system

Abstract

We provide the system. [Solution] A means of analyzing natural language input received from the user and extracting requirements, A design generation means that generates design drawings and models based on extracted requirements, A means for evaluating the patentability of the generated design and comparing it with existing patent information, In the design generation process, a means of automatically creating the structure as a product concept and including variable elements, To support product design within the factory, a means of immediately displaying the results of design generation and patentability evaluation on a mobile device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to the complexity of its expertise, hardware design has become an obstacle for many individuals and companies to realize ideas. For this reason, there is a problem that ideas that are not sufficiently mature often end up not being realized. Also, since it is difficult to determine patent infringement and novelty, it is also an issue that it involves legal risks.

Means for Solving the Problems

[0005] This invention provides means for analyzing natural language input received from a user and extracting requirements. It includes design generation means for generating design drawings and models based on these extracted requirements. Furthermore, by providing means for evaluating the patentability of the generated design and comparing it with existing patent information, it is possible to mitigate legal concerns during the design process. A function to automatically modify the design based on user feedback enables the rapid provision of an optimal design that reflects the user's intentions.

[0006] A "user" is an individual or legal entity that uses the system to input ideas in natural language.

[0007] "Natural language" refers to the language that humans use on a daily basis, and is the form of text that a system analyzes to extract requirements.

[0008] "Analysis" is the process of interpreting input data and extracting its meaning and intent.

[0009] "Requirements" are a definition of the specifications and conditions necessary for the idea that the user wants to realize.

[0010] A "design drawing" is a graphic representation of a specific product, generated by a system.

[0011] A "model" refers to a 3D structure or prototype created based on a design, representing the virtual form of the product.

[0012] A "design generation means" is a part of a system that has the function of automatically generating design drawings and models based on requirements.

[0013] "Patentability" refers to the characteristics used to determine whether a particular invention is novel and possesses inventive step.

[0014] "Existing patent information" refers to information on patent documents that have already been published or registered patent rights.

[0015] "Feedback" refers to opinions and requests provided by users regarding the design, and is information useful for improving the design.

[0016] "Correction" is a process of improving or changing the design based on user feedback.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] [[ID=4,4]]It is a sequence diagram showing the processing flow of the data processing system in Example 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 Example 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

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of the arithmetic unit 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.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of the non-volatile storage device include a flash memory (SSD (Solid State Drive)), a magnetic disk (e.g., a hard disk), or a magnetic tape, etc.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system that accepts ideas from users in natural language, analyzes them, extracts necessary requirements, and then creates a concrete design using a design generation means. Furthermore, this system also has a function to evaluate patentability, allowing users to receive comprehensive support, including legal aspects of the design.

[0039] In implementing this system, users first input their ideas and requirements for the product they want to create using natural language through their devices. For example, a specific desire such as "I want to create original accessories that light up." The server then uses a language analysis algorithm to analyze the input text and extract the requirements and conditions necessary for the design.

[0040] Based on the extraction results, the server launches a design generation program and automatically generates design drawings and models. This design generation method includes the automatic creation of CAD data and 3D models, and can also provide suggestions regarding physical properties and materials to be used. The generated designs are presented to the user via a terminal, allowing them to review the contents.

[0041] Subsequently, the user provides feedback on the presented design and sends requests for further improvements or changes to the server. The server automatically modifies the design based on this feedback, and by repeating this process, it can provide a design that best meets the user's requirements.

[0042] Once the final design is determined, the server uses a patent-specific program to assess the patentability of that design. This program searches existing patent databases to check for similar patents and legal risks. The user is then provided with the patentability assessment results and related advice on their terminal, allowing them to make additional modifications to the design or proceed with preparing a patent application as needed.

[0043] Thus, the present invention not only quickly and efficiently realizes users' ideas but also comprehensively supports the legal aspects of design, thereby providing users with a valuable product development process.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user inputs their product idea in natural language through the device's interface. This input includes a basic description of the product and the desired characteristics.

[0047] Step 2:

[0048] The server receives user input and analyzes the input using a large-scale language model. From the analyzed data, it extracts necessary design requirements and conditions.

[0049] Step 3:

[0050] The server activates the design generation mechanism based on the extracted requirements. A design-specialized generation AI automatically generates design drawings and models. Information regarding material properties and product functionality is also considered during this process.

[0051] Step 4:

[0052] The server sends the generated design data to the terminal and presents it to the user. This allows the user to visually confirm and evaluate the design.

[0053] Step 5:

[0054] Users can send their opinions and feedback on the presented design from their device to the server. This can include specific suggestions for improvements and additional requests.

[0055] Step 6:

[0056] The server modifies the design data based on user feedback. If necessary, it restarts the design generation system, generates the improved design, and presents it to the user again.

[0057] Step 7:

[0058] Finally, the server launches a patent-specific program to evaluate the patentability of the completed design. It searches existing patent databases and checks for similarities with relevant patents.

[0059] Step 8:

[0060] The server sends the patentability assessment results and advice to the terminal, providing them to the user. Based on this, the user can make decisions regarding patent applications and further adjust the design.

[0061] (Example 1)

[0062] 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."

[0063] In modern product development processes, it is crucial to quickly and efficiently realize user ideas and address their legal aspects. However, accurately analyzing natural language input from users, extracting necessary design requirements, automatically generating design drawings and 3D models, and evaluating patentability are not easy tasks. There is a need to efficiently integrate these multiple processes, reducing effort while obtaining high-quality results.

[0064] 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.

[0065] In this invention, the server includes a processing unit that analyzes natural language information received from the user and extracts technical requirements; a processing unit that generates structural diagrams and three-dimensional models based on the extracted requirements; a processing unit that automatically modifies the structure based on user feedback; a processing unit that evaluates the patentability of the finally generated structure and compares it with existing legal information; and a device that presents the generated structure and the patentability evaluation results to the user. This enables the efficient realization of the user's ideas and comprehensive product development support, including legal considerations of the design.

[0066] "Information in natural language" refers to information that users input as words or sentences, and is data provided in normal linguistic expression.

[0067] "Technical requirements" refer to the conditions and standards necessary for the design of a product or system, and include specifications regarding specific functions and performance.

[0068] A "processing device" refers to hardware or software that includes mechanisms and programs for processing data from input and generating output.

[0069] A "structural drawing" is a drawing that serves as the basis for a design, and it is a visual representation used to show the shape and arrangement of parts of a product or system.

[0070] A "three-dimensional model" is digital data that represents an object or structure in three-dimensional space, and is created to reproduce the shape and proportions of the real thing.

[0071] "Evaluating patentability" is the process of determining whether something is novel and inventive compared to existing technologies and rights, and it is the act of determining the likelihood of obtaining a patent.

[0072] "Legal information" refers to existing patent databases and data on relevant laws, which are used to verify the patent status and its relevance to technology.

[0073] A "presenting device" refers to a device, including terminals and screens, that visually displays information to the user and enables interaction.

[0074] In this system, users first input product ideas and requests in natural language using their own devices. A concrete example of this input is, "I want to create original accessories that light up."

[0075] Next, the server receives this information and performs detailed linguistic analysis using a generative AI model (e.g., GPT-4®). Through this analysis, technical requirements are clarified and the conditions necessary for design are extracted. An example of a prompt message is, "Please extract the necessary design requirements based on the input idea."

[0076] Based on the extracted conditions, the server automatically generates design drawings and 3D models using software such as AutoCAD and Blender. This process also takes into account physical properties and the selection of optimal materials.

[0077] The generated design data is provided to the user via a terminal. The terminal provides the user with a visual interface to review the design and evaluate its various details. The user can, for example, suggest changes to the design's size or design options.

[0078] When a user provides feedback, the server automatically modifies the design based on that feedback and sends the improved version back to the user's device. This process is repeated until the optimal design is determined.

[0079] Finally, the server uses a dedicated program to evaluate patentability and compare it with existing legal information. This evaluation result is also communicated to the user via the terminal, and the user decides whether to modify the design or proceed with filing a patent application based on this information.

[0080] In this way, user ideas are materialized efficiently and structurally, and detailed support regarding legal aspects is also provided. This integrated system enables users to develop products effectively and comprehensively.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user inputs their product idea in natural language using their own device. This input is the starting point for the system; for example, they might write, "I want to make original accessories that light up." The entered information is then sent to the server.

