the task and emotion to be sent to the server.

[0401] Step 2:

[0402] The server analyzes the set of tasks and emotions received from the terminal. It uses natural language processing techniques to break down the tasks and extract technical requirements. Specifically, it tokenizes the input task text, identifies keywords, and searches the database for corresponding technical requirements. The input is a set of tasks and emotions, and the output is a list of technical requirements.

[0403] Step 3:

[0404] The server selects the appropriate specialized artificial intelligence based on the extracted technical requirements and emotional information. The selected AI combinations are then prioritized based on the emotional information. The input is a list of technical requirements and emotional data, and the output is a list of specialized AIs.

[0405] Step 4:

[0406] The server complements the functions required by the selected specialized artificial intelligence and integrates them into a system. To complement these functions, it adds additional modules to existing AI capabilities, improving overall performance. The input is a list of specialized AIs, and the output is the AI ​​system with complemented functions.

[0407] Step 5:

[0408] The server generates applications for autonomous vehicles using complementary artificial intelligence. The generated applications are designed while confirming operating conditions using prompt statements. Specific operating procedures are programmed into the generated applications. The input is the complementary AI system, and the output is an executable application file.

[0409] Step 6:

[0410] The server distributes the generated application to the user's terminal. The terminal properly installs the received application and configures its operation settings based on the vehicle environment. The input is an executable application file, and the output is the application running on the terminal.

[0411] Step 7:

[0412] The device continuously recognizes passengers' emotions in real time and dynamically adjusts the in-car environment and driving style. For example, if a passenger is relaxed, it will recline the seat and change the background music. The input is passenger emotion data, and the output is the adjusted in-car settings.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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".

[0429] This invention is a system for providing AI solutions that optimally combine specialized artificial intelligence necessary for users to solve specific problems. This system consists of a user, a terminal, and a server, each performing a specific role.

[0430] User interface

[0431] Users utilize an on-device interface for inputting tasks in natural language. This interface is intuitive, allowing users to access the system and communicate their requests without requiring specialized knowledge.

[0432] Server Processing

[0433] When the server receives a natural language task submitted by a user, it performs an analysis using natural language processing (NLP) technology internally. Technical requirements are extracted from the analysis results, and based on this information, the server searches for multiple specialized AIs nationwide and selects the optimal combination. After selection, the server adds components and programs that complement the functions of the selected AI as needed. This creates an AI solution that completely solves the user's problem.

[0434] Application generation and distribution

[0435] The server automatically generates applications based on the combined AI solutions. These applications are customized according to the user's terminal environment and quickly prepared for execution. The generated applications are sent from the server to the user's terminal, where they are installed and initially configured.

[0436] Specific example

[0437] As an example, consider a case where a user inputs a problem such as "I want to predict sales for local retail stores and suggest efficient product placement." In this case, the server selects a "sales forecasting AI" and a "product placement optimization AI," combines them, and adds a function to collect data tailored to the purchasing trends and climate conditions of the specific region. By distributing the application generated in this way to the user's terminal and making it available for real-time use on-site, the user can improve the efficiency of store operations.

[0438] This system allows users to access quick and appropriate AI solutions, significantly reducing the time and effort required to select and combine individual, specialized AIs.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The user uses the terminal's input interface to enter the problem they want to solve in natural language. For example, they might enter a specific problem such as "I want to improve logistics efficiency."

[0442] Step 2:

[0443] The terminal sends the task entered by the user to the server. During this process, the task data is transmitted using a secure and efficient communication protocol.

[0444] Step 3:

[0445] The server analyzes the received problem data using a natural language processing (NLP) engine. This analysis extracts keywords such as "logistics optimization" and "efficiency improvement" as technical requirements necessary to solve the problem.

[0446] Step 4:

[0447] Based on the extracted technical requirements, the server queries a database of specialized AIs located throughout the country and lists relevant AIs. For example, "Delivery Route Optimization AI" and "Demand Forecasting AI" might be selected.

[0448] Step 5:

[0449] The server applies combination patterns to the selected specialized AI. During this process, it may customize the functionality to meet the specific needs of a particular region or client, thereby enhancing the completeness of the solution.

[0450] Step 6:

[0451] The server automatically generates platform-specific executable applications based on these AI solutions. Code generation and build processes are performed internally to create the desired application.

[0452] Step 7:

[0453] The server sends the generated application to the terminal. The terminal receives the application and starts the installation process. After the installation is complete, the user is presented with the application's initial setup screen.

[0454] Step 8:

[0455] The user enters the necessary information on the initial setup screen displayed on the device, making the application ready for use. This allows the user to quickly utilize the AI ​​solutions provided by the system.

[0456] (Example 1)

[0457] 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."

[0458] The challenge lies in rapidly providing individually optimized artificial intelligence solutions for the diverse challenges of today, particularly the need to ensure that users can effectively utilize these solutions without requiring specialized knowledge. Furthermore, there is a demand for combining artificial intelligence technologies from different domains to provide comprehensive solutions to complex problems.

[0459] 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.

[0460] In this invention, the server includes means for inputting a problem in natural language, means for analyzing the input problem and extracting relevant technical requirements, and means for selecting and combining multiple specialized artificial intelligences based on the extracted technical requirements. This makes it possible for users to quickly utilize the optimal solution using specialized artificial intelligence, even without specialized knowledge.

[0461] The term "issue" refers to a specific problem or requirement that needs to be solved.

[0462] "Natural language" refers to the language that humans use on a daily basis, and is in contrast to computer program code, for example.

[0463] "Natural language processing technology" is the technology that enables computers to understand and process human language.

[0464] "Technical requirements" refer to the technical conditions and specifications necessary to solve a particular problem.

[0465] "Specialized artificial intelligence" refers to artificial intelligence optimized for specific tasks, exhibiting higher performance in particular areas than general tasks.

[0466] A "data collection function" is a function that collects information necessary to solve a problem.

[0467] An "application" is a computer program developed to provide a specific function.

[0468] A "secure connection" is a connection method that ensures data security during communication.

[0469] "Geographical characteristics" refer to the physical and cultural features unique to a given region.

[0470] "Market trends" refer to the general trends in consumer behavior and demand within a particular market.

[0471] This invention is a system designed to allow users to easily utilize the latest artificial intelligence technology to solve specific problems. Users can input problems in natural language via an interface on their terminal, and this interface is designed to be intuitive to use. This system is implemented using a cloud server and user terminals, and is responsible for the data processing that is crucial for providing artificial intelligence solutions.

[0472] When the server receives a task submitted by a user, it analyzes its content using natural language processing (NLP) technology. Specifically, it uses open-source NLP libraries and a dedicated AI platform to accurately extract relevant technical requirements from the task. This clarifies the type and functionality of the required artificial intelligence, laying the foundation for selecting a specialized AI in the next step.

