system

Abstract

We provide the system. [Solution] A means of analyzing natural language information input by the user to extract key elements and requirements, A means for generating design specifications based on extracted requirements, A means of automatically generating design drawings and data based on the generated design specifications, A system that includes means for verifying whether a generated design is patentable and determining whether or not patent infringement has occurred.

Description

Technical Field

[0004] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional hardware design, specialized knowledge and know-how are essential, and it has been difficult for many individuals and small and medium-sized enterprises to implement their own ideas. In particular, there are problems that it is difficult to confirm that the process of implementing an idea into a specific design and that the design does not conflict with patent infringement or regulations. Also, converting the designed data into a form that can be used by a specific manufacturing device has been a technical hurdle.

Means for Solving the Problems

[0005] This invention provides a means for analyzing natural language information input by a user to extract key elements and requirements, and for generating design specifications based on them. It also includes means for automatically generating design drawings and data based on the generated specifications, and means for determining patentability and patent infringement, thereby reducing complexity in the design process. Furthermore, it includes means for confirming that the design has been made in consideration of legal regulations and safety standards, and provides means for converting design data into a specific manufacturing device format, thereby supporting the overall process of realizing an idea as an actual product.

[0006] A "user" is an individual or organization that provides design ideas or requirements to a system.

[0007] "Natural language" refers to the language that humans use on a daily basis, and is a form of information that can be processed through analysis by computers.

[0008] "Information analysis" is the process of extracting meaning from given data and organizing and classifying related information.

[0009] "Requirements extraction" is the process of clarifying the elements and conditions necessary for design based on user input information.

[0010] A "design specification" is a document that describes in detail the purpose, function, constraints, and other aspects of a design.

[0011] A "design drawing" is a diagram or digital data that shows the physical structure and configuration of hardware.

[0012] "Automatic generation" refers to the process by which a program creates blueprints and data without human intervention.

[0013] "Patentability" is a criterion for evaluating whether an invention is novel and possesses an inventive step.

[0014] "Patent infringement" refers to the act of improperly using the patent rights of others and is regulated by law.

[0015] "Legal regulations" refer to the set of laws and standards that products and technologies must comply with.

[0016] "Safety standards" are guidelines for not harming people and the environment in the use and production of products.

[0017] "Format for manufacturing devices" is a format in which design data is organized so that specific manufacturing equipment can understand and operate.

Brief Description of the Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] 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.

[0022] 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.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is a system for streamlining the hardware design process, aiming to realize user ideas and requirements as concrete products. This system is implemented in the following manner.

[0040] Users input ideas and requirements in natural language via a terminal. This information is sent from the terminal to the server. The server uses a large-scale language model to analyze the user's input and extract key elements and requirements. This clarifies ambiguities and generates a design specification.

[0041] The generated design specifications are input by the server into a multimodal, design-focused generation AI, which automatically generates design drawings and digital data. This data includes component selection, material properties, and manufacturing process simulations based on user requirements.

[0042] Furthermore, the server uses patent-specific generation AI to verify whether the generated design is patentable or does not infringe on existing patents. It also checks for compliance with legal regulations and safety standards, and provides feedback to the user. The user can review the design based on this information and, if necessary, instruct modifications via their device.

[0043] As a concrete example, consider a scenario where a user wants to design a special toy for children. The user inputs "I want to design a safe robot toy that children can enjoy" into the terminal. The server analyzes this input and creates a design specification document suggesting suitable functions and materials. The automatically generated design is then checked from a patent and legal regulation perspective, and after confirming that there are no problems, it is provided to the user. The user can then review this and prepare to proceed to the manufacturing process.

[0044] This system allows even users without specialized knowledge to reliably turn their ideas into products and create business opportunities.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] Users input their concepts and requirements into the device using natural language. This information includes the purpose, target users, functionality, and design image.

[0048] Step 2:

[0049] The terminal receives user input and sends the data to the server. The server receives this information and begins analysis using a large-scale language model.

[0050] Step 3:

[0051] The server analyzes the user's input to understand it and extract key elements and requirements. This process clarifies ambiguous parts of the idea and generates a design specification.

[0052] Step 4:

[0053] Based on the generated design specifications, the server utilizes a design-specific generation AI to automatically generate design drawings and digital data. This process includes component selection, material property analysis, and manufacturing process simulations.

[0054] Step 5:

[0055] The server runs a patent-specific generation AI to evaluate the patentability of the generated designs and check for infringement of existing patents. It also evaluates whether the designs comply with legal regulations and safety standards.

[0056] Step 6:

[0057] The terminal provides the user with design data received from the server, patentability evaluation results, and legal compliance check results. The user then reviews the design based on this information and requests modifications if necessary.

[0058] Step 7:

[0059] The server modifies and adjusts the design data based on user feedback. The modified design data is then converted into a manufacturing-ready format.

[0060] Step 8:

[0061] The terminal provides the user with the final design data and notifies them of guidelines to begin the manufacturing process. Based on this, the user can proceed with the actual product manufacturing.

[0062] (Example 1)

[0063] 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."

[0064] Traditional hardware design processes often required a high level of expertise to translate user ideas into concrete forms, and were time-consuming and laborious. Furthermore, considering patents and legal regulations during the design phase necessitated additional experts and resources, leading to project delays and increased costs. Therefore, there was a need for a system that would allow users without specialized knowledge to efficiently advance the design process while easily meeting legal requirements.

[0065] 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.

[0066] In this invention, the server includes means for analyzing natural language information input by the user to extract key elements and requirements, means for generating design specifications based on the extracted requirements, means for automatically generating the generated design drawings and data, and means for verifying whether the generated design is patentable and complies with legal regulations and safety standards. This enables users to efficiently and legally realize their ideas without specialized knowledge and to execute a comprehensive design process, including verification of patents and legal regulations.

[0067] "Natural language" refers to the language that humans use on a daily basis, and it requires analysis to be understood by computers.

[0068] "Key elements" refer to the core characteristics and requirements of the design, identified from the user's input information.

[0069] "Requirements" refer to the conditions or specifications that must be met for a particular idea or product.

[0070] A "design specification" is a document that describes the detailed specifications and requirements of the object being designed, and it forms the basis of the design activity.

[0071] A "design drawing" is a diagram that visually shows the shape, dimensions, and layout of a product, and serves as a guideline for manufacturing.

[0072] "Automatic generation" refers to a process where a computer system handles the necessary data and documents without manual intervention.

[0073] "Patentability" refers to the fact that an invention or idea is novel, highly inventive, and eligible for legal protection.

[0074] "Patent infringement" refers to the act of using or imitating technology or products that are already protected by patents without permission.

[0075] "Legal regulations" refer to laws and rules that apply to specific industries or products, and are established to ensure safety and quality.

[0076] "Safety standards" are criteria set to ensure that products and processes are operated safely.

[0077] "Two-way communication" is a form of communication in which users and systems can exchange information with each other and interact in real time.

[0078] This invention is a system for transforming user ideas into concrete forms and efficiently advancing the hardware design process. The system is built using a terminal and a server. The user first inputs their ideas and requirements in natural language via the terminal. This information is then transmitted from the terminal to the server.

[0079] The server utilizes a Large-Scale Language Model (LLM) to analyze user input. During this analysis, key elements and requirements are extracted from the user's request. The server then uses a generative AI model to automatically generate a design specification based on the extracted information. This design specification includes specific product functions, required components, and recommended materials.

[0080] Next, the server uses a design-specific generation AI to generate design drawings and digital data based on the design specifications. This process also automatically performs component selection and manufacturing process simulations. Through this system, users can design products from multiple perspectives and comprehensively.

[0081] Furthermore, the server utilizes patent-specific generation AI to verify whether the generated design is patentable. In this process, the server refers to existing patent data to determine whether or not there is patent infringement. It also verifies that the design complies with legal regulations and safety standards. The results of the verification are fed back to the user, who can then review and modify the design based on that feedback.

[0082] As a concrete example, let's consider a scenario where a user inputs a request into a terminal stating, "I want to design a safe robot toy that children can play with." The server receives this information and creates a design specification for the robot toy with appropriate materials and functions. Subsequently, it provides the user with the automatically generated design drawings along with the results of patent and regulatory clearance procedures.

[0083] Furthermore, an example of a prompt message is: "Generate a design specification for a safe robot toy that children can play with. This toy must be durable, and the materials used must meet safety standards." This prompt allows the user to quickly proceed with the specific product design process.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user inputs ideas and requests into the terminal in natural language. This is the initial input to the system. The input data includes specific purposes and required functions. This information is sent from the terminal to the server.

[0087] Step 2:

[0088] The server analyzes the received natural language data using a Large-Scale Language Model (LLM). This analysis extracts key elements and requirements from the input text. For example, from the input information "safe robot toy," safety requirements and standards are identified. The output of the analysis is a list of extracted requirements.

[0089] Step 3:

[0090] The server generates design specifications using an AI model based on the extracted requirements. During this process, the system generates a recommended list of necessary parts and information about the materials to be used. The specification output includes details such as part type, quantity, and characteristics.

[0091] Step 4:

[0092] The server inputs the generated design specifications into a design-specialized generation AI, which automatically generates detailed design drawings and digital data. At this stage, it processes a wide range of data, including material properties and manufacturing simulations, and outputs a visual design. The output results include CAD data and manufacturing procedures.

[0093] Step 5:

[0094] The server uses a patent-specific generation AI to compare the generated data with a patent database to determine whether the design is patentable or does not infringe on existing patents. This process yields the patent clearance results. The output is a patent evaluation report.

[0095] Step 6:

[0096] The server verifies compliance with legal regulations and safety standards. It checks that the design adheres to relevant regulations and generates the results. The output includes a legal compliance assessment.

[0097] Step 7:

[0098] Users can view design and evaluation results sent from the server on their terminal. Users can modify the design as needed and provide new input based on feedback. New input based on user responses triggers a return to step 1, starting the next processing cycle.

[0099] (Application Example 1)

[0100] 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."

[0101] In today's product design process, it is difficult for users to materialize their ideas and turn them into products without specialized knowledge. Furthermore, the process of checking legal regulations, safety standards, and patents during product development is complex and requires considerable time and effort. Therefore, there is a need for efficient and automated design systems.

[0102] 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.

[0103] In this invention, the server includes means for analyzing natural language information input by a user to extract key elements and requirements; means for generating design specifications based on the extracted requirements; means for automatically generating design drawings and data based on the generated design specifications; means for generating proposed component lists, material lists, and manufacturing processes in response to specific functional requests from the user; and means for representing the details of the production process using the system and verifying its suitability. This enables users to easily and quickly realize their ideas and advance product development.

[0104] "Natural language information" refers to information expressed in the form of words and sentences that users use on a daily basis.

[0105] "Key elements" is a term that refers to important components or features in design and product development.

[0106] A "requirement" is a condition or standard that is necessary to achieve a specific objective.

[0107] A "design specification" is a document that outlines the detailed requirements and elements related to the design of a product or system.

[0108] "Specific feature requests from users" refers to specific wishes or requests regarding features or characteristics that users particularly desire.

[0109] "Whether a generated design is patentable" refers to evaluating whether the design possesses novelty and inventiveness, and whether it is eligible for patent application.

[0110] "Means for determining whether or not a patent infringes" refers to a method of checking whether a new design infringes on other patents by comparing it with existing patents.

[0111] "Means of verifying conformity" refers to the process of checking whether a design or product conforms to the required standards and specifications.

[0112] "Components" is a term that refers to the individual parts or elements that make up a product or system.

[0113] A "materials list" is a list of the types and quantities of materials required to manufacture a product.

[0114] "Production process" refers to a series of tasks and procedures necessary to complete a product.

[0115] The implementation of this invention begins with the user inputting ideas and requirements in natural language via a terminal. The terminal sends this information to a server, which analyzes the input using a large-scale language model. Specifically, machine learning libraries such as TENSORFLOW® and PyTorch are used, and models such as OpenAI®'s GPT-4® are applied. This makes it possible to extract key elements and requirements.

[0116] The server generates design specifications based on the extracted information. Next, the server automatically generates design drawings and digital data using the CAD software's API. At this stage, software such as AutoCAD is used.

[0117] Furthermore, to verify that the generated design is patentable and does not infringe on any patents, the server accesses a patent database. For this purpose, it uses Python to retrieve information from the Lens.org database and check for compliance.

[0118] This system can also generate proposed component lists, material lists, and manufacturing processes in response to specific feature requests from users. This allows users to efficiently proceed with the process of realizing their ideas.

[0119] For example, if a user inputs "I want to design a household robot that can clean and do simple cooking," the server will generate a design specification and propose a design that meets safety standards and legal regulations. An example of a prompt message would be, "If you want to design a new household robot, please enter the desired functions and characteristics. Example: 'A robot that can take care of pets'."

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] Users input ideas and requirements specifications in natural language via their terminal. This input information is sent to the server in text format. This input data serves as the foundational information to be analyzed.

[0123] Step 2:

[0124] The server analyzes the received natural language information using a large-scale language model (e.g., GPT-4). At this stage, natural language processing techniques are used to extract key elements and requirements. The input to this process is natural language input from the user, and the output is a list of design requirements and key elements.