[0084] Step 2:

[0085] The server analyzes natural language input received from the user using a generative AI model (e.g., GPT-4). The generative model is given the prompt, "Extract the necessary design requirements based on the input idea." This analysis extracts the technical requirements. The output provides the detailed extracted design requirements.

[0086] Step 3:

[0087] Based on the extracted technical requirements, the server automatically generates design drawings and 3D models using design generation software (e.g., AutoCAD or Blender). The input is the requirements data from step 2, and the output is specific design drawings and 3D models.

[0088] Step 4:

[0089] The generated design is provided to the user via a terminal. The terminal visually displays the generated design data and provides an interface for the user to review the details. The output is a digital design in a format that the user can view.

[0090] Step 5:

[0091] Users provide feedback on the provided design using natural language and send it to the server via their device. For example, they might request a modification such as "I want to change the color to red." This feedback becomes the input to the server.

[0092] Step 6:

[0093] The server analyzes user feedback and modifies the design as needed. The input is feedback information, and an improved design, including configuration updates, is output. The improved design is then sent back to the terminal for user confirmation.

[0094] Step 7:

[0095] The server uses a patent evaluation program to determine the patentability of the finalized design. The design finalized in step 6 is used as input, and the evaluation results, compared with existing legal information, are obtained as output.

[0096] Step 8:

[0097] The patent evaluation results are presented to the user via a terminal. The output includes a decision on whether or not to grant a patent and related advice, which the user can use to revise their design or proceed with preparing their patent application.

[0098] (Application Example 1)

[0099] 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."

[0100] There is a growing need to accelerate and optimize product design in factories. However, conventional methods involve time-consuming requirements extraction, design generation, and patentability assessment in the initial stages of design, and these processes are fragmented, resulting in reduced overall efficiency. Therefore, there is a demand for a system that provides integrated support for each stage of product design, delivers rapid and highly accurate design proposals, and also takes patentability into consideration.

[0101] 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.

[0102] In this invention, the server includes means for analyzing natural language input received from a user and extracting requirements; means for generating design drawings and models based on the extracted requirements; means for evaluating the patentability of the generated design and comparing it with existing patent information; means for automatically creating a structure as a product concept and including variable elements during the design generation process; and means for immediately displaying the results of design generation and patentability evaluation on a mobile terminal to support product design in the factory. This enables users to receive rapid design generation and patentability evaluation based on natural language input.

[0103] "Natural language input" refers to information that uses everyday words and phrases and is not in a mechanically formatted form.

[0104] "Means for extracting requirements" refers to technologies or systems for identifying and extracting the conditions and specifications necessary for design from received information.

[0105] "Design generation means" refers to a technology or method for constructing specific design drawings or models based on extracted requirements.

[0106] "Means for evaluating the patentability of a generated design" refers to technologies or processes for verifying the originality and legal protectability of a design and comparing it with existing patents.

[0107] "Existing patent information" refers to databases and records of patents registered in the past, and is used to verify similar technologies.

[0108] "A means of automatically creating a product's conceptual structure and including variable elements" refers to a technology that automates the basic conceptual design of a product and allows for flexible modification of elements in response to user requirements.

[0109] "Means for immediate display on a mobile device" refers to a technology or method for displaying generated information or evaluation results in real time on the screen of a portable electronic device.

[0110] The system that realizes this application example can perform everything from design generation to patentability evaluation in one go, based on the user's natural language input.

[0111] The server receives natural language input from the user and uses natural language processing libraries such as spaCy and Google Cloud Natural Language API to extract requirements. This clarifies the specifications and conditions necessary for the design from the input sentence.

[0112] Next, the server uses CAD software and 3D modeling tools, such as the Fusion 360 API, to automatically generate design drawings and models based on the extracted requirements. This generates a concrete design that aligns with the user's desired product concept.

[0113] Furthermore, the server uses the USPTO API and the J-PlatPat database to evaluate the patentability of the generated designs. This is to confirm novelty and legal protectionability by comparing them with existing patent information.

[0114] The terminal displays the results of these design generation and patentability evaluations to the user in real time, allowing the user to instantly review them via their mobile device. This streamlines the new product development process in the factory and allows for simultaneous verification of the originality and marketability of the design.

[0115] As a concrete example, a factory site engineer could use the system with a prompt message such as, "Develop a detailed design proposal for a variable robotic arm aimed at automating a logistics center, and search for relevant patents to confirm its novelty." This prompt would allow the engineer to quickly receive feedback on the design proposal and adjust the design direction on the spot.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user uses their own device to input an idea for a product they want to create in natural language. The input is in the form of a prompt message, such as, "Develop a detailed design proposal for a variable robotic arm aimed at automating logistics centers, and research relevant patents to confirm its novelty." The input data is sent directly to the server.

[0119] Step 2:

[0120] The server processes the received natural language input and extracts requirements using language analysis algorithms (e.g., spaCy or Google Cloud Natural Language API). It identifies key conditions and specifications from the input data and compiles them as necessary information during the design phase. This results in the extracted requirements.

[0121] Step 3:

[0122] The server generates design drawings and models based on the extracted requirements. Specifically, it uses CAD software and 3D modeling tools (e.g., Fusion 360 API) to automatically create design proposals that meet the requirements. The input is requirements information, and the output is the generated design drawings and 3D models.

[0123] Step 4:

[0124] The server evaluates the patentability of the generated design proposals. Using the USPTO API and the J-PlatPat database, it compares the design against existing patent information to confirm its novelty and legal protectability. The input is the design proposal, and the output is the patentability evaluation result.

[0125] Step 5:

[0126] Design proposals and patentability evaluation results from the server are immediately delivered to the terminal and displayed to the user. The user reviews the results on the mobile device screen and sends additional feedback or modifications to the server as needed. The output is the evaluation result, which is then converted into the next feedback information as input.

[0127] Step 6:

[0128] If new feedback is received, the server will re-run the design and evaluation process and generate an optimal design proposal that reflects the user's requirements. The regenerated design will be the final output.

[0129] 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.

[0130] This invention is a system that, in addition to a function to extract requests from the user's natural language input and generate designs, incorporates an emotion engine that recognizes the user's emotions. The aim of this system is to improve the user experience, and it provides more appropriate design suggestions and feedback based on the user's emotional state.

[0131] Users access the system through a terminal and input ideas they want to realize. At this time, users may include phrases that express emotions, such as "I want to make fun and colorful toys." The server receives this input and not only analyzes the requirements using natural language processing technology, but also recognizes the user's emotions using an emotion engine. For example, if the emotion "fun" is recognized, the server prioritizes selecting design concepts that correspond to that emotion.

[0132] Based on the analyzed requirements and emotional information, the server uses design generation tools to create specific design drawings and models. This generation process utilizes the emotional information obtained by the emotional engine and includes elements that respond to emotions, such as appropriate color schemes and shape selections.

[0133] The generated design is presented to the user via the device, and the user can review its contents. The user's feedback on the presented design is also analyzed by the emotion engine and contributes to improving the design. For example, if the feedback is "I want a more fun design," the server will add more colorful and interactive elements to the design.

[0134] Once the final design is determined, the server uses a patent-specific program to evaluate its patentability and check for similarities with existing patents. This result is sent to the terminal, allowing the user to make decisions regarding the patent application.

[0135] This invention enables users to gain a more satisfying product development experience through designs that reflect their own emotions.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users use the terminal interface to input ideas and requests regarding their project in natural language. This input may include specific requests such as "a design with fun colors" or "user-friendly features."

[0139] Step 2:

[0140] The server analyzes the user's input. First, it uses natural language processing techniques to extract the requirements and conditions necessary for the design. Simultaneously, it activates an emotion engine to recognize the user's emotional state and analyze emotions such as "enjoyment" and "comfort" from the input.

[0141] Step 3:

[0142] Based on the analyzed requirements and emotional information, the server automatically generates an initial design using design generation tools. At this stage, colors and shapes that resonate with the user's emotions are selected, and for example, a "colorful and friendly" design might be proposed.

[0143] Step 4:

[0144] The generated initial design is sent to the device and presented visually to the user. The user can review the presented design and provide feedback for further optimization based on the emotions they experience.

[0145] Step 5:

[0146] When a user submits feedback, the server analyzes its content again using the emotion engine. Based on the emotional expression in the feedback, such as "I want more colors," the server improves the design, adjusts the design generation method, and creates a new design.

[0147] Step 6:

[0148] When the final design matches the user's intent, the server uses a patent-specific program to evaluate the patentability of the design. By comparing it with existing patent information, it verifies the novelty and originality of the design.