[0473] The server searches a nationwide database for the most suitable specialized artificial intelligence based on the analysis results and determines the optimal combination. After selection, it builds an AI solution that fully addresses the user's challenges by adding data collection functions and complementary programs as needed.

[0474] The generated AI solution is transformed into a customized application adapted to the user's device environment. The server then sends this application to the user's device using a secure communication method, and installation and initial setup are performed automatically or according to user instructions.

[0475] As a concrete example, consider a scenario where a user inputs a prompt into the system such as, "I want to predict sales for local retail stores and suggest efficient product placement." In this case, the server selects an AI specialized in sales forecasting and another AI for optimizing product placement, combines them, and automatically generates an application by adding functions to collect region-specific purchasing trends and weather data. Through this process, the user can utilize a highly customized AI solution to manage their store operations more efficiently.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The user uses an interface on their device to input the problem they want to solve in natural language. Specifically, the user sends the problem as text data to the system using a keyboard or voice input device. This input serves as the basic data for the server to perform natural language processing.

[0479] Step 2:

[0480] The server analyzes text data received from the user using natural language processing (NLP) techniques. It receives a problem text as input and extracts technical requirements through analysis. Specifically, an NLP engine is used to extract keywords and analyze their structure, generating a list of technical requirements as output.

[0481] Step 3:

[0482] The server selects multiple specialized artificial intelligences based on the extracted technical requirements. It receives a list of technical requirements as input and searches the database for the most suitable AI module. This process selects and combines AI modules that match the conditions, determining the optimal combination of artificial intelligences as output.

[0483] Step 4:

[0484] The server adds components and programs to the selected specialized AI modules to complement their necessary functions. It receives a combination of AI modules as input and programs additional functions to address specific challenges. The output is a program with the functionality of a complete AI solution.

[0485] Step 5:

[0486] The server automatically generates customized applications based on the AI ​​solution it has built, tailored to the user's terminal environment. It receives a complete AI program as input, transforms the program while considering the terminal's specifications and constraints, and creates an executable application package as output.

[0487] Step 6:

[0488] The server sends the generated application to the user's terminal via a secure connection for installation and initial setup. The server takes the application package as input and sends it using a communication protocol. The terminal receives this data, runs the installation script to configure the environment, and builds a usable application as output.

[0489] Step 7:

[0490] Users operate the application on their device and solve problems based on real-time data. They provide user instructions and additional data as input, which the application processes to output optimal suggestions and predictions. Using these results, users can make efficient decisions.

[0491] (Application Example 1)

[0492] 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."

[0493] In modern society, retail store and brick-and-mortar store operators face challenges related to sales forecasting and optimizing product placement. Solving these challenges requires significant time and specialized knowledge, making it difficult to obtain quick and appropriate solutions. This invention aims to provide a method for efficiently and automatically resolving these operational challenges.

[0494] 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.

[0495] In this invention, the server includes means for inputting a problem in natural language, means for analyzing the input problem and extracting relevant technical requirements, means for selecting and combining multiple specialized information processing devices located in the region based on the extracted technical requirements, means for supplementing the selected information processing devices with necessary functions, means for automatically generating user software using the supplemented information processing devices, and means for distributing the generated user software to user devices and providing support functions for predicting trends and optimizing product placement in the regional sales network. As a result, store managers can quickly perform sales forecasting and optimize product placement without requiring specialized knowledge.

[0496] "Natural language" refers to the language that humans use in everyday life, and is the format necessary for computers to understand and analyze it.

[0497] "Technical requirements" are the technical conditions and specifications necessary to solve a problem.

[0498] A "specialized information processing system" is a data processing system optimized for specific functions or tasks.

[0499] "Software used" refers to a group of programs installed on a computer to perform specific actions or functions.

[0500] "User equipment" refers to all electronic devices that users use on a daily basis, including smartphones and personal computers.

[0501] A "sales network" is a collection of routes and channels used in the process of getting a product from the manufacturer to the consumer.

[0502] "Trend forecasting" refers to making predictions in advance about future events or market movements.

[0503] "Optimizing product placement" is a technique aimed at improving sales and customer satisfaction by improving how products are displayed within a store.

[0504] The system for realizing this invention consists of a user terminal, a server, and a specialized information processing device. The user inputs the task in natural language using a terminal such as a smartphone. For example, a prompt such as, "Tell me which product is predicted to sell the most during the next sale period." This information is transmitted to the server via the internet.

[0505] The server utilizes NLP (Neural Language Programming) on ​​a high-performance computer to analyze the received natural language processing tasks. This analysis uses machine learning libraries such as TensorFlow and PyTorch to extract technical requirements. Based on these extracted technical requirements, cloud-based computing resources are used to select and combine the most suitable specialized AI according to regional characteristics.

[0506] The server builds programs that complement the functions of the selected information processing devices as needed, and generates the software to be used. This software is intended to optimize trend forecasting and product placement within the regional sales network.

[0507] The generated software is delivered to the user's terminal via the internet and installed on the terminal. Using this software, users can instantly obtain information to support data analysis and decision-making in store operations. As a concrete example of how the generated software can suggest optimized in-store layouts and actually increase sales, one bookstore can input "Please tell me the best-selling book genre next weekend" and receive product placement suggestions optimized for that region and time.

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] Users open the application on a device such as a smartphone or tablet and input a question using natural language. An example of an input question is, "Tell me what type of product will sell the most in the next campaign." This input is immediately sent to the server.

[0511] Step 2:

[0512] The server analyzes the received natural language task using NLP (Neuro-Language Programming) techniques. This analysis is performed using frameworks such as TensorFlow, and extracts technical requirements from the task. The input is the user's task statement, and the output of the analysis is a list of technical requirements. For example, the need for sales forecasting and placement suggestions might be extracted.

[0513] Step 3:

[0514] The server selects a specialized AI model tailored to the regional characteristics based on the extracted technical requirements. This involves selecting multiple models, such as predictive AI and optimization AI. The input for this step is a list of technical requirements, and the output is the selected AI models.

[0515] Step 4:

[0516] The server adds necessary complementary functions to the selected AI models and generates customized software for use. Examples of complementary functions include a more accurate prediction module using local sales data. The input is the selected AI models, and the output is the final software module.

[0517] Step 5:

[0518] The server delivers the generated software to the user's terminal, making it available for real-time use. The input for this step is customized software, and the output is an application that is installed and ready to run on the user's terminal.

[0519] Step 6:

[0520] Users can use the delivered software to receive real-time sales forecasts and product placement suggestions, enabling them to make decisions to improve store operations. For example, it can predict best-selling book genres over the weekend and incorporate that information into actual sales strategies.