[0125] Step 3:

[0126] The server automatically generates a design specification based on the extracted requirements list. The generated design specification is used in the next step. This specification is a formalized document of the requirements, with the requirements list as input and the design specification as output.

[0127] Step 4:

[0128] The server uses the generated design specifications to automatically generate design drawings and digital data using the CAD software's API. In this step, detailed design drawings are created using software such as AutoCAD. The input is the design specifications, and the output is CAD files and digital data.

[0129] Step 5:

[0130] To verify whether the generated design is patentable, the server accesses a patent database and checks for patent existence. In this step, Python is used to retrieve patent information from the Lens.org database. The input is digital data, and the output is the determination of patentability and patent infringement.

[0131] Step 6:

[0132] The server generates a list of proposed components and materials, as well as a manufacturing process, in response to the user's specific functional requirements. The input for this step is the functional requirement, and the output is a list of proposed components, a list of materials, and a manufacturing process. This generation is automated and performed efficiently.

[0133] Step 7:

[0134] The generated design and related information are fed back to the user, allowing for design review and modification. In this step, the user makes a final productization decision based on the provided information. Inputs are design drawings and digital data, while outputs are user review and modification instructions.

[0135] 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.

[0136] This invention enables a hardware design system that better meets user needs by analyzing user emotions and incorporating them into the design process. The system is implemented in the following steps.

[0137] First, the user inputs their design ideas and requirements in natural language via a terminal. The terminal receives this information and sends it to the server. The server extracts requirements from this information and automatically generates a design specification. During this process, data on the user's initial emotions is also acquired.

[0138] The emotion engine analyzes the user's emotions from their input. Based on this analysis, the design concept is adjusted to match the user's emotions. For example, if the user is seeking "fun," the selection of colors and designs will be adjusted to be more colorful and playful.

[0139] Subsequently, the server uses a design-specific generative AI to create blueprints and digital data. Here, specific specifications are determined, reflecting the extracted requirements and sentiment analysis results. Furthermore, it is checked whether the design is patentable and whether it complies with legal regulations and safety standards.

[0140] The device monitors the user's emotional state in real time through sentiment analysis and provides feedback accordingly. If the user is satisfied with the results and shows a positive reaction, the design process moves to the next stage. This information is stored in the sentiment engine and used for future improvements.

[0141] As a concrete example, consider a case where a user wants to design an educational robot for children. The user inputs into the terminal that they want a "robot that children can learn from in a fun way." The server receives this intention and uses an emotion engine to analyze the emotion of "fun." Based on this emotion, the design is adjusted, and the design AI suggests various functions. As a result, a colorful and interactive robot design is automatically generated and provided to the user.

[0142] This system integrates user emotions with design intent, resulting in a more human-centered process and improved user satisfaction.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] Users input their design ideas and requirements in natural language via their device. This information includes the design's purpose, desired characteristics, and target users.

[0146] Step 2:

[0147] The terminal receives user input data and sends it to the server. The server then launches a large-scale language model to analyze the information and extract key requirements.

[0148] Step 3:

[0149] The server utilizes an emotion engine to analyze the user's emotional state from their input. This analysis detects the strongest emotion the user is experiencing and prepares it to be reflected in the design process.

[0150] Step 4:

[0151] The server generates a design specification based on the extracted requirements and sentiment analysis results. This specification includes design elements and feature suggestions that align with the user's emotions.

[0152] Step 5:

[0153] Using a design-focused generation AI, the server automatically generates design drawings and digital data. Here, design selections are made that align with the user's emotions, and specific specifications are defined.

[0154] Step 6:

[0155] The server evaluates whether the generated design is patentable and determines whether there is any patent infringement. At the same time, it also checks whether it complies with legal regulations and safety standards.

[0156] Step 7:

[0157] The terminal displays the design data and analysis results received from the server to the user. The user can then review the design based on this information and, if necessary, send modification requests to the server via the terminal.

[0158] Step 8:

[0159] The server receives user feedback and modifies / updates the design as needed. The final design data is converted to a format for manufacturing devices.

[0160] Step 9:

[0161] The device notifies the user of the revised final design data and provides guidelines for proceeding to the specific manufacturing stage. It then prepares the product to reflect the user's design intent and vision.

[0162] (Example 2)

[0163] 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".

[0164] Conventional design systems could not only extract key components and requirements from users' natural language requests, but also model user emotions and reflect them in the design process. Therefore, achieving human-centered design that considered user emotions and design intentions was difficult.

[0165] 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.

[0166] In this invention, the server includes means for analyzing natural language information input by the user to extract key components and requirements, means for generating specifications based on the extracted requirements and user sentiment information, and means for analyzing the user's sentiment to adjust the design concept. This makes it possible to incorporate user sentiment into the process from the early stages of design, resulting in a design that better meets user needs.

[0167] "Natural language information" refers to data expressed in the form of human language used in everyday life.

[0168] "Major components" refer to the fundamental and essential parts in the design of a system or product.

[0169] "Means for extracting requirements" refers to methods or techniques for identifying and extracting the conditions and requests necessary for design from the information provided by the user.

[0170] A "specification document" refers to a document that describes the detailed specifications and functions of a designed system or product.

[0171] "Emotional information" refers to data that expresses a user's emotional state or sensibilities.

[0172] "Means of adjusting the design concept" refers to methods or techniques for fine-tuning the basic policy and direction of the design based on user emotional information.

[0173] A "design drawing" refers to a diagram that visually represents the structure and function of a system or product.

[0174] A "generative model that automatically generates data" refers to a mathematical or computer-based model that uses algorithms to automatically create design drawings and specification data based on input information.

[0175] "Protectableness" refers to the possibility and conditions under which an invention or design can be legally protected as intellectual property.

[0176] "Means for determining infringement" refers to methods or technologies for evaluating and determining whether or not one's intellectual property rights are being infringed.

[0177] "Standard manufacturing equipment data format" refers to a data format that allows manufacturing equipment to correctly interpret design data and perform manufacturing.

[0178] "Regulations and safety standards" refer to the laws and safety guidelines that designs and products must comply with.

[0179] This system enables more user-oriented design by integrating user emotions into the design process. Users connect to the system via a terminal and input their design ideas and requirements in natural language. The terminal uses an application called the "Design Interaction Platform" to collect this information and send it to the server.

[0180] After receiving the input natural language information, the server analyzes the requirements using a natural language processing tool such as the "NLTK Library" and extracts the main components. At the same time, it uses sentiment analysis tools such as "IBM Watson® Tone Analyzer" to obtain sentiment information from the user's input and identify the emotions the user is seeking.

[0181] By incorporating these requirements and emotional information, the server analyzes the user's emotions and adjusts the design concept accordingly. Specifically, colors, shapes, and design sensibilities are adjusted based on the user's emotions. The server then automatically generates design drawings and digital data using a design-specific generative AI model such as "Autodesk Generative Design." This generation process also verifies whether the design is patentable and compliant with legal regulations.

[0182] The device has the ability to monitor the user's emotional state in real time and provide appropriate feedback. If the user is satisfied with the design results, the system proceeds to the next stage of the design process. This feedback information is also stored in the emotion engine for future process improvements.

[0183] For example, if a user wants a "robot that children can learn from in a fun way," they would input this into the terminal. An example of a prompt might be, "Please extract the element of fun from the user's input and propose a design for a colorful, interactive educational robot." Based on this input, the server extracts the emotional element of "fun" and adjusts the design accordingly. The design AI model uses this information to automatically generate robot designs that meet various requirements, and the final result is provided to the user.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] Users input their design ideas and requirements in natural language via a terminal. This input data is collected by the "Design Interaction Platform." The input information is organized as text data representing the user's concept and prepared for transmission to the server.

[0187] Step 2:

[0188] The terminal sends user input to the server. Upon receiving this information, the server first analyzes the requirements using natural language processing technology and extracts the main components. The "NLTK library" is used here to analyze the input text data and identify the requirements necessary for the design. As output, requirements data that will form the basis of the specification document is generated.

[0189] Step 3:

[0190] The server simultaneously uses an "emotion analysis tool" to analyze the user's emotional information. In this step, emotions are identified from the natural language input by the user, and emotional elements that should be reflected in the design are revealed. For example, emotional data such as "fun" and "security" may be extracted.

[0191] Step 4:

[0192] The server adjusts the design concept based on the acquired requirements and emotional information. This process fine-tunes the design direction to match user emotions and determines specific design policies. As a result of this adjustment, emotionally-driven design guidelines are generated.

[0193] Step 5:

[0194] The server uses a design-specific generative AI model to automatically generate design drawings and digital data based on the refined design concept. Utilizing a "generative AI model," it integrates input requirements data and emotional information to generate output data that embodies the user's intent. This output serves as a guideline for realizing specific product designs.

[0195] Step 6:

[0196] The device displays the generated design data in real time and monitors the user's emotional state. When the user is satisfied with the results or provides positive feedback, this information is fed back into the emotion engine and used to improve the design process in the future. Ultimately, once the user is satisfied with the design results, the system is ready to move on to the next stage of the design process.

[0197] (Application Example 2)

[0198] 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".

[0199] Traditional hardware design systems failed to reflect user emotions, making it difficult to design products that accurately met individual user needs. Furthermore, the lack of a process to verify whether the design met individual needs sometimes resulted in decreased user satisfaction.

[0200] 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.

[0201] In this invention, the server includes means for analyzing natural language information input by the user to extract key components and requirements, means for generating a design specification document based on the extracted requirements, and means for acquiring and analyzing user emotion data. This enables flexible hardware design that responds to the user's emotions.

[0202] "Natural language information" refers to language information in the form that users use on a daily basis, and is input in the form of text, audio, etc.

[0203] "Major components" refer to the essential elements that form the basis of the design, and are the foundation of the specific design.

[0204] "Requirements" refer to conditions or criteria that specifically express the user's demands or desires.

[0205] A "design specification document" is a document that details the content, functions, and requirements to be designed.

[0206] A "design drawing" is a diagram that visually shows the shape, structure, and mechanism of a design.

[0207] "Information" is a general term for all data and materials related to the design.

[0208] "Emotional data" refers to information that quantitatively or descriptively represents a user's emotional state.

[0209] "Means of analysis" refer to methods and techniques for analyzing given information and extracting necessary data and results.

[0210] "Methods for adjusting the design concept" refer to techniques for changing the direction and style of a design based on analyzed emotional data.

[0211] "Patentable" means that an invention possesses novelty, inventiveness, and industrial applicability.

[0212] "Patent infringement" refers to the act of infringing on the rights of an already registered patent without permission.

[0213] A "specific manufacturing equipment format" refers to a particular format or protocol suitable for product manufacturing.

[0214] "Legal regulations" refers to the laws and regulatory standards that a design must adhere to.

[0215] "Safety standards" are criteria and guidelines that designs must follow to ensure safety for users and their surroundings.

[0216] The embodiment of the invention provides a system for designing hardware that reflects the user's emotions. This system begins with the user inputting design requirements in natural language via a smartphone or other input device. The device receives this information and transmits the data to a server.

[0217] The server analyzes the received information, extracts key components and requirements using natural language processing techniques, and generates a design specification document. This process incorporates an emotion engine that simultaneously acquires and analyzes the user's emotional data. The design concept is adjusted according to the emotional state. For example, if the emotion "happy" is detected, the design changes to become more colorful and playful.

[0218] Based on the design specifications, design drawings and other data are automatically generated using an AI model. During the generation process, patentability and compliance with laws, regulations, and safety standards are checked. This allows for an automated evaluation of whether the proposed design is novel to prior art and whether there are any legal or safety issues.

[0219] Users can review design proposals in real time and provide feedback along the way. If a positive response is given, the system proceeds to the next stage of the design process. The final design data is converted to a format suitable for specific manufacturing equipment and output in a form usable for the manufacturing process.

[0220] For example, a user might want to design a robot that children can learn from in a fun way. In this case, the design AI analyzes the user's input and emotional data to determine what constitutes "fun," and then automatically generates a colorful, interactive robot design. An example of a prompt to the generating AI model would be, "What should a robot design look like when a user is looking for fun?"

[0221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0222] Step 1:

[0223] The user inputs design requirements in natural language via an input terminal.

[0224] The input natural language data is sent from the terminal to the server. The input here is text data containing the user's wishes and requests. The output is the raw natural language data sent to the server.

[0225] Step 2:

[0226] The server analyzes the received natural language data and uses natural language processing techniques to extract key components and requirements.

[0227] Specifically, the input text data is tokenized, and syntactic and semantic analysis is performed to clarify the user's design intent. The output is a list of extracted requirements and key components.

[0228] Step 3:

[0229] The server uses an emotion engine to analyze the user's emotional data.

[0230] The input consists of text data from Step 1 and the user's past sentiment history. The system infers the sentiment state based on keywords and context, and the output is the analyzed sentiment data.

[0231] Step 4:

[0232] The server adjusts its design concept based on the analyzed sentiment data.

[0233] Specifically, this involves selecting a design theme based on emotions and determining the direction of color and shape. The inputs are requirements and emotional data, and the output is a refined design concept.

[0234] Step 5:

[0235] Based on the refined design concept, the server automatically generates blueprints and other digital data using a generative AI model.

[0236] The AI ​​model generates design concepts and outputs generated design data, based on prompt text inputs.