[0149] Step 7:

[0150] The patentability evaluation results are transmitted to the terminal and presented to the user. Based on these results, the user can decide whether to file a patent application or whether further design adjustments are necessary.

[0151] Through the above process, users can materialize their ideas by going through a design process that reflects their own emotions.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0154] Conventional design generation systems could generate designs based on user requirements, but they did not take into account the user's emotional state. This made it difficult to provide designs that reflected the emotions users desired, resulting in limited improvements to the user experience. Furthermore, there was room for improvement in the process of effectively incorporating user feedback.

[0155] 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.

[0156] In this invention, the server includes information processing means for analyzing natural language input received from a user and extracting requirements and emotional states; generation means for generating design drawings and models based on the extracted requirements and emotional states; and evaluation means for evaluating the patentability of the generated design and comparing it with existing patent information. This makes it possible to provide designs that reflect the user's emotions and to improve the design while taking feedback into consideration.

[0157] "Information processing means" refers to technologies for analyzing natural language input from users and extracting their requests and emotional states.

[0158] "Generative means" refers to the technology of creating design drawings and models based on extracted requirements and emotional states.

[0159] "Evaluation means" refers to a technology that evaluates the patentability of a generated design and compares it with existing patent information.

[0160] This invention relates to a system that generates designs that take into account emotional states based on natural language input from a user. This system is implemented through the following steps.

[0161] The server receives natural language data entered by the user through their terminal. This input data is processed using AI technology. Specifically, a common generative AI model (e.g., GPT-3®) is used for natural language processing to extract requests and intentions from the input.

[0162] The extracted data is analyzed using an emotion recognition engine (e.g., an emotion analysis API) to determine the user's emotional state. This allows us to identify the emotional direction of the design the user desires.

[0163] Next, the server uses the obtained requirements and sentiment data to execute the design generation process. During this process, design generation tools (e.g., creative generation tools) are used to determine specific design elements such as shapes and color patterns.

[0164] The generated design results are presented to the user via the device. The user can provide feedback on this presented design, and this feedback is also analyzed based on an emotion engine, with the feedback data used to revise the design.

[0165] Ultimately, the server performs a patentability assessment. This process verifies how similar the generated design is to existing patents and whether it meets the requirements for patenting. This assessment uses evaluation tools that refer to patent databases.

[0166] This system allows users to experience highly satisfying designs that reflect their own emotions, and also enables them to develop these designs further and establish criteria for filing patent applications.

[0167] For example, a prompt might be used such as, "I want to create a new toy design. It should be bright, fun, and colorful." This allows the user to receive design suggestions that meet their expectations and are tailored to their specific needs.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The user inputs their design requirements in natural language through their device. This input is sent to the server in the form of prompts. The input includes phrases that indicate the user's wishes and ideas. For example, "I want to make fun and colorful toys."

[0171] Step 2:

[0172] The server analyzes the received natural language input and extracts the user's requirements. This analysis uses a generative AI model. The input is the prompt text from Step 1, which is processed and converted into a specific data format to extract the requirements. Specifically, the AI ​​model processes the text and recognizes keywords such as "fun" and "colorful." A list of requirements is generated as output.

[0173] Step 3:

[0174] The server uses an emotion recognition engine to recognize the user's emotional state from their input. The input is the prompt text from Step 1. This is analyzed by the emotion recognition algorithm to identify the user's emotional state. As output, the server obtains emotion-based data, such as "happy." A specific action involves generating an emotion vector.

[0175] Step 4:

[0176] The server generates design drawings and models using design generation tools based on the extracted requirements and sentiment information. The requirements list and sentiment data generated in steps 2 and 3 are used as input. For data processing, the design generation tool is used to determine design elements such as color and shape. Initial design drawings and 3D models are generated as output.

[0177] Step 5:

[0178] The generated design is presented to the user via a terminal. The user visually reviews the design and inputs their satisfaction rating and suggestions for improvement. The input includes specific opinions accompanied by user feedback. Specifically, a view of the design is displayed on the screen, and a format is provided for the user to add comments.

[0179] Step 6:

[0180] The server receives user feedback and analyzes it again using the sentiment engine. The input is the feedback obtained in step 5. Based on this data, the design is modified. The output is the generation of improved design drawings or models. Specifically, an optimization process may be implemented to incorporate the feedback into the requirements.

[0181] Step 7:

[0182] The server performs a patentability assessment on the final design. The design data finalized in step 6 is used as input. The evaluation tool compares the design with existing patent databases to check for similarity. The output is the patentability assessment result, which is reported to the user. Specifically, a patent evaluation tool is used, and the agreement rate is measured using a comparison algorithm.

[0183] (Application Example 2)

[0184] 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".

[0185] This solution addresses the challenge of users not receiving content recommendations tailored to their individual emotional states when selecting content. It also addresses the difficulty of designing and selecting content that takes user emotions into account.

[0186] 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.

[0187] In this invention, the server includes means for analyzing natural language input received from the user and extracting requirements; design generation means for generating design drawings and models based on the extracted requirements and user sentiment information; and means for providing the generated design and content based on the user's sentiment as content. This makes it possible to suggest content that responds to the user's sentiment, thereby improving the user experience.

[0188] "Natural language received from the user" refers to the linguistic information that the user transcribes or speaks, and is the initial data that the system should process.

[0189] "Requirements extraction" is the process of analyzing the meaning of the user's natural language input to clarify the required functions and conditions.

[0190] "Emotional information" refers to data that identifies a user's emotional state and reflects that state.

[0191] "Design generation means" refers to a function that automatically creates specific design drawings and models based on requirements and sentiment information obtained from the user.

[0192] "Means of providing content" refers to a method of presenting generated designs or models to users and proposing them as content that responds to the users' emotions.

[0193] "Existing patent information" refers to detailed information about patented technologies that are already publicly known, and is used to evaluate novelty and inventiveness.

[0194] This invention realizes a system that processes information entered by users in natural language and provides sentiment-based content. The server uses natural language processing techniques to analyze the natural language input sent from the user through the terminal and extract requirements. This utilizes common natural language processing libraries and APIs (e.g., Python's NLTK and spaCy).

[0195] Next, an algorithm is executed on the server to generate design drawings and models based on the extracted requirements and emotional information. A generative AI model is used for design generation, resulting in designs that reflect subjective elements. This enables interactive content based on emotional information.

[0196] User sentiment information is automatically extracted by a sentiment analysis engine, and recommended content is generated in a way that corresponds to those sentiments. This information processing uses a deep learning-based sentiment recognition model, enabling highly accurate detection of emotions contained in text data.

[0197] For example, if a user types "I'm in a really good mood today, so I want to watch a funny movie" on their smartphone, the system recognizes the emotion of "fun" and prioritizes recommending comedy movies. An example of a prompt would be, "Please recommend a movie that will put me in a good mood."

[0198] Through this system, users can receive personalized suggestions that take their emotions into consideration, enabling them to enjoy a more satisfying user experience.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The user sends natural language input from the terminal to the server. This input includes words that express the user's requests and their emotions at the time. Text-based sentences are used as the input data.

[0202] Step 2:

[0203] The server analyzes the received natural language input and extracts requirements. This process uses natural language processing libraries (e.g., NLTK and spaCy) to extract keywords from the input text. The analysis results in the user's requests and intentions.

[0204] Step 3:

[0205] The server uses an emotion recognition engine to extract user emotion information based on natural language input. By applying a deep learning model, it analyzes the emotions contained in the text and obtains a classification result.

[0206] Step 4:

[0207] The server combines the requirements extracted in step 2 with the emotional information obtained in step 3, and uses design generation tools to generate appropriate design drawings and content models. This generation process uses a generation AI model to generate content suggestions that match the emotions.

[0208] Step 5:

[0209] The generated designs and content are presented to the user via their device. The user can review the proposals and submit feedback. As output, content information tailored to the user's selections is provided.

[0210] Step 6:

[0211] The server receives feedback from users and modifies the design and content as needed. The regenerated content, based on the feedback, is then presented to the user again.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention is a system that accepts ideas from users in natural language, analyzes them, extracts necessary requirements, and then creates a concrete design using a design generation means. Furthermore, this system also has a function to evaluate patentability, allowing users to receive comprehensive support, including legal aspects of the design.

[0229] In implementing this system, users first input their ideas and requirements for the product they want to create using natural language through their devices. For example, a specific desire such as "I want to create original accessories that light up." The server then uses a language analysis algorithm to analyze the input text and extract the requirements and conditions necessary for the design.