[0521] 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.

[0522] This invention is a system that combines specialized AI to solve problems entered by the user, and further recognizes the user's emotional state to select the appropriate AI and customize the application. The user, terminal, and server each perform specific functions, thereby providing a solution that meets the user's needs.

[0523] User Interface and Emotion Recognition

[0524] Users input their challenges in natural language using an interface provided on their device. This interface incorporates an emotion engine to detect the user's emotions and analyze the emotional elements included in the input. For example, if a user says, "I urgently need to improve logistics efficiency," the emotion engine recognizes from the keyword "urgent" that the user is feeling anxious.

[0525] Server processing and AI selection

[0526] Upon receiving user issues and emotional information, the server uses natural language processing (NLP) techniques to analyze the issues and extract technical requirements. Based on the emotional information, it adjusts the priority of selected AIs and selects relevant specialized AIs, taking into account factors such as urgency. In this case, "logistics optimization AI" and "real-time monitoring AI" may be selected.

[0527] AI combinations and functional complementarity

[0528] When combining selected specialized AIs, the server complements the necessary functions based on the user's emotional state. For example, for users who are feeling anxious, it is designed to reduce stress by adding rich dashboards and timely alert functions.

[0529] Application generation and customization

[0530] The server automatically generates an executable application based on the final AI solution. This application's interface and functionality are customized based on information obtained through the emotion engine. For example, it might include relaxing color tones and audio feedback for the user.

[0531] Distribution and Initial Setup

[0532] The generated application is sent from the server to the user's terminal and then installed on the terminal. The user can then perform initial setup and begin using the application optimized for their emotional state.

[0533] In this way, the present invention provides an AI solution that takes into account the user's technical requirements and emotions, enabling a more personalized user experience.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] The user inputs their task in natural language through the device's interface. At the same time, an emotion engine operates, analyzing the user's facial expressions and tone of voice to detect their emotional state.

[0537] Step 2:

[0538] The device sends the entered task and detected emotion data to the server. This includes text data and emotion information obtained through speech recognition and image analysis.

[0539] Step 3:

[0540] The server passes the received data to a natural language processing (NLP) engine, which analyzes the content of the problem and extracts the technical requirements. In doing so, it takes emotional information into consideration to determine which requirements should be prioritized.

[0541] Step 4:

[0542] The server references a nationwide database of specialized AIs based on technical requirements and selects the most relevant AI. Using emotional information, for example, if urgency is indicated, it prioritizes selecting an AI capable of responding quickly.

[0543] Step 5:

[0544] The server adds necessary supplementary functions based on the selected AI. For example, if the user is feeling anxious, it adds infographics or explanatory content to reassure them.

[0545] Step 6:

[0546] The server automatically generates applications based on the completed AI solution, tailored to the user's emotional state. These applications feature customized interfaces and notification methods based on the user's emotions.

[0547] Step 7:

[0548] The generated application is sent from the server to the terminal and automatically installed on the terminal. After installation, an interface opens, guiding the user through the initial setup process.

[0549] Step 8:

[0550] The user enters the necessary information by following the setup prompts and begins using the application. Application operation is supported by continuously monitoring the user's emotional state and adaptively optimizing the interface.

[0551] (Example 2)

[0552] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0553] In today's complex social environment, users face a wide range of challenges, and their urgency and importance depend heavily on their emotional state. However, conventional systems have struggled to provide efficient solutions that take users' emotional states into account. In particular, generating personalized application programs that reflect emotional information has been technically complex.

[0554] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0555] In this invention, the server includes means for inputting a problem in descriptive format, means for analyzing the input problem and extracting relevant requirements, and means for analyzing emotional elements included in the input from individual devices. This enables the provision of rapid and personalized solutions that take into account the user's emotional state.

[0556] A "problem" refers to a specific issue or request that a user needs to resolve.

[0557] "Descriptive format" refers to a text-based input method using natural language.

[0558] "Requirements" refer to the abstract or specific conditions or specifications necessary to solve a problem.

[0559] An "intelligent machine" refers to a system or tool that possesses artificial intelligence technology designed to address a specific problem.

[0560] "Individual device" refers to an independent device used directly by a specific user.

[0561] "Emotional elements" refer to the expression of emotions and feelings included in the user's input.

[0562] An "application program" refers to software that has a specific function or role and can be executed by a user.

[0563] "Emotional information" refers to data and analysis results related to users' emotions and feelings.

[0564] "Personalization" refers to a state that is customized according to the specific needs and emotional state of each individual user.

[0565] The present invention provides an optimal artificial intelligence solution to a problem that a user wishes to solve. The user inputs the problem in natural language using a terminal. The terminal is equipped with an emotion engine that analyzes emotional elements from the input.

[0566] Task and emotional information transmitted from the terminal are received by the server. The server analyzes the input task using natural language processing techniques and extracts relevant requirements. Common natural language processing tools and libraries are used for this process. The analyzed information is used to select the most suitable intelligent machine from among multiple intelligent machines stored in the server's database.

[0567] In selecting intelligent machines, priority is given to those that match the extracted requirements, and the priority is adjusted based on the user's emotional information. For example, if a user enters the prompt "I want to increase sales by 20% right now," the server can prioritize selecting intelligent machines such as "Sales Forecasting AI" or "Marketing Optimization AI."

[0568] Furthermore, the server combines selected intelligent machines, complements functions according to the user's emotional state, and automatically generates application programs. These application programs are distributed to individual devices, and users can utilize customized programs tailored to their needs by performing initial setup. Throughout this entire process, emotional information-based color tones and interface designs are reflected in the applications, resulting in a more personalized experience.

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

[0570] Step 1:

[0571] The user inputs a task into the device. The input is in natural language, and the device's built-in emotion engine analyzes this input in real time. The input describes the specific problem or request the user is facing. The emotion engine extracts emotional elements from the text and outputs the user's emotional state as numerical data. This result is used in subsequent processing.

[0572] Step 2:

[0573] The terminal sends the entered task and sentiment data to the server. The server receives this data and analyzes the task using natural language processing technology. It tokenizes the text data received as input and extracts keywords and phrases. This outputs the technical requirements, and the processing priorities are determined accordingly.

[0574] Step 3:

[0575] The server selects the most suitable intelligent machine from multiple specific intelligent machines in the database based on extracted technical requirements and sentiment data. It uses technical requirements and sentiment data as input and ranks the multiple intelligent machines by evaluation scores. The output is a list of the selected intelligent machines, which optimizes the necessary functions and characteristics.

