system

Abstract

We provide the system. [Solution] A means of analyzing design ideas in natural language received from users to generate design requirements, Means for automatically generating detailed design drawings and models based on the aforementioned design requirements, A means for reviewing existing technical information and material information related to the generated design, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Hardware design requires specialized techniques and knowledge, presenting a very high hurdle for non-experts. As a result, there is a current situation where many ideas end up not being realized. Additionally, the design process is complex and time-consuming and labor-intensive with conventional methods. Moreover, in an industry where knowledge inheritance is difficult, it is also challenging to cultivate new engineers.

Means for Solving the Problems

[0005] This invention provides a technology that analyzes design ideas provided by users in natural language using a large-scale language model to generate specific design requirements. Furthermore, it constructs a system that automatically generates detailed design drawings and models using generative AI that supports multiple design domains, based on those requirements. This allows for the rapid and high-quality realization of ideas, even without specialized design knowledge. In addition, by automatically reviewing existing technical information and material information, patent infringement can be avoided and appropriate materials can be selected. Furthermore, the system improves usability by incorporating a function to modify the design based on user feedback.

[0006] A "user" is an individual or legal entity that uses the system to provide design ideas and wishes for them to be realized.

[0007] "Natural language" refers to the forms of language that people use for everyday communication, and is a means of expressing information in a form different from programming languages.

[0008] A "design idea" is a concept or suggestion regarding the function and design of a product that a user provides to the system with the aim of developing new hardware.

[0009] "Analysis" is the process of information processing used by a system to understand the input design idea and derive specific design requirements based on that understanding.

[0010] "Design requirements" are the technical and functional conditions and specifications extracted from the design idea, and they serve as guidelines for the specific design process.

[0011] "Generative artificial intelligence" is an AI technology that uses machine learning models to generate blueprints and models based on user input.

[0012] "Design drawings and models" are specific visual representations and plans created based on design requirements, and they form the basis for advancing product development.

[0013] "Existing technical information" refers to existing patents and publicly available technical information in the market, which are referenced to ensure that new designs do not infringe upon them.

[0014] "Material information" refers to data about the properties and applicability of materials used in product design, and is a factor that affects the performance and cost of the product.

[0015] "Feedback" refers to the opinions and requests for modifications that users provide regarding the generated blueprints and models, and is an important process that influences the final form of the product. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] 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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention describes a system that uses generative AI technology to quickly and accurately realize hardware design ideas proposed by users. The system functions through the interaction of users, servers, and terminals. Specific embodiments of each component are shown below.

[0038] First, the user inputs their design idea in natural language through an interface on their device. It can handle a wide range of inputs, from simple sentences to specific technical requirements. For example, they could input, "I want to design an embedded device that utilizes renewable energy."

[0039] Next, the server uses a large-scale language model to analyze the received natural language input. The analysis results are documented as technical specifications and functional requirements, generating specific design requirements. At this stage, the purpose and function of the device, the properties of the desired materials, and other conditions are clarified.

[0040] Subsequently, the server uses a multimodal, design-specific generative AI to create detailed blueprints and 3D models according to the design requirements. This process leverages the latest technologies across multiple design domains to provide an optimal design that considers functionality, aesthetics, and efficiency. The blueprints and models include the details necessary for manufacturing and market launch preparation.

[0041] Furthermore, the server automatically reviews newly generated designs to ensure they do not infringe on existing technology patents and selects the most suitable materials. This guarantees legally safe and cost-effective product designs.

[0042] Finally, the terminal presents the generated design to the user and requests feedback. If the user wishes to improve the design or functionality, they can input instructions through the terminal. Based on this, the server modifies the design, obtains final approval, and outputs a complete design file for product development.

[0043] This system will serve as a platform that easily enables individuals and companies without specialized hardware design expertise to materialize their ideas and promotes innovative product development.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users access the terminal and input their hardware design ideas in natural language through the interface. They might present specific needs, such as "I want to design a home electronic product that uses sustainable energy."

[0047] Step 2:

[0048] The terminal sends input from the user to the server. The server receives this input and parses it using a large-scale language model. This extracts and documents design requirements from ideas.

[0049] Step 3:

[0050] The server then uses analysis to define design requirements, including functional conditions, material requirements, and design metrics. Based on this information, a design-focused generation AI automatically generates design drawings and 3D models.

[0051] Step 4:

[0052] The server automatically checks for patent infringement risks by searching an internal database of existing technology patents for the generated design drawings and models. It also selects potential materials applicable to the design and estimates their costs.

[0053] Step 5:

[0054] The device presents the user with an initial design proposal. The user reviews the design and provides feedback on improvements to the functionality and design.

[0055] Step 6:

[0056] The server receives user feedback and incorporates the specified modifications into the design. It updates the design drawings and models as needed, generating the final version.

[0057] Step 7:

[0058] The terminal presents the final design to the user, who then approves it. After approval, the server generates all the necessary files for product development and provides the user with a download link.

[0059] (Example 1)

[0060] 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."

[0061] In hardware design, the process of quickly and accurately realizing a user's idea requires specialized technical knowledge and considerable time. Furthermore, verifying that the design does not infringe on existing technology patents and selecting appropriate materials are also challenges. Therefore, there is a need for a system that effectively realizes ideas and enables designs that meet legal and physical requirements.

[0062] 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.

[0063] In this invention, the server includes means for analyzing design ideas received from a user in natural language to generate technical specifications and functional requirements; means for automatically generating detailed design drawings and three-dimensional models based on the technical specifications and functional requirements; and means for reviewing existing technical information and material information related to the generated design to ensure legal compliance and select materials. This makes it possible to quickly convert a user's ideas into a detailed design and efficiently obtain a design that meets legal and technical requirements.

[0064] "Natural language" refers to the language that humans use on a daily basis, and it is required that it be converted into a format that machines can understand.

[0065] A "design idea" is a concept or idea for a device or a component of a device that has a specific function or purpose.

[0066] A "technical specification" is a document that outlines the specific technical aspects and performance requirements of a designed product.

[0067] "Functional requirements" define the conditions for the functions and performance that a designed product must meet.

[0068] A "design drawing" is a diagram that visually represents the shape, dimensions, materials, and other details of a product or part.

[0069] A "three-dimensional model" is data that digitally represents the 3D shape of an actual object.

[0070] "Legal compliance" refers to a state in which the generated design does not infringe on existing patents or laws and is legally usable.

[0071] "Material selection" is the process of choosing the optimal material to meet the required characteristics of a product.

[0072] "Feedback" refers to opinions and suggestions for improvement provided by users, and is used to improve the design.

[0073] This invention provides a system that utilizes generative AI technology to quickly and thoroughly realize design ideas proposed by users. The specific forms for carrying out the invention are as follows:

[0074] First, the user inputs their design idea in natural language through the device's interface. This device can be a standard computer or mobile device, accessible via a web browser or dedicated application. For example, the user might input a design idea such as, "I want to design a smart cooling device that utilizes renewable energy."

[0075] Next, the server uses software that implements a large-scale language model to analyze user input. This analysis converts the input natural language into technical specifications and functional requirements. Specifically, it uses text analysis algorithms to extract important keywords and phrases, and based on these, identifies the device's purpose, desired functions, and required material characteristics.

[0076] Next, the server uses a design-specific generative AI to generate detailed design drawings and 3D models from the analyzed information. Here, CAD software and 3D modeling tools are used to visualize the design. In this step, an optimal design is automatically created that considers not only technical requirements but also aesthetics and manufacturing efficiency.

[0077] Furthermore, the server reviews patent databases to ensure that the generated design does not infringe on existing patents. It also makes appropriate selections of materials, taking into account market availability and cost. This ensures a legally safe and cost-effective design.

[0078] Finally, the terminal presents the generated design to the user and requests feedback. The user can input opinions and modifications to the design through the interface. For example, they can input specific improvement requests such as "I want to further improve the efficiency of the cooling function." Based on this, the server adjusts the design again, and the design is refined until the user finally approves it.

[0079] A concrete example of a prompt might be, "Design a portable cooler incorporating new, energy-efficient cooling technology." The key feature of this system is that it provides a platform that allows even users without specialized knowledge to easily realize innovative product designs.

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

[0081] Step 1:

[0082] The user inputs design ideas in natural language through the terminal's interface. This input could be specific, such as "I want to design a smart device that utilizes renewable energy." The input design idea in natural language is then sent directly to the server. In this step, the terminal receives the user's text input and forwards it to the server.

[0083] Step 2:

[0084] The server analyzes the received natural language input using a large-scale language model. It tokenizes the input text and performs data processing to extract important keywords and phrases. The output here is semantic information, including the device's technical specifications and functional requirements. Specifically, the AI ​​model understands the context of the sentence and identifies the elements necessary for the design.

[0085] Step 3:

[0086] The server generates detailed design drawings and 3D models using design-specific generative AI based on the analyzed technical specifications and functional requirements. This process involves data calculations that generate visual output using CAD software and 3D modeling tools. The output includes design drawings and 3D models containing specific dimensions and material information. Specific operations include a process of automatically operating design software to create image data.

[0087] Step 4:

[0088] The server searches and reviews patent databases to ensure that the generated design does not violate existing patents. It also performs data calculations to select the optimal materials, taking into account supply availability and cost. The output confirms that the design is legally compliant and that the material selection is optimized. This step specifically involves legal database searches and verification.

[0089] Step 5:

[0090] The terminal presents the generated design to the user and requests feedback. The terminal receives the user's feedback and sends it back to the server. The input consists of the user's opinions and suggestions, and the output is feedback data based on those. Specific actions include the user accessing the interface again and writing additional instructions.

[0091] Step 6:

[0092] The server modifies the design based on user feedback. Data processing is performed to update the design drawings and 3D models according to the revised technical specifications and design. The final design file is output in an improved state. Specifically, there is a process of generating a new design plan using AI and design software again.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In manufacturing environments, there is a need to quickly design new robots and machinery, avoid patent risks, and efficiently integrate them into the manufacturing process. Traditional methods involve significant time and cost from design to prototype manufacturing, and also carry the risk of patent infringement; therefore, a solution is needed to address these issues.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] This invention includes a server that includes means for analyzing design ideas in natural language received from a user to generate design requirements, means for automatically generating detailed design drawings and three-dimensional models based on the design requirements, and means for verifying the legal security of the generated design using a patent information database. This enables users to quickly materialize designs and reflect them in the manufacturing process while reducing patent risk.

[0098] "Natural language design ideas" refer to design concepts and ideas expressed in the language that users use on a daily basis.

[0099] "Design requirements" refer to the technical conditions and specifications that are concretized based on the analyzed design ideas.

[0100] "Detailed design drawings and three-dimensional models" refer to design drawings and three-dimensional models that visually embody the generated design requirements.

[0101] A "patent information database" is a collection of data that stores existing patent information and is used to check whether newly generated designs conflict with other patents.

[0102] "Legal safety" refers to a state in which the generated design does not infringe on existing patents and is guaranteed to be free from legal issues.

[0103] "Portable devices" refer to electronic devices that users can carry with them and that are used for checking and modifying designs.

[0104] This system begins with the user inputting design ideas in natural language using a mobile device. This mobile device is equipped with an interface to accurately receive the user's design intent and communicate with the server. The design ideas entered by the user are sent to the server and analyzed using a large-scale language model. Based on the analyzed information, the server generates design requirements and creates detailed design drawings and 3D models.

[0105] The server's software includes a generative AI model, enabling the generation of designs adaptable to multiple design domains. Furthermore, the server accesses a patent database and compares the newly generated designs with existing patent information to verify legal security. During this process, users can provide real-time feedback and instruct the server to modify the design as needed.

[0106] Through this process, designs that meet user requirements can be quickly materialized and prepared for commercialization. For example, the design process begins when a user sends a prompt message to the server from a mobile device stating, "We need a robotic arm for a new assembly line. Please design it using lightweight and environmentally friendly materials." The AI ​​model then uses this input to provide a system that rapidly generates designs and simultaneously verifies their legal safety.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] Users input design ideas in natural language using their mobile devices. This input is sent from the device to the server as a "prompt message." The prompt message includes the design's purpose, characteristics, and desired materials.