[0237] Step 6:

[0238] The server verifies the patentability of the generated design data and checks for compliance with laws, regulations, and safety standards.

[0239] The input is the generated design data. By referencing patent databases and legal and regulatory databases, the output is the result of verifying patentability and compliance with laws and regulations.

[0240] Step 7:

[0241] Users review the design generated by the server and provide feedback.

[0242] Specifically, the generated design data is displayed on the terminal, prompting the user for confirmation. The input is the design data, and the output is user feedback.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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).

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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".

[0259] This invention is a system for streamlining the hardware design process, aiming to realize user ideas and requirements as concrete products. This system is implemented in the following form:

[0260] Users input ideas and requirements in natural language via a terminal. This information is sent from the terminal to the server. The server uses a large-scale language model to analyze the user's input and extract key elements and requirements. This clarifies ambiguities and generates a design specification.

[0261] The generated design specifications are input by the server into a multimodal, design-focused generation AI, which automatically generates design drawings and digital data. This data includes component selection, material properties, and manufacturing process simulations based on user requirements.

[0262] Furthermore, the server uses patent-specific generation AI to verify whether the generated design is patentable or does not infringe on existing patents. It also checks for compliance with legal regulations and safety standards, and provides feedback to the user. The user can review the design based on this information and, if necessary, instruct modifications via their device.

[0263] As a concrete example, consider a scenario where a user wants to design a special toy for children. The user inputs "I want to design a safe robot toy that children can enjoy" into the terminal. The server analyzes this input and creates a design specification document suggesting suitable functions and materials. The automatically generated design is then checked from a patent and legal regulation perspective, and after confirming that there are no problems, it is provided to the user. The user can then review this and prepare to proceed to the manufacturing process.

[0264] This system allows even users without specialized knowledge to reliably turn their ideas into products and create business opportunities.

[0265] The following describes the processing flow.

[0266] Step 1:

[0267] Users input their concepts and requirements into the device using natural language. This information includes the purpose, target users, functionality, and design image.

[0268] Step 2:

[0269] The terminal receives user input and sends the data to the server. The server receives this information and begins analysis using a large-scale language model.

[0270] Step 3:

[0271] The server analyzes the user's input to understand it and extract key elements and requirements. This process clarifies ambiguous parts of the idea and generates a design specification.

[0272] Step 4:

[0273] Based on the generated design specifications, the server utilizes a design-specific generation AI to automatically generate design drawings and digital data. This process includes component selection, material property analysis, and manufacturing process simulations.

[0274] Step 5:

[0275] The server runs a patent-specific generation AI to evaluate the patentability of the generated designs and check for infringement of existing patents. It also evaluates whether the designs comply with legal regulations and safety standards.

[0276] Step 6:

[0277] The terminal provides the user with design data received from the server, patentability evaluation results, and legal compliance check results. The user then reviews the design based on this information and requests modifications if necessary.

[0278] Step 7:

[0279] The server modifies and adjusts the design data based on user feedback. The modified design data is then converted into a manufacturing-ready format.

[0280] Step 8:

[0281] The terminal provides the user with the final design data and notifies them of guidelines to begin the manufacturing process. Based on this, the user can proceed with the actual product manufacturing.

[0282] (Example 1)

[0283] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0284] The conventional hardware design process requires high expertise to embody the user's idea in a specific form, and is often time-consuming and labor-intensive. Furthermore, additional experts and resources are required to consider patents and regulations at the design stage, which has been a factor causing project delays and cost increases. Therefore, there has been a demand for a system that allows users without expertise to efficiently proceed with the design process and easily meet legal requirements.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0286] In this invention, the server includes means for analyzing natural language information input from a user to extract main elements and requirements, means for generating a design specification based on the extracted requirements, means for automatically generating generated design drawings and data, and means for checking whether the generated design has patentability or complies with regulations and safety standards. As a result, the user can efficiently and legally embody their idea without having expertise, and can execute an overall design process including checking patents and regulations.

[0287] "Natural language" refers to words that humans use daily and requires analysis for the computer to understand.

[0288] The "main element" is something that identifies the core characteristics and requirements of the design from the user's input information.

[0289] The "requirement" refers to the conditions and specifications that should be satisfied in a specific idea or product.

[0290] A "design specification" is a document that describes the detailed specifications and requirements of the object being designed, and it forms the basis of the design activity.

[0291] A "design drawing" is a diagram that visually shows the shape, dimensions, and layout of a product, and serves as a guideline for manufacturing.

[0292] "Automatic generation" refers to a process where a computer system handles the necessary data and documents without manual intervention.

[0293] "Patentability" refers to the fact that an invention or idea is novel, highly inventive, and eligible for legal protection.

[0294] "Patent infringement" refers to the act of using or imitating technology or products that are already protected by patents without permission.

[0295] "Legal regulations" refer to laws and rules that apply to specific industries or products, and are established to ensure safety and quality.

[0296] "Safety standards" are criteria set to ensure that products and processes are operated safely.

[0297] "Two-way communication" is a form of communication in which users and systems can exchange information with each other and interact in real time.

[0298] This invention is a system for transforming user ideas into concrete forms and efficiently advancing the hardware design process. The system is built using a terminal and a server. The user first inputs their ideas and requirements in natural language via the terminal. This information is then transmitted from the terminal to the server.

[0299] The server utilizes a Large-Scale Language Model (LLM) to analyze user input. During this analysis, key elements and requirements are extracted from the user's request. The server then uses a generative AI model to automatically generate a design specification based on the extracted information. This design specification includes specific product functions, required components, and recommended materials.

[0300] Next, the server uses a design-specific generation AI to generate design drawings and digital data based on the design specifications. This process also automatically performs component selection and manufacturing process simulations. Through this system, users can design products from multiple perspectives and comprehensively.

[0301] Furthermore, the server utilizes patent-specific generation AI to verify whether the generated design is patentable. In this process, the server refers to existing patent data to determine whether or not there is patent infringement. It also verifies that the design complies with legal regulations and safety standards. The results of the verification are fed back to the user, who can then review and modify the design based on that feedback.

[0302] As a concrete example, let's consider a scenario where a user inputs a request into a terminal stating, "I want to design a safe robot toy that children can play with." The server receives this information and creates a design specification for the robot toy with appropriate materials and functions. Subsequently, it provides the user with the automatically generated design drawings along with the results of patent and regulatory clearance procedures.

[0303] Furthermore, an example of a prompt message is: "Generate a design specification for a safe robot toy that children can play with. This toy must be durable, and the materials used must meet safety standards." This prompt allows the user to quickly proceed with the specific product design process.

[0304] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0305] Step 1:

[0306] The user inputs ideas and requirements in natural language into the terminal. This becomes the first input to the system. The input data includes specific purposes and required functions. This information is sent from the terminal to the server.

[0307] Step 2:

[0308] The server analyzes the received natural language data by leveraging a large language model (LLM). Through this analysis, key elements and requirement specifications are extracted from the input text. For example, from the input information of "safe robot toy", requirements and criteria related to safety are identified. The output of the analysis is a list of extracted requirements.

[0309] Step 3:

[0310] The server creates a design specification using an AI model based on the extracted requirements. In this process, the system generates information about the recommended list of necessary components and materials to be used. The output of the specification includes the type, quantity, characteristics, etc. of the components.

[0311] Step 4:

[0312] The server inputs the generated design specification into a design - specialized generative AI to automatically generate specific design drawings and digital data. At this stage, a wide range of data such as material properties and manufacturing simulations are processed to output a visual design object. The output results include CAD data and manufacturing procedures.

[0313] Step 5:

[0314] The server uses a patent - specialized generative AI to compare the generated data with the patent database to check whether the design has patentability or does not conflict with existing patents. Through this process, the result of patent clearance is obtained. The output is an evaluation report regarding the patent.

[0315] Step 6:

[0316] The server verifies compliance with legal regulations and safety standards. It checks that the design adheres to relevant regulations and generates the results. The output includes a legal compliance assessment.

[0317] Step 7:

[0318] Users can view design and evaluation results sent from the server on their terminal. Users can modify the design as needed and provide new input based on feedback. New input based on user responses triggers a return to step 1, starting the next processing cycle.

[0319] (Application Example 1)

[0320] 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."

[0321] In today's product design process, it is difficult for users to materialize their ideas and turn them into products without specialized knowledge. Furthermore, the process of checking legal regulations, safety standards, and patents during product development is complex and requires considerable time and effort. Therefore, there is a need for efficient and automated design systems.

[0322] 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.

[0323] In this invention, the server includes means for analyzing natural language information input by a user to extract key elements and requirements; means for generating design specifications based on the extracted requirements; means for automatically generating design drawings and data based on the generated design specifications; means for generating proposed component lists, material lists, and manufacturing processes in response to specific functional requests from the user; and means for representing the details of the production process using the system and verifying its suitability. This enables users to easily and quickly realize their ideas and advance product development.

[0324] "Natural language information" refers to information expressed in the form of words and sentences that users use on a daily basis.

[0325] "Key elements" is a term that refers to important components or features in design and product development.

[0326] A "requirement" is a condition or standard that is necessary to achieve a specific objective.

[0327] A "design specification" is a document that outlines the detailed requirements and elements related to the design of a product or system.

[0328] "Specific feature requests from users" refers to specific wishes or requests regarding features or characteristics that users particularly desire.

[0329] "Whether a generated design is patentable" refers to evaluating whether the design possesses novelty and inventiveness, and whether it is eligible for patent application.

[0330] "Means for determining whether or not a patent infringes" refers to a method of checking whether a new design infringes on other patents by comparing it with existing patents.

[0331] "Means of verifying conformity" refers to the process of checking whether a design or product conforms to the required standards and specifications.

[0332] "Components" is a term that refers to the individual parts or elements that make up a product or system.

[0333] A "materials list" is a list of the types and quantities of materials required to manufacture a product.

[0334] "Production process" refers to a series of tasks and procedures necessary to complete a product.

[0335] The implementation of this invention begins with the user inputting ideas and requirements in natural language via a terminal. The terminal sends this information to a server, which analyzes the input using a large-scale language model. Specifically, machine learning libraries such as TensorFlow and PyTorch are used, and models such as OpenAI's GPT-4 are applied. This makes it possible to extract key elements and requirements.

[0336] The server generates design specifications based on the extracted information. Next, the server automatically generates design drawings and digital data using the CAD software's API. At this stage, software such as AutoCAD is used.

[0337] Furthermore, to verify that the generated design is patentable and does not infringe on any patents, the server accesses a patent database. For this purpose, it uses Python to retrieve information from the Lens.org database and check for compliance.

[0338] This system can also generate proposed component lists, material lists, and manufacturing processes in response to specific feature requests from users. This allows users to efficiently proceed with the process of realizing their ideas.

[0339] For example, if a user inputs "I want to design a household robot that can clean and do simple cooking," the server will generate a design specification and propose a design that meets safety standards and legal regulations. An example of a prompt message would be, "If you want to design a new household robot, please enter the desired functions and characteristics. Example: 'A robot that can take care of pets'."

[0340] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0341] Step 1:

[0342] Users input ideas and requirements specifications in natural language via their terminal. This input information is sent to the server in text format. This input data serves as the foundational information to be analyzed.

[0343] Step 2:

[0344] The server analyzes the received natural language information using a large-scale language model (e.g., GPT-4). At this stage, natural language processing techniques are used to extract key elements and requirements. The input to this process is natural language input from the user, and the output is a list of design requirements and key elements.

[0345] Step 3:

[0346] The server automatically generates a design specification based on the extracted requirements list. The generated design specification is used in the next step. This specification is a formalized document of the requirements, with the requirements list as input and the design specification as output.

[0347] Step 4:

[0348] The server uses the generated design specifications to automatically generate design drawings and digital data using the CAD software's API. In this step, detailed design drawings are created using software such as AutoCAD. The input is the design specifications, and the output is CAD files and digital data.

[0349] Step 5:

[0350] To verify whether the generated design is patentable, the server accesses a patent database and checks for patent existence. In this step, Python is used to retrieve patent information from the Lens.org database. The input is digital data, and the output is the determination of patentability and patent infringement.

[0351] Step 6:

[0352] The server generates a list of proposed components and materials, as well as a manufacturing process, in response to the user's specific functional requirements. The input for this step is the functional requirement, and the output is a list of proposed components, a list of materials, and a manufacturing process. This generation is automated and performed efficiently.

[0353] Step 7:

[0354] The generated design and related information are fed back to the user, allowing for design review and modification. In this step, the user makes a final productization decision based on the provided information. Inputs are design drawings and digital data, while outputs are user review and modification instructions.

[0355] 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.

[0356] This invention enables hardware design that better meets user needs by analyzing user emotions and incorporating them into the design process. The system is implemented in the following steps.

[0357] First, the user inputs their design ideas and requirements in natural language via a terminal. The terminal receives this information and sends it to the server. The server extracts requirements from this information and automatically generates a design specification. During this process, data on the user's initial emotions is also acquired.

[0358] The emotion engine analyzes the user's emotions from their input. Based on this analysis, the design concept is adjusted to match the user's emotions. For example, if the user is seeking "fun," the selection of colors and designs will be adjusted to be more colorful and playful.