[0230] Based on the extraction results, the server launches a design generation program and automatically generates design drawings and models. This design generation method includes the automatic creation of CAD data and 3D models, and can also provide suggestions regarding physical properties and materials to be used. The generated designs are presented to the user via a terminal, allowing them to review the contents.

[0231] Subsequently, the user provides feedback on the presented design and sends requests for further improvements or changes to the server. The server automatically modifies the design based on this feedback, and by repeating this process, it can provide a design that best meets the user's requirements.

[0232] Once the final design is determined, the server uses a patent-specific program to assess the patentability of that design. This program searches existing patent databases to check for similar patents and legal risks. The user is then provided with the patentability assessment results and related advice on their terminal, allowing them to make additional modifications to the design or proceed with preparing a patent application as needed.

[0233] Thus, the present invention not only quickly and efficiently realizes users' ideas but also comprehensively supports the legal aspects of design, thereby providing users with a valuable product development process.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user inputs their product idea in natural language through the device's interface. This input includes a basic description of the product and the desired characteristics.

[0237] Step 2:

[0238] The server receives user input and analyzes the input using a large-scale language model. From the analyzed data, it extracts necessary design requirements and conditions.

[0239] Step 3:

[0240] The server activates the design generation mechanism based on the extracted requirements. A design-specialized generation AI automatically generates design drawings and models. Information regarding material properties and product functionality is also considered during this process.

[0241] Step 4:

[0242] The server sends the generated design data to the terminal and presents it to the user. This allows the user to visually confirm and evaluate the design.

[0243] Step 5:

[0244] Users can send their opinions and feedback on the presented design from their device to the server. This can include specific suggestions for improvements and additional requests.

[0245] Step 6:

[0246] The server modifies the design data based on user feedback. If necessary, it restarts the design generation system, generates the improved design, and presents it to the user again.

[0247] Step 7:

[0248] Finally, the server launches a patent-specific program to evaluate the patentability of the completed design. It searches existing patent databases and checks for similarities with relevant patents.

[0249] Step 8:

[0250] The server sends the patentability assessment results and advice to the terminal, providing them to the user. Based on this, the user can make decisions about filing a patent application and make further design adjustments.

[0251] (Example 1)

[0252] 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."

[0253] In modern product development processes, it is crucial to quickly and efficiently realize user ideas and address their legal aspects. However, accurately analyzing natural language input from users, extracting necessary design requirements, automatically generating design drawings and 3D models, and evaluating patentability are not easy tasks. There is a need to efficiently integrate these multiple processes, reducing effort while obtaining high-quality results.

[0254] 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.

[0255] In this invention, the server includes a processing unit that analyzes natural language information received from the user and extracts technical requirements; a processing unit that generates structural diagrams and three-dimensional models based on the extracted requirements; a processing unit that automatically modifies the structure based on user feedback; a processing unit that evaluates the patentability of the finally generated structure and compares it with existing legal information; and a device that presents the generated structure and the patentability evaluation results to the user. This enables the efficient realization of the user's ideas and comprehensive product development support, including legal considerations of the design.

[0256] "Information in natural language" refers to information that users input as words or sentences, and is data provided in normal linguistic expression.

[0257] "Technical requirements" refer to the conditions and standards necessary for the design of a product or system, and include specifications regarding specific functions and performance.

[0258] A "processing device" refers to hardware or software that includes mechanisms and programs for processing data from input and generating output.

[0259] A "structural drawing" is a drawing that serves as the basis for a design, and it is a visual representation used to show the shape and arrangement of parts of a product or system.

[0260] A "three-dimensional model" is digital data that represents an object or structure in three-dimensional space, and is created to reproduce the shape and proportions of the real thing.

[0261] "Evaluating patentability" is the process of determining whether something is novel and inventive compared to existing technologies and rights, and it is the act of determining the likelihood of obtaining a patent.

[0262] "Legal information" refers to existing patent databases and data on relevant laws, which are used to verify the patent status and its relevance to technology.

[0263] A "presenting device" refers to a device, including terminals and screens, that visually displays information to the user and enables interaction.

[0264] In this system, users first input product ideas and requests in natural language using their own devices. A concrete example of this input is, "I want to create original accessories that light up."

[0265] Next, the server receives this information and performs detailed linguistic analysis using a generative AI model (e.g., GPT-4). Through this analysis, technical requirements are clarified and the conditions necessary for design are extracted. An example of a prompt message is, "Please extract the necessary design requirements based on the input idea."

[0266] Based on the extracted conditions, the server automatically generates design drawings and 3D models using software such as AutoCAD and Blender. This process also takes into account physical properties and the selection of optimal materials.

[0267] The generated design data is provided to the user via a terminal. The terminal provides the user with a visual interface to review the design and evaluate its various details. The user can, for example, suggest changes to the design's size or design options.

[0268] When a user provides feedback, the server automatically modifies the design based on that feedback and sends the improved version back to the user's device. This process is repeated until the optimal design is determined.

[0269] Finally, the server uses a dedicated program to evaluate patentability and compare it with existing legal information. This evaluation result is also communicated to the user via the terminal, and the user decides whether to modify the design or proceed with filing a patent application based on this information.

[0270] In this way, user ideas are materialized efficiently and structurally, and detailed support regarding legal aspects is also provided. This integrated system enables users to develop products effectively and comprehensively.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The user inputs their product idea in natural language using their own device. This input is the starting point for the system; for example, they might write, "I want to make original accessories that light up." The entered information is then sent to the server.

[0274] Step 2:

[0275] The server analyzes natural language input received from the user using a generative AI model (e.g., GPT-4). The generative model is given the prompt, "Extract the necessary design requirements based on the input idea." This analysis extracts the technical requirements. The output provides the detailed extracted design requirements.

[0276] Step 3:

[0277] Based on the extracted technical requirements, the server automatically generates design drawings and 3D models using design generation software (e.g., AutoCAD or Blender). The input is the requirements data from step 2, and the output is specific design drawings and 3D models.

[0278] Step 4:

[0279] The generated design is provided to the user via a terminal. The terminal visually displays the generated design data and provides an interface for the user to review the details. The output is a digital design in a format that the user can view.

[0280] Step 5:

[0281] Users provide feedback on the provided design in natural language and send it to the server via their device. For example, they might request a modification such as "I want to change the color to red." This feedback becomes the input to the server.

[0282] Step 6:

[0283] The server analyzes the feedback from the user and modifies the design as necessary. The input is the feedback information, and an improved version of the design including the update of the settings is output. The improved design is sent to the terminal again to obtain the confirmation from the user.

[0284] Step 7:

[0285] For the finally determined design, the server uses a patent evaluation program to determine the patentability. The design finalized in Step 6 is used as the input, and the evaluation result compared with the existing legal information is obtained as the output.

[0286] Step 8:

[0287] The patent evaluation result is presented to the user through the terminal. The output is the judgment on whether the patent is available and related advice, and the user reconsiders the design or proceeds with the preparation for patent application based on this.

[0288] (Application Example 1)

[0289] 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".

[0290] There is a demand for speeding up and optimizing product design in factories. However, in the conventional method, requirement extraction, design generation, and patentability evaluation in the initial stage of design are laborious, and since these processes are fragmented, the overall efficiency has been reduced. Therefore, it is desired to realize a system that supports each stage of product design in a lump sum, provides a quick and accurate design plan, and also takes patentability into consideration.

[0291] 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.

[0292] In this invention, the server includes means for analyzing natural language input received from a user and extracting requirements; means for generating design drawings and models based on the extracted requirements; means for evaluating the patentability of the generated design and comparing it with existing patent information; means for automatically creating a structure as a product concept and including variable elements during the design generation process; and means for immediately displaying the results of design generation and patentability evaluation on a mobile terminal to support product design in the factory. This enables users to receive rapid design generation and patentability evaluation based on natural language input.

[0293] "Natural language input" refers to information that uses everyday words and phrases and is not in a mechanically formatted form.

[0294] "Means for extracting requirements" refers to technologies or systems for identifying and extracting the conditions and specifications necessary for design from received information.

[0295] "Design generation means" refers to a technology or method for constructing specific design drawings or models based on extracted requirements.

[0296] "Means for evaluating the patentability of a generated design" refers to technologies or processes for verifying the originality and legal protectability of a design and comparing it with existing patents.