[0110] Step 2:

[0111] The server analyzes the received prompt message using a large-scale language model. This analysis process extracts specific "design requirements" from the prompt message. These design requirements describe the detailed technical conditions necessary for generating design drawings and 3D models. The server saves these analysis results for use in the next step.

[0112] Step 3:

[0113] The server generates detailed blueprints and 3D models based on design requirements extracted using a generative AI model. It receives design requirements as input and creates visual blueprints and 3D models as output. The generated designs are then prepared for subsequent stages such as patent checks and user feedback.

[0114] Step 4:

[0115] The server compares the generated design with the patent information database to verify its legal security. It retrieves relevant information from the patent database and compares it with the generated design to perform verification to avoid patent infringement. If there are no legal issues, it saves the details.

[0116] Step 5:

[0117] Users can view design drawings and 3D models sent from the server on their mobile devices and provide real-time feedback. The server receives user feedback as input, modifies the design as needed, and incorporates these modifications as new design requirements.

[0118] Step 6:

[0119] The server generates manufacturing information based on the final revised design, preparing it for production. The final output is a design file containing all the necessary manufacturing details, which is then provided to the user and the manufacturing department.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention is a system that uses generative AI technology combined with an emotion engine to design hardware while taking user emotions into consideration. In this system, the user, server, and terminal work together to incorporate emotional elements into the design process.

[0122] First, the user uses an interface on their device to input their design idea in natural language. For example, they can communicate a specific need such as, "I want to design lighting fixtures that create a comfortable, relaxing space." At this time, the device has an emotion recognition function built in, which detects the user's emotions from their tone of voice, facial expressions, etc.

[0123] Next, the server analyzes this input using a large-scale language model to generate design requirements. Simultaneously, it uses an emotion engine to extract the user's emotional tone and fine-tune the design requirements to reflect those nuances. This is a process to specifically weave the emotions and atmosphere the user desires into the design.

[0124] Subsequently, the server automatically generates detailed blueprints and models based on the design requirements, using generative artificial intelligence that supports multiple design domains. This generation process optimizes the design and functionality according to the user's emotions. This process results in highly unique products that resonate with the user's feelings.

[0125] Furthermore, the server reviews existing technical and material information for the generated design. Functions for assessing patent infringement risk, selecting appropriate materials, and estimating optimal costs are also utilized here.

[0126] Finally, the device presents the generated design to the user. The user visually reviews the design and, if necessary, provides emotionally-based feedback. This feedback is sent back to the server for further modifications.

[0127] In this way, by incorporating emotion recognition, we can realize hardware designs optimized for individuals and accelerate the development of products that highly match the sensibilities and needs of users.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] When a user accesses a device and inputs design ideas in natural language, the emotion engine analyzes the user's voice and facial expressions to recognize their emotions. This analysis is then used to gain a deeper understanding of the user's wishes and expectations.

[0131] Step 2:

[0132] The device sends design ideas from the user and recognized emotion data to the server. The server uses a large-scale language model to analyze design requirements from natural language and adjusts those requirements based on the emotion data. For example, if a relaxed emotion is detected, the design will incorporate soft curves and warm colors.

[0133] Step 3:

[0134] The server automatically generates detailed blueprints and 3D models using multimodal generative artificial intelligence based on design requirements that reflect emotions. The generated designs reflect insights gained from emotion recognition, taking into account the optimal product layout and functionality that responds to the user's emotions.

[0135] Step 4:

[0136] The server checks existing technical information against the generated design and assesses the risk of patent infringement. Simultaneously, it selects potential materials to be used and estimates the cost for each material.

[0137] Step 5:

[0138] The device presents the generated design proposals to the user. The user can review the proposed designs and provide sentiment-based feedback and additional requests.

[0139] Step 6:

[0140] The server receives user feedback and makes further revisions to the design drawings and 3D models. This revision process can be cycled multiple times as needed, continuing until the final design is completed.

[0141] Step 7:

[0142] After the final design is approved, the terminal generates download links to provide the user with all the files necessary for product development. The user can then use these files to proceed with the product manufacturing and distribution process.

[0143] (Example 2)

[0144] 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".

[0145] In conventional systems, it was difficult to consider the emotional state of users during the design process, resulting in challenges in designing products that fully met users' true needs. Furthermore, methods for quickly incorporating post-design feedback and providing more refined, customized designs were limited. Solving these challenges is essential.

[0146] 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.

[0147] In this invention, the server includes means for analyzing design ideas in natural language received from a user to generate design requirements, means for acquiring user emotion data and adjusting the design requirements based on that data, and means for automatically generating detailed design drawings and models based on the design requirements. This makes it possible to generate an optimal design that takes the user's emotions into consideration.

[0148] "Natural language" refers to the linguistic forms that users typically use in everyday conversation and writing, and which are easily understood by humans.

[0149] A "design idea" is the initial concept or image of a product or system that the user wants to realize.

[0150] "Analysis" is the act of breaking down and analyzing input information and data in detail to clarify its structure and meaning.

[0151] "Design requirements" are guidelines that clarify the specifications and necessary conditions for a product or system.

[0152] "Emotional data" refers to information that expresses a user's emotional state using numerical values ​​or categories.

[0153] "Adjustment" refers to the act of making changes or modifications as needed to optimize the process.

[0154] A "design drawing" is a diagram that shows the structure and dimensions of a product or system in detail.

[0155] A "model" is a visual representation, or digital data, of the concept and shape of a product or system based on design drawings.

[0156] "Automatic generation" refers to the process by which a system autonomously creates blueprints and models without human intervention.

[0157] This invention is a system that uses generative AI technology to enable product design that takes user emotions into consideration. The system is configured in which the user, server, and terminal work together, and is designed to incorporate emotional elements into the design process.

[0158] First, the user inputs their design idea in natural language using a device. For example, they might input a request such as, "I want to design lighting fixtures that create a comfortable, relaxing space." At this time, the device's camera and microphone are used to acquire emotional data through the user's facial expressions and tone of voice. This incorporates the user's emotional state into the system.

[0159] The server analyzes input information sent from the terminal using a Large-Scale Language Model (LGM). Specifically, it uses models such as OpenAI's GPT series and Google's BERT to perform text analysis. Furthermore, it extracts the user's emotional tone using an emotion engine and incorporates it into the design requirements. This adjustment allows for the incorporation of soft designs and colors suitable for a relaxing space.

[0160] Next, the server automatically generates detailed blueprints and models using a generative AI model based on the adjusted design requirements. In this process, the design AI selects appropriate shapes and materials, and the generated design reflects the user's emotions.

[0161] A concrete example of a prompt would be, "Please suggest a lighting design that creates a relaxing atmosphere." Based on this, the system can generate a design that meets the user's needs.

[0162] In this way, through a design process that utilizes emotion recognition, it becomes possible to design products that respond to users' sensibilities and needs with high accuracy.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] Users input design ideas in natural language through their devices. During this process, the camera and microphone capture the user's facial expressions and tone of voice as emotional data. The input natural language data and emotional data are preprocessed by emotion recognition software and sent to the server. This allows the system to clearly recognize the user's emotional needs regarding the design.

[0166] Step 2:

[0167] The server receives natural language data sent from the terminal and analyzes its content using a generative AI model. Specifically, it processes the input text with a large-scale language model such as the GPT series to generate design requirements. Here, natural language processing technology is used to convert user requests from text data into concrete design specifications.

[0168] Step 3:

[0169] The server uses an emotion engine to analyze the acquired emotional data. Based on this data, the process of fine-tuning design requirements involves finding the optimal match between the emotional data and the design requirements. For example, if the user is seeking a relaxing tone, the design requirements will be changed to emphasize soft colors and curves.

[0170] Step 4:

[0171] The server automatically generates design drawings and models using a generative AI model based on the adjusted design requirements. The design AI works in conjunction with 3D modeling software to bring the design proposals to life. Here, it performs data calculations on the design data to generate a detailed product model.

[0172] Step 5:

[0173] The server compares the generated design with existing technical information to assess material selection and patent infringement risk. This assessment involves searching for information using a database, obtaining and analyzing data on the latest technologies and materials.

[0174] Step 6:

[0175] The terminal presents the generated blueprints and models to the user. The user visually evaluates them and provides feedback as needed. The terminal then sends the user's feedback back to the server, where it is used to further improve the design.

[0176] (Application Example 2)

[0177] 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".

[0178] In the design of autonomous mobile devices, user emotions are often disregarded, potentially compromising the quality of the user experience. Furthermore, it is difficult to assess potential patent infringement risks during the design process, and feedback for efficient design improvement is insufficient. These factors hinder the development of high-quality products that align with user emotions and needs.

[0179] 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.

[0180] This invention includes a server that analyzes design ideas received from a user in natural language, recognizes emotions in the process, and generates design requirements; a server that automatically generates detailed design drawings and models based on the design requirements and optimizes the interior of the autonomous mobile device; and a server that reviews existing technical information and material information related to the generated design and evaluates the risk of patent infringement in the process. This enables a design process that takes user emotions into consideration and minimizes patent risk.

[0181] "Natural language" refers to the language that humans use on a daily basis, and is a format that can be intuitively manipulated by non-experts in computer systems.

[0182] "Emotional recognition" is the act of understanding a person's emotional state by analyzing their tone of voice, facial expressions, or other biometric information.

[0183] "Design requirements" are specifications that include various conditions such as the functions, performance, and user experience necessary for the generated design or model.

[0184] "Automatic generation" refers to a process in which a computer system generates blueprints or models based on algorithms, minimizing human intervention.

[0185] An "autonomous mobile device" is a mechanical device that has the ability to move on its own, such as an autonomous vehicle.

[0186] "Optimizing the interior" means adjusting the design of the device's interior to the best possible state in accordance with the user's emotions and needs.

[0187] "Existing technical information" refers to a collection of information including past and present technological trends, technical literature, and patent information.

[0188] "Patent infringement risk" refers to the possibility that a particular technology or design infringes on existing patent rights.

[0189] "Feedback" refers to information obtained from users, including their opinions and emotional responses, that is used to improve the design.

[0190] To implement this invention, the user first uses a smartphone or tablet to input natural language requests regarding the interior design of an autonomous mobile device. The device is equipped with a camera and microphone to analyze facial expressions and tone of voice to recognize emotions. An emotion recognition API is used for this emotion analysis.

[0191] Next, the information entered by the user is sent to the server, which analyzes it using a large-scale language model. This analysis converts natural language requests into specific design requirements, taking emotional data into consideration. Generative AI technologies from companies like Autodesk are used to generate the design and detailed models. This results in a design that matches the user's emotions.

[0192] The generated designs are reviewed by the server based on existing technical and material information, and simultaneously, the risk of patent infringement is assessed. Technical literature and past patent information from the database are used for patent searches and material selection. Optimization of the design and materials used promotes an efficient manufacturing process.

[0193] The design is then presented to the user via the device, and the user visually confirms the design. If necessary, the user provides emotion-based feedback. This feedback is sent back to the server, and the design is adjusted and optimized.

[0194] As a concrete example, in the interior design of an autonomous vehicle, if a user inputs "I want to design a seat that allows me to relax," the system uses emotion recognition to understand the relaxation the user desires, and generates a seat design with soft lines and warm colors.

[0195] An example of a prompt message sent to the generating AI model would be, "The user has indicated they want to relax. Design the seat incorporating soft lines and warm colors." This would then provide a customized design that reflects the user's emotions.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user inputs design requirements in natural language using a device. This requirement is received by a device equipped with emotion recognition capabilities. The device's camera and microphone detect the user's facial expressions and tone of voice, and analyze them as emotion data. The input design requirements and the analyzed emotion data are then sent to the next step.

[0199] Step 2:

[0200] Design requests and sentiment data sent from the terminal are transferred to the server. The server uses a large-scale language model to analyze the natural language requests and extract them as design requirements. In this process, sentiment data is taken into consideration and the requirements are adjusted. Specific design requirements are generated as output and used in the next model generation step.

[0201] Step 3:

[0202] The server automatically generates interior designs for autonomous mobile devices using an AI model based on design requirements. This model generation incorporates emotional data to ensure a user-optimized design. The generated designs then proceed to the next review and evaluation steps.