[0576] Step 4:

[0577] The server combines selected intelligent machines to generate application programs that complement the user's emotional state. The inputs used are a list of selected intelligent machines and the user's emotional information. The generating AI model designs the program and outputs it as an application. This application includes a user-friendly interface.

[0578] Step 5:

[0579] The server sends the generated application program to the terminal. The application is then installed on the terminal, and the user performs the initial setup. The input is program data from the server, and the output is the user's customized application ready to begin using. The application deployed on the terminal includes color tones and audio feedback that respond to the user's emotional state.

[0580] (Application Example 2)

[0581] 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."

[0582] Autonomous vehicles are required to appropriately adjust the driving style and in-vehicle environment according to the emotional state of the user, providing a comfortable and safe travel experience. However, conventional systems lack sufficient technology to effectively recognize passengers' emotions and automatically adjust the environment accordingly, resulting in a challenge in meeting the diverse needs of users.

[0583] 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.

[0584] In this invention, the server includes means for inputting a task in natural language, means for analyzing the input task and extracting relevant technical requirements, means for selecting and combining multiple specialized artificial intelligences based on the extracted technical requirements, means for supplementing the selected artificial intelligence with necessary functions, means for automatically generating an application using the supplemented artificial intelligence, means for distributing the generated application to the user terminal, and means for recognizing passenger emotions and adjusting the driving style and spatial environment. This makes it possible to operate an autonomous vehicle that accurately reflects the emotional state of the user.

[0585] "Means for inputting tasks in natural language" refers to an interface configured to allow users to input tasks into the system using everyday language.

[0586] "Means for analyzing input issues and extracting relevant technical requirements" refers to a mechanism that uses natural language processing technology to identify necessary technical requirements from the content of issues entered by the user.

[0587] "Methods for selecting and combining multiple specialized artificial intelligences" refers to methods for selecting appropriate artificial intelligences based on extracted technical requirements and enabling them to work together.

[0588] "Means of supplementing the necessary functions for the selected artificial intelligence" refers to steps taken to ensure that the selected artificial intelligence possesses all the functions necessary to solve the problem.

[0589] "Methods for automatically generating applications using complementary artificial intelligence" refers to the process of automatically constructing executable software programs based on the capabilities of complementary artificial intelligence.

[0590] "Means of distributing the generated application to the user's terminal" refers to a mechanism that sends the completed application to the user's device and makes it usable.

[0591] "Means of recognizing passenger emotions and adjusting driving style and spatial environment" refers to technology that analyzes emotions from the user's facial expressions and voice, and dynamically changes the settings of the autonomous vehicle based on that analysis.

[0592] In this system, to improve passenger comfort inside autonomous vehicles, users input challenges they face in natural language via a terminal. The terminal uses hardware such as a camera and microphone to recognize the user's emotions and sends the collected emotional data to a server. Based on this, the server uses natural language processing technology to extract technical requirements and selects and combines appropriate specialized artificial intelligence. The selected artificial intelligence is then integrated into the system with the necessary functions complemented.

[0593] The server leverages the capabilities of specialized artificial intelligence suitable for embedded systems to automatically generate applications for autonomous vehicles. These applications are distributed to user terminals, enabling adjustments to the driving style and in-vehicle environment settings based on passenger emotions.

[0594] For example, if a business person wants to concentrate on work during their commute, the system can recognize this emotion from their facial expressions and voice, and then lower the volume in the car and adjust the seat angle to provide a comfortable environment.

[0595] Here, the following prompt can be used as input to the generative AI model: "If the passenger's mood is detected as relaxed, suggest an AI combination that reclines the seats in the car, plays relaxing music, and smooths the drive."

[0596] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0597] Step 1:

[0598] The user inputs the task in natural language using a device. The device acquires the input task as text data and activates the emotion engine. The emotion engine uses the camera and microphone to detect the user's emotional state from their facial expressions and voice. The input is the task text and emotion data, and the output is a set of the task and emotion to be sent to the server.

[0599] Step 2:

[0600] The server analyzes the set of tasks and emotions received from the terminal. It uses natural language processing techniques to break down the tasks and extract technical requirements. Specifically, it tokenizes the input task text, identifies keywords, and searches the database for corresponding technical requirements. The input is a set of tasks and emotions, and the output is a list of technical requirements.

[0601] Step 3:

[0602] The server selects the appropriate specialized artificial intelligence based on the extracted technical requirements and emotional information. The selected AI combinations are then prioritized based on the emotional information. The input is a list of technical requirements and emotional data, and the output is a list of specialized AIs.

[0603] Step 4:

[0604] The server complements the functions required by the selected specialized artificial intelligence and integrates them into a system. To complement it, it adds additional modules to existing AI functions, improving overall performance. The input is a list of specialized AIs, and the output is the AI ​​system with complemented functions.

[0605] Step 5:

[0606] The server generates applications for autonomous vehicles using complementary artificial intelligence. The generated applications are designed while confirming operating conditions using prompt statements. Specific operating procedures are programmed into the generated applications. The input is the complementary AI system, and the output is an executable application file.

[0607] Step 6:

[0608] The server distributes the generated application to the user's terminal. The terminal properly installs the received application and configures its operation settings based on the vehicle environment. The input is an executable application file, and the output is the application running on the terminal.

[0609] Step 7:

[0610] The device continuously recognizes passengers' emotions in real time and dynamically adjusts the in-car environment and driving style. For example, if a passenger is relaxed, it will recline the seat and change the background music. The input is passenger emotion data, and the output is the adjusted in-car settings.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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".

[0628] This invention is a system for providing AI solutions that optimally combine specialized artificial intelligence necessary for users to solve specific problems. This system consists of a user, a terminal, and a server, each performing a specific role.

[0629] User interface

[0630] Users utilize an on-device interface for inputting tasks in natural language. This interface is intuitive, allowing users to access the system and communicate their requests without requiring specialized knowledge.

[0631] Server Processing

[0632] When the server receives a natural language task submitted by a user, it performs an analysis using natural language processing (NLP) technology internally. Technical requirements are extracted from the analysis results, and based on this information, the server searches for multiple specialized AIs nationwide and selects the optimal combination. After selection, the server adds components and programs that complement the functions of the selected AI as needed. This creates an AI solution that completely solves the user's problem.

[0633] Application generation and distribution

[0634] The server automatically generates applications based on the combined AI solutions. These applications are customized according to the user's terminal environment and quickly prepared for execution. The generated applications are sent from the server to the user's terminal, where they are installed and initially configured.

[0635] Specific example

[0636] As an example, consider a case where a user inputs a problem such as "I want to predict sales for local retail stores and suggest efficient product placement." In this case, the server selects a "sales forecasting AI" and a "product placement optimization AI," combines them, and adds a function to collect data tailored to the purchasing trends and climate conditions of the specific region. By distributing the application generated in this way to the user's terminal and making it available for real-time use on-site, the user can improve the efficiency of store operations.