[0359] Subsequently, the server uses a design-specific generative AI to create blueprints and digital data. Here, specific specifications are determined, reflecting the extracted requirements and sentiment analysis results. Furthermore, it is checked whether the design is patentable and whether it complies with legal regulations and safety standards.

[0360] The device monitors the user's emotional state in real time through sentiment analysis and provides feedback accordingly. If the user is satisfied with the results and shows a positive reaction, the design process moves to the next stage. This information is stored in the sentiment engine and used for future improvements.

[0361] As a concrete example, consider a case where a user wants to design an educational robot for children. The user inputs into the terminal that they want a "robot that children can learn from in a fun way." The server receives this intention and uses an emotion engine to analyze the emotion of "fun." Based on this emotion, the design is adjusted, and the design AI suggests various functions. As a result, a colorful and interactive robot design is automatically generated and provided to the user.

[0362] This system integrates user emotions with design intent, resulting in a more human-centered process and improved user satisfaction.

[0363] The following describes the processing flow.

[0364] Step 1:

[0365] Users input their design ideas and requirements in natural language via their device. This information includes the design's purpose, desired characteristics, and target users.

[0366] Step 2:

[0367] The terminal receives user input data and sends it to the server. The server then launches a large-scale language model to analyze the information and extract key requirements.

[0368] Step 3:

[0369] The server utilizes an emotion engine to analyze the user's emotional state from their input. This analysis detects the strongest emotion the user is experiencing and prepares it to be reflected in the design process.

[0370] Step 4:

[0371] The server generates a design specification based on the extracted requirements and sentiment analysis results. This specification includes design elements and feature suggestions that align with the user's emotions.

[0372] Step 5:

[0373] Using a design-focused generation AI, the server automatically generates design drawings and digital data. Here, design selections are made that align with the user's emotions, and specific specifications are defined.

[0374] Step 6:

[0375] The server evaluates whether the generated design is patentable and determines whether there is any patent infringement. At the same time, it also checks whether it complies with legal regulations and safety standards.

[0376] Step 7:

[0377] The terminal displays the design data and analysis results received from the server to the user. The user can then review the design based on this information and, if necessary, send modification requests to the server via the terminal.

[0378] Step 8:

[0379] The server receives user feedback and modifies / updates the design as needed. The final design data is converted to a format for manufacturing devices.

[0380] Step 9:

[0381] The device notifies the user of the revised final design data and provides guidelines for proceeding to the specific manufacturing stage. It then prepares the product to reflect the user's design intent and vision.

[0382] (Example 2)

[0383] 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".

[0384] Conventional design systems could not only extract key components and requirements from users' natural language requests, but also model user emotions and reflect them in the design process. Therefore, achieving human-centered design that considered user emotions and design intentions was difficult.

[0385] 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.

[0386] In this invention, the server includes means for analyzing natural language information input by the user to extract key components and requirements, means for generating specifications based on the extracted requirements and user sentiment information, and means for analyzing the user's sentiment to adjust the design concept. This makes it possible to incorporate user sentiment into the process from the early stages of design, resulting in a design that better meets user needs.

[0387] "Natural language information" refers to data expressed in the form of human language used in everyday life.

[0388] "Key components" refer to the fundamental and important parts in the design of a system or product.

[0389] "Means for extracting requirements" refers to methods or techniques for identifying and extracting the conditions and requests necessary for design from the information provided by the user.

[0390] A "specification document" refers to a document that describes the detailed specifications and functions of a designed system or product.

[0391] "Emotional information" refers to data that expresses a user's emotional state or sensibilities.

[0392] "Means of adjusting the design concept" refers to methods or techniques for fine-tuning the basic policy and direction of the design based on user emotional information.

[0393] A "design drawing" refers to a diagram that visually represents the structure and function of a system or product.

[0394] A "generative model that automatically generates data" refers to a mathematical or computer-based model that uses algorithms to automatically create design drawings and specification data based on input information.

[0395] "Protectableness" refers to the possibility and conditions under which an invention or design can be legally protected as intellectual property.

[0396] "Means for determining infringement" refers to methods or technologies for evaluating and determining whether or not one's intellectual property rights are being infringed.

[0397] "Standard manufacturing equipment data format" refers to a data format that allows manufacturing equipment to correctly interpret design data and perform manufacturing.

[0398] "Regulations and safety standards" refer to the laws and safety guidelines that designs and products must comply with.

[0399] This system enables more user-oriented design by integrating user emotions into the design process. Users connect to the system via a terminal and input their design ideas and requirements in natural language. The terminal uses an application called the "Design Interaction Platform" to collect this information and send it to the server.

[0400] After receiving the input natural language information, the server analyzes the requirements using a natural language processing tool such as the "NLTK Library" and extracts the main components. At the same time, it uses sentiment analysis tools such as "IBM Watson Tone Analyzer" to obtain sentiment information from the user's input and identify the emotions the user is seeking.

[0401] By incorporating these requirements and emotional information, the server analyzes the user's emotions and adjusts the design concept accordingly. Specifically, colors, shapes, and design sensibilities are adjusted based on the user's emotions. The server then automatically generates design drawings and digital data using a design-specific generative AI model such as "Autodesk Generative Design." This generation process also verifies whether the design is patentable and compliant with legal regulations.

[0402] The device has the ability to monitor the user's emotional state in real time and provide appropriate feedback. If the user is satisfied with the design results, the system proceeds to the next stage of the design process. This feedback information is also stored in the emotion engine for future process improvements.

[0403] For example, if a user wants a "robot that children can learn from in a fun way," they would input this into the terminal. An example of a prompt might be, "Please extract the element of fun from the user's input and propose a design for a colorful, interactive educational robot." Based on this input, the server extracts the emotional element of "fun" and adjusts the design accordingly. The design AI model uses this information to automatically generate robot designs that meet various requirements, and the final result is provided to the user.

[0404] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0405] Step 1:

[0406] Users input their design ideas and requirements in natural language via a terminal. This input data is collected by the "Design Interaction Platform." The input information is organized as text data representing the user's concept and prepared for transmission to the server.

[0407] Step 2:

[0408] The terminal sends user input to the server. Upon receiving this information, the server first analyzes the requirements using natural language processing technology and extracts the main components. The "NLTK library" is used here to analyze the input text data and identify the requirements necessary for the design. As output, requirements data that will form the basis of the specification document is generated.

[0409] Step 3:

[0410] The server simultaneously uses an "emotion analysis tool" to analyze the user's emotional information. In this step, emotions are identified from the natural language input by the user, and emotional elements that should be reflected in the design are revealed. For example, emotional data such as "fun" and "security" may be extracted.

[0411] Step 4:

[0412] The server adjusts the design concept based on the acquired requirements and emotional information. This process fine-tunes the design direction to match user emotions and determines specific design policies. As a result of this adjustment, emotionally-driven design guidelines are generated.

[0413] Step 5:

[0414] The server uses a design-specific generative AI model to automatically generate design drawings and digital data based on the refined design concept. Utilizing a "generative AI model," it integrates input requirements data and emotional information to generate output data that embodies the user's intent. This output serves as a guideline for realizing specific product designs.

[0415] Step 6:

[0416] The device displays the generated design data in real time and monitors the user's emotional state. When the user is satisfied with the results or provides positive feedback, this information is fed back into the emotion engine and used to improve the design process in the future. Ultimately, once the user is satisfied with the design results, the system is ready to move on to the next stage of the design process.

[0417] (Application Example 2)

[0418] 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."

[0419] Traditional hardware design systems failed to reflect user emotions, making it difficult to design products that accurately met individual user needs. Furthermore, the lack of a process to verify whether the design met individual needs sometimes resulted in decreased user satisfaction.

[0420] 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.

[0421] In this invention, the server includes means for analyzing natural language information input by the user to extract key components and requirements, means for generating a design specification document based on the extracted requirements, and means for acquiring and analyzing user emotion data. This enables flexible hardware design that responds to the user's emotions.

[0422] "Natural language information" refers to language information in the form that users use on a daily basis, and is input in the form of text, audio, etc.

[0423] "Major components" refer to the essential elements that form the basis of the design, and are the foundation of the specific design.

[0424] "Requirements" refer to conditions or criteria that specifically express the user's demands or desires.

[0425] A "design specification document" is a document that details the content, functions, and requirements to be designed.

[0426] A "design drawing" is a diagram that visually shows the shape, structure, and mechanism of a design.

[0427] "Information" is a general term for all data and materials related to the design.

[0428] "Emotional data" refers to information that quantitatively or descriptively represents a user's emotional state.

[0429] "Means of analysis" refer to methods and techniques for analyzing given information and extracting necessary data and results.

[0430] "Methods for adjusting the design concept" refer to techniques for changing the direction and style of a design based on analyzed emotional data.

[0431] "Patentable" means that an invention possesses novelty, inventiveness, and industrial applicability.

[0432] "Patent infringement" refers to the act of infringing on the rights of an already registered patent without permission.

[0433] A "specific manufacturing equipment format" refers to a particular format or protocol suitable for product manufacturing.

[0434] "Legal regulations" refers to the laws and regulatory standards that a design must adhere to.

[0435] "Safety standards" are criteria and guidelines that designs must follow to ensure safety for users and their surroundings.

[0436] The embodiment of the invention provides a system for designing hardware that reflects the user's emotions. This system begins with the user inputting design requirements in natural language via a smartphone or other input device. The device receives this information and transmits the data to a server.

[0437] The server analyzes the received information, extracts key components and requirements using natural language processing techniques, and generates a design specification document. This process incorporates an emotion engine that simultaneously acquires and analyzes the user's emotional data. The design concept is adjusted according to the emotional state. For example, if the emotion "happy" is detected, the design changes to become more colorful and playful.

[0438] Based on the design specifications, design drawings and other data are automatically generated using an AI model. During the generation process, patentability and compliance with laws, regulations, and safety standards are checked. This allows for an automated evaluation of whether the proposed design is novel to prior art and whether there are any legal or safety issues.

[0439] Users can review design proposals in real time and provide feedback along the way. If a positive response is given, the system proceeds to the next stage of the design process. The final design data is converted to a format suitable for specific manufacturing equipment and output in a form usable for the manufacturing process.

[0440] For example, a user might want to design a robot that children can learn from in a fun way. In this case, the design AI analyzes the user's input and emotional data to determine what constitutes "fun," and then automatically generates a colorful, interactive robot design. An example of a prompt to the generating AI model would be, "What should a robot design look like when a user is looking for fun?"

[0441] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0442] Step 1:

[0443] The user inputs design requirements in natural language via an input terminal.

[0444] The input natural language data is sent from the terminal to the server. The input here is text data containing the user's wishes and requests. The output is the raw natural language data sent to the server.

[0445] Step 2:

[0446] The server analyzes the received natural language data and uses natural language processing techniques to extract key components and requirements.

[0447] Specifically, the input text data is tokenized, and syntactic and semantic analysis is performed to clarify the user's design intent. The output is a list of extracted requirements and key components.

[0448] Step 3:

[0449] The server uses an emotion engine to analyze the user's emotional data.

[0450] The input consists of text data from Step 1 and the user's past sentiment history. The system infers the sentiment state based on keywords and context, and the output is the analyzed sentiment data.

[0451] Step 4:

[0452] The server adjusts its design concept based on the analyzed sentiment data.

[0453] Specifically, a design theme is selected based on emotions, and the color and shape guidelines are determined. The inputs are requirements and emotional data, and the output is a refined design concept.

[0454] Step 5:

[0455] Based on the refined design concept, the server automatically generates blueprints and other digital data using a generative AI model.

[0456] The AI ​​generator is given prompt text to propose specific designs and functions. The input is a design concept, and the output is the generated design data.

[0457] Step 6:

[0458] The server verifies the patentability of the generated design data and checks for compliance with laws, regulations, and safety standards.

[0459] The input is the generated design data. By referencing patent databases and legal and regulatory databases, the output is the result of verifying patentability and compliance with laws and regulations.

[0460] Step 7:

[0461] Users review the design generated by the server and provide feedback.

[0462] Specifically, the generated design data is displayed on the terminal, prompting the user for confirmation. The input is the design data, and the output is user feedback.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] [Third Embodiment]

[0467] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0468] 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.

[0469] 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).

[0470] 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.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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".

[0479] This invention is a system for streamlining the hardware design process, aiming to realize user ideas and requirements as concrete products. This system is implemented in the following form:

[0480] Users input ideas and requirements in natural language via a terminal. This information is sent from the terminal to the server. The server uses a large-scale language model to analyze the user's input and extract key elements and requirements. This clarifies ambiguities and generates a design specification.

[0481] The generated design specifications are input by the server into a multimodal, design-focused generation AI, which automatically generates design drawings and digital data. This data includes component selection, material properties, and manufacturing process simulations based on user requirements.

[0482] Furthermore, the server uses patent-specific generation AI to verify whether the generated design is patentable or does not infringe on existing patents. It also checks for compliance with legal regulations and safety standards, and provides feedback to the user. The user can review the design based on this information and, if necessary, instruct modifications via their device.

[0483] As a concrete example, consider a scenario where a user wants to design a special toy for children. The user inputs "I want to design a safe robot toy that children can enjoy" into the terminal. The server analyzes this input and creates a design specification document suggesting suitable functions and materials. The automatically generated design is then checked from a patent and legal regulation perspective, and after confirming that there are no problems, it is provided to the user. The user can then review this and prepare to proceed to the manufacturing process.