[0203] Step 4:

[0204] The server reviews the generated design based on existing technical information and material information. In this step, it assesses the risk of patent infringement and determines whether or not such a risk exists. It also selects the necessary material information and outputs an optimized design and material information.

[0205] Step 5:

[0206] Users visually review the design sent from the server using their device. They provide feedback on the design through the UI provided by their device. This feedback is sent back to the server as evaluation data, including emotional information.

[0207] Step 6:

[0208] After receiving user feedback, the server readjusts the design. It uses the feedback data to further optimize the design and finalize it. The adjusted design is then provided to the user as the final output.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] This invention describes a system that uses generative AI technology to quickly and accurately realize hardware design ideas proposed by users. The system functions through the interaction of users, servers, and terminals. Specific embodiments of each component are shown below.

[0226] First, the user inputs their design idea in natural language through an interface on their device. It can handle a wide range of inputs, from simple sentences to specific technical requirements. For example, they could input, "I want to design an embedded device that utilizes renewable energy."

[0227] Next, the server uses a large-scale language model to analyze the received natural language input. The analysis results are documented as technical specifications and functional requirements, generating specific design requirements. At this stage, the purpose and function of the device, the properties of the desired materials, and other conditions are clarified.

[0228] Subsequently, the server uses a multimodal, design-specific generative AI to create detailed blueprints and 3D models according to the design requirements. This process leverages the latest technologies across multiple design domains to provide an optimal design that considers functionality, aesthetics, and efficiency. The blueprints and models include the details necessary for manufacturing and market launch preparation.

[0229] Furthermore, the server automatically reviews newly generated designs to ensure they do not infringe on existing technology patents and selects the most suitable materials. This guarantees legally safe and cost-effective product designs.

[0230] Finally, the terminal presents the generated design to the user and requests feedback. If the user wishes to improve the design or functionality, they can input instructions through the terminal. Based on this, the server modifies the design, obtains final approval, and outputs a complete design file for product development.

[0231] This system will serve as a platform that easily enables individuals and companies without specialized hardware design expertise to materialize their ideas and promotes innovative product development.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] Users access the terminal and input their hardware design ideas in natural language through the interface. They might present specific needs, such as "I want to design a home electronic product that uses sustainable energy."

[0235] Step 2:

[0236] The terminal sends input from the user to the server. The server receives this input and parses it using a large-scale language model. This extracts and documents design requirements from ideas.

[0237] Step 3:

[0238] The server then uses analysis to define design requirements, including functional conditions, material requirements, and design metrics. Based on this information, a design-focused generation AI automatically generates design drawings and 3D models.

[0239] Step 4:

[0240] The server automatically checks for patent infringement risks by searching an internal database of existing technology patents for the generated design drawings and models. It also selects potential materials applicable to the design and estimates their costs.

[0241] Step 5:

[0242] The device presents the user with an initial design proposal. The user reviews the design and provides feedback on improvements to the functionality and design.

[0243] Step 6:

[0244] The server receives user feedback and incorporates the specified modifications into the design. It updates the design drawings and models as needed, generating the final version.

[0245] Step 7:

[0246] The terminal presents the final design to the user, who then approves it. After approval, the server generates all the necessary files for product development and provides the user with a download link.

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] In hardware design, the process of quickly and accurately realizing a user's idea requires specialized technical knowledge and considerable time. Furthermore, verifying that the design does not infringe on existing technology patents and selecting appropriate materials are also challenges. Therefore, there is a need for a system that effectively realizes ideas and enables designs that meet legal and physical requirements.

[0250] 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.

[0251] In this invention, the server includes means for analyzing design ideas received from a user in natural language to generate technical specifications and functional requirements; means for automatically generating detailed design drawings and three-dimensional models based on the technical specifications and functional requirements; and means for reviewing existing technical information and material information related to the generated design to ensure legal compliance and select materials. This makes it possible to quickly convert a user's ideas into a detailed design and efficiently obtain a design that meets legal and technical requirements.

[0252] "Natural language" refers to the language that humans use on a daily basis, and it is required that it be converted into a format that machines can understand.

[0253] A "design idea" is a concept or idea for a device or a component of a device that has a specific function or purpose.

[0254] A "technical specification" is a document that outlines the specific technical aspects and performance requirements of a designed product.

[0255] "Functional requirements" define the conditions for the functions and performance that a designed product must meet.

[0256] A "design drawing" is a diagram that visually represents the shape, dimensions, materials, and other details of a product or part.

[0257] A "three-dimensional model" is data that digitally represents the 3D shape of an actual object.

[0258] "Legal compliance" refers to a state in which the generated design does not infringe on existing patents or laws and is legally usable.

[0259] "Material selection" is the process of choosing the optimal material to meet the required characteristics of a product.

[0260] "Feedback" refers to opinions and suggestions for improvement provided by users, and is used to improve the design.

[0261] This invention provides a system that utilizes generative AI technology to quickly and thoroughly realize design ideas proposed by users. The specific forms for carrying out the invention are as follows:

[0262] First, the user inputs their design idea in natural language through the device's interface. This device can be a standard computer or mobile device, accessible via a web browser or dedicated application. For example, the user might input a design idea such as, "I want to design a smart cooling device that utilizes renewable energy."

[0263] Next, the server uses software that implements a large-scale language model to analyze user input. This analysis converts the input natural language into technical specifications and functional requirements. Specifically, it uses text analysis algorithms to extract important keywords and phrases, and based on these, identifies the device's purpose, desired functions, and required material characteristics.

[0264] Next, the server uses a design-specific generative AI to generate detailed design drawings and 3D models from the analyzed information. Here, CAD software and 3D modeling tools are used to visualize the design. In this step, an optimal design is automatically created that considers not only technical requirements but also aesthetics and manufacturing efficiency.

[0265] Furthermore, the server reviews patent databases to ensure that the generated design does not infringe on existing patents. It also makes appropriate selections of materials, taking into account market availability and cost. This ensures a legally safe and cost-effective design.

[0266] Finally, the terminal presents the generated design to the user and requests feedback. The user can input opinions and modifications to the design through the interface. For example, they can input specific improvement requests such as "I want to further improve the efficiency of the cooling function." Based on this, the server adjusts the design again, and the design is refined until the user finally approves it.

[0267] A concrete example of a prompt might be, "Design a portable cooler incorporating new, energy-efficient cooling technology." The key feature of this system is that it provides a platform that allows even users without specialized knowledge to easily realize innovative product designs.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The user inputs design ideas in natural language through the terminal's interface. This input could be specific, such as "I want to design a smart device that utilizes renewable energy." The input design idea in natural language is then sent directly to the server. In this step, the terminal receives the user's text input and forwards it to the server.

[0271] Step 2:

[0272] The server analyzes the received natural language input using a large-scale language model. It tokenizes the input text and performs data processing to extract important keywords and phrases. The output here is semantic information, including the device's technical specifications and functional requirements. Specifically, the AI ​​model understands the context of the sentence and identifies the elements necessary for the design.

[0273] Step 3:

[0274] The server generates detailed design drawings and 3D models using design-specific generative AI based on the analyzed technical specifications and functional requirements. This process involves data calculations that generate visual output using CAD software and 3D modeling tools. The output includes design drawings and 3D models containing specific dimensions and material information. Specific operations include a process of automatically operating design software to create image data.

[0275] Step 4:

[0276] The server searches and reviews patent databases to ensure that the generated design does not violate existing patents. It also performs data calculations to select the optimal materials, taking into account supply availability and cost. The output confirms that the design is legally compliant and that the material selection is optimized. This step specifically involves legal database searches and verification.

[0277] Step 5:

[0278] The terminal performs the process of presenting the generated design content to the user and seeking feedback. The terminal receives the feedback from the user and returns it to the server again. The input is the user's opinions and suggestions, and the output is the feedback data based on them. Specific operations include the user accessing the interface again and writing additional instructions.

[0279] Step 6:

[0280] The server modifies the design based on the feedback obtained from the user. Data processing is performed to update the design drawings and three-dimensional models according to the modified technical specifications and designs. The final version of the design file is output in an improved state. As a specific operation, there is a process of generating a new design plan using AI and design software again.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] In a manufacturing site, it is required to quickly design new robots and mechanical devices, avoid patent risks, and efficiently reflect them in the manufacturing process. In the conventional method, it takes a lot of time and cost from design to prototype manufacturing, and there is also a risk of patent infringement, so means to solve this are necessary.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means for analyzing a design idea in natural language received from a user to generate design requirements, means for automatically generating detailed design drawings and three-dimensional models based on the design requirements, and means for confirming the legal safety of the generated design using a patent information database. As a result, the user can quickly materialize the design while reducing patent risks and reflect it in the manufacturing process.

[0286] The "design idea in natural language" refers to the构想 and concept of a design expressed in the language that users commonly use.

[0287] The "design requirements" refer to the technical conditions and specifications materialized based on the analyzed design idea.

[0288] The "detailed design drawings and three-dimensional models" refer to the visual drawings and three-dimensional models of the design materialized based on the generated design requirements.

[0289] The "patent information database" is a data collection that accumulates existing patent information and is used to confirm whether a newly generated design conflicts with other patents.

[0290] The "legal safety" means that the generated design does not infringe on existing patents and guarantees a state without legal problems.

[0291] The "portable device" refers to an electronic device that a user can carry and is used to confirm and modify a design.

[0292] This system starts when a user inputs a design idea in natural language using a portable device. This portable device accurately receives the user's design intention and has an interface for communicating with the server. The design idea input by the user is sent to the server and analyzed using a large language model. The server generates design requirements based on the analyzed information and creates detailed design drawings and three-dimensional models.

[0293] The server's software includes a generative AI model, enabling the generation of designs adaptable to multiple design domains. Furthermore, the server accesses a patent database and compares the newly generated designs with existing patent information to verify legal security. During this process, users can provide real-time feedback and instruct the server to modify the design as needed.

[0294] Through this process, designs that meet user requirements can be quickly materialized and prepared for commercialization. For example, the design process begins when a user sends a prompt message to the server from a mobile device stating, "We need a robotic arm for a new assembly line. Please design it using lightweight and environmentally friendly materials." The AI ​​model then uses this input to provide a system that rapidly generates designs and simultaneously verifies their legal safety.

[0295] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0296] Step 1:

[0297] Users input design ideas in natural language using their mobile devices. This input is sent from the device to the server as a "prompt message." The prompt message includes the design's purpose, characteristics, and desired materials.

[0298] Step 2:

[0299] The server analyzes the received prompt message using a large-scale language model. This analysis process extracts specific "design requirements" from the prompt message. These design requirements describe the detailed technical conditions necessary for generating design drawings and 3D models. The server saves these analysis results for use in the next step.

[0300] Step 3:

[0301] The server generates a detailed design drawing and a three-dimensional model based on the design requirements extracted using the generative AI model. It receives the design requirements as input and creates a visual design drawing and a three-dimensional model as output. The generated design is prepared to proceed to the subsequent patent check and user feedback stages.

[0302] Step 4:

[0303] The server compares the generated design with the patent information database to confirm legal safety. It obtains relevant information from the patent database and compares it with the generated design, and conducts a confirmation operation to avoid patent infringement. If there are no legal issues, it saves the details.

[0304] Step 5:

[0305] The user can view the design drawing and three-dimensional model sent from the server on a mobile device and provide real-time feedback. The server receives the user's feedback as input, modifies the design as needed, and reflects the modified content as new design requirements.

[0306] Step 6:

[0307] The server finally generates manufacturing information based on the revised design and prepares for productization. As the final output, a design file containing all the detailed information required for manufacturing is generated and provided to the user and the manufacturing department.

[0308] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0309] This invention is a system that uses generative AI technology combined with an emotion engine to design hardware while taking user emotions into consideration. In this system, the user, server, and terminal work together to incorporate emotional elements into the design process.

[0310] First, the user uses an interface on their device to input their design idea in natural language. For example, they can communicate a specific need such as, "I want to design lighting fixtures that create a comfortable, relaxing space." At this time, the device has an emotion recognition function built in, which detects the user's emotions from their tone of voice, facial expressions, etc.