[0637] This system allows users to access quick and appropriate AI solutions, significantly reducing the time and effort required to select and combine individual, specialized AIs.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The user uses the terminal's input interface to enter the problem they want to solve in natural language. For example, they might enter a specific problem such as "I want to improve logistics efficiency."

[0641] Step 2:

[0642] The terminal sends the task entered by the user to the server. During this process, the task data is transmitted using a secure and efficient communication protocol.

[0643] Step 3:

[0644] The server analyzes the received problem data using a natural language processing (NLP) engine. This analysis extracts keywords such as "logistics optimization" and "efficiency improvement" as technical requirements necessary to solve the problem.

[0645] Step 4:

[0646] Based on the extracted technical requirements, the server queries a database of specialized AIs located throughout the country and lists relevant AIs. For example, "Delivery Route Optimization AI" and "Demand Forecasting AI" might be selected.

[0647] Step 5:

[0648] The server applies combination patterns to the selected specialized AI. During this process, it may customize the functionality to meet the specific needs of a particular region or client, thereby enhancing the completeness of the solution.

[0649] Step 6:

[0650] The server automatically generates platform-specific executable applications based on these AI solutions. Code generation and build processes are performed internally to create the desired application.

[0651] Step 7:

[0652] The server sends the generated application to the terminal. The terminal receives the application and starts the installation process. After the installation is complete, the user is presented with the application's initial setup screen.

[0653] Step 8:

[0654] The user enters the necessary information on the initial setup screen displayed on the device, making the application ready for use. This allows the user to quickly utilize the AI ​​solutions provided by the system.

[0655] (Example 1)

[0656] 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".

[0657] The challenge lies in rapidly providing individually optimized artificial intelligence solutions for the diverse challenges of today, particularly the need to ensure that users can effectively utilize these solutions without requiring specialized knowledge. Furthermore, there is a demand for combining artificial intelligence technologies from different domains to provide comprehensive solutions to complex problems.

[0658] 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.

[0659] In this invention, the server includes means for inputting a problem in natural language, means for analyzing the input problem and extracting relevant technical requirements, and means for selecting and combining multiple specialized artificial intelligences based on the extracted technical requirements. This makes it possible for users to quickly utilize the optimal solution using specialized artificial intelligence, even without specialized knowledge.

[0660] The term "issue" refers to a specific problem or requirement that needs to be solved.

[0661] "Natural language" refers to the language that humans use on a daily basis, and is in contrast to computer program code, for example.

[0662] "Natural language processing technology" is the technology that enables computers to understand and process human language.

[0663] "Technical requirements" refer to the technical conditions and specifications necessary to solve a particular problem.

[0664] "Specialized artificial intelligence" refers to artificial intelligence optimized for specific tasks, exhibiting higher performance in particular areas than general tasks.

[0665] A "data collection function" is a function that collects information necessary to solve a problem.

[0666] An "application" is a computer program developed to provide a specific function.

[0667] A "secure connection" is a connection method that ensures data security during communication.

[0668] "Geographical characteristics" refer to the physical and cultural features unique to a given region.

[0669] "Market trends" refer to the general trends in consumer behavior and demand within a particular market.

[0670] This invention is a system designed to allow users to easily utilize the latest artificial intelligence technology to solve specific problems. Users can input problems in natural language via an interface on their terminal, and this interface is designed to be intuitive to use. This system is implemented using a cloud server and user terminals, and is responsible for the data processing that is crucial for providing artificial intelligence solutions.

[0671] When the server receives a task submitted by a user, it analyzes its content using natural language processing (NLP) technology. Specifically, it uses open-source NLP libraries and a dedicated AI platform to accurately extract relevant technical requirements from the task. This clarifies the type and functionality of the required artificial intelligence, laying the foundation for selecting a specialized AI in the next step.

[0672] The server searches a nationwide database for the most suitable specialized artificial intelligence based on the analysis results and determines the optimal combination. After selection, it builds an AI solution that fully addresses the user's challenges by adding data collection functions and complementary programs as needed.

[0673] The generated AI solution is transformed into a customized application adapted to the user's device environment. The server then sends this application to the user's device using a secure communication method, and installation and initial setup are performed automatically or according to user instructions.

[0674] As a concrete example, consider a scenario where a user inputs a prompt into the system such as, "I want to predict sales for local retail stores and suggest efficient product placement." In this case, the server selects an AI specialized in sales forecasting and another AI for optimizing product placement, combines them, and automatically generates an application by adding functions to collect region-specific purchasing trends and weather data. Through this process, the user can utilize a highly customized AI solution to manage their store operations more efficiently.

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The user uses an interface on their device to input the problem they want to solve in natural language. Specifically, the user sends the problem as text data to the system using a keyboard or voice input device. This input serves as the basic data for the server to perform natural language processing.

[0678] Step 2:

[0679] The server analyzes text data received from the user using natural language processing (NLP) techniques. It receives a problem text as input and extracts technical requirements through analysis. Specifically, an NLP engine is used to extract keywords and analyze their structure, generating a list of technical requirements as output.

[0680] Step 3:

[0681] The server selects multiple specialized artificial intelligences based on the extracted technical requirements. It receives a list of technical requirements as input and searches the database for the most suitable AI module. This process selects and combines AI modules that match the conditions, determining the optimal combination of artificial intelligences as output.

[0682] Step 4:

[0683] The server adds components and programs to the selected specialized AI modules to complement their necessary functions. It receives a combination of AI modules as input and programs additional functions to address specific challenges. The output is a program with the functionality of a complete AI solution.

[0684] Step 5:

[0685] The server automatically generates customized applications based on the AI ​​solution it has built, tailored to the user's terminal environment. It receives a complete AI program as input, transforms the program while considering the terminal's specifications and constraints, and creates an executable application package as output.

[0686] Step 6:

[0687] The server sends the generated application to the user's terminal via a secure connection for installation and initial setup. The server takes the application package as input and sends it using a communication protocol. The terminal receives this data, runs the installation script to configure the environment, and builds a usable application as output.

[0688] Step 7:

[0689] Users operate the application on their device and solve problems based on real-time data. They provide user instructions and additional data as input, which the application processes to output optimal suggestions and predictions. Using these results, users can make efficient decisions.

[0690] (Application Example 1)

[0691] 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".

[0692] In modern society, retail store and brick-and-mortar store operators face challenges related to sales forecasting and optimizing product placement. Solving these challenges requires significant time and specialized knowledge, making it difficult to obtain quick and appropriate solutions. This invention aims to provide a method for efficiently and automatically resolving these operational challenges.