[0484] This system allows even users without specialized knowledge to reliably turn their ideas into products and create business opportunities.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] Users input their concepts and requirements into the device using natural language. This information includes the purpose, target users, functionality, and design image.

[0488] Step 2:

[0489] The terminal receives user input and sends the data to the server. The server receives this information and begins analysis using a large-scale language model.

[0490] Step 3:

[0491] The server analyzes the user's input to understand it and extract key elements and requirements. This process clarifies ambiguous parts of the idea and generates a design specification.

[0492] Step 4:

[0493] Based on the generated design specifications, the server utilizes a design-specific generation AI to automatically generate design drawings and digital data. This process includes component selection, material property analysis, and manufacturing process simulations.

[0494] Step 5:

[0495] The server runs a patent-specific generation AI to evaluate the patentability of the generated designs and check for infringement of existing patents. It also evaluates whether the designs comply with legal regulations and safety standards.

[0496] Step 6:

[0497] The terminal provides the user with design data received from the server, patentability evaluation results, and legal compliance check results. The user then reviews the design based on this information and requests modifications if necessary.

[0498] Step 7:

[0499] The server modifies and adjusts the design data based on user feedback. The modified design data is then converted into a manufacturing-ready format.

[0500] Step 8:

[0501] The terminal provides the user with the final design data and notifies them of guidelines to begin the manufacturing process. Based on this, the user can proceed with the actual product manufacturing.

[0502] (Example 1)

[0503] 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."

[0504] Traditional hardware design processes often required a high level of expertise to translate user ideas into concrete forms, and were time-consuming and laborious. Furthermore, considering patents and legal regulations during the design phase necessitated additional experts and resources, leading to project delays and increased costs. Therefore, there was a need for a system that would allow users without specialized knowledge to efficiently advance the design process while easily meeting legal requirements.

[0505] 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.

[0506] In this invention, the server includes means for analyzing natural language information input by the user to extract key elements and requirements, means for generating design specifications based on the extracted requirements, means for automatically generating the generated design drawings and data, and means for verifying whether the generated design is patentable and complies with legal regulations and safety standards. This enables users to efficiently and legally realize their ideas without specialized knowledge and to execute a comprehensive design process, including verification of patents and legal regulations.

[0507] "Natural language" refers to the language that humans use on a daily basis, and it requires analysis to be understood by computers.

[0508] "Key elements" refer to the core characteristics and requirements of the design, identified from the user's input information.

[0509] "Requirements" refer to the conditions or specifications that must be met for a particular idea or product.

[0510] A "design specification" is a document that describes the detailed specifications and requirements of the object being designed, and it forms the basis of the design activity.

[0511] A "design drawing" is a diagram that visually shows the shape, dimensions, and layout of a product, and serves as a guideline for manufacturing.

[0512] "Automatic generation" refers to a process where a computer system handles the necessary data and documents without manual intervention.

[0513] "Patentability" refers to the fact that an invention or idea is novel, highly inventive, and eligible for legal protection.

[0514] "Patent infringement" refers to the act of using or imitating technology or products that are already protected by patents without permission.

[0515] "Legal regulations" refer to laws and rules that apply to specific industries or products, and are established to ensure safety and quality.

[0516] "Safety standards" are criteria set to ensure that products and processes are operated safely.

[0517] "Two-way communication" is a form of communication in which users and systems can exchange information with each other and interact in real time.

[0518] This invention is a system for transforming user ideas into concrete forms and efficiently advancing the hardware design process. The system is built using a terminal and a server. The user first inputs their ideas and requirements in natural language via the terminal. This information is then transmitted from the terminal to the server.

[0519] The server utilizes a Large-Scale Language Model (LLM) to analyze user input. During this analysis, key elements and requirements are extracted from the user's request. The server then uses a generative AI model to automatically generate a design specification based on the extracted information. This design specification includes specific product functions, required components, and recommended materials.

[0520] Next, the server uses a design-specific generation AI to generate design drawings and digital data based on the design specifications. This process also automatically performs component selection and manufacturing process simulations. Through this system, users can design products from multiple perspectives and comprehensively.

[0521] Furthermore, the server utilizes patent-specific generation AI to verify whether the generated design is patentable. In this process, the server refers to existing patent data to determine whether or not there is patent infringement. It also verifies that the design complies with legal regulations and safety standards. The results of the verification are fed back to the user, who can then review and modify the design based on that feedback.

[0522] As a concrete example, let's consider a scenario where a user inputs a request into a terminal stating, "I want to design a safe robot toy that children can play with." The server receives this information and creates a design specification for the robot toy with appropriate materials and functions. Subsequently, it provides the user with the automatically generated design drawings along with the results of patent and regulatory clearance procedures.

[0523] Furthermore, an example of a prompt message is: "Generate a design specification for a safe robot toy that children can play with. This toy must be durable, and the materials used must meet safety standards." This prompt allows the user to quickly proceed with the specific product design process.

[0524] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0525] Step 1:

[0526] The user inputs ideas and requests into the terminal in natural language. This is the initial input to the system. The input data includes specific purposes and required functions. This information is sent from the terminal to the server.

[0527] Step 2:

[0528] The server analyzes the received natural language data using a Large-Scale Language Model (LLM). This analysis extracts key elements and requirements from the input text. For example, from the input information "safe robot toy," safety requirements and standards are identified. The output of the analysis is a list of extracted requirements.

[0529] Step 3:

[0530] The server generates design specifications using an AI model based on the extracted requirements. During this process, the system generates a recommended list of necessary parts and information about the materials to be used. The specification output includes part types, quantities, and characteristics.

[0531] Step 4:

[0532] The server inputs the generated design specifications into a design-specialized generation AI, which automatically generates detailed design drawings and digital data. At this stage, it processes a wide range of data, including material properties and manufacturing simulations, and outputs a visual design. The output results include CAD data and manufacturing procedures.

[0533] Step 5:

[0534] The server uses a patent-specific generation AI to compare the generated data with a patent database to determine whether the design is patentable or does not infringe on existing patents. This process yields the patent clearance results. The output is a patent evaluation report.

[0535] Step 6:

[0536] The server verifies compliance with legal regulations and safety standards. It checks that the design adheres to relevant regulations and generates the results. The output includes a legal compliance assessment.

[0537] Step 7:

[0538] Users can view design and evaluation results sent from the server on their terminal. Users can modify the design as needed and provide new input based on feedback. New input based on user responses triggers a return to step 1, starting the next processing cycle.

[0539] (Application Example 1)

[0540] 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."

[0541] In today's product design process, it is difficult for users to materialize their ideas and turn them into products without specialized knowledge. Furthermore, the process of checking legal regulations, safety standards, and patents during product development is complex and requires considerable time and effort. Therefore, there is a need for efficient and automated design systems.

[0542] 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.

[0543] In this invention, the server includes means for analyzing natural language information input by a user to extract key elements and requirements; means for generating design specifications based on the extracted requirements; means for automatically generating design drawings and data based on the generated design specifications; means for generating proposed component lists, material lists, and manufacturing processes in response to specific functional requests from the user; and means for representing the details of the production process using the system and verifying its suitability. This enables users to easily and quickly realize their ideas and advance product development.

[0544] "Natural language information" refers to information expressed in the form of words and sentences that users use on a daily basis.

[0545] "Key elements" is a term that refers to important components or features in design and product development.

[0546] A "requirement" is a condition or standard that is necessary to achieve a specific objective.

[0547] A "design specification" is a document that outlines the detailed requirements and elements related to the design of a product or system.

[0548] "Specific feature requests from users" refers to specific wishes or requests regarding features or characteristics that users particularly desire.

[0549] "Whether a generated design is patentable" refers to evaluating whether the design possesses novelty and inventiveness, and whether it is eligible for patent application.

[0550] "Means for determining whether or not a patent infringes" refers to a method of checking whether a new design infringes on other patents by comparing it with existing patents.

[0551] "Means of verifying conformity" refers to the process of checking whether a design or product conforms to the required standards and specifications.

[0552] "Components" is a term that refers to the individual parts or elements that make up a product or system.

[0553] A "materials list" is a list of the types and quantities of materials required to manufacture a product.

[0554] "Production process" refers to a series of tasks and procedures necessary to complete a product.

[0555] The implementation of this invention begins with the user inputting ideas and requirements in natural language via a terminal. The terminal sends this information to a server, which analyzes the input using a large-scale language model. Specifically, machine learning libraries such as TensorFlow and PyTorch are used, and models such as OpenAI's GPT-4 are applied. This makes it possible to extract key elements and requirements.

[0556] The server generates design specifications based on the extracted information. Next, the server automatically generates design drawings and digital data using the CAD software's API. At this stage, software such as AutoCAD is used.

[0557] Furthermore, to verify that the generated design is patentable and does not infringe on any patents, the server accesses a patent database. For this purpose, it uses Python to retrieve information from the Lens.org database and check for compliance.

[0558] This system can also generate proposed component lists, material lists, and manufacturing processes in response to specific feature requests from users. This allows users to efficiently proceed with the process of realizing their ideas.

[0559] For example, if a user inputs "I want to design a household robot that can clean and do simple cooking," the server will generate a design specification and propose a design that meets safety standards and legal regulations. An example of a prompt message would be, "If you want to design a new household robot, please enter the desired functions and characteristics. Example: 'A robot that can take care of pets'."

[0560] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0561] Step 1:

[0562] Users input ideas and requirements specifications in natural language via their terminal. This input information is sent to the server in text format. This input data serves as the foundational information to be analyzed.

[0563] Step 2:

[0564] The server analyzes the received natural language information using a large-scale language model (e.g., GPT-4). At this stage, natural language processing techniques are used to extract key elements and requirements. The input to this process is natural language input from the user, and the output is a list of design requirements and key elements.

[0565] Step 3:

[0566] The server automatically generates a design specification based on the extracted requirements list. The generated design specification is used in the next step. This specification is a formalized document of the requirements, with the requirements list as input and the design specification as output.

[0567] Step 4:

[0568] The server uses the generated design specifications to automatically generate design drawings and digital data using the CAD software's API. In this step, detailed design drawings are created using software such as AutoCAD. The input is the design specifications, and the output is CAD files and digital data.

[0569] Step 5:

[0570] To verify whether the generated design is patentable, the server accesses a patent database and checks for patent existence. In this step, Python is used to retrieve patent information from the Lens.org database. The input is digital data, and the output is the determination of patentability and patent infringement.

[0571] Step 6:

[0572] The server generates a list of proposed components and materials, as well as a manufacturing process, in response to the user's specific functional requirements. The input for this step is the functional requirement, and the output is a list of proposed components, a list of materials, and a manufacturing process. This generation is automated and performed efficiently.

[0573] Step 7:

[0574] The generated design and related information are fed back to the user, allowing for design review and modification. In this step, the user makes a final productization decision based on the provided information. Inputs are design drawings and digital data, while outputs are user review and modification instructions.

[0575] 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.

[0576] This invention enables hardware design that better meets user needs by analyzing user emotions and incorporating them into the design process. The system is implemented in the following steps.

[0577] First, the user inputs their design ideas and requirements in natural language via a terminal. The terminal receives this information and sends it to the server. The server extracts requirements from this information and automatically generates a design specification. During this process, data on the user's initial emotions is also acquired.

[0578] The emotion engine analyzes the user's emotions from their input. Based on this analysis, the design concept is adjusted to match the user's emotions. For example, if the user is seeking "fun," the selection of colors and designs will be adjusted to be more colorful and playful.

[0579] Subsequently, the server uses a design-specific generative AI to create blueprints and digital data. Here, specific specifications are determined, reflecting the extracted requirements and sentiment analysis results. Furthermore, it is checked whether the design is patentable and whether it complies with legal regulations and safety standards.

[0580] The device monitors the user's emotional state in real time through sentiment analysis and provides feedback accordingly. If the user is satisfied with the results and shows a positive reaction, the design process moves to the next stage. This information is stored in the sentiment engine and used for future improvements.

[0581] As a concrete example, consider a case where a user wants to design an educational robot for children. The user inputs into the terminal that they want a "robot that children can learn from in a fun way." The server receives this intention and uses an emotion engine to analyze the emotion of "fun." Based on this emotion, the design is adjusted, and the design AI suggests various functions. As a result, a colorful and interactive robot design is automatically generated and provided to the user.

[0582] This system integrates user emotions with design intent, resulting in a more human-centered process and improved user satisfaction.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] Users input their design ideas and requirements in natural language via their device. This information includes the design's purpose, desired characteristics, and target users.

[0586] Step 2:

[0587] The terminal receives user input data and sends it to the server. The server then launches a large-scale language model to analyze the information and extract key requirements.

[0588] Step 3:

[0589] The server utilizes an emotion engine to analyze the user's emotional state from their input. This analysis detects the strongest emotion the user is experiencing and prepares it to be reflected in the design process.

[0590] Step 4:

[0591] The server generates a design specification based on the extracted requirements and sentiment analysis results. This specification includes design elements and feature suggestions that align with the user's emotions.

[0592] Step 5:

[0593] Using a design-focused generation AI, the server automatically generates design drawings and digital data. Here, design selections are made that align with the user's emotions, and specific specifications are defined.