[0311] Next, the server analyzes this input using a large-scale language model to generate design requirements. Simultaneously, it uses an emotion engine to extract the user's emotional tone and fine-tune the design requirements to reflect those nuances. This is a process to specifically weave the emotions and atmosphere the user desires into the design.

[0312] Subsequently, the server automatically generates detailed blueprints and models based on the design requirements, using generative artificial intelligence that supports multiple design domains. This generation process optimizes the design and functionality according to the user's emotions. This process results in highly unique products that resonate with the user's feelings.

[0313] Furthermore, the server reviews existing technical and material information for the generated design. Functions for assessing patent infringement risk, selecting appropriate materials, and estimating optimal costs are also utilized here.

[0314] Finally, the device presents the generated design to the user. The user visually reviews the design and, if necessary, provides emotionally-based feedback. This feedback is sent back to the server for further modifications.

[0315] In this way, by incorporating emotion recognition, we can realize hardware designs optimized for individuals and accelerate the development of products that highly match the sensibilities and needs of users.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] When a user accesses the device and inputs design ideas in natural language, the emotion engine analyzes the user's voice and facial expressions to recognize their emotions. This analysis is then used to gain a deeper understanding of the user's wishes and expectations.

[0319] Step 2:

[0320] The device sends design ideas from the user and recognized emotion data to the server. The server uses a large-scale language model to analyze design requirements from natural language and adjusts those requirements based on the emotion data. For example, if a relaxed emotion is detected, the design will incorporate soft curves and warm colors.

[0321] Step 3:

[0322] The server automatically generates detailed blueprints and 3D models using multimodal generative artificial intelligence based on design requirements that reflect emotions. The generated designs reflect insights gained from emotion recognition, taking into account the optimal product layout and functionality that responds to the user's emotions.

[0323] Step 4:

[0324] The server checks existing technical information against the generated design and assesses the risk of patent infringement. Simultaneously, it selects potential materials to be used and estimates the cost for each material.

[0325] Step 5:

[0326] The device presents the generated design proposals to the user. The user can review the proposed designs and provide sentiment-based feedback and additional requests.

[0327] Step 6:

[0328] The server receives user feedback and makes further revisions to the design drawings and 3D models. This revision process can be cycled multiple times as needed, continuing until the final design is completed.

[0329] Step 7:

[0330] After the final design is approved, the terminal generates download links to provide the user with all the files necessary for product development. The user can then use these files to proceed with the product manufacturing and distribution process.

[0331] (Example 2)

[0332] 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".

[0333] In conventional systems, it was difficult to consider the emotional state of users during the design process, resulting in challenges in designing products that fully met users' true needs. Furthermore, methods for quickly incorporating post-design feedback and providing more refined, customized designs were limited. Solving these challenges is essential.

[0334] 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.

[0335] In this invention, the server includes means for analyzing design ideas in natural language received from a user to generate design requirements, means for acquiring user emotion data and adjusting the design requirements based on that data, and means for automatically generating detailed design drawings and models based on the design requirements. This makes it possible to generate an optimal design that takes the user's emotions into consideration.

[0336] "Natural language" refers to the linguistic forms that users typically use in everyday conversation and writing, and which are easily understood by humans.

[0337] A "design idea" is the initial concept or image of a product or system that the user wants to realize.

[0338] "Analysis" is the act of breaking down and analyzing input information and data in detail to clarify its structure and meaning.

[0339] "Design requirements" are guidelines that clarify the specifications and necessary conditions for a product or system.

[0340] "Emotional data" refers to information that expresses a user's emotional state using numerical values ​​or categories.

[0341] "Adjustment" refers to the act of making changes or modifications as needed to optimize the process.

[0342] A "design drawing" is a diagram that shows the structure and dimensions of a product or system in detail.

[0343] A "model" is a visual representation, or digital data, of the concept and shape of a product or system based on design drawings.

[0344] "Automatic generation" refers to the process by which a system autonomously creates blueprints and models without human intervention.

[0345] This invention is a system that uses generative AI technology to enable product design that takes user emotions into consideration. The system is configured in which the user, server, and terminal work together, and is designed to incorporate emotional elements into the design process.

[0346] First, the user inputs their design idea in natural language using a device. For example, they might input a request such as, "I want to design lighting fixtures that create a comfortable, relaxing space." At this time, the device's camera and microphone are used to acquire emotional data through the user's facial expressions and tone of voice. This incorporates the user's emotional state into the system.

[0347] The server analyzes input information sent from the terminal using a Large-Scale Language Model (LGM). Specifically, it uses models such as OpenAI's GPT series and Google's BERT to perform text analysis. Furthermore, it extracts the user's emotional tone using an emotion engine and incorporates it into the design requirements. This adjustment allows for the incorporation of soft designs and colors suitable for a relaxing space.

[0348] Next, the server automatically generates detailed blueprints and models using a generative AI model based on the adjusted design requirements. In this process, the design AI selects appropriate shapes and materials, and the generated design reflects the user's emotions.

[0349] A concrete example of a prompt would be, "Please suggest a lighting design that creates a relaxing atmosphere." Based on this, the system can generate a design that meets the user's needs.

[0350] In this way, through a design process that utilizes emotion recognition, it becomes possible to design products that respond to users' sensibilities and needs with high accuracy.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] Users input design ideas in natural language through their devices. During this process, the camera and microphone capture the user's facial expressions and tone of voice as emotional data. The input natural language data and emotional data are preprocessed by emotion recognition software and sent to the server. This allows the system to clearly recognize the user's emotional needs regarding the design.

[0354] Step 2:

[0355] The server receives natural language data sent from the terminal and analyzes its content using a generative AI model. Specifically, it processes the input text with a large-scale language model such as the GPT series to generate design requirements. Here, natural language processing technology is used to convert user requests from text data into concrete design specifications.

[0356] Step 3:

[0357] The server uses an emotion engine to analyze the acquired emotional data. Based on this data, the process of fine-tuning design requirements involves finding the optimal match between the emotional data and the design requirements. For example, if the user is seeking a relaxing tone, the design requirements will be changed to emphasize soft colors and curves.

[0358] Step 4:

[0359] The server automatically generates design drawings and models using a generative AI model based on the adjusted design requirements. The design AI works in conjunction with 3D modeling software to bring the design proposal to life. Here, it performs data calculations on the design data to generate a detailed product model.

[0360] Step 5:

[0361] The server compares the generated design with existing technical information to assess material selection and patent infringement risk. This assessment involves searching for information using a database, obtaining and analyzing data on the latest technologies and materials.

[0362] Step 6:

[0363] The terminal presents the generated blueprints and models to the user. The user visually evaluates them and provides feedback as needed. The terminal then sends the user's feedback back to the server, where it is used to further improve the design.

[0364] (Application Example 2)

[0365] 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."

[0366] In the design of autonomous mobile devices, user emotions are often disregarded, potentially compromising the quality of the user experience. Furthermore, it is difficult to assess potential patent infringement risks during the design process, and feedback for efficient design improvement is insufficient. These factors hinder the development of high-quality products that align with user emotions and needs.

[0367] 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.

[0368] This invention includes a server that analyzes design ideas received from a user in natural language, recognizes emotions in the process, and generates design requirements; a server that automatically generates detailed design drawings and models based on the design requirements and optimizes the interior of the autonomous mobile device; and a server that reviews existing technical information and material information related to the generated design and evaluates the risk of patent infringement in the process. This enables a design process that takes user emotions into consideration and minimizes patent risk.

[0369] "Natural language" refers to the language that humans use on a daily basis, and is a form that can be intuitively manipulated by non-experts in computer systems.

[0370] "Emotional recognition" is the act of understanding a person's emotional state by analyzing their tone of voice, facial expressions, or other biometric information.

[0371] "Design requirements" are specifications that include various conditions such as the functions, performance, and user experience necessary for the generated design or model.

[0372] "Automatic generation" refers to a process in which a computer system generates blueprints or models based on algorithms, minimizing human intervention.

[0373] An "autonomous mobile device" is a mechanical device that has the ability to move on its own, such as an autonomous vehicle.

[0374] "Optimizing the interior" means adjusting the design of the device's interior to the best possible state in accordance with the user's emotions and needs.

[0375] "Existing technical information" refers to a collection of information including past and present technological trends, technical literature, and patent information.

[0376] "Patent infringement risk" refers to the possibility that a particular technology or design infringes on existing patent rights.

[0377] "Feedback" refers to information obtained from users, including their opinions and emotional responses, that is used to improve the design.

[0378] To implement this invention, the user first uses a smartphone or tablet to input natural language requests regarding the interior design of an autonomous mobile device. The device is equipped with a camera and microphone to analyze facial expressions and tone of voice to recognize emotions. An emotion recognition API is used for this emotion analysis.

[0379] Next, the information entered by the user is sent to the server, which analyzes it using a large-scale language model. This analysis converts natural language requests into specific design requirements, taking emotional data into consideration. Generative AI technologies from companies like Autodesk are used to generate the design and detailed models. This results in a design that matches the user's emotions.

[0380] The generated designs are reviewed by the server based on existing technical and material information, and simultaneously, the risk of patent infringement is assessed. Technical literature and past patent information from the database are used for patent searches and material selection. Optimization of the design and materials used promotes an efficient manufacturing process.

[0381] The design is then presented to the user via the device, and the user visually confirms the design. If necessary, the user provides emotion-based feedback. This feedback is sent back to the server, and the design is adjusted and optimized.

[0382] As a concrete example, in the interior design of an autonomous vehicle, if a user inputs "I want to design a seat that allows me to relax," the system uses emotion recognition to understand the relaxation the user desires, and generates a seat design with soft lines and warm colors.

[0383] An example of a prompt message sent to the generating AI model would be, "The user has indicated they want to relax. Design the seat incorporating soft lines and warm colors." This would then provide a customized design that reflects the user's emotions.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The user inputs design requirements in natural language using a device. This requirement is received by a device equipped with emotion recognition capabilities. The device's camera and microphone detect the user's facial expressions and tone of voice, and analyze them as emotion data. The input design requirements and the analyzed emotion data are then sent to the next step.

[0387] Step 2:

[0388] Design requests and sentiment data sent from the terminal are transferred to the server. The server uses a large-scale language model to analyze the natural language requests and extract them as design requirements. In this process, sentiment data is taken into consideration and the requirements are adjusted. Specific design requirements are generated as output and used in the next model generation step.

[0389] Step 3:

[0390] The server automatically generates interior designs for autonomous mobile devices using an AI model based on design requirements. This model generation incorporates emotional data to ensure a user-optimized design. The generated designs then proceed to the next review and evaluation steps.

[0391] Step 4:

[0392] The server reviews the generated design based on existing technical information and material information. In this step, it assesses the risk of patent infringement and determines whether or not such a risk exists. It also selects the necessary material information and outputs an optimized design and material information.

[0393] Step 5:

[0394] Users visually review the design sent from the server using their device. They provide feedback on the design through the UI provided by their device. This feedback is sent back to the server as evaluation data, including emotional information.

[0395] Step 6:

[0396] After receiving user feedback, the server readjusts the design. It uses the feedback data to further optimize the design and finalize it. The adjusted design is then provided to the user as the final output.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] This invention describes a system that uses generative AI technology to quickly and accurately realize hardware design ideas proposed by users. The system functions through the interaction of users, servers, and terminals. Specific embodiments of each component are shown below.

[0414] First, the user inputs their design idea in natural language through an interface on their device. It can handle a wide range of inputs, from simple sentences to specific technical requirements. For example, they could input, "I want to design an embedded device that utilizes renewable energy."

[0415] Next, the server uses a large-scale language model to analyze the received natural language input. The analysis results are documented as technical specifications and functional requirements, generating specific design requirements. At this stage, the purpose and function of the device, the properties of the desired materials, and other conditions are clarified.

[0416] Subsequently, the server uses a multimodal, design-specific generative AI to create detailed blueprints and 3D models according to the design requirements. This process leverages the latest technologies across multiple design domains to provide an optimal design that considers functionality, aesthetics, and efficiency. The blueprints and models include the details necessary for manufacturing and market launch preparation.