[0693] 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.

[0694] In this invention, the server includes means for inputting a problem in natural language, means for analyzing the input problem and extracting relevant technical requirements, means for selecting and combining multiple specialized information processing devices located in the region based on the extracted technical requirements, means for supplementing the selected information processing devices with necessary functions, means for automatically generating user software using the supplemented information processing devices, and means for distributing the generated user software to user devices and providing support functions for predicting trends and optimizing product placement in the regional sales network. As a result, store managers can quickly perform sales forecasting and optimize product placement without requiring specialized knowledge.

[0695] "Natural language" refers to the language that humans use in everyday life, and is the format necessary for computers to understand and analyze it.

[0696] "Technical requirements" are the technical conditions and specifications necessary to solve a problem.

[0697] A "specialized information processing system" is a data processing system optimized for specific functions or tasks.

[0698] "Software used" refers to a group of programs installed on a computer to perform specific actions or functions.

[0699] "User equipment" refers to all electronic devices that users use on a daily basis, including smartphones and personal computers.

[0700] A "sales network" is a collection of routes and channels used in the process of getting a product from the manufacturer to the consumer.

[0701] "Trend forecasting" refers to making predictions in advance about future events or market movements.

[0702] "Optimizing product placement" is a technique aimed at improving sales and customer satisfaction by improving how products are displayed within a store.

[0703] The system for realizing this invention consists of a user terminal, a server, and a specialized information processing device. The user inputs the task in natural language using a terminal such as a smartphone. For example, a prompt such as, "Tell me which product is predicted to sell the most during the next sale period." This information is transmitted to the server via the internet.

[0704] The server utilizes NLP (Neural Language Programming) on ​​a high-performance computer to analyze the received natural language processing tasks. This analysis uses machine learning libraries such as TensorFlow and PyTorch to extract technical requirements. Based on these extracted technical requirements, cloud-based computing resources are used to select and combine the most suitable specialized AI according to regional characteristics.

[0705] The server builds programs that complement the functions of the selected information processing devices as needed, and generates the software to be used. This software is intended to optimize trend forecasting and product placement within the regional sales network.

[0706] The generated software is delivered to the user's terminal via the internet and installed on the terminal. Using this software, users can instantly obtain information to support data analysis and decision-making in store operations. As a concrete example of how the generated software can suggest optimized in-store layouts and actually increase sales, one bookstore can input "Please tell me the best-selling book genre next weekend" and receive product placement suggestions optimized for that region and time.

[0707] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0708] Step 1:

[0709] Users open the application on a device such as a smartphone or tablet and input a question using natural language. An example of an input question is, "Tell me what type of product will sell the most in the next campaign." This input is immediately sent to the server.

[0710] Step 2:

[0711] The server analyzes the received natural language task using NLP (Neuro-Language Programming) techniques. This analysis is performed using frameworks such as TensorFlow, and extracts technical requirements from the task. The input is the user's task statement, and the output of the analysis is a list of technical requirements. For example, the need for sales forecasting and placement suggestions might be extracted.

[0712] Step 3:

[0713] The server selects a specialized AI model tailored to the regional characteristics based on the extracted technical requirements. This involves selecting multiple models, such as predictive AI and optimization AI. The input for this step is a list of technical requirements, and the output is the selected AI models.

[0714] Step 4:

[0715] The server adds necessary complementary functions to the selected AI models and generates customized software for use. Examples of complementary functions include a more accurate prediction module using local sales data. The input is the selected AI models, and the output is the final software module.

[0716] Step 5:

[0717] The server delivers the generated software to the user's terminal, making it available for real-time use. The input for this step is customized software, and the output is an application that is installed and ready to run on the user's terminal.

[0718] Step 6:

[0719] Users can use the delivered software to receive real-time sales forecasts and product placement suggestions, enabling them to make decisions to improve store operations. For example, it can predict best-selling book genres over the weekend and incorporate that information into actual sales strategies.

[0720] 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.

[0721] This invention is a system that combines specialized AI to solve problems entered by the user, and further recognizes the user's emotional state to select the appropriate AI and customize the application. The user, terminal, and server each perform specific functions, thereby providing a solution that meets the user's needs.

[0722] User Interface and Emotion Recognition

[0723] Users input their challenges in natural language using an interface provided on their device. This interface incorporates an emotion engine to detect the user's emotions and analyze the emotional elements included in the input. For example, if a user says, "I urgently need to improve logistics efficiency," the emotion engine recognizes from the keyword "urgent" that the user is feeling anxious.

[0724] Server processing and AI selection

[0725] Upon receiving user issues and emotional information, the server uses natural language processing (NLP) techniques to analyze the issues and extract technical requirements. Based on the emotional information, it adjusts the priority of selected AIs and selects relevant specialized AIs, taking into account factors such as urgency. In this case, "logistics optimization AI" and "real-time monitoring AI" may be selected.

[0726] AI combinations and functional complementarity

[0727] When combining selected specialized AIs, the server complements the necessary functions based on the user's emotional state. For example, for users who are feeling anxious, it is designed to reduce stress by adding rich dashboards and timely alert functions.

[0728] Application generation and customization

[0729] The server automatically generates an executable application based on the final AI solution. This application's interface and functionality are customized based on information obtained through the emotion engine. For example, it might include relaxing color tones and audio feedback for the user.

[0730] Distribution and Initial Setup

[0731] The generated application is sent from the server to the user's terminal and then installed on the terminal. The user can then perform initial setup and begin using the application optimized for their emotional state.

[0732] In this way, the present invention provides an AI solution that takes into account the user's technical requirements and emotions, enabling a more personalized user experience.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] The user inputs their task in natural language through the device's interface. At the same time, an emotion engine operates, analyzing the user's facial expressions and tone of voice to detect their emotional state.

[0736] Step 2:

[0737] The device sends the entered task and detected emotion data to the server. This includes text data and emotion information obtained through speech recognition and image analysis.

[0738] Step 3:

[0739] The server passes the received data to a natural language processing (NLP) engine, which analyzes the content of the problem and extracts the technical requirements. In doing so, it takes emotional information into consideration to determine which requirements should be prioritized.

[0740] Step 4:

[0741] The server references a nationwide database of specialized AIs based on technical requirements and selects the most relevant AI. Using emotional information, for example, if urgency is indicated, it prioritizes selecting an AI capable of responding quickly.

[0742] Step 5:

[0743] The server adds necessary supplementary functions based on the selected AI. For example, if the user is feeling anxious, it adds infographics or explanatory content to reassure them.

[0744] Step 6:

[0745] The server automatically generates applications based on the completed AI solution, tailored to the user's emotional state. These applications feature customized interfaces and notification methods based on the user's emotions.