[0297] "Existing patent information" refers to databases and records of patents registered in the past, and is used to verify similar technologies.

[0298] "A means of automatically creating a product's conceptual structure and including variable elements" refers to a technology that automates the basic conceptual design of a product and allows for flexible modification of elements in response to user requirements.

[0299] "Means for immediate display on a mobile device" refers to a technology or method for displaying generated information or evaluation results in real time on the screen of a portable electronic device.

[0300] The system that realizes this application example can perform everything from design generation to patentability evaluation in one go, based on the user's natural language input.

[0301] The server receives natural language input from the user and uses natural language processing libraries such as spaCy and the Google Cloud Natural Language API to extract requirements. This clarifies the specifications and conditions necessary for the design from the input sentence.

[0302] Next, the server uses CAD software and 3D modeling tools, such as the Fusion 360 API, to automatically generate design drawings and models based on the extracted requirements. This generates a concrete design that aligns with the user's desired product concept.

[0303] Furthermore, the server uses the USPTO API and the J-PlatPat database to evaluate the patentability of the generated designs. This is to confirm novelty and legal protectionability by comparing them with existing patent information.

[0304] The terminal displays the results of these design generation and patentability evaluations to the user in real time, allowing the user to instantly review them via their mobile device. This streamlines the new product development process in the factory and allows for simultaneous verification of the originality and marketability of the design.

[0305] As a concrete example, a factory site engineer could use the system with a prompt message such as, "Develop a detailed design proposal for a variable robotic arm aimed at automating a logistics center, and search for relevant patents to confirm its novelty." This prompt would allow the engineer to quickly receive feedback on the design proposal and adjust the design direction on the spot.

[0306] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0307] Step 1:

[0308] The user uses their own terminal to input ideas in natural language for the product they want to achieve. The input is in the form of a prompt sentence such as "Please formulate a detailed design plan for a variable robotic arm aiming for automation in a logistics center and conduct a search for relevant patents to confirm novelty". The input data is directly sent to the server.

[0309] Step 2:

[0310] The server processes the received natural language input and extracts requirements using a language analysis algorithm (e.g., spaCy or Google Cloud Natural Language API). It identifies important conditions and specifications from the input data and summarizes them as the information required in the design stage. Thus, the extracted requirements are obtained.

[0311] Step 3:

[0312] The server generates design drawings and models based on the extracted requirements. Specifically, it uses CAD software or 3D modeling tools (e.g., the API of Fusion 360) to automatically create a design plan according to the requirements. The input is the requirement information, and the generated design drawings and 3D models are obtained as the output.

[0313] Step 4:

[0314] For the generated design plan, the server evaluates its patentability. It uses the API of the USPTO or the database of J-PlatPat to compare with existing patent information and confirm the novelty and legal protectability of the design. The input is the design plan, and the evaluation result of patentability is obtained as the output.

[0315] Step 5:

[0316] Design proposals and patentability evaluation results from the server are immediately delivered to the terminal and displayed to the user. The user reviews the results on the mobile device screen and sends additional feedback or modifications to the server as needed. The output is the evaluation result, which is then converted into the next feedback information as input.

[0317] Step 6:

[0318] If new feedback is received, the server will re-run the design and evaluation process and generate an optimal design proposal that reflects the user's requirements. The regenerated design will be the final output.

[0319] 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.

[0320] This invention is a system that, in addition to a function to extract requests from the user's natural language input and generate designs, incorporates an emotion engine that recognizes the user's emotions. The aim of this system is to improve the user experience, and it provides more appropriate design suggestions and feedback based on the user's emotional state.

[0321] Users access the system through a terminal and input ideas they want to realize. At this time, users may include phrases that express emotions, such as "I want to make fun and colorful toys." The server receives this input and not only analyzes the requirements using natural language processing technology, but also recognizes the user's emotions using an emotion engine. For example, if the emotion "fun" is recognized, the server prioritizes selecting design concepts that correspond to that emotion.

[0322] Based on the analyzed requirements and emotional information, the server uses design generation tools to create specific design drawings and models. This generation process utilizes the emotional information obtained by the emotional engine and includes elements that respond to emotions, such as appropriate color schemes and shape selections.

[0323] The generated design is presented to the user via the device, and the user can review its contents. The user's feedback on the presented design is also analyzed by the emotion engine and contributes to improving the design. For example, if the feedback is "I want a more fun design," the server will add more colorful and interactive elements to the design.

[0324] Once the final design is determined, the server uses a patent-specific program to evaluate its patentability and check for similarities with existing patents. This result is sent to the terminal, allowing the user to make decisions regarding the patent application.

[0325] This invention enables users to gain a more satisfying product development experience through designs that reflect their own emotions.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] Users use the terminal interface to input ideas and requests regarding their project in natural language. This input may include specific requests such as "a design with fun colors" or "user-friendly features."

[0329] Step 2:

[0330] The server analyzes the user's input. First, it uses natural language processing techniques to extract the requirements and conditions necessary for the design. Simultaneously, it activates an emotion engine to recognize the user's emotional state and analyze emotions such as "enjoyment" and "comfort" from the input.

[0331] Step 3:

[0332] Based on the analyzed requirements and emotional information, the server automatically generates an initial design using design generation tools. At this stage, colors and shapes that resonate with the user's emotions are selected, and for example, a "colorful and friendly" design might be proposed.

[0333] Step 4:

[0334] The generated initial design is sent to the device and presented visually to the user. The user can review the presented design and provide feedback for further optimization based on the emotions they experience.

[0335] Step 5:

[0336] When a user submits feedback, the server analyzes its content again using the emotion engine. Based on the emotional expression in the feedback, such as "I want more colors," the server improves the design, adjusts the design generation method, and creates a new design.

[0337] Step 6:

[0338] When the final design matches the user's intent, the server uses a patent-specific program to evaluate the patentability of the design. By comparing it with existing patent information, it verifies the novelty and originality of the design.

[0339] Step 7:

[0340] The patentability evaluation results are transmitted to the terminal and presented to the user. Based on these results, the user can decide whether to file a patent application or whether further design adjustments are necessary.

[0341] Through the above process, users can materialize their ideas by going through a design process that reflects their own emotions.

[0342] (Example 2)

[0343] 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".

[0344] Conventional design generation systems could generate designs based on user requirements, but they did not take into account the user's emotional state. This made it difficult to provide designs that reflected the emotions users desired, resulting in limited improvements to the user experience. Furthermore, there was room for improvement in the process of effectively incorporating user feedback.

[0345] 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.

[0346] In this invention, the server includes information processing means for analyzing natural language input received from a user and extracting requirements and emotional states; generation means for generating design drawings and models based on the extracted requirements and emotional states; and evaluation means for evaluating the patentability of the generated design and comparing it with existing patent information. This makes it possible to provide designs that reflect the user's emotions and to improve the design while taking feedback into consideration.

[0347] "Information processing means" refers to technologies for analyzing natural language input from users and extracting their requests and emotional states.

[0348] "Generative means" refers to the technology of creating design drawings and models based on extracted requirements and emotional states.

[0349] "Evaluation means" refers to a technology that evaluates the patentability of a generated design and compares it with existing patent information.

[0350] This invention relates to a system that generates designs that take into account emotional states based on natural language input from a user. This system is implemented through the following steps.

[0351] The server receives natural language data entered by the user through their terminal. This input data is processed using AI technology. Specifically, a common generative AI model (e.g., GPT-3) is used for natural language processing to extract requests and intentions from the input.

[0352] The extracted data is analyzed using an emotion recognition engine (e.g., an emotion analysis API) to determine the user's emotional state. This allows us to identify the emotional direction of the design the user desires.

[0353] Next, the server uses the obtained requirements and sentiment data to execute the design generation process. During this process, design generation tools (e.g., creative generation tools) are used to determine specific design elements such as shapes and color patterns.

[0354] The generated design results are presented to the user via the device. The user can provide feedback on this presented design, and this feedback is also analyzed based on an emotion engine, with the feedback data used to revise the design.

[0355] Ultimately, the server performs a patentability assessment. This process verifies how similar the generated design is to existing patents and whether it meets the requirements for patenting. This assessment uses evaluation tools that refer to patent databases.

[0356] This system allows users to experience highly satisfying designs that reflect their own emotions, and also enables them to develop these designs further and establish criteria for filing patent applications.