[0417] Furthermore, the server automatically reviews newly generated designs to ensure they do not infringe on existing technology patents and selects the most suitable materials. This guarantees legally safe and cost-effective product designs.

[0418] Finally, the terminal presents the generated design to the user and requests feedback. If the user wishes to improve the design or functionality, they can input instructions through the terminal. Based on this, the server modifies the design, obtains final approval, and outputs a complete design file for product development.

[0419] This system will serve as a platform that easily enables individuals and companies without specialized hardware design expertise to materialize their ideas and promotes innovative product development.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] Users access the terminal and input their hardware design ideas in natural language through the interface. They might present specific needs, such as "I want to design a home electronic product that uses sustainable energy."

[0423] Step 2:

[0424] The terminal sends input from the user to the server. The server receives this input and parses it using a large-scale language model. This extracts and documents design requirements from ideas.

[0425] Step 3:

[0426] The server then uses analysis to define design requirements, including functional conditions, material requirements, and design metrics. Based on this information, a design-focused generation AI automatically generates design drawings and 3D models.

[0427] Step 4:

[0428] The server automatically checks for patent infringement risks by searching an internal database of existing technology patents for the generated design drawings and models. It also selects potential materials applicable to the design and estimates their costs.

[0429] Step 5:

[0430] The device presents the user with an initial design proposal. The user reviews the design and provides feedback on improvements to the functionality and design.

[0431] Step 6:

[0432] The server receives user feedback and incorporates the specified modifications into the design. It updates the design drawings and models as needed, generating the final version.

[0433] Step 7:

[0434] The terminal presents the final design to the user, who then approves it. After approval, the server generates all the necessary files for product development and provides the user with a download link.

[0435] (Example 1)

[0436] 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."

[0437] In hardware design, the process of quickly and accurately realizing a user's idea requires specialized technical knowledge and considerable time. Furthermore, verifying that the design does not infringe on existing technology patents and selecting appropriate materials are also challenges. Therefore, there is a need for a system that effectively realizes ideas and enables designs that meet legal and physical requirements.

[0438] 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.

[0439] In this invention, the server includes means for analyzing design ideas received from a user in natural language to generate technical specifications and functional requirements; means for automatically generating detailed design drawings and three-dimensional models based on the technical specifications and functional requirements; and means for reviewing existing technical information and material information related to the generated design to ensure legal compliance and select materials. This makes it possible to quickly convert a user's ideas into a detailed design and efficiently obtain a design that meets legal and technical requirements.

[0440] "Natural language" refers to the language that humans use on a daily basis, and it is required that it be converted into a format that machines can understand.

[0441] A "design idea" is a concept or idea for a device or a component of a device that has a specific function or purpose.

[0442] A "technical specification" is a document that outlines the specific technical aspects and performance requirements of a designed product.

[0443] "Functional requirements" define the conditions for the functions and performance that a designed product must meet.

[0444] A "design drawing" is a diagram that visually represents the shape, dimensions, materials, and other details of a product or part.

[0445] A "three-dimensional model" is data that digitally represents the 3D shape of an actual object.

[0446] "Legal compliance" refers to a state in which the generated design does not infringe on existing patents or laws and is legally usable.

[0447] "Material selection" is the process of choosing the optimal material to meet the required characteristics of a product.

[0448] "Feedback" refers to opinions and suggestions for improvement provided by users, and is used to improve the design.

[0449] This invention provides a system that utilizes generative AI technology to quickly and thoroughly realize design ideas proposed by users. The specific forms for carrying out the invention are as follows:

[0450] First, the user inputs their design idea in natural language through the device's interface. This device can be a standard computer or mobile device, accessible via a web browser or dedicated application. For example, the user might input a design idea such as, "I want to design a smart cooling device that utilizes renewable energy."

[0451] Next, the server uses software that implements a large-scale language model to analyze user input. This analysis converts the input natural language into technical specifications and functional requirements. Specifically, it uses text analysis algorithms to extract important keywords and phrases, and based on these, identifies the device's purpose, desired functions, and required material characteristics.

[0452] Next, the server uses a design-specific generative AI to generate detailed design drawings and 3D models from the analyzed information. Here, CAD software and 3D modeling tools are used to visualize the design. In this step, an optimal design is automatically created that considers not only technical requirements but also aesthetics and manufacturing efficiency.

[0453] Furthermore, the server reviews patent databases to ensure that the generated design does not infringe on existing patents. It also makes appropriate selections of materials, taking into account market availability and cost. This ensures a legally safe and cost-effective design.

[0454] Finally, the terminal presents the generated design to the user and requests feedback. The user can input opinions and modifications to the design through the interface. For example, they can input specific improvement requests such as "I want to further improve the efficiency of the cooling function." Based on this, the server adjusts the design again, and the design is refined until the user finally approves it.

[0455] A concrete example of a prompt might be, "Design a portable cooler incorporating new, energy-efficient cooling technology." The key feature of this system is that it provides a platform that allows even users without specialized knowledge to easily realize innovative product designs.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The user inputs design ideas in natural language through the terminal's interface. This input could be specific, such as "I want to design a smart device that utilizes renewable energy." The input design idea in natural language is then sent directly to the server. In this step, the terminal receives the user's text input and forwards it to the server.

[0459] Step 2:

[0460] The server analyzes the received natural language input using a large-scale language model. It tokenizes the input text and performs data processing to extract important keywords and phrases. The output here is semantic information, including the device's technical specifications and functional requirements. Specifically, the AI ​​model understands the context of the sentence and identifies the elements necessary for the design.

[0461] Step 3:

[0462] The server generates detailed design drawings and 3D models using design-specific generative AI based on the analyzed technical specifications and functional requirements. This process involves data calculations that generate visual output using CAD software and 3D modeling tools. The output includes design drawings and 3D models containing specific dimensions and material information. Specific operations include a process of automatically operating design software to create image data.

[0463] Step 4:

[0464] The server searches and reviews patent databases to ensure that the generated design does not violate existing patents. It also performs data calculations to select the optimal materials, taking into account supply availability and cost. The output confirms that the design is legally compliant and that the material selection is optimized. This step specifically involves legal database searches and verification.

[0465] Step 5:

[0466] The terminal presents the generated design to the user and requests feedback. The terminal receives the user's feedback and sends it back to the server. The input consists of the user's opinions and suggestions, and the output is feedback data based on those. Specific actions include the user accessing the interface again and writing additional instructions.

[0467] Step 6:

[0468] The server modifies the design based on user feedback. Data processing is performed to update the design drawings and 3D models according to the revised technical specifications and design. The final design file is output in an improved state. Specifically, there is a process of generating a new design plan using AI and design software again.

[0469] (Application Example 1)

[0470] 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."

[0471] In manufacturing environments, there is a need to quickly design new robots and machinery, avoid patent risks, and efficiently integrate them into the manufacturing process. Traditional methods involve significant time and cost from design to prototype manufacturing, and also carry the risk of patent infringement; therefore, a solution is needed to address these issues.

[0472] 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.

[0473] This invention includes a server that includes means for analyzing design ideas in natural language received from a user to generate design requirements, means for automatically generating detailed design drawings and three-dimensional models based on the design requirements, and means for verifying the legal security of the generated design using a patent information database. This enables users to quickly materialize designs and reflect them in the manufacturing process while reducing patent risk.

[0474] "Natural language design ideas" refer to design concepts and ideas expressed in the language that users use on a daily basis.

[0475] "Design requirements" refer to the technical conditions and specifications that are concretized based on the analyzed design ideas.

[0476] "Detailed design drawings and three-dimensional models" refer to design drawings and three-dimensional models that visually embody the generated design requirements.

[0477] A "patent information database" is a collection of data that stores existing patent information and is used to check whether newly generated designs conflict with other patents.

[0478] "Legal safety" refers to a state in which the generated design does not infringe on existing patents and is guaranteed to be free from legal issues.

[0479] "Portable devices" refer to electronic devices that users can carry with them and that are used for checking and modifying designs.

[0480] This system begins with the user inputting design ideas in natural language using a mobile device. This mobile device is equipped with an interface to accurately receive the user's design intent and communicate with the server. The design ideas entered by the user are sent to the server and analyzed using a large-scale language model. Based on the analyzed information, the server generates design requirements and creates detailed design drawings and 3D models.

[0481] The server's software includes a generative AI model, enabling the generation of designs adaptable to multiple design domains. Furthermore, the server accesses a patent database and compares the newly generated designs with existing patent information to verify legal security. During this process, users can provide real-time feedback and instruct the server to modify the design as needed.

[0482] Through this process, designs that meet user requirements can be quickly materialized and prepared for commercialization. For example, the design process begins when a user sends a prompt message to the server from a mobile device stating, "We need a robotic arm for a new assembly line. Please design it using lightweight and environmentally friendly materials." The AI ​​model then uses this input to provide a system that rapidly generates designs and simultaneously verifies their legal safety.

[0483] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0484] Step 1:

[0485] Users input design ideas in natural language using their mobile devices. This input is sent from the device to the server as a "prompt message." The prompt message includes the design's purpose, characteristics, and desired materials.

[0486] Step 2:

[0487] The server analyzes the received prompt message using a large-scale language model. This analysis process extracts specific "design requirements" from the prompt message. These design requirements describe the detailed technical conditions necessary for generating design drawings and 3D models. The server saves these analysis results for use in the next step.

[0488] Step 3:

[0489] The server generates detailed blueprints and 3D models based on design requirements extracted using a generative AI model. It receives design requirements as input and creates visual blueprints and 3D models as output. The generated designs are then prepared for subsequent stages such as patent checks and user feedback.

[0490] Step 4:

[0491] The server compares the generated design with the patent information database to verify its legal security. It retrieves relevant information from the patent database and compares it with the generated design to perform verification to avoid patent infringement. If there are no legal issues, it saves the details.

[0492] Step 5:

[0493] Users can view design drawings and 3D models sent from the server on their mobile devices and provide real-time feedback. The server receives user feedback as input, modifies the design as needed, and incorporates these modifications as new design requirements.

[0494] Step 6:

[0495] The server generates manufacturing information based on the final revised design, preparing it for production. The final output is a design file containing all the necessary manufacturing details, which is then provided to the user and the manufacturing department.

[0496] 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.

[0497] This invention is a system that uses generative AI technology combined with an emotion engine to design hardware while taking user emotions into consideration. In this system, the user, server, and terminal work together to incorporate emotional elements into the design process.

[0498] First, the user uses an interface on their device to input their design idea in natural language. For example, they can communicate a specific need such as, "I want to design lighting fixtures that create a comfortable, relaxing space." At this time, the device has an emotion recognition function built in, which detects the user's emotions from their tone of voice, facial expressions, etc.

[0499] Next, the server analyzes this input using a large-scale language model to generate design requirements. Simultaneously, it uses an emotion engine to extract the user's emotional tone and fine-tune the design requirements to reflect those nuances. This is a process to specifically weave the emotions and atmosphere the user desires into the design.

[0500] Subsequently, the server automatically generates detailed blueprints and models based on the design requirements, using generative artificial intelligence that supports multiple design domains. This generation process optimizes the design and functionality according to the user's emotions. This process results in highly unique products that resonate with the user's feelings.

[0501] Furthermore, the server reviews existing technical and material information for the generated design. Functions for assessing patent infringement risk, selecting appropriate materials, and estimating optimal costs are also utilized here.

[0502] Finally, the device presents the generated design to the user. The user visually reviews the design and, if necessary, provides emotionally-based feedback. This feedback is sent back to the server for further modifications.

[0503] In this way, by incorporating emotion recognition, we can realize hardware designs optimized for individuals and accelerate the development of products that highly match the sensibilities and needs of users.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] When a user accesses the device and inputs design ideas in natural language, the emotion engine analyzes the user's voice and facial expressions to recognize their emotions. This analysis is then used to gain a deeper understanding of the user's wishes and expectations.

[0507] Step 2:

[0508] The device sends design ideas from the user and recognized emotion data to the server. The server uses a large-scale language model to analyze design requirements from natural language and adjusts those requirements based on the emotion data. For example, if a relaxed emotion is detected, the design will incorporate soft curves and warm colors.