[0594] Step 6:

[0595] The server evaluates whether the generated design is patentable and determines whether there is any patent infringement. At the same time, it also checks whether it complies with legal regulations and safety standards.

[0596] Step 7:

[0597] The terminal displays the design data and analysis results received from the server to the user. The user can then review the design based on this information and, if necessary, send modification requests to the server via the terminal.

[0598] Step 8:

[0599] The server receives user feedback and modifies / updates the design as needed. The final design data is converted to a format for manufacturing devices.

[0600] Step 9:

[0601] The device notifies the user of the revised final design data and provides guidelines for proceeding to the specific manufacturing stage. It then prepares the product to reflect the user's design intent and vision.

[0602] (Example 2)

[0603] 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."

[0604] Conventional design systems could not only extract key components and requirements from users' natural language requests, but also model user emotions and reflect them in the design process. Therefore, achieving human-centered design that considered user emotions and design intentions was difficult.

[0605] 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.

[0606] In this invention, the server includes means for analyzing natural language information input by the user to extract key components and requirements, means for generating specifications based on the extracted requirements and user sentiment information, and means for analyzing the user's sentiment to adjust the design concept. This makes it possible to incorporate user sentiment into the process from the early stages of design, resulting in a design that better meets user needs.

[0607] "Natural language information" refers to data expressed in the form of human language used in everyday life.

[0608] "Key components" refer to the fundamental and important parts in the design of a system or product.

[0609] "Means for extracting requirements" refers to methods or techniques for identifying and extracting the conditions and requests necessary for design from the information provided by the user.

[0610] A "specification document" refers to a document that describes the detailed specifications and functions of a designed system or product.

[0611] "Emotional information" refers to data that expresses a user's emotional state or sensibilities.

[0612] "Means of adjusting the design concept" refers to methods or techniques for fine-tuning the basic policy and direction of the design based on user emotional information.

[0613] A "design drawing" refers to a diagram that visually represents the structure and function of a system or product.

[0614] A "generative model that automatically generates data" refers to a mathematical or computer-based model that uses algorithms to automatically create design drawings and specification data based on input information.

[0615] "Protectableness" refers to the possibility and conditions under which an invention or design can be legally protected as intellectual property.

[0616] "Means for determining infringement" refers to methods or technologies for evaluating and determining whether or not one's intellectual property rights are being infringed.

[0617] "Standard manufacturing equipment data format" refers to a data format that allows manufacturing equipment to correctly interpret design data and perform manufacturing.

[0618] "Regulations and safety standards" refer to the laws and safety guidelines that designs and products must comply with.

[0619] This system enables more user-oriented design by integrating user emotions into the design process. Users connect to the system via a terminal and input their design ideas and requirements in natural language. The terminal uses an application called the "Design Interaction Platform" to collect this information and send it to the server.

[0620] After receiving the input natural language information, the server analyzes the requirements using a natural language processing tool such as the "NLTK Library" and extracts the main components. At the same time, it uses sentiment analysis tools such as "IBM Watson Tone Analyzer" to obtain sentiment information from the user's input and identify the emotions the user is seeking.

[0621] By incorporating these requirements and emotional information, the server analyzes the user's emotions and adjusts the design concept accordingly. Specifically, colors, shapes, and design sensibilities are adjusted based on the user's emotions. The server then automatically generates design drawings and digital data using a design-specific generative AI model such as "Autodesk Generative Design." This generation process also verifies whether the design is patentable and compliant with legal regulations.

[0622] The device has the ability to monitor the user's emotional state in real time and provide appropriate feedback. If the user is satisfied with the design results, the system proceeds to the next stage of the design process. This feedback information is also stored in the emotion engine for future process improvements.

[0623] For example, if a user wants a "robot that children can learn from in a fun way," they would input this into the terminal. An example of a prompt might be, "Please extract the element of fun from the user's input and propose a design for a colorful, interactive educational robot." Based on this input, the server extracts the emotional element of "fun" and adjusts the design accordingly. The design AI model uses this information to automatically generate robot designs that meet various requirements, and the final result is provided to the user.

[0624] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0625] Step 1:

[0626] Users input their design ideas and requirements in natural language via a terminal. This input data is collected by the "Design Interaction Platform." The input information is organized as text data representing the user's concept and prepared for transmission to the server.

[0627] Step 2:

[0628] The terminal sends user input to the server. Upon receiving this information, the server first analyzes the requirements using natural language processing technology and extracts the main components. The "NLTK library" is used here to analyze the input text data and identify the requirements necessary for the design. As output, requirements data that will form the basis of the specification document is generated.

[0629] Step 3:

[0630] The server simultaneously uses an "emotion analysis tool" to analyze the user's emotional information. In this step, emotions are identified from the natural language input by the user, and emotional elements that should be reflected in the design are revealed. For example, emotional data such as "fun" and "security" may be extracted.

[0631] Step 4:

[0632] The server adjusts the design concept based on the acquired requirements and emotional information. This process fine-tunes the design direction to match user emotions and determines specific design policies. As a result of this adjustment, emotionally-driven design guidelines are generated.

[0633] Step 5:

[0634] The server uses a design-specific generative AI model to automatically generate design drawings and digital data based on the refined design concept. Utilizing a "generative AI model," it integrates input requirements data and emotional information to generate output data that embodies the user's intent. This output serves as a guideline for realizing specific product designs.

[0635] Step 6:

[0636] The device displays the generated design data in real time and monitors the user's emotional state. When the user is satisfied with the results or provides positive feedback, this information is fed back into the emotion engine and used to improve the design process in the future. Ultimately, once the user is satisfied with the design results, the system is ready to move on to the next stage of the design process.

[0637] (Application Example 2)

[0638] 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."

[0639] Traditional hardware design systems failed to reflect user emotions, making it difficult to design products that accurately met individual user needs. Furthermore, the lack of a process to verify whether the design met individual needs sometimes resulted in decreased user satisfaction.

[0640] 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.

[0641] In this invention, the server includes means for analyzing natural language information input by the user to extract key components and requirements, means for generating a design specification document based on the extracted requirements, and means for acquiring and analyzing user emotion data. This enables flexible hardware design that responds to the user's emotions.

[0642] "Natural language information" refers to language information in the form that users use on a daily basis, and is input in the form of text, audio, etc.

[0643] "Major components" refer to the essential elements that form the basis of the design, and are the foundation of the specific design.

[0644] "Requirements" refer to conditions or criteria that specifically express the user's demands or desires.

[0645] A "design specification document" is a document that details the content, functions, and requirements to be designed.

[0646] A "design drawing" is a diagram that visually shows the shape, structure, and mechanism of a design.

[0647] "Information" is a general term for all data and materials related to the design.

[0648] "Emotional data" refers to information that quantitatively or descriptively represents a user's emotional state.

[0649] "Means of analysis" refer to methods and techniques for analyzing given information and extracting necessary data and results.

[0650] "Methods for adjusting the design concept" refer to techniques for changing the direction and style of a design based on analyzed emotional data.

[0651] "Patentable" means that an invention possesses novelty, inventiveness, and industrial applicability.

[0652] "Patent infringement" refers to the act of infringing on the rights of an already registered patent without permission.

[0653] A "specific manufacturing equipment format" refers to a particular format or protocol suitable for product manufacturing.

[0654] "Legal regulations" refers to the laws and regulatory standards that a design must adhere to.

[0655] "Safety standards" are criteria and guidelines that designs must follow to ensure safety for users and their surroundings.

[0656] The embodiment of the invention provides a system for designing hardware that reflects the user's emotions. This system begins with the user inputting design requirements in natural language via a smartphone or other input device. The device receives this information and transmits the data to a server.

[0657] The server analyzes the received information, extracts key components and requirements using natural language processing techniques, and generates a design specification document. This process incorporates an emotion engine that simultaneously acquires and analyzes the user's emotional data. The design concept is adjusted according to the emotional state. For example, if the emotion "happy" is detected, the design changes to become more colorful and playful.

[0658] Based on the design specifications, design drawings and other data are automatically generated using an AI model. During the generation process, patentability and compliance with laws, regulations, and safety standards are checked. This allows for an automated evaluation of whether the proposed design is novel to prior art and whether there are any legal or safety issues.

[0659] Users can review design proposals in real time and provide feedback along the way. If a positive response is given, the system proceeds to the next stage of the design process. The final design data is converted to a format suitable for specific manufacturing equipment and output in a form usable for the manufacturing process.

[0660] For example, a user might want to design a robot that children can learn from in a fun way. In this case, the design AI analyzes the user's input and emotional data to determine what constitutes "fun," and then automatically generates a colorful, interactive robot design. An example of a prompt to the generating AI model would be, "What should a robot design look like when a user is looking for fun?"

[0661] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0662] Step 1:

[0663] The user inputs design requirements in natural language via an input terminal.

[0664] The input natural language data is sent from the terminal to the server. The input here is text data containing the user's wishes and requests. The output is the raw natural language data sent to the server.

[0665] Step 2:

[0666] The server analyzes the received natural language data and uses natural language processing techniques to extract key components and requirements.

[0667] Specifically, the input text data is tokenized, and syntactic and semantic analysis is performed to clarify the user's design intent. The output is a list of extracted requirements and key components.

[0668] Step 3:

[0669] The server uses an emotion engine to analyze the user's emotional data.

[0670] The input consists of text data from Step 1 and the user's past sentiment history. The system infers the sentiment state based on keywords and context, and the output is the analyzed sentiment data.

[0671] Step 4:

[0672] The server adjusts its design concept based on the analyzed sentiment data.

[0673] Specifically, a design theme is selected based on emotions, and the color and shape guidelines are determined. The inputs are requirements and emotional data, and the output is a refined design concept.

[0674] Step 5:

[0675] Based on the refined design concept, the server automatically generates blueprints and other digital data using a generative AI model.

[0676] The AI ​​generator is given prompt text to propose specific designs and functions. The input is a design concept, and the output is the generated design data.

[0677] Step 6:

[0678] The server verifies the patentability of the generated design data and checks for compliance with laws, regulations, and safety standards.

[0679] The input is the generated design data. By referencing patent databases and legal and regulatory databases, the output is the result of verifying patentability and compliance with laws and regulations.

[0680] Step 7:

[0681] Users review the design generated by the server and provide feedback.

[0682] Specifically, the generated design data is displayed on the terminal, prompting the user for confirmation. The input is the design data, and the output is user feedback.

[0683] 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.

[0684] 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.

[0685] 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.

[0686] [Fourth Embodiment]

[0687] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0688] 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.

[0689] 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).

[0690] 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.

[0691] 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.

[0692] 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).

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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".

[0700] This invention is a system for streamlining the hardware design process, aiming to realize user ideas and requirements as concrete products. This system is implemented in the following form:

[0701] Users input ideas and requirements in natural language via a terminal. This information is sent from the terminal to the server. The server uses a large-scale language model to analyze the user's input and extract key elements and requirements. This clarifies ambiguities and generates a design specification.

[0702] The generated design specifications are input by the server into a multimodal, design-focused generation AI, which automatically generates design drawings and digital data. This data includes component selection, material properties, and manufacturing process simulations based on user requirements.

[0703] Furthermore, the server uses patent-specific generation AI to verify whether the generated design is patentable or does not infringe on existing patents. It also checks for compliance with legal regulations and safety standards, and provides feedback to the user. The user can review the design based on this information and, if necessary, instruct modifications via their device.

[0704] As a concrete example, consider a scenario where a user wants to design a special toy for children. The user inputs "I want to design a safe robot toy that children can enjoy" into the terminal. The server analyzes this input and creates a design specification document suggesting suitable functions and materials. The automatically generated design is then checked from a patent and legal regulation perspective, and after confirming that there are no problems, it is provided to the user. The user can then review this and prepare to proceed to the manufacturing process.

[0705] This system allows even users without specialized knowledge to reliably turn their ideas into products and create business opportunities.

[0706] The following describes the processing flow.

[0707] Step 1:

[0708] Users input their concepts and requirements into the device using natural language. This information includes the purpose, target users, functionality, and design image.

[0709] Step 2:

[0710] The terminal receives user input and sends the data to the server. The server receives this information and begins analysis using a large-scale language model.

[0711] Step 3:

[0712] The server analyzes the user's input to understand it and extract key elements and requirements. This process clarifies ambiguous parts of the idea and generates a design specification.

[0713] Step 4:

[0714] Based on the generated design specifications, the server utilizes a design-specific generation AI to automatically generate design drawings and digital data. This process includes component selection, material property analysis, and manufacturing process simulations.

[0715] Step 5:

[0716] The server runs a patent-specific generation AI to evaluate the patentability of the generated designs and check for infringement of existing patents. It also evaluates whether the designs comply with legal regulations and safety standards.

[0717] Step 6:

[0718] The terminal provides the user with design data received from the server, patentability evaluation results, and legal compliance check results. The user then reviews the design based on this information and requests modifications if necessary.

[0719] Step 7:

[0720] The server modifies and adjusts the design data based on user feedback. The modified design data is then converted into a manufacturing-ready format.

[0721] Step 8:

[0722] The terminal provides the user with the final design data and notifies them of guidelines to begin the manufacturing process. Based on this, the user can proceed with the actual product manufacturing.