[0357] For example, a prompt might be used such as, "I want to create a new toy design. It should be bright, fun, and colorful." This allows the user to receive design suggestions that meet their expectations and are tailored to their specific needs.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] Users input their design requests in natural language through their device. This input is sent to the server in the form of prompts. The input includes phrases that indicate the user's wishes and ideas. For example, "I want to make fun and colorful toys."

[0361] Step 2:

[0362] The server analyzes the received natural language input and extracts the user's requirements. This analysis uses a generative AI model. The input is the prompt text from Step 1, which is processed and converted into a specific data format to extract the requirements. Specifically, the AI ​​model processes the text and recognizes keywords such as "fun" and "colorful." A list of requirements is generated as output.

[0363] Step 3:

[0364] The server uses an emotion recognition engine to recognize the user's emotional state from their input. The input is the prompt text from Step 1. This is analyzed by the emotion recognition algorithm to identify the user's emotional state. As output, the server obtains emotion-based data, such as "happy." A specific action involves generating an emotion vector.

[0365] Step 4:

[0366] The server generates design drawings and models using design generation tools based on the extracted requirements and sentiment information. The requirements list and sentiment data generated in steps 2 and 3 are used as input. For data processing, the design generation tool is used to determine design elements such as color and shape. Initial design drawings and 3D models are generated as output.

[0367] Step 5:

[0368] The generated design is presented to the user via a terminal. The user visually reviews the design and inputs their satisfaction rating and suggestions for improvement. The input includes specific opinions accompanied by user feedback. Specifically, a view of the design is displayed on the screen, and a format is provided for the user to add comments.

[0369] Step 6:

[0370] The server receives user feedback and analyzes it again using the sentiment engine. The input is the feedback obtained in step 5. Based on this data, the design is modified. The output is the generation of improved design drawings or models. Specifically, an optimization process may be implemented to incorporate the feedback into the requirements.

[0371] Step 7:

[0372] The server performs a patentability assessment on the final design. The design data finalized in step 6 is used as input. The evaluation tool compares the design with existing patent databases to check for similarity. The output is the patentability assessment result, which is reported to the user. Specifically, a patent evaluation tool is used, and the agreement rate is measured using a comparison algorithm.

[0373] (Application Example 2)

[0374] 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."

[0375] This solution addresses the challenge of users not receiving content recommendations tailored to their individual emotional states when selecting content. It also addresses the difficulty of designing and selecting content that takes user emotions into account.

[0376] 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.

[0377] In this invention, the server includes means for analyzing natural language input received from the user and extracting requirements; design generation means for generating design drawings and models based on the extracted requirements and user sentiment information; and means for providing the generated design and content based on the user's sentiment as content. This makes it possible to suggest content that responds to the user's sentiment, thereby improving the user experience.

[0378] "Natural language received from the user" refers to the linguistic information that the user transcribes or speaks, and is the initial data that the system should process.

[0379] "Requirements extraction" is the process of analyzing the meaning of the user's natural language input to clarify the required functions and conditions.

[0380] "Emotional information" refers to data that identifies a user's emotional state and reflects that state.

[0381] "Design generation means" refers to a function that automatically creates specific design drawings and models based on requirements and sentiment information obtained from the user.

[0382] "Means of providing content" refers to a method of presenting generated designs or models to users and proposing them as content that responds to the users' emotions.

[0383] "Existing patent information" refers to detailed information about patented technologies that are already publicly known, and is used to evaluate novelty and inventiveness.

[0384] This invention realizes a system that processes information entered by users in natural language and provides sentiment-based content. The server uses natural language processing techniques to analyze the natural language input sent from the user through the terminal and extract requirements. This utilizes common natural language processing libraries and APIs (e.g., Python's NLTK and spaCy).

[0385] Next, an algorithm is executed on the server to generate design drawings and models based on the extracted requirements and emotional information. A generative AI model is used for design generation, resulting in designs that reflect subjective elements. This enables interactive content based on emotional information.

[0386] User sentiment information is automatically extracted by a sentiment analysis engine, and recommended content is generated in a way that corresponds to those sentiments. This information processing uses a deep learning-based sentiment recognition model, enabling highly accurate detection of emotions contained in text data.

[0387] For example, if a user types "I'm in a really good mood today, so I want to watch a funny movie" on their smartphone, the system recognizes the emotion of "fun" and prioritizes recommending comedy movies. An example of a prompt would be, "Please recommend a movie that will put me in a good mood."

[0388] Through this system, users can receive personalized suggestions that take their emotions into consideration, enabling them to enjoy a more satisfying user experience.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The user sends natural language input from the terminal to the server. This input includes words that express the user's requests and their emotions at the time. Text-based sentences are used as the input data.

[0392] Step 2:

[0393] The server analyzes the received natural language input and extracts requirements. This process uses natural language processing libraries (e.g., NLTK and spaCy) to extract keywords from the input text. The analysis results in the user's requests and intentions.

[0394] Step 3:

[0395] The server uses an emotion recognition engine to extract user emotion information based on natural language input. By applying a deep learning model, it analyzes the emotions contained in the text and obtains a classification result.

[0396] Step 4:

[0397] The server combines the requirements extracted in step 2 with the emotional information obtained in step 3, and uses design generation tools to generate appropriate design drawings and content models. This generation process uses a generation AI model to generate content suggestions that match the emotions.

[0398] Step 5:

[0399] The generated designs and content are presented to the user via their device. The user can review the proposals and submit feedback. As output, content information tailored to the user's selections is provided.

[0400] Step 6:

[0401] The server receives feedback from users and modifies the design and content as needed. The regenerated content, based on the feedback, is then presented to the user again.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] This invention is a system that accepts ideas from users in natural language, analyzes them, extracts necessary requirements, and then creates a concrete design using a design generation means. Furthermore, this system also has a function to evaluate patentability, allowing users to receive comprehensive support, including legal aspects of the design.

[0419] In implementing this system, users first input their ideas and requirements for the product they want to create using natural language through their devices. For example, a specific desire such as "I want to create original accessories that light up." The server then uses a language analysis algorithm to analyze the input text and extract the requirements and conditions necessary for the design.

[0420] Based on the extraction results, the server launches a design generation program and automatically generates design drawings and models. This design generation method includes the automatic creation of CAD data and 3D models, and can also provide suggestions regarding physical properties and materials to be used. The generated designs are presented to the user via a terminal, allowing them to review the contents.

[0421] Subsequently, the user provides feedback on the presented design and sends requests for further improvements or changes to the server. The server automatically modifies the design based on this feedback, and by repeating this process, it can provide a design that best meets the user's requirements.

[0422] Once the final design is determined, the server uses a patent-specific program to assess the patentability of that design. This program searches existing patent databases to check for similar patents and legal risks. The user is then provided with the patentability assessment results and related advice on their terminal, allowing them to make additional modifications to the design or proceed with preparing a patent application as needed.

[0423] Thus, the present invention not only quickly and efficiently realizes users' ideas but also comprehensively supports the legal aspects of design, thereby providing users with a valuable product development process.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The user inputs their product idea in natural language through the device's interface. This input includes a basic description of the product and the desired characteristics.

[0427] Step 2:

[0428] The server receives user input and analyzes the input using a large-scale language model. From the analyzed data, it extracts necessary design requirements and conditions.

[0429] Step 3:

[0430] The server activates the design generation mechanism based on the extracted requirements. A design-specialized generation AI automatically generates design drawings and models. Information regarding material properties and product functionality is also considered during this process.

[0431] Step 4:

[0432] The server sends the generated design data to the terminal and presents it to the user. This allows the user to visually confirm and evaluate the design.

[0433] Step 5:

[0434] Users can send their opinions and feedback on the presented design from their device to the server. This can include specific suggestions for improvements and additional requests.

[0435] Step 6:

[0436] The server modifies the design data based on user feedback. If necessary, it restarts the design generation system, generates the improved design, and presents it to the user again.

[0437] Step 7:

[0438] Finally, the server launches a patent-specific program to evaluate the patentability of the completed design. It searches existing patent databases and checks for similarities with relevant patents.

[0439] Step 8:

[0440] The server sends the patentability assessment results and advice to the terminal, providing them to the user. Based on this, the user can make decisions about filing a patent application and make further design adjustments.

[0441] (Example 1)

[0442] 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."

[0443] In modern product development processes, it is crucial to quickly and efficiently realize user ideas and address their legal aspects. However, accurately analyzing natural language input from users, extracting necessary design requirements, automatically generating design drawings and 3D models, and evaluating patentability are not easy tasks. There is a need to efficiently integrate these multiple processes, reducing effort while obtaining high-quality results.