[0509] Step 3:

[0510] The server automatically generates detailed blueprints and 3D models using multimodal generative artificial intelligence based on design requirements that reflect emotions. The generated designs reflect insights gained from emotion recognition, taking into account the optimal product layout and functionality that responds to the user's emotions.

[0511] Step 4:

[0512] The server checks existing technical information against the generated design and assesses the risk of patent infringement. Simultaneously, it selects potential materials to be used and estimates the cost for each material.

[0513] Step 5:

[0514] The device presents the generated design proposals to the user. The user can review the proposed designs and provide sentiment-based feedback and additional requests.

[0515] Step 6:

[0516] The server receives user feedback and makes further revisions to the design drawings and 3D models. This revision process can be cycled multiple times as needed, continuing until the final design is completed.

[0517] Step 7:

[0518] After the final design is approved, the terminal generates download links to provide the user with all the files necessary for product development. The user can then use these files to proceed with the product manufacturing and distribution process.

[0519] (Example 2)

[0520] 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."

[0521] In conventional systems, it was difficult to consider the emotional state of users during the design process, resulting in challenges in designing products that fully met users' true needs. Furthermore, methods for quickly incorporating post-design feedback and providing more refined, customized designs were limited. Solving these challenges is essential.

[0522] 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.

[0523] In this invention, the server includes means for analyzing design ideas in natural language received from a user to generate design requirements, means for acquiring user emotion data and adjusting the design requirements based on that data, and means for automatically generating detailed design drawings and models based on the design requirements. This makes it possible to generate an optimal design that takes the user's emotions into consideration.

[0524] "Natural language" refers to the linguistic forms that users typically use in everyday conversation and writing, and which are easily understood by humans.

[0525] A "design idea" is the initial concept or image of a product or system that the user wants to realize.

[0526] "Analysis" is the act of breaking down and analyzing input information and data in detail to clarify its structure and meaning.

[0527] "Design requirements" are guidelines that clarify the specifications and necessary conditions for a product or system.

[0528] "Emotional data" refers to information that expresses a user's emotional state using numerical values ​​or categories.

[0529] "Adjustment" refers to the act of making changes or modifications as needed to optimize the process.

[0530] A "design drawing" is a diagram that shows the structure and dimensions of a product or system in detail.

[0531] A "model" is a visual representation, or digital data, of the concept and shape of a product or system based on design drawings.

[0532] "Automatic generation" refers to the process by which a system autonomously creates blueprints and models without human intervention.

[0533] This invention is a system that uses generative AI technology to enable product design that takes user emotions into consideration. The system is configured in which the user, server, and terminal work together, and is designed to incorporate emotional elements into the design process.

[0534] First, the user inputs their design idea in natural language using a device. For example, they might input a request such as, "I want to design lighting fixtures that create a comfortable, relaxing space." At this time, the device's camera and microphone are used to acquire emotional data through the user's facial expressions and tone of voice. This incorporates the user's emotional state into the system.

[0535] The server analyzes input information sent from the terminal using a Large-Scale Language Model (LGM). Specifically, it uses models such as OpenAI's GPT series and Google's BERT to perform text analysis. Furthermore, it extracts the user's emotional tone using an emotion engine and incorporates it into the design requirements. This adjustment allows for the incorporation of soft designs and colors suitable for a relaxing space.

[0536] Next, the server automatically generates detailed blueprints and models using a generative AI model based on the adjusted design requirements. In this process, the design AI selects appropriate shapes and materials, and the generated design reflects the user's emotions.

[0537] A concrete example of a prompt would be, "Please suggest a lighting design that creates a relaxing atmosphere." Based on this, the system can generate a design that meets the user's needs.

[0538] In this way, through a design process that utilizes emotion recognition, it becomes possible to design products that respond to users' sensibilities and needs with high accuracy.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] Users input design ideas in natural language through their devices. During this process, the camera and microphone capture the user's facial expressions and tone of voice as emotional data. The input natural language data and emotional data are preprocessed by emotion recognition software and sent to the server. This allows the system to clearly recognize the user's emotional needs regarding the design.

[0542] Step 2:

[0543] The server receives natural language data sent from the terminal and analyzes its content using a generative AI model. Specifically, it processes the input text with a large-scale language model such as the GPT series to generate design requirements. Here, natural language processing technology is used to convert user requests from text data into concrete design specifications.

[0544] Step 3:

[0545] The server uses an emotion engine to analyze the acquired emotional data. Based on this data, the process of fine-tuning design requirements involves finding the optimal match between the emotional data and the design requirements. For example, if the user is seeking a relaxing tone, the design requirements will be changed to emphasize soft colors and curves.

[0546] Step 4:

[0547] The server automatically generates design drawings and models using a generative AI model based on the adjusted design requirements. The design AI works in conjunction with 3D modeling software to bring the design proposal to life. Here, it performs data calculations on the design data to generate a detailed product model.

[0548] Step 5:

[0549] The server compares the generated design with existing technical information to assess material selection and patent infringement risk. This assessment involves searching for information using a database, obtaining and analyzing data on the latest technologies and materials.

[0550] Step 6:

[0551] The terminal presents the generated blueprints and models to the user. The user visually evaluates them and provides feedback as needed. The terminal then sends the user's feedback back to the server, where it is used to further improve the design.

[0552] (Application Example 2)

[0553] 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."

[0554] In the design of autonomous mobile devices, user emotions are often disregarded, potentially compromising the quality of the user experience. Furthermore, it is difficult to assess potential patent infringement risks during the design process, and feedback for efficient design improvement is insufficient. These factors hinder the development of high-quality products that align with user emotions and needs.

[0555] 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.

[0556] This invention includes a server that analyzes design ideas received from a user in natural language, recognizes emotions in the process, and generates design requirements; a server that automatically generates detailed design drawings and models based on the design requirements and optimizes the interior of the autonomous mobile device; and a server that reviews existing technical information and material information related to the generated design and evaluates the risk of patent infringement in the process. This enables a design process that takes user emotions into consideration and minimizes patent risk.

[0557] "Natural language" refers to the language that humans use on a daily basis, and is a form that can be intuitively manipulated by non-experts in computer systems.

[0558] "Emotional recognition" is the act of understanding a person's emotional state by analyzing their tone of voice, facial expressions, or other biometric information.

[0559] "Design requirements" are specifications that include various conditions such as the functions, performance, and user experience necessary for the generated design or model.

[0560] "Automatic generation" refers to a process in which a computer system generates blueprints or models based on algorithms, minimizing human intervention.

[0561] An "autonomous mobile device" is a mechanical device that has the ability to move on its own, such as an autonomous vehicle.

[0562] "Optimizing the interior" means adjusting the design of the device's interior to the best possible state in accordance with the user's emotions and needs.

[0563] "Existing technical information" refers to a collection of information including past and present technological trends, technical literature, and patent information.

[0564] "Patent infringement risk" refers to the possibility that a particular technology or design infringes on existing patent rights.

[0565] "Feedback" refers to information obtained from users, including their opinions and emotional responses, that is used to improve the design.

[0566] To implement this invention, the user first uses a smartphone or tablet to input natural language requests regarding the interior design of an autonomous mobile device. The device is equipped with a camera and microphone to analyze facial expressions and tone of voice to recognize emotions. An emotion recognition API is used for this emotion analysis.

[0567] Next, the information entered by the user is sent to the server, which analyzes it using a large-scale language model. This analysis converts natural language requests into specific design requirements, taking emotional data into consideration. Generative AI technologies from companies like Autodesk are used to generate the design and detailed models. This results in a design that matches the user's emotions.

[0568] The generated designs are reviewed by the server based on existing technical and material information, and simultaneously, the risk of patent infringement is assessed. Technical literature and past patent information from the database are used for patent searches and material selection. Optimization of the design and materials used promotes an efficient manufacturing process.

[0569] The design is then presented to the user via the device, and the user visually confirms the design. If necessary, the user provides emotion-based feedback. This feedback is sent back to the server, and the design is adjusted and optimized.

[0570] As a concrete example, in the interior design of an autonomous vehicle, if a user inputs "I want to design a seat that allows me to relax," the system uses emotion recognition to understand the relaxation the user desires, and generates a seat design with soft lines and warm colors.

[0571] An example of a prompt message sent to the generating AI model would be, "The user has indicated they want to relax. Design the seat incorporating soft lines and warm colors." This would then provide a customized design that reflects the user's emotions.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The user inputs design requirements in natural language using a device. This requirement is received by a device equipped with emotion recognition capabilities. The device's camera and microphone detect the user's facial expressions and tone of voice, and analyze them as emotion data. The input design requirements and the analyzed emotion data are then sent to the next step.

[0575] Step 2:

[0576] Design requests and sentiment data sent from the terminal are transferred to the server. The server uses a large-scale language model to analyze the natural language requests and extract them as design requirements. In this process, sentiment data is taken into consideration and the requirements are adjusted. Specific design requirements are generated as output and used in the next model generation step.

[0577] Step 3:

[0578] The server automatically generates interior designs for autonomous mobile devices using an AI model based on design requirements. This model generation incorporates emotional data to ensure a user-optimized design. The generated designs then proceed to the next review and evaluation steps.

[0579] Step 4:

[0580] The server reviews the generated design based on existing technical information and material information. In this step, it assesses the risk of patent infringement and determines whether or not such a risk exists. It also selects the necessary material information and outputs an optimized design and material information.

[0581] Step 5:

[0582] Users visually review the design sent from the server using their device. They provide feedback on the design through the UI provided by their device. This feedback is sent back to the server as evaluation data, including emotional information.

[0583] Step 6:

[0584] After receiving user feedback, the server readjusts the design. It uses the feedback data to further optimize the design and finalize it. The adjusted design is then provided to the user as the final output.

[0585] 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.

[0586] 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 those described above. 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 shown 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.

[0587] 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.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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".

[0602] This invention describes a system that uses generative AI technology to quickly and accurately realize hardware design ideas proposed by users. The system functions through the interaction of users, servers, and terminals. Specific embodiments of each component are shown below.

[0603] First, the user inputs their design idea in natural language through an interface on their device. It can handle a wide range of inputs, from simple sentences to specific technical requirements. For example, they could input, "I want to design an embedded device that utilizes renewable energy."

[0604] Next, the server uses a large-scale language model to analyze the received natural language input. The analysis results are documented as technical specifications and functional requirements, generating specific design requirements. At this stage, the purpose and function of the device, the properties of the desired materials, and other conditions are clarified.

[0605] Subsequently, the server uses a multimodal, design-specific generative AI to create detailed blueprints and 3D models according to the design requirements. This process leverages the latest technologies across multiple design domains to provide an optimal design that considers functionality, aesthetics, and efficiency. The blueprints and models include the details necessary for manufacturing and market launch preparation.

[0606] Furthermore, the server automatically reviews newly generated designs to ensure they do not infringe on existing technology patents and selects the most suitable materials. This guarantees legally safe and cost-effective product designs.

[0607] Finally, the terminal presents the generated design to the user and requests feedback. If the user wishes to improve the design or functionality, they can input instructions through the terminal. Based on this, the server modifies the design, obtains final approval, and outputs a complete design file for product development.

[0608] This system will serve as a platform that allows individuals and companies without specialized hardware design expertise to easily materialize their ideas and promote innovative product development.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] Users access the terminal and input their hardware design ideas in natural language through the interface. They might present specific needs, such as "I want to design a home electronic product that uses sustainable energy."

[0612] Step 2:

[0613] The terminal sends input from the user to the server. The server receives this input and analyzes it using a large-scale language model. This extracts and documents design requirements from ideas.

[0614] Step 3:

[0615] The server then uses analysis to define design requirements, including functional conditions, material requirements, and design metrics. Based on this information, a design-focused generation AI automatically generates design drawings and 3D models.

[0616] Step 4:

[0617] The server automatically checks for patent infringement risks by searching an internal database of existing technology patents for the generated design drawings and models. It also selects potential materials applicable to the design and estimates their costs.

[0618] Step 5:

[0619] The device presents the user with an initial design proposal. The user reviews the design and provides feedback on improvements to the functionality and design.

[0620] Step 6:

[0621] The server receives user feedback and incorporates the specified modifications into the design. It updates the design drawings and models as needed, generating the final version.