[0723] (Example 1)

[0724] 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".

[0725] Traditional hardware design processes often required a high level of expertise to translate user ideas into concrete forms, and were time-consuming and laborious. Furthermore, considering patents and legal regulations during the design phase necessitated additional experts and resources, leading to project delays and increased costs. Therefore, there was a need for a system that would allow users without specialized knowledge to efficiently advance the design process while easily meeting legal requirements.

[0726] 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.

[0727] In this invention, the server includes means for analyzing natural language information input by the user to extract key elements and requirements, means for generating design specifications based on the extracted requirements, means for automatically generating the generated design drawings and data, and means for verifying whether the generated design is patentable and complies with legal regulations and safety standards. This enables users to efficiently and legally realize their ideas without specialized knowledge and to execute a comprehensive design process, including verification of patents and legal regulations.

[0728] "Natural language" refers to the language that humans use on a daily basis, and it requires analysis to be understood by computers.

[0729] "Key elements" refer to the core characteristics and requirements of the design, identified from the user's input information.

[0730] "Requirements" refer to the conditions or specifications that must be met for a particular idea or product.

[0731] A "design specification" is a document that describes the detailed specifications and requirements of the object being designed, and it forms the basis of the design activity.

[0732] A "design drawing" is a diagram that visually shows the shape, dimensions, and layout of a product, and serves as a guideline for manufacturing.

[0733] "Automatic generation" refers to a process where a computer system handles the necessary data and documents without manual intervention.

[0734] "Patentability" refers to the fact that an invention or idea is novel, highly inventive, and eligible for legal protection.

[0735] "Patent infringement" refers to the act of using or imitating technology or products that are already protected by patents without permission.

[0736] "Legal regulations" refer to laws and rules that apply to specific industries or products, and are established to ensure safety and quality.

[0737] "Safety standards" are criteria set to ensure that products and processes are operated safely.

[0738] "Two-way communication" is a form of communication in which users and systems can exchange information with each other and interact in real time.

[0739] This invention is a system for transforming user ideas into concrete forms and efficiently advancing the hardware design process. The system is built using a terminal and a server. The user first inputs their ideas and requirements in natural language via the terminal. This information is then transmitted from the terminal to the server.

[0740] The server utilizes a Large-Scale Language Model (LLM) to analyze user input. During this analysis, key elements and requirements are extracted from the user's request. The server then uses a generative AI model to automatically generate a design specification based on the extracted information. This design specification includes specific product functions, required components, and recommended materials.

[0741] Next, the server uses a design-specific generation AI to generate design drawings and digital data based on the design specifications. This process also automatically performs component selection and manufacturing process simulations. Through this system, users can design products from multiple perspectives and comprehensively.

[0742] Furthermore, the server utilizes patent-specific generation AI to verify whether the generated design is patentable. In this process, the server refers to existing patent data to determine whether or not there is patent infringement. It also verifies that the design complies with legal regulations and safety standards. The results of the verification are fed back to the user, who can then review and modify the design based on that feedback.

[0743] As a concrete example, let's consider a scenario where a user inputs a request into a terminal stating, "I want to design a safe robot toy that children can play with." The server receives this information and creates a design specification for the robot toy with appropriate materials and functions. Subsequently, it provides the user with the automatically generated design drawings along with the results of patent and regulatory clearance procedures.

[0744] Furthermore, an example of a prompt message is: "Generate a design specification for a safe robot toy that children can play with. This toy must be durable, and the materials used must meet safety standards." This prompt allows the user to quickly proceed with the specific product design process.

[0745] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0746] Step 1:

[0747] The user inputs ideas and requests into the terminal in natural language. This is the initial input to the system. The input data includes specific purposes and required functions. This information is sent from the terminal to the server.

[0748] Step 2:

[0749] The server analyzes the received natural language data using a Large-Scale Language Model (LLM). This analysis extracts key elements and requirements from the input text. For example, from the input information "safe robot toy," safety requirements and standards are identified. The output of the analysis is a list of extracted requirements.

[0750] Step 3:

[0751] The server generates design specifications using an AI model based on the extracted requirements. During this process, the system generates a recommended list of necessary parts and information about the materials to be used. The specification output includes part types, quantities, and characteristics.

[0752] Step 4:

[0753] The server inputs the generated design specifications into a design-specialized generation AI, which automatically generates detailed design drawings and digital data. At this stage, it processes a wide range of data, including material properties and manufacturing simulations, and outputs a visual design. The output results include CAD data and manufacturing procedures.

[0754] Step 5:

[0755] The server uses a patent-specific generation AI to compare the generated data with a patent database to determine whether the design is patentable or does not infringe on existing patents. This process yields the patent clearance results. The output is a patent evaluation report.

[0756] Step 6:

[0757] The server verifies compliance with legal regulations and safety standards. It checks that the design adheres to relevant regulations and generates the results. The output includes a legal compliance assessment.

[0758] Step 7:

[0759] Users can view design and evaluation results sent from the server on their terminal. Users can modify the design as needed and provide new input based on feedback. New input based on user responses triggers a return to step 1, starting the next processing cycle.

[0760] (Application Example 1)

[0761] 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".

[0762] In today's product design process, it is difficult for users to materialize their ideas and turn them into products without specialized knowledge. Furthermore, the process of checking legal regulations, safety standards, and patents during product development is complex and requires considerable time and effort. Therefore, there is a need for efficient and automated design systems.

[0763] 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.

[0764] In this invention, the server includes means for analyzing natural language information input by a user to extract key elements and requirements; means for generating design specifications based on the extracted requirements; means for automatically generating design drawings and data based on the generated design specifications; means for generating proposed component lists, material lists, and manufacturing processes in response to specific functional requests from the user; and means for representing the details of the production process using the system and verifying its suitability. This enables users to easily and quickly realize their ideas and advance product development.

[0765] "Natural language information" refers to information expressed in the form of words and sentences that users use on a daily basis.

[0766] "Key elements" is a term that refers to important components or features in design and product development.

[0767] A "requirement" is a condition or standard that is necessary to achieve a specific objective.

[0768] A "design specification" is a document that outlines the detailed requirements and elements related to the design of a product or system.

[0769] "Specific feature requests from users" refers to specific wishes or requests regarding features or characteristics that users particularly desire.

[0770] "Whether a generated design is patentable" refers to evaluating whether the design possesses novelty and inventiveness, and whether it is eligible for patent application.

[0771] "Means for determining whether or not a patent infringes" refers to a method of checking whether a new design infringes on other patents by comparing it with existing patents.

[0772] "Means of verifying conformity" refers to the process of checking whether a design or product conforms to the required standards and specifications.

[0773] "Components" is a term that refers to the individual parts or elements that make up a product or system.

[0774] A "materials list" is a list of the types and quantities of materials required to manufacture a product.

[0775] "Production process" refers to a series of tasks and procedures necessary to complete a product.

[0776] The implementation of this invention begins with the user inputting ideas and requirements in natural language via a terminal. The terminal sends this information to a server, which analyzes the input using a large-scale language model. Specifically, machine learning libraries such as TensorFlow and PyTorch are used, and models such as OpenAI's GPT-4 are applied. This makes it possible to extract key elements and requirements.

[0777] The server generates design specifications based on the extracted information. Next, the server automatically generates design drawings and digital data using the CAD software's API. At this stage, software such as AutoCAD is used.

[0778] Furthermore, to verify that the generated design is patentable and does not infringe on any patents, the server accesses a patent database. For this purpose, it uses Python to retrieve information from the Lens.org database and check for compliance.

[0779] This system can also generate proposed component lists, material lists, and manufacturing processes in response to specific feature requests from users. This allows users to efficiently proceed with the process of realizing their ideas.

[0780] For example, if a user inputs "I want to design a household robot that can clean and do simple cooking," the server will generate a design specification and propose a design that meets safety standards and legal regulations. An example of a prompt message would be, "If you want to design a new household robot, please enter the desired functions and characteristics. Example: 'A robot that can take care of pets'."

[0781] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0782] Step 1:

[0783] Users input ideas and requirements specifications in natural language via their terminal. This input information is sent to the server in text format. This input data serves as the foundational information to be analyzed.

[0784] Step 2:

[0785] The server analyzes the received natural language information using a large-scale language model (e.g., GPT-4). At this stage, natural language processing techniques are used to extract key elements and requirements. The input to this process is natural language input from the user, and the output is a list of design requirements and key elements.

[0786] Step 3:

[0787] The server automatically generates a design specification based on the extracted requirements list. The generated design specification is used in the next step. This specification is a formalized document of the requirements, with the requirements list as input and the design specification as output.

[0788] Step 4:

[0789] The server uses the generated design specifications to automatically generate design drawings and digital data using the CAD software's API. In this step, detailed design drawings are created using software such as AutoCAD. The input is the design specifications, and the output is CAD files and digital data.

[0790] Step 5:

[0791] To verify whether the generated design is patentable, the server accesses a patent database and checks for patent existence. In this step, Python is used to retrieve patent information from the Lens.org database. The input is digital data, and the output is the determination of patentability and patent infringement.

[0792] Step 6:

[0793] The server generates a list of proposed components and materials, as well as a manufacturing process, in response to the user's specific functional requirements. The input for this step is the functional requirement, and the output is a list of proposed components, a list of materials, and a manufacturing process. This generation is automated and performed efficiently.

[0794] Step 7:

[0795] The generated design and related information are fed back to the user, allowing for design review and modification. In this step, the user makes a final productization decision based on the provided information. Inputs are design drawings and digital data, while outputs are user review and modification instructions.

[0796] 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.

[0797] This invention enables hardware design that better meets user needs by analyzing user emotions and incorporating them into the design process. The system is implemented in the following steps.

[0798] First, the user inputs their design ideas and requirements in natural language via a terminal. The terminal receives this information and sends it to the server. The server extracts requirements from this information and automatically generates a design specification. During this process, data on the user's initial emotions is also acquired.

[0799] The emotion engine analyzes the user's emotions from their input. Based on this analysis, the design concept is adjusted to match the user's emotions. For example, if the user is seeking "fun," the selection of colors and designs will be adjusted to be more colorful and playful.

[0800] Subsequently, the server uses a design-specific generative AI to create blueprints and digital data. Here, specific specifications are determined, reflecting the extracted requirements and sentiment analysis results. Furthermore, it is checked whether the design is patentable and whether it complies with legal regulations and safety standards.

[0801] The device monitors the user's emotional state in real time through sentiment analysis and provides feedback accordingly. If the user is satisfied with the results and shows a positive reaction, the design process moves to the next stage. This information is stored in the sentiment engine and used for future improvements.

[0802] As a concrete example, consider a case where a user wants to design an educational robot for children. The user inputs into the terminal that they want a "robot that children can learn from in a fun way." The server receives this intention and uses an emotion engine to analyze the emotion of "fun." Based on this emotion, the design is adjusted, and the design AI suggests various functions. As a result, a colorful and interactive robot design is automatically generated and provided to the user.

[0803] This system integrates user emotions with design intent, resulting in a more human-centered process and improved user satisfaction.

[0804] The following describes the processing flow.

[0805] Step 1:

[0806] Users input their design ideas and requirements in natural language via their device. This information includes the design's purpose, desired characteristics, and target users.

[0807] Step 2:

[0808] The terminal receives user input data and sends it to the server. The server then launches a large-scale language model to analyze the information and extract key requirements.

[0809] Step 3:

[0810] The server utilizes an emotion engine to analyze the user's emotional state from their input. This analysis detects the strongest emotion the user is experiencing and prepares it to be reflected in the design process.

[0811] Step 4:

[0812] The server generates a design specification based on the extracted requirements and sentiment analysis results. This specification includes design elements and feature suggestions that align with the user's emotions.

[0813] Step 5:

[0814] Using a design-focused generation AI, the server automatically generates design drawings and digital data. Here, design selections are made that align with the user's emotions, and specific specifications are defined.

[0815] Step 6:

[0816] The server evaluates whether the generated design is patentable and determines whether there is any patent infringement. At the same time, it also checks whether it complies with legal regulations and safety standards.

[0817] Step 7:

[0818] The terminal displays the design data and analysis results received from the server to the user. The user can then review the design based on this information and, if necessary, send modification requests to the server via the terminal.

[0819] Step 8:

[0820] The server receives user feedback and modifies / updates the design as needed. The final design data is converted to a format for manufacturing devices.

[0821] Step 9:

[0822] The device notifies the user of the revised final design data and provides guidelines for proceeding to the specific manufacturing stage. It then prepares the product to reflect the user's design intent and vision.

[0823] (Example 2)

[0824] 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".

[0825] Conventional design systems could not only extract key components and requirements from users' natural language requests, but also model user emotions and reflect them in the design process. Therefore, achieving human-centered design that considered user emotions and design intentions was difficult.

[0826] 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.

[0827] In this invention, the server includes means for analyzing natural language information input by the user to extract key components and requirements, means for generating specifications based on the extracted requirements and user sentiment information, and means for analyzing the user's sentiment to adjust the design concept. This makes it possible to incorporate user sentiment into the process from the early stages of design, resulting in a design that better meets user needs.

[0828] "Natural language information" refers to data expressed in the form of human language used in everyday life.