[0444] 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.

[0445] In this invention, the server includes a processing unit that analyzes natural language information received from the user and extracts technical requirements; a processing unit that generates structural diagrams and three-dimensional models based on the extracted requirements; a processing unit that automatically modifies the structure based on user feedback; a processing unit that evaluates the patentability of the finally generated structure and compares it with existing legal information; and a device that presents the generated structure and the patentability evaluation results to the user. This enables the efficient realization of the user's ideas and comprehensive product development support, including legal considerations of the design.

[0446] "Information in natural language" refers to information that users input as words or sentences, and is data provided in normal linguistic expression.

[0447] "Technical requirements" refer to the conditions and standards necessary for the design of a product or system, and include specifications regarding specific functions and performance.

[0448] A "processing device" refers to hardware or software that includes mechanisms and programs for processing data from input and generating output.

[0449] A "structural drawing" is a drawing that serves as the basis for a design, and it is a visual representation used to show the shape and arrangement of parts of a product or system.

[0450] A "three-dimensional model" is digital data that represents an object or structure in three-dimensional space, and is created to reproduce the shape and proportions of the real thing.

[0451] "Evaluating patentability" is the process of determining whether something is novel and inventive compared to existing technologies and rights, and it is the act of determining the likelihood of obtaining a patent.

[0452] "Legal information" refers to existing patent databases and data on relevant laws, which are used to verify the patent status and its relevance to technology.

[0453] A "presenting device" refers to a device, including terminals and screens, that visually displays information to the user and enables interaction.

[0454] In this system, users first input product ideas and requests in natural language using their own devices. A concrete example of this input is, "I want to create original accessories that light up."

[0455] Next, the server receives this information and performs detailed linguistic analysis using a generative AI model (e.g., GPT-4). Through this analysis, technical requirements are clarified and the conditions necessary for design are extracted. An example of a prompt message is, "Please extract the necessary design requirements based on the input idea."

[0456] Based on the extracted conditions, the server automatically generates design drawings and 3D models using software such as AutoCAD and Blender. This process also takes into account physical properties and the selection of optimal materials.

[0457] The generated design data is provided to the user via a terminal. The terminal provides the user with a visual interface to review the design and evaluate its various details. The user can, for example, suggest changes to the design's size or design options.

[0458] When a user provides feedback, the server automatically modifies the design based on that feedback and sends the improved version back to the user's device. This process is repeated until the optimal design is determined.

[0459] Finally, the server uses a dedicated program to evaluate patentability and compare it with existing legal information. This evaluation result is also communicated to the user via the terminal, and the user decides whether to modify the design or proceed with filing a patent application based on this information.

[0460] In this way, user ideas are materialized efficiently and structurally, and detailed support regarding legal aspects is also provided. This integrated system enables users to develop products effectively and comprehensively.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The user inputs their product idea in natural language using their own device. This input is the starting point for the system; for example, they might write, "I want to make original accessories that light up." The entered information is then sent to the server.

[0464] Step 2:

[0465] The server analyzes natural language input received from the user using a generative AI model (e.g., GPT-4). The generative model is given the prompt, "Extract the necessary design requirements based on the input idea." This analysis extracts the technical requirements. The output provides the detailed extracted design requirements.

[0466] Step 3:

[0467] Based on the extracted technical requirements, the server automatically generates design drawings and 3D models using design generation software (e.g., AutoCAD or Blender). The input is the requirements data from step 2, and the output is specific design drawings and 3D models.

[0468] Step 4:

[0469] The generated design is provided to the user via a terminal. The terminal visually displays the generated design data and provides an interface for the user to review the details. The output is a digital design in a format that the user can view.

[0470] Step 5:

[0471] Users provide feedback on the provided design in natural language and send it to the server via their device. For example, they might request a modification such as "I want to change the color to red." This feedback becomes the input to the server.

[0472] Step 6:

[0473] The server analyzes user feedback and modifies the design as needed. The input is feedback information, and an improved version of the design, including configuration updates, is output. The improved design is then sent back to the terminal for user confirmation.

[0474] Step 7:

[0475] The server uses a patent evaluation program to determine the patentability of the finalized design. The design finalized in step 6 is used as input, and the evaluation results, compared with existing legal information, are obtained as output.

[0476] Step 8:

[0477] The patent evaluation results are presented to the user via a terminal. The output includes a decision on whether or not to grant a patent and related advice, which the user can use to revise their design or proceed with preparing their patent application.

[0478] (Application Example 1)

[0479] 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."

[0480] There is a growing need to accelerate and optimize product design in factories. However, conventional methods involve time-consuming requirements extraction, design generation, and patentability assessment in the initial stages of design, and these processes are fragmented, resulting in reduced overall efficiency. Therefore, there is a demand for a system that provides integrated support for each stage of product design, delivers rapid and highly accurate design proposals, and also takes patentability into consideration.

[0481] 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.

[0482] In this invention, the server includes means for analyzing natural language input received from a user and extracting requirements; means for generating design drawings and models based on the extracted requirements; means for evaluating the patentability of the generated design and comparing it with existing patent information; means for automatically creating a structure as a product concept and including variable elements during the design generation process; and means for immediately displaying the results of design generation and patentability evaluation on a mobile terminal to support product design in the factory. This enables users to receive rapid design generation and patentability evaluation based on natural language input.

[0483] "Natural language input" refers to information that uses everyday words and phrases and is not in a mechanically formatted form.

[0484] "Means for extracting requirements" refers to technologies or systems for identifying and extracting the conditions and specifications necessary for design from received information.

[0485] "Design generation means" refers to a technology or method for constructing specific design drawings or models based on extracted requirements.

[0486] "Means for evaluating the patentability of a generated design" refers to technologies or processes for verifying the originality and legal protectability of a design and comparing it with existing patents.

[0487] "Existing patent information" refers to databases and records of patents registered in the past, and is used to verify similar technologies.

[0488] "A means of automatically creating a product's conceptual structure and including variable elements" refers to a technology that automates the basic conceptual design of a product and allows for flexible modification of elements in response to user requirements.

[0489] "Means for immediate display on a mobile device" refers to a technology or method for displaying generated information or evaluation results in real time on the screen of a portable electronic device.

[0490] The system that realizes this application example can perform everything from design generation to patentability evaluation in one go, based on the user's natural language input.

[0491] The server receives natural language input from the user and uses natural language processing libraries such as spaCy and the Google Cloud Natural Language API to extract requirements. This clarifies the specifications and conditions necessary for the design from the input sentence.

[0492] Next, the server uses CAD software and 3D modeling tools, such as the Fusion 360 API, to automatically generate design drawings and models based on the extracted requirements. This generates a concrete design that aligns with the user's desired product concept.

[0493] Furthermore, the server uses the USPTO API and the J-PlatPat database to evaluate the patentability of the generated designs. This is to confirm novelty and legal protectionability by comparing them with existing patent information.

[0494] The terminal displays the results of these design generation and patentability evaluations to the user in real time, allowing the user to instantly review them via their mobile device. This streamlines the new product development process in the factory and allows for simultaneous verification of the originality and marketability of the design.

[0495] As a concrete example, a factory site engineer could use the system with a prompt message such as, "Develop a detailed design proposal for a variable robotic arm aimed at automating a logistics center, and search for relevant patents to confirm its novelty." This prompt would allow the engineer to quickly receive feedback on the design proposal and adjust the design direction on the spot.

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The user uses their own device to input an idea for a product they want to create in natural language. The input is in the form of a prompt message, such as, "Develop a detailed design proposal for a variable robotic arm aimed at automating logistics centers, and research relevant patents to confirm its novelty." The input data is sent directly to the server.

[0499] Step 2:

[0500] The server processes the received natural language input and extracts requirements using language analysis algorithms (e.g., spaCy or Google Cloud Natural Language API). It identifies key conditions and specifications from the input data and compiles them as necessary information during the design phase. This results in the extracted requirements.

[0501] Step 3:

[0502] The server generates design drawings and models based on the extracted requirements. Specifically, it uses CAD software and 3D modeling tools (e.g., Fusion 360 API) to automatically create design proposals that meet the requirements. The input is requirements information, and the output is the generated design drawings and 3D models.

[0503] Step 4:

[0504] The server evaluates the patentability of the generated design proposals. Using the USPTO API and the J-PlatPat database, it compares the design against existing patent information to confirm its novelty and legal protectability. The input is the design proposal, and the output is the patentability evaluation result.