[0622] Step 7:

[0623] The terminal presents the final design to the user, who then approves it. After approval, the server generates all the necessary files for product development and provides the user with a download link.

[0624] (Example 1)

[0625] 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".

[0626] In hardware design, the process of quickly and accurately realizing a user's idea requires specialized technical knowledge and considerable time. Furthermore, verifying that the design does not infringe on existing technology patents and selecting appropriate materials are also challenges. Therefore, there is a need for a system that effectively realizes ideas and enables designs that meet legal and physical requirements.

[0627] 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.

[0628] In this invention, the server includes means for analyzing design ideas received from a user in natural language to generate technical specifications and functional requirements; means for automatically generating detailed design drawings and three-dimensional models based on the technical specifications and functional requirements; and means for reviewing existing technical information and material information related to the generated design to ensure legal compliance and select materials. This makes it possible to quickly convert a user's ideas into a detailed design and efficiently obtain a design that meets legal and technical requirements.

[0629] "Natural language" refers to the language that humans use on a daily basis, and it is required that it be converted into a format that machines can understand.

[0630] A "design idea" is a concept or idea for a device or a component of a device that has a specific function or purpose.

[0631] A "technical specification" is a document that outlines the specific technical aspects and performance requirements of a designed product.

[0632] "Functional requirements" define the conditions for the functions and performance that a designed product must meet.

[0633] A "design drawing" is a diagram that visually represents the shape, dimensions, materials, and other details of a product or part.

[0634] A "three-dimensional model" is data that digitally represents the 3D shape of an actual object.

[0635] "Legal compliance" refers to a state in which the generated design does not infringe on existing patents or laws and is legally usable.

[0636] "Material selection" is the process of choosing the optimal material to meet the required characteristics of a product.

[0637] "Feedback" refers to opinions and suggestions for improvement provided by users, and is used to improve the design.

[0638] This invention provides a system that utilizes generative AI technology to quickly and thoroughly realize design ideas proposed by users. The specific forms for carrying out the invention are as follows:

[0639] First, the user inputs their design idea in natural language through the device's interface. This device can be a standard computer or mobile device, accessible via a web browser or dedicated application. For example, the user might input a design idea such as, "I want to design a smart cooling device that utilizes renewable energy."

[0640] Next, the server uses software that implements a large-scale language model to analyze user input. This analysis converts the input natural language into technical specifications and functional requirements. Specifically, it uses text analysis algorithms to extract important keywords and phrases, and based on these, identifies the device's purpose, desired functions, and required material characteristics.

[0641] Next, the server uses a design-specific generative AI to generate detailed design drawings and 3D models from the analyzed information. Here, CAD software and 3D modeling tools are used to visualize the design. In this step, an optimal design is automatically created that considers not only technical requirements but also aesthetics and manufacturing efficiency.

[0642] Furthermore, the server reviews patent databases to ensure that the generated design does not infringe on existing patents. It also makes appropriate selections of materials, taking into account market availability and cost. This ensures a legally safe and cost-effective design.

[0643] Finally, the terminal presents the generated design to the user and requests feedback. The user can input opinions and modifications to the design through the interface. For example, they can input specific improvement requests such as "I want to further improve the efficiency of the cooling function." Based on this, the server adjusts the design again, and the design is refined until the user finally approves it.

[0644] A concrete example of a prompt might be, "Design a portable cooler incorporating new, energy-efficient cooling technology." The key feature of this system is that it provides a platform that allows even users without specialized knowledge to easily realize innovative product designs.

[0645] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0646] Step 1:

[0647] The user inputs design ideas in natural language through the terminal's interface. This input could be specific, such as "I want to design a smart device that utilizes renewable energy." The input design idea in natural language is then sent directly to the server. In this step, the terminal receives the user's text input and forwards it to the server.

[0648] Step 2:

[0649] The server analyzes the received natural language input using a large-scale language model. It tokenizes the input text and performs data processing to extract important keywords and phrases. The output here is semantic information, including the device's technical specifications and functional requirements. Specifically, the AI ​​model understands the context of the sentence and identifies the elements necessary for the design.

[0650] Step 3:

[0651] The server generates detailed design drawings and 3D models using design-specific generative AI based on the analyzed technical specifications and functional requirements. This process involves data calculations that generate visual output using CAD software and 3D modeling tools. The output includes design drawings and 3D models containing specific dimensions and material information. Specific operations include a process of automatically operating design software to create image data.

[0652] Step 4:

[0653] The server searches and reviews patent databases to ensure that the generated design does not violate existing patents. It also performs data calculations to select the optimal materials, taking into account supply availability and cost. The output confirms that the design is legally compliant and that the material selection is optimized. This step specifically involves legal database searches and verification.

[0654] Step 5:

[0655] The terminal presents the generated design to the user and requests feedback. The terminal receives the user's feedback and sends it back to the server. The input consists of the user's opinions and suggestions, and the output is feedback data based on those. Specific actions include the user accessing the interface again and writing additional instructions.

[0656] Step 6:

[0657] The server modifies the design based on user feedback. Data processing is performed to update the design drawings and 3D models according to the revised technical specifications and design. The final design file is output in an improved state. Specifically, there is a process of generating a new design plan using AI and design software again.

[0658] (Application Example 1)

[0659] 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".

[0660] In manufacturing environments, there is a need to quickly design new robots and machinery, avoid patent risks, and efficiently integrate them into the manufacturing process. Traditional methods involve significant time and cost from design to prototype manufacturing, and also carry the risk of patent infringement; therefore, a solution is needed to address these issues.

[0661] 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.

[0662] This invention includes a server that includes means for analyzing design ideas in natural language received from a user to generate design requirements, means for automatically generating detailed design drawings and three-dimensional models based on the design requirements, and means for verifying the legal security of the generated design using a patent information database. This enables users to quickly materialize designs and reflect them in the manufacturing process while reducing patent risk.

[0663] "Natural language design ideas" refer to design concepts and ideas expressed in the language that users use on a daily basis.

[0664] "Design requirements" refer to the technical conditions and specifications that are concretized based on the analyzed design ideas.

[0665] "Detailed design drawings and three-dimensional models" refer to design drawings and three-dimensional models that visually embody the generated design requirements.

[0666] A "patent information database" is a collection of data that stores existing patent information and is used to check whether newly generated designs conflict with other patents.

[0667] "Legal safety" refers to a state in which the generated design does not infringe on existing patents and is guaranteed to be free from legal issues.

[0668] "Portable devices" refer to electronic devices that users can carry with them and that are used for checking and modifying designs.

[0669] This system begins with the user inputting design ideas in natural language using a mobile device. This mobile device is equipped with an interface to accurately receive the user's design intent and communicate with the server. The design ideas entered by the user are sent to the server and analyzed using a large-scale language model. Based on the analyzed information, the server generates design requirements and creates detailed design drawings and 3D models.

[0670] The server's software includes a generative AI model, enabling the generation of designs adaptable to multiple design domains. Furthermore, the server accesses a patent database and compares the newly generated designs with existing patent information to verify legal security. During this process, users can provide real-time feedback and instruct the server to modify the design as needed.

[0671] Through this process, designs that meet user requirements can be quickly materialized and prepared for commercialization. For example, the design process begins when a user sends a prompt message to the server from a mobile device stating, "We need a robotic arm for a new assembly line. Please design it using lightweight and environmentally friendly materials." The AI ​​model then uses this input to provide a system that rapidly generates designs and simultaneously verifies their legal safety.

[0672] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0673] Step 1:

[0674] Users input design ideas in natural language using their mobile devices. This input is sent from the device to the server as a "prompt message." The prompt message includes the design's purpose, characteristics, and desired materials.

[0675] Step 2:

[0676] The server analyzes the received prompt message using a large-scale language model. This analysis process extracts specific "design requirements" from the prompt message. These design requirements describe the detailed technical conditions necessary for generating design drawings and 3D models. The server saves these analysis results for use in the next step.

[0677] Step 3:

[0678] The server generates detailed blueprints and 3D models based on design requirements extracted using a generative AI model. It receives design requirements as input and creates visual blueprints and 3D models as output. The generated designs are then prepared for subsequent stages such as patent checks and user feedback.

[0679] Step 4:

[0680] The server compares the generated design with the patent information database to verify its legal security. It retrieves relevant information from the patent database and compares it with the generated design to perform verification to avoid patent infringement. If there are no legal issues, it saves the details.

[0681] Step 5:

[0682] Users can view design drawings and 3D models sent from the server on their mobile devices and provide real-time feedback. The server receives user feedback as input, modifies the design as needed, and incorporates these modifications as new design requirements.

[0683] Step 6:

[0684] The server generates manufacturing information based on the final revised design, preparing it for production. The final output is a design file containing all the necessary manufacturing details, which is then provided to the user and the manufacturing department.

[0685] 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.

[0686] This invention is a system that uses generative AI technology combined with an emotion engine to design hardware while taking user emotions into consideration. In this system, the user, server, and terminal work together to incorporate emotional elements into the design process.

[0687] First, the user uses an interface on their device to input their design idea in natural language. For example, they can communicate a specific need such as, "I want to design lighting fixtures that create a comfortable, relaxing space." At this time, the device has an emotion recognition function built in, which detects the user's emotions from their tone of voice, facial expressions, etc.

[0688] Next, the server analyzes this input using a large-scale language model to generate design requirements. Simultaneously, it uses an emotion engine to extract the user's emotional tone and fine-tune the design requirements to reflect those nuances. This is a process to specifically weave the emotions and atmosphere the user desires into the design.

[0689] Subsequently, the server automatically generates detailed blueprints and models based on the design requirements, using generative artificial intelligence that supports multiple design domains. This generation process optimizes the design and functionality according to the user's emotions. This process results in highly unique products that resonate with the user's feelings.

[0690] Furthermore, the server reviews existing technical and material information for the generated design. Functions for assessing patent infringement risk, selecting appropriate materials, and estimating optimal costs are also utilized here.

[0691] Finally, the device presents the generated design to the user. The user visually reviews the design and, if necessary, provides emotionally-based feedback. This feedback is sent back to the server for further modifications.

[0692] In this way, by incorporating emotion recognition, we can realize hardware designs optimized for individuals and accelerate the development of products that highly match the sensibilities and needs of users.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] When a user accesses the device and inputs design ideas in natural language, the emotion engine analyzes the user's voice and facial expressions to recognize their emotions. This analysis is then used to gain a deeper understanding of the user's wishes and expectations.

[0696] Step 2:

[0697] The device sends design ideas from the user and recognized emotion data to the server. The server uses a large-scale language model to analyze design requirements from natural language and adjusts those requirements based on the emotion data. For example, if a relaxed emotion is detected, the design will incorporate soft curves and warm colors.

[0698] Step 3:

[0699] The server automatically generates detailed blueprints and 3D models using multimodal generative artificial intelligence based on design requirements that reflect emotions. The generated designs reflect insights gained from emotion recognition, taking into account the optimal product layout and functionality that responds to the user's emotions.

[0700] Step 4:

[0701] The server checks existing technical information against the generated design and assesses the risk of patent infringement. Simultaneously, it selects potential materials to be used and estimates the cost for each material.

[0702] Step 5:

[0703] The device presents the generated design proposals to the user. The user can review the proposed designs and provide sentiment-based feedback and additional requests.

[0704] Step 6:

[0705] The server receives user feedback and makes further revisions to the design drawings and 3D models. This revision process can be cycled multiple times as needed, continuing until the final design is completed.

[0706] Step 7:

[0707] After the final design is approved, the terminal generates download links to provide the user with all the files necessary for product development. The user can then use these files to proceed with the product manufacturing and distribution process.

[0708] (Example 2)

[0709] 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".

[0710] In conventional systems, it was difficult to consider the emotional state of users during the design process, resulting in challenges in designing products that fully met users' true needs. Furthermore, methods for quickly incorporating post-design feedback and providing more refined, customized designs were limited. Solving these challenges is essential.

[0711] 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.

[0712] In this invention, the server includes means for analyzing design ideas in natural language received from a user to generate design requirements, means for acquiring user emotion data and adjusting the design requirements based on that data, and means for automatically generating detailed design drawings and models based on the design requirements. This makes it possible to generate an optimal design that takes the user's emotions into consideration.