[0746] Step 7:

[0747] The generated application is sent from the server to the terminal and automatically installed on the terminal. After installation, an interface opens, guiding the user through the initial setup process.

[0748] Step 8:

[0749] The user enters the necessary information by following the setup prompts and begins using the application. Application operation is supported by continuously monitoring the user's emotional state and adaptively optimizing the interface.

[0750] (Example 2)

[0751] 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".

[0752] In today's complex social environment, users face a wide range of challenges, and their urgency and importance depend heavily on their emotional state. However, conventional systems have struggled to provide efficient solutions that take users' emotional states into account. In particular, generating personalized application programs that reflect emotional information has been technically complex.

[0753] 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.

[0754] In this invention, the server includes means for inputting a problem in descriptive format, means for analyzing the input problem and extracting relevant requirements, and means for analyzing emotional elements included in the input from individual devices. This enables the provision of rapid and personalized solutions that take into account the user's emotional state.

[0755] A "problem" refers to a specific issue or request that a user needs to resolve.

[0756] "Descriptive format" refers to a text-based input method using natural language.

[0757] "Requirements" refer to the abstract or specific conditions or specifications necessary to solve a problem.

[0758] An "intelligent machine" refers to a system or tool that possesses artificial intelligence technology designed to address a specific problem.

[0759] "Individual device" refers to an independent device used directly by a specific user.

[0760] "Emotional elements" refer to the expression of emotions and feelings included in the user's input.

[0761] An "application program" refers to software that has a specific function or role and can be executed by a user.

[0762] "Emotional information" refers to data and analysis results related to users' emotions and feelings.

[0763] "Personalization" refers to a state that is customized according to the specific needs and emotional state of each individual user.

[0764] The present invention provides an optimal artificial intelligence solution to a problem that a user wishes to solve. The user inputs the problem in natural language using a terminal. The terminal is equipped with an emotion engine that analyzes emotional elements from the input.

[0765] Task and emotional information transmitted from the terminal are received by the server. The server analyzes the input task using natural language processing techniques and extracts relevant requirements. Common natural language processing tools and libraries are used for this process. The analyzed information is used to select the most suitable intelligent machine from among multiple intelligent machines stored in the server's database.

[0766] In selecting intelligent machines, priority is given to those that match the extracted requirements, and the priority is adjusted based on the user's emotional information. For example, if a user enters the prompt "I want to increase sales by 20% right now," the server can prioritize selecting intelligent machines such as "Sales Forecasting AI" or "Marketing Optimization AI."

[0767] Furthermore, the server combines selected intelligent machines, complements functions according to the user's emotional state, and automatically generates application programs. These application programs are distributed to individual devices, and users can utilize customized programs tailored to their needs by performing initial setup. Throughout this entire process, emotional information-based color tones and interface designs are reflected in the applications, resulting in a more personalized experience.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The user inputs a task into the device. The input is in natural language, and the device's built-in emotion engine analyzes this input in real time. The input describes the specific problem or request the user is facing. The emotion engine extracts emotional elements from the text and outputs the user's emotional state as numerical data. This result is used in subsequent processing.

[0771] Step 2:

[0772] The terminal sends the entered task and sentiment data to the server. The server receives this data and analyzes the task using natural language processing technology. It tokenizes the text data received as input and extracts keywords and phrases. This outputs the technical requirements, and the processing priorities are determined accordingly.

[0773] Step 3:

[0774] The server selects the most suitable intelligent machine from multiple specific intelligent machines in the database based on extracted technical requirements and sentiment data. It uses technical requirements and sentiment data as input and ranks the multiple intelligent machines by evaluation scores. The output is a list of the selected intelligent machines, which optimizes the necessary functions and characteristics.

[0775] Step 4:

[0776] The server combines selected intelligent machines to generate application programs that complement the user's emotional state. The inputs used are a list of selected intelligent machines and the user's emotional information. The generating AI model designs the program and outputs it as an application. This application includes a user-friendly interface.

[0777] Step 5:

[0778] The server sends the generated application program to the terminal. The application is then installed on the terminal, and the user performs the initial setup. The input is program data from the server, and the output is the user's customized application ready to begin using. The application deployed on the terminal includes color tones and audio feedback that respond to the user's emotional state.

[0779] (Application Example 2)

[0780] 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".

[0781] Autonomous vehicles are required to appropriately adjust the driving style and in-vehicle environment according to the emotional state of the user, providing a comfortable and safe travel experience. However, conventional systems lack sufficient technology to effectively recognize passengers' emotions and automatically adjust the environment accordingly, resulting in a challenge in meeting the diverse needs of users.

[0782] 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.

[0783] In this invention, the server includes means for inputting a task in natural language, means for analyzing the input task and extracting relevant technical requirements, means for selecting and combining multiple specialized artificial intelligences based on the extracted technical requirements, means for supplementing the selected artificial intelligence with necessary functions, means for automatically generating an application using the supplemented artificial intelligence, means for distributing the generated application to the user terminal, and means for recognizing passenger emotions and adjusting the driving style and spatial environment. This makes it possible to operate an autonomous vehicle that accurately reflects the emotional state of the user.

[0784] "Means for inputting tasks in natural language" refers to an interface configured to allow users to input tasks into the system using everyday language.

[0785] "Means for analyzing input issues and extracting relevant technical requirements" refers to a mechanism that uses natural language processing technology to identify necessary technical requirements from the content of issues entered by the user.

[0786] "Methods for selecting and combining multiple specialized artificial intelligences" refers to methods for selecting appropriate artificial intelligences based on extracted technical requirements and enabling them to work together.

[0787] "Means of supplementing the necessary functions for the selected artificial intelligence" refers to steps taken to ensure that the selected artificial intelligence possesses all the functions necessary to solve the problem.

[0788] "Methods for automatically generating applications using complementary artificial intelligence" refers to the process of automatically constructing executable software programs based on the capabilities of complementary artificial intelligence.

[0789] "Means of distributing the generated application to the user's terminal" refers to a mechanism that sends the completed application to the user's device and makes it usable.

[0790] "Means of recognizing passenger emotions and adjusting driving style and spatial environment" refers to technology that analyzes emotions from the user's facial expressions and voice, and dynamically changes the settings of the autonomous vehicle based on that analysis.

[0791] In this system, to improve passenger comfort inside autonomous vehicles, users input challenges they face in natural language via a terminal. The terminal uses hardware such as a camera and microphone to recognize the user's emotions and sends the collected emotional data to a server. Based on this, the server uses natural language processing technology to extract technical requirements and selects and combines appropriate specialized artificial intelligence. The selected artificial intelligence is then integrated into the system with the necessary functions complemented.