[0829] "Key components" refer to the fundamental and important parts in the design of a system or product.

[0830] "Means for extracting requirements" refers to methods or techniques for identifying and extracting the conditions and requests necessary for design from the information provided by the user.

[0831] A "specification document" refers to a document that describes the detailed specifications and functions of a designed system or product.

[0832] "Emotional information" refers to data that expresses a user's emotional state or sensibilities.

[0833] "Means of adjusting the design concept" refers to methods or techniques for fine-tuning the basic policy and direction of the design based on user emotional information.

[0834] A "design drawing" refers to a diagram that visually represents the structure and function of a system or product.

[0835] A "generative model that automatically generates data" refers to a mathematical or computer-based model that uses algorithms to automatically create design drawings and specification data based on input information.

[0836] "Protectableness" refers to the possibility and conditions under which an invention or design can be legally protected as intellectual property.

[0837] "Means for determining infringement" refers to methods or technologies for evaluating and determining whether or not one's intellectual property rights are being infringed.

[0838] "Standard manufacturing equipment data format" refers to a data format that allows manufacturing equipment to correctly interpret design data and perform manufacturing.

[0839] "Regulations and safety standards" refer to the laws and safety guidelines that designs and products must comply with.

[0840] This system enables more user-oriented design by integrating user emotions into the design process. Users connect to the system via a terminal and input their design ideas and requirements in natural language. The terminal uses an application called the "Design Interaction Platform" to collect this information and send it to the server.

[0841] After receiving the input natural language information, the server analyzes the requirements using a natural language processing tool such as the "NLTK Library" and extracts the main components. At the same time, it uses sentiment analysis tools such as "IBM Watson Tone Analyzer" to obtain sentiment information from the user's input and identify the emotions the user is seeking.

[0842] By incorporating these requirements and emotional information, the server analyzes the user's emotions and adjusts the design concept accordingly. Specifically, colors, shapes, and design sensibilities are adjusted based on the user's emotions. The server then automatically generates design drawings and digital data using a design-specific generative AI model such as "Autodesk Generative Design." This generation process also verifies whether the design is patentable and compliant with legal regulations.

[0843] The device has the ability to monitor the user's emotional state in real time and provide appropriate feedback. If the user is satisfied with the design results, the system proceeds to the next stage of the design process. This feedback information is also stored in the emotion engine for future process improvements.

[0844] For example, if a user wants a "robot that children can learn from in a fun way," they would input this into the terminal. An example of a prompt might be, "Please extract the element of fun from the user's input and propose a design for a colorful, interactive educational robot." Based on this input, the server extracts the emotional element of "fun" and adjusts the design accordingly. The design AI model uses this information to automatically generate robot designs that meet various requirements, and the final result is provided to the user.

[0845] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0846] Step 1:

[0847] Users input their design ideas and requirements in natural language via a terminal. This input data is collected by the "Design Interaction Platform." The input information is organized as text data representing the user's concept and prepared for transmission to the server.

[0848] Step 2:

[0849] The terminal sends user input to the server. Upon receiving this information, the server first analyzes the requirements using natural language processing technology and extracts the main components. The "NLTK library" is used here to analyze the input text data and identify the requirements necessary for the design. As output, requirements data that will form the basis of the specification document is generated.

[0850] Step 3:

[0851] The server simultaneously uses an "emotion analysis tool" to analyze the user's emotional information. In this step, emotions are identified from the natural language input by the user, and emotional elements that should be reflected in the design are revealed. For example, emotional data such as "fun" and "security" may be extracted.

[0852] Step 4:

[0853] The server adjusts the design concept based on the acquired requirements and emotional information. This process fine-tunes the design direction to match user emotions and determines specific design policies. As a result of this adjustment, emotionally-driven design guidelines are generated.

[0854] Step 5:

[0855] The server uses a design-specific generative AI model to automatically generate design drawings and digital data based on the refined design concept. Utilizing a "generative AI model," it integrates input requirements data and emotional information to generate output data that embodies the user's intent. This output serves as a guideline for realizing specific product designs.

[0856] Step 6:

[0857] The device displays the generated design data in real time and monitors the user's emotional state. When the user is satisfied with the results or provides positive feedback, this information is fed back into the emotion engine and used to improve the design process in the future. Ultimately, once the user is satisfied with the design results, the system is ready to move on to the next stage of the design process.

[0858] (Application Example 2)

[0859] 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".

[0860] Traditional hardware design systems failed to reflect user emotions, making it difficult to design products that accurately met individual user needs. Furthermore, the lack of a process to verify whether the design met individual needs sometimes resulted in decreased user satisfaction.

[0861] 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.

[0862] In this invention, the server includes means for analyzing natural language information input by the user to extract key components and requirements, means for generating a design specification document based on the extracted requirements, and means for acquiring and analyzing user emotion data. This enables flexible hardware design that responds to the user's emotions.

[0863] "Natural language information" refers to language information in the form that users use on a daily basis, and is input in the form of text, audio, etc.

[0864] "Major components" refer to the essential elements that form the basis of the design, and are the foundation of the specific design.

[0865] "Requirements" refer to conditions or criteria that specifically express the user's demands or desires.

[0866] A "design specification document" is a document that details the content, functions, and requirements to be designed.

[0867] A "design drawing" is a diagram that visually shows the shape, structure, and mechanism of a design.

[0868] "Information" is a general term for all data and materials related to the design.

[0869] "Emotional data" refers to information that quantitatively or descriptively represents a user's emotional state.

[0870] "Means of analysis" refer to methods and techniques for analyzing given information and extracting necessary data and results.

[0871] "Methods for adjusting the design concept" refer to techniques for changing the direction and style of a design based on analyzed emotional data.

[0872] "Patentable" means that an invention possesses novelty, inventiveness, and industrial applicability.

[0873] "Patent infringement" refers to the act of infringing on the rights of an already registered patent without permission.

[0874] A "specific manufacturing equipment format" refers to a particular format or protocol suitable for product manufacturing.

[0875] "Legal regulations" refers to the laws and regulatory standards that a design must adhere to.

[0876] "Safety standards" are criteria and guidelines that designs must follow to ensure safety for users and their surroundings.

[0877] The embodiment of the invention provides a system for designing hardware that reflects the user's emotions. This system begins with the user inputting design requirements in natural language via a smartphone or other input device. The device receives this information and transmits the data to a server.

[0878] The server analyzes the received information, extracts key components and requirements using natural language processing techniques, and generates a design specification document. This process incorporates an emotion engine that simultaneously acquires and analyzes the user's emotional data. The design concept is adjusted according to the emotional state. For example, if the emotion "happy" is detected, the design changes to become more colorful and playful.

[0879] Based on the design specifications, design drawings and other data are automatically generated using an AI model. During the generation process, patentability and compliance with laws, regulations, and safety standards are checked. This allows for an automated evaluation of whether the proposed design is novel to prior art and whether there are any legal or safety issues.

[0880] Users can review design proposals in real time and provide feedback along the way. If a positive response is given, the system proceeds to the next stage of the design process. The final design data is converted to a format suitable for specific manufacturing equipment and output in a form usable for the manufacturing process.

[0881] For example, a user might want to design a robot that children can learn from in a fun way. In this case, the design AI analyzes the user's input and emotional data to determine what constitutes "fun," and then automatically generates a colorful, interactive robot design. An example of a prompt to the generating AI model would be, "What should a robot design look like when a user is looking for fun?"

[0882] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0883] Step 1:

[0884] The user inputs design requirements in natural language via an input terminal.

[0885] The input natural language data is sent from the terminal to the server. The input here is text data containing the user's wishes and requests. The output is the raw natural language data sent to the server.

[0886] Step 2:

[0887] The server analyzes the received natural language data and uses natural language processing techniques to extract key components and requirements.

[0888] Specifically, the input text data is tokenized, and syntactic and semantic analysis is performed to clarify the user's design intent. The output is a list of extracted requirements and key components.

[0889] Step 3:

[0890] The server uses an emotion engine to analyze the user's emotional data.

[0891] The input consists of text data from Step 1 and the user's past sentiment history. The system infers the sentiment state based on keywords and context, and the output is the analyzed sentiment data.

[0892] Step 4:

[0893] The server adjusts its design concept based on the analyzed sentiment data.

[0894] Specifically, a design theme is selected based on emotions, and the color and shape guidelines are determined. The inputs are requirements and emotional data, and the output is a refined design concept.

[0895] Step 5:

[0896] Based on the refined design concept, the server automatically generates blueprints and other digital data using a generative AI model.

[0897] The AI ​​generator is given prompt text to propose specific designs and functions. The input is a design concept, and the output is the generated design data.

[0898] Step 6:

[0899] The server verifies the patentability of the generated design data and checks for compliance with laws, regulations, and safety standards.

[0900] The input is the generated design data. By referencing patent databases and legal and regulatory databases, the output is the result of verifying patentability and compliance with laws and regulations.

[0901] Step 7:

[0902] Users review the design generated by the server and provide feedback.

[0903] Specifically, the generated design data is displayed on the terminal, prompting the user for confirmation. The input is the design data, and the output is user feedback.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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."

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] The following is further disclosed regarding the embodiments described above.

[0926] (Claim 1)

[0927] A means of analyzing natural language information input by the user to extract key elements and requirements,

[0928] A means for generating design specifications based on extracted requirements,

[0929] A means of automatically generating design drawings and data based on the generated design specifications,

[0930] A system that includes means for verifying whether a generated design is patentable and determining whether or not patent infringement has occurred.

[0931] (Claim 2)

[0932] The system according to claim 1, comprising means for converting generated design data into a format for a specific manufacturing device.

[0933] (Claim 3)

[0934] The system according to claim 1, comprising means for verifying that the design takes into account legal regulations and safety standards.

[0935] "Example 1"

[0936] (Claim 1)

[0937] A means of analyzing natural language information input by the user to extract key elements and requirements,

[0938] A means for generating design specifications based on extracted requirements,

[0939] A means of automatically generating design drawings and data based on the generated design specifications,

[0940] A means for verifying whether the generated design is patentable and determining whether or not patent infringement has occurred,

[0941] A means to verify whether the generated design data and inventions comply with patent law and legal regulations,

[0942] A system that includes means to enable two-way communication for users to review design details and provide modification instructions.

[0943] (Claim 2)

[0944] The system according to claim 1, comprising means for converting generated design data into a format for a specific manufacturing apparatus.

[0945] (Claim 3)

[0946] The system according to claim 1, comprising means for verifying that the design takes into account legal regulations and safety standards.

[0947] "Application Example 1"

[0948] (Claim 1)

[0949] A means of analyzing natural language information input by the user to extract key elements and requirements,

[0950] A means for generating design specifications based on extracted requirements,

[0951] A means of automatically generating design drawings and data based on the generated design specifications,

[0952] A means for verifying whether the generated design is patentable and determining whether or not patent infringement has occurred,

[0953] A means for generating a proposed list of components and materials and a manufacturing process in response to specific functional requests from users,

[0954] A system that uses a system to represent the details of the production process and includes means for verifying its suitability.

[0955] (Claim 2)

[0956] The system according to claim 1, comprising means for converting generated design data into a format for a specific manufacturing device.

[0957] (Claim 3)

[0958] The system according to claim 1, comprising means for verifying that the design takes into account legal regulations and safety standards.

[0959] "Example 2 of combining an emotion engine"

[0960] (Claim 1)

[0961] A means for analyzing natural language information input by the user to extract key components and requirements,

[0962] A means for generating a specification document based on extracted requirements and user sentiment information,

[0963] A means of analyzing user emotions and adjusting the design concept,

[0964] A means of using a generative model that automatically generates design drawings and data based on a refined design concept,

[0965] A system that includes means for verifying whether the generated design is protectable and for determining whether an infringement has occurred.

[0966] (Claim 2)

[0967] The system according to claim 1, comprising means for converting generated design data into a data format for standard manufacturing equipment.

[0968] (Claim 3)

[0969] The system according to claim 1, comprising means for ensuring that the design takes into account regulations and safety standards.

[0970] "Application example 2 when combining with an emotional engine"

[0971] (Claim 1)

[0972] A means for analyzing natural language information input by the user to extract key components and requirements,

[0973] A means for generating a design specification document based on the extracted requirements,

[0974] A means of automatically generating design drawings and information based on the generated design specification document,

[0975] A means of acquiring and analyzing user emotional data,

[0976] A means of adjusting the design concept based on analyzed emotional data,

[0977] A system that includes means for verifying whether a generated design is patentable and determining whether or not patent infringement has occurred.

[0978] (Claim 2)

[0979] The system according to claim 1, comprising means for converting generated design information into a format for a specific manufacturing apparatus.

[0980] (Claim 3)

[0981] The system according to claim 1, including means for confirming that the design takes into account legal regulations and safety standards. [Explanation of symbols]

[0982] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing natural language information input by the user to extract key elements and requirements, A means for generating design specifications based on extracted requirements, A means of automatically generating design drawings and data based on the generated design specifications, A system that includes means for verifying whether a generated design is patentable and determining whether or not patent infringement has occurred.

2. The system according to claim 1, comprising means for converting generated design data into a format for a specific manufacturing device.

3. The system according to claim 1, comprising means for confirming that the design takes into account legal regulations and safety standards.

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