[0713] "Natural language" refers to the linguistic forms that users typically use in everyday conversation and writing, and which are easily understood by humans.

[0714] A "design idea" is the initial concept or image of a product or system that the user wants to realize.

[0715] "Analysis" is the act of breaking down and analyzing input information and data in detail to clarify its structure and meaning.

[0716] "Design requirements" are guidelines that clarify the specifications and necessary conditions for a product or system.

[0717] "Emotional data" refers to information that expresses a user's emotional state using numerical values ​​or categories.

[0718] "Adjustment" refers to the act of making changes or modifications as needed to optimize the process.

[0719] A "design drawing" is a diagram that shows the structure and dimensions of a product or system in detail.

[0720] A "model" is a visual representation, or digital data, of the concept and shape of a product or system based on design drawings.

[0721] "Automatic generation" refers to the process by which a system autonomously creates blueprints and models without human intervention.

[0722] This invention is a system that uses generative AI technology to enable product design that takes user emotions into consideration. The system is configured in which the user, server, and terminal work together, and is designed to incorporate emotional elements into the design process.

[0723] First, the user inputs their design idea in natural language using a device. For example, they might input a request such as, "I want to design lighting fixtures that create a comfortable, relaxing space." At this time, the device's camera and microphone are used to acquire emotional data through the user's facial expressions and tone of voice. This incorporates the user's emotional state into the system.

[0724] The server analyzes input information sent from the terminal using a Large-Scale Language Model (LGM). Specifically, it uses models such as OpenAI's GPT series and Google's BERT to perform text analysis. Furthermore, it extracts the user's emotional tone using an emotion engine and incorporates it into the design requirements. This adjustment allows for the incorporation of soft designs and colors suitable for a relaxing space.

[0725] Next, the server automatically generates detailed blueprints and models using a generative AI model based on the adjusted design requirements. In this process, the design AI selects appropriate shapes and materials, and the generated design reflects the user's emotions.

[0726] A concrete example of a prompt would be, "Please suggest a lighting design that creates a relaxing atmosphere." Based on this, the system can generate a design that meets the user's needs.

[0727] In this way, through a design process that utilizes emotion recognition, it becomes possible to design products that respond to users' sensibilities and needs with high accuracy.

[0728] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0729] Step 1:

[0730] Users input design ideas in natural language through their devices. During this process, the camera and microphone capture the user's facial expressions and tone of voice as emotional data. The input natural language data and emotional data are preprocessed by emotion recognition software and sent to the server. This allows the system to clearly recognize the user's emotional needs regarding the design.

[0731] Step 2:

[0732] The server receives natural language data sent from the terminal and analyzes its content using a generative AI model. Specifically, it processes the input text with a large-scale language model such as the GPT series to generate design requirements. Here, natural language processing technology is used to convert user requests from text data into concrete design specifications.

[0733] Step 3:

[0734] The server uses an emotion engine to analyze the acquired emotional data. Based on this data, the process of fine-tuning design requirements involves finding the optimal match between the emotional data and the design requirements. For example, if the user is seeking a relaxing tone, the design requirements will be changed to emphasize soft colors and curves.

[0735] Step 4:

[0736] The server automatically generates design drawings and models using a generative AI model based on the adjusted design requirements. The design AI works in conjunction with 3D modeling software to bring the design proposals to life. Here, it performs data calculations on the design data to generate a detailed product model.

[0737] Step 5:

[0738] The server compares the generated design with existing technical information to assess material selection and patent infringement risk. This assessment involves searching for information using a database, obtaining and analyzing data on the latest technologies and materials.

[0739] Step 6:

[0740] The terminal presents the generated blueprints and models to the user. The user visually evaluates them and provides feedback as needed. The terminal then sends the user's feedback back to the server, where it is used to further improve the design.

[0741] (Application Example 2)

[0742] 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".

[0743] In the design of autonomous mobile devices, user emotions are often disregarded, potentially compromising the quality of the user experience. Furthermore, it is difficult to assess potential patent infringement risks during the design process, and feedback for efficient design improvement is insufficient. These factors hinder the development of high-quality products that align with user emotions and needs.

[0744] 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.

[0745] This invention includes a server that analyzes design ideas received from a user in natural language, recognizes emotions in the process, and generates design requirements; a server that automatically generates detailed design drawings and models based on the design requirements and optimizes the interior of the autonomous mobile device; and a server that reviews existing technical information and material information related to the generated design and evaluates the risk of patent infringement in the process. This enables a design process that takes user emotions into consideration and minimizes patent risk.

[0746] "Natural language" refers to the language that humans use on a daily basis, and is a form that can be intuitively manipulated by non-experts in computer systems.

[0747] "Emotional recognition" is the act of understanding a person's emotional state by analyzing their tone of voice, facial expressions, or other biometric information.

[0748] "Design requirements" are specifications that include various conditions such as the functions, performance, and user experience necessary for the generated design or model.

[0749] "Automatic generation" refers to a process in which a computer system generates blueprints or models based on algorithms, minimizing human intervention.

[0750] An "autonomous mobile device" is a mechanical device that has the ability to move on its own, such as an autonomous vehicle.

[0751] "Optimizing the interior" means adjusting the design of the device's interior to the best possible state in accordance with the user's emotions and needs.

[0752] "Existing technical information" refers to a collection of information including past and present technological trends, technical literature, and patent information.

[0753] "Patent infringement risk" refers to the possibility that a particular technology or design infringes on existing patent rights.

[0754] "Feedback" refers to information obtained from users, including their opinions and emotional responses, that is used to improve the design.

[0755] To implement this invention, the user first uses a smartphone or tablet to input natural language requests regarding the interior design of an autonomous mobile device. The device is equipped with a camera and microphone to analyze facial expressions and tone of voice to recognize emotions. An emotion recognition API is used for this emotion analysis.

[0756] Next, the information entered by the user is sent to the server, which analyzes it using a large-scale language model. This analysis converts natural language requests into specific design requirements, taking emotional data into consideration. Generative AI technologies from companies like Autodesk are used to generate the design and detailed models. This results in a design that matches the user's emotions.

[0757] The generated designs are reviewed by the server based on existing technical and material information, and simultaneously, the risk of patent infringement is assessed. Technical literature and past patent information from the database are used for patent searches and material selection. Optimization of the design and materials used promotes an efficient manufacturing process.

[0758] The design is then presented to the user via the device, and the user visually confirms the design. If necessary, the user provides emotion-based feedback. This feedback is sent back to the server, and the design is adjusted and optimized.

[0759] As a concrete example, in the interior design of an autonomous vehicle, if a user inputs "I want to design a seat that allows me to relax," the system uses emotion recognition to understand the relaxation the user desires, and generates a seat design with soft lines and warm colors.

[0760] An example of a prompt message sent to the generating AI model would be, "The user has indicated they want to relax. Design the seat incorporating soft lines and warm colors." This would then provide a customized design that reflects the user's emotions.

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The user inputs design requirements in natural language using a device. This requirement is received by a device equipped with emotion recognition capabilities. The device's camera and microphone detect the user's facial expressions and tone of voice, and analyze them as emotion data. The input design requirements and the analyzed emotion data are then sent to the next step.

[0764] Step 2:

[0765] Design requests and sentiment data sent from the terminal are transferred to the server. The server uses a large-scale language model to analyze the natural language requests and extract them as design requirements. In this process, sentiment data is taken into consideration and the requirements are adjusted. Specific design requirements are generated as output and used in the next model generation step.

[0766] Step 3:

[0767] The server automatically generates interior designs for autonomous mobile devices using an AI model based on design requirements. This model generation incorporates emotional data to ensure a user-optimized design. The generated designs then proceed to the next review and evaluation steps.

[0768] Step 4:

[0769] The server reviews the generated design based on existing technical information and material information. In this step, it assesses the risk of patent infringement and determines whether or not such a risk exists. It also selects the necessary material information and outputs an optimized design and material information.

[0770] Step 5:

[0771] Users visually review the design sent from the server using their device. They provide feedback on the design through the UI provided by their device. This feedback is sent back to the server as evaluation data, including emotional information.

[0772] Step 6:

[0773] After receiving user feedback, the server readjusts the design. It uses the feedback data to further optimize the design and finalize it. The adjusted design is then provided to the user as the final output.

[0774] 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.

[0775] 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 those described above. 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 shown 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.

[0776] 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 robot 414.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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."

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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 this memory.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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 as being incorporated by reference.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] A means of analyzing design ideas in natural language received from users to generate design requirements,

[0798] Means for automatically generating detailed design drawings and models based on the aforementioned design requirements,

[0799] A means for reviewing existing technical information and material information related to the generated design,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, comprising means for using generative artificial intelligence corresponding to multiple design domains when generating design drawings and models based on the aforementioned design requirements.

[0803] (Claim 3)

[0804] The system according to claim 1, further comprising means for modifying design drawings and models based on user feedback.

[0805] "Example 1"

[0806] (Claim 1)

[0807] A means for analyzing design ideas in natural language received from users to generate technical specifications and functional requirements,

[0808] A means for automatically generating detailed design drawings and three-dimensional models based on the aforementioned technical specifications and functional requirements,

[0809] A means of reviewing existing technical and material information related to the generated design, and performing legal compliance and material selection,

[0810] A means of presenting the design details to users and obtaining their feedback,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, comprising means for using design-specific generation artificial intelligence corresponding to multiple design domains when generating design drawings and three-dimensional models based on the aforementioned technical specifications and functional requirements.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising means for modifying design drawings and three-dimensional models based on user feedback and generating a final design file.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] A means of analyzing design ideas in natural language received from users to generate design requirements,

[0819] A means for automatically generating detailed design drawings and three-dimensional models based on the aforementioned design requirements,

[0820] A means for reviewing existing technical information and material information related to the generated design,

[0821] A means of verifying the legal security of a design generated using a patent information database,

[0822] A means for users to use mobile devices to verify and modify designs and 3D models in real time,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, comprising means for using generation artificial intelligence corresponding to multiple design domains when generating design drawings and three-dimensional models based on the aforementioned design requirements, and outputting processing information of the generated three-dimensional model.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising means for modifying design drawings and three-dimensional models based on user feedback and quickly reflecting these changes in the manufacturing process.

[0828] "Example 2 of combining an emotion engine"

[0829] (Claim 1)

[0830] A means of analyzing design ideas in natural language received from users to generate design requirements,

[0831] A means of acquiring user emotional data and adjusting design requirements based on that data,

[0832] Means for automatically generating detailed design drawings and models based on the aforementioned design requirements,

[0833] A means for reviewing existing technical information and material information related to the generated design,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising means for using generative artificial intelligence corresponding to multiple design domains when generating design drawings and models based on the aforementioned design requirements.

[0837] (Claim 3)

[0838] The system according to claim 1, further comprising means for modifying design drawings and models based on user feedback.

[0839] "Application example 2 when combining with an emotional engine"

[0840] (Claim 1)

[0841] A method for analyzing design ideas received from users in natural language, recognizing emotions in the process, and generating design requirements,

[0842] A means for automatically generating detailed design drawings and models based on the aforementioned design requirements and optimizing the interior of the autonomous mobile device,

[0843] A means of reviewing existing technical information and material information related to the generated design, and in the process assessing the risk of patent infringement,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, comprising means for acquiring and analyzing emotional data and using generative machine learning techniques in various design domains when generating design drawings and models based on the aforementioned design requirements.

[0847] (Claim 3)

[0848] The system according to claim 1, further comprising means for modifying design drawings and models based on emotional feedback from users to achieve a more user-friendly design. [Explanation of Symbols]

[0849] 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 design ideas in natural language received from users to generate design requirements, Means for automatically generating detailed design drawings and models based on the aforementioned design requirements, A means for reviewing existing technical information and material information related to the generated design, A system that includes this.

2. The system according to claim 1, comprising means for using generative artificial intelligence corresponding to multiple design domains when generating design drawings and models based on the aforementioned design requirements.

3. The system according to claim 1, further comprising means for modifying design drawings and models based on user feedback.

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