[0792] The server leverages the capabilities of specialized artificial intelligence suitable for embedded systems to automatically generate applications for autonomous vehicles. These applications are distributed to user terminals, enabling adjustments to the driving style and in-vehicle environment settings based on passenger emotions.

[0793] For example, if a business person wants to concentrate on work during their commute, the system can recognize this emotion from their facial expressions and voice, and then lower the volume in the car and adjust the seat angle to provide a comfortable environment.

[0794] Here, the following prompt can be used as input to the generative AI model: "If the passenger's mood is detected as relaxed, suggest an AI combination that reclines the seats in the car, plays relaxing music, and smooths the drive."

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The user inputs the task in natural language using a device. The device acquires the input task as text data and activates the emotion engine. The emotion engine uses the camera and microphone to detect the user's emotional state from their facial expressions and voice. The input is the task text and emotion data, and the output is a set of the task and emotion to be sent to the server.

[0798] Step 2:

[0799] The server analyzes the set of tasks and emotions received from the terminal. It uses natural language processing techniques to break down the tasks and extract technical requirements. Specifically, it tokenizes the input task text, identifies keywords, and searches the database for corresponding technical requirements. The input is a set of tasks and emotions, and the output is a list of technical requirements.

[0800] Step 3:

[0801] The server selects the appropriate specialized artificial intelligence based on the extracted technical requirements and emotional information. The selected AI combinations are then prioritized based on the emotional information. The input is a list of technical requirements and emotional data, and the output is a list of specialized AIs.

[0802] Step 4:

[0803] The server complements the functions required by the selected specialized artificial intelligence and integrates them into a system. To complement it, it adds additional modules to existing AI functions, improving overall performance. The input is a list of specialized AIs, and the output is the AI ​​system with complemented functions.

[0804] Step 5:

[0805] The server generates applications for autonomous vehicles using complementary artificial intelligence. The generated applications are designed while confirming operating conditions using prompt statements. Specific operating procedures are programmed into the generated applications. The input is the complementary AI system, and the output is an executable application file.

[0806] Step 6:

[0807] The server distributes the generated application to the user's terminal. The terminal properly installs the received application and configures its operation settings based on the vehicle environment. The input is an executable application file, and the output is the application running on the terminal.

[0808] Step 7:

[0809] The device continuously recognizes passengers' emotions in real time and dynamically adjusts the in-car environment and driving style. For example, if a passenger is relaxed, it will recline the seat and change the background music. The input is passenger emotion data, and the output is the adjusted in-car settings.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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."

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] The following is further disclosed regarding the embodiments described above.

[0832] (Claim 1)

[0833] A means of inputting the task in natural language,

[0834] A means of analyzing the input problem and extracting relevant technical requirements,

[0835] A means of selecting and combining multiple specialized artificial intelligence systems located throughout the country based on extracted technical requirements,

[0836] Means to complement the necessary functions for the selected artificial intelligence,

[0837] A means of automatically generating applications using complementary artificial intelligence,

[0838] A means of distributing the generated application to user terminals,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, wherein the extraction of the aforementioned technical requirements is performed using natural language processing technology.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the application generation is customized according to the user's regional characteristics.

[0844] "Example 1"

[0845] (Claim 1)

[0846] A means of inputting the task in natural language,

[0847] A means of analyzing the input problem and extracting relevant technical requirements,

[0848] A means of selecting and combining multiple specialized artificial intelligences based on extracted technical requirements,

[0849] Means to complement the necessary functions for the selected artificial intelligence,

[0850] A means for automatically generating applications according to the user's computing environment using complementary artificial intelligence,

[0851] A means for distributing the generated application to the user's computing device using a secure connection, and for performing installation and initial setup,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, wherein technical requirements are extracted using natural language processing technology, and a data collection function is integrated into an AI solution that combines selected specialized artificial intelligence.

[0855] (Claim 3)

[0856] The system according to claim 1, wherein application generation is rapidly customized according to the geographical characteristics and market trends of the user.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] A means of inputting the task in natural language,

[0860] A means of analyzing the input problem and extracting relevant technical requirements,

[0861] A means of selecting and combining multiple specialized information processing devices located in the region based on extracted technical requirements,

[0862] Means to complement the necessary functions of the selected information processing device,

[0863] A means for automatically generating software using a complementary information processing device,

[0864] A means for distributing generated software to user devices and providing support functions for predicting trends and optimizing product placement within a regional sales network,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, wherein the extraction of the aforementioned technical requirements is performed using natural language processing technology.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein the software used is customized according to regional characteristics during generation, with the aim of improving the efficiency of the sales network.

[0870] "Example 2 of combining an emotion engine"

[0871] (Claim 1)

[0872] A means for inputting the assignment in written form,

[0873] A means of analyzing the input issue and extracting relevant requirements,

[0874] A means for selecting and combining multiple specific intelligent machines based on extracted requirements,

[0875] Means to complement the necessary functions of the selected intelligent machine,

[0876] A means of automatically generating application programs using a complementary intelligent machine,

[0877] A means for distributing the generated application program to individual devices,

[0878] A means for analyzing the emotional elements contained in the input from individual devices,

[0879] A means for adjusting the application program according to the user's emotional state based on the analyzed emotional information,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, wherein the extraction of the aforementioned requirements is performed using information processing technology.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein the application program generation is customized according to the user's emotional state.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] A means of inputting the task in natural language,

[0888] A means of analyzing the input problem and extracting relevant technical requirements,

[0889] A means of selecting and combining multiple specialized artificial intelligences based on extracted technical requirements,

[0890] Means to complement the necessary functions for the selected artificial intelligence,

[0891] A means of automatically generating applications using complementary artificial intelligence,

[0892] A means of distributing the generated application to user terminals,

[0893] A means of recognizing passengers' emotions and adjusting the driving style and spatial environment accordingly,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, wherein the extraction of the aforementioned technical requirements is performed using natural language processing technology.

[0897] (Claim 3)

[0898] The system according to claim 1, wherein the application generation is customized according to the user's emotional state. [Explanation of symbols]

[0899] 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 inputting the task in natural language, A means of analyzing the input problem and extracting relevant technical requirements, A means of selecting and combining multiple specialized artificial intelligence systems located throughout the country based on extracted technical requirements, Means to complement the necessary functions for the selected artificial intelligence, A means of automatically generating applications using complementary artificial intelligence, A means of distributing the generated application to user terminals, A system that includes this.

2. The system according to claim 1, wherein the extraction of the aforementioned technical requirements is performed using natural language processing technology.

3. The system according to claim 1, wherein the application generation is customized according to the user's regional characteristics.

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