[0505] Step 5:

[0506] Design proposals and patentability evaluation results from the server are immediately delivered to the terminal and displayed to the user. The user reviews the results on the mobile device screen and sends additional feedback or modifications to the server as needed. The output is the evaluation result, which is then converted into the next feedback information as input.

[0507] Step 6:

[0508] If new feedback is received, the server will re-run the design and evaluation process and generate an optimal design proposal that reflects the user's requirements. The regenerated design will be the final output.

[0509] 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.

[0510] This invention is a system that, in addition to a function to extract requests from the user's natural language input and generate designs, incorporates an emotion engine that recognizes the user's emotions. The aim of this system is to improve the user experience, and it provides more appropriate design suggestions and feedback based on the user's emotional state.

[0511] Users access the system through a terminal and input ideas they want to realize. At this time, users may include phrases that express emotions, such as "I want to make fun and colorful toys." The server receives this input and not only analyzes the requirements using natural language processing technology, but also recognizes the user's emotions using an emotion engine. For example, if the emotion "fun" is recognized, the server prioritizes selecting design concepts that correspond to that emotion.

[0512] Based on the analyzed requirements and emotional information, the server uses design generation tools to create specific design drawings and models. This generation process utilizes the emotional information obtained by the emotional engine and includes elements that respond to emotions, such as appropriate color schemes and shape selections.

[0513] The generated design is presented to the user via the device, and the user can review its contents. The user's feedback on the presented design is also analyzed by the emotion engine and contributes to improving the design. For example, if the feedback is "I want a more fun design," the server will add more colorful and interactive elements to the design.

[0514] Once the final design is determined, the server uses a patent-specific program to evaluate its patentability and check for similarities with existing patents. This result is sent to the terminal, allowing the user to make decisions regarding the patent application.

[0515] This invention enables users to gain a more satisfying product development experience through designs that reflect their own emotions.

[0516] The following describes the processing flow.

[0517] Step 1:

[0518] Users use the terminal interface to input ideas and requests regarding their project in natural language. This input may include specific requests such as "a design with fun colors" or "user-friendly features."

[0519] Step 2:

[0520] The server analyzes the user's input. First, it uses natural language processing techniques to extract the requirements and conditions necessary for the design. Simultaneously, it activates an emotion engine to recognize the user's emotional state and analyze emotions such as "enjoyment" and "comfort" from the input.

[0521] Step 3:

[0522] Based on the analyzed requirements and emotional information, the server automatically generates an initial design using design generation tools. At this stage, colors and shapes that resonate with the user's emotions are selected, and for example, a "colorful and friendly" design might be proposed.

[0523] Step 4:

[0524] The generated initial design is sent to the device and presented visually to the user. The user can review the presented design and provide feedback for further optimization based on the emotions they experience.

[0525] Step 5:

[0526] When a user submits feedback, the server analyzes its content again using the emotion engine. Based on the emotional expression in the feedback, such as "I want more colors," the server improves the design, adjusts the design generation method, and creates a new design.

[0527] Step 6:

[0528] When the final design matches the user's intent, the server uses a patent-specific program to evaluate the patentability of the design. By comparing it with existing patent information, it verifies the novelty and originality of the design.

[0529] Step 7:

[0530] The patentability evaluation results are transmitted to the terminal and presented to the user. Based on these results, the user can decide whether to file a patent application or whether further design adjustments are necessary.

[0531] Through the above process, users can materialize their ideas by going through a design process that reflects their own emotions.

[0532] (Example 2)

[0533] 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."

[0534] Conventional design generation systems could generate designs based on user requirements, but they did not take into account the user's emotional state. This made it difficult to provide designs that reflected the emotions users desired, resulting in limited improvements to the user experience. Furthermore, there was room for improvement in the process of effectively incorporating user feedback.

[0535] 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.

[0536] In this invention, the server includes information processing means for analyzing natural language input received from a user and extracting requirements and emotional states; generation means for generating design drawings and models based on the extracted requirements and emotional states; and evaluation means for evaluating the patentability of the generated design and comparing it with existing patent information. This makes it possible to provide designs that reflect the user's emotions and to improve the design while taking feedback into consideration.

[0537] "Information processing means" refers to technologies for analyzing natural language input from users and extracting their requests and emotional states.

[0538] "Generative means" refers to the technology of creating design drawings and models based on extracted requirements and emotional states.

[0539] "Evaluation means" refers to a technology that evaluates the patentability of a generated design and compares it with existing patent information.

[0540] This invention relates to a system that generates designs that take into account emotional states based on natural language input from a user. This system is implemented through the following steps.

[0541] The server receives natural language data entered by the user through their terminal. This input data is processed using AI technology. Specifically, a common generative AI model (e.g., GPT-3) is used for natural language processing to extract requests and intentions from the input.

[0542] The extracted data is analyzed using an emotion recognition engine (e.g., an emotion analysis API) to determine the user's emotional state. This allows us to identify the emotional direction of the design the user desires.

[0543] Next, the server uses the obtained requirements and sentiment data to execute the design generation process. During this process, design generation tools (e.g., creative generation tools) are used to determine specific design elements such as shapes and color patterns.

[0544] The generated design results are presented to the user via the device. The user can provide feedback on this presented design, and this feedback is also analyzed based on an emotion engine, with the feedback data used to revise the design.

[0545] Ultimately, the server performs a patentability assessment. This process verifies how similar the generated design is to existing patents and whether it meets the requirements for patenting. This assessment uses evaluation tools that refer to patent databases.

[0546] This system allows users to experience highly satisfying designs that reflect their own emotions, and also enables them to develop these designs further and establish criteria for filing patent applications.

[0547] For example, a prompt might be used such as, "I want to create a new toy design. It should be bright, fun, and colorful." This allows the user to receive design suggestions that meet their expectations and are tailored to their specific needs.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] Users input their design requests in natural language through their device. This input is sent to the server in the form of prompts. The input includes phrases that indicate the user's wishes and ideas. For example, "I want to make fun and colorful toys."

[0551] Step 2:

[0552] The server analyzes the received natural language input and extracts the user's requirements. This analysis uses a generative AI model. The input is the prompt text from Step 1, which is processed and converted into a specific data format to extract the requirements. Specifically, the AI ​​model processes the text and recognizes keywords such as "fun" and "colorful." A list of requirements is generated as output.

[0553] Step 3:

[0554] The server uses an emotion recognition engine to recognize the user's emotional state from their input. The input is the prompt text from Step 1. This is analyzed by the emotion recognition algorithm to identify the user's emotional state. As output, the server obtains emotion-based data, such as "happy." A specific action involves generating an emotion vector.

[0555] Step 4:

[0556] The server generates design drawings and models using design generation tools based on the extracted requirements and sentiment information. The requirements list and sentiment data generated in steps 2 and 3 are used as input. For data processing, the design generation tool is used to determine design elements such as color and shape. Initial design drawings and 3D models are generated as output.

[0557] Step 5:

[0558] The generated design is presented to the user via a terminal. The user visually reviews the design and inputs their satisfaction rating and suggestions for improvement. The input includes specific opinions accompanied by user feedback. Specifically, a view of the design is displayed on the screen, and a format is provided for the user to add comments.

[0559] Step 6:

[0560] The server receives user feedback and analyzes it again using the sentiment engine. The input is the feedback obtained in step 5. Based on this data, the design is modified. The output is the generation of improved design drawings or models. Specifically, an optimization process may be implemented to incorporate the feedback into the requirements.

[0561] Step 7:

[0562] The server performs a patentability assessment on the final design. The design data finalized in step 6 is used as input. The evaluation tool compares the design with existing patent databases to check for similarity. The output is the patentability assessment result, which is reported to the user. Specifically, a patent evaluation tool is used, and the agreement rate is measured using a comparison algorithm.

[0563] (Application Example 2)

[0564] 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."

[0565] This solution addresses the challenge of users not receiving content recommendations tailored to their individual emotional states when selecting content. It also addresses the difficulty of designing and selecting content that takes user emotions into account.

[0566] 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.

[0567] In this invention, the server includes means for analyzing natural language input received from the user and extracting requirements; design generation means for generating design drawings and models based on the extracted requirements and user sentiment information; and means for providing the generated design and content based on the user's sentiment as content. This makes it possible to suggest content that responds to the user's sentiment, thereby improving the user experience.

[0568] "Natural language received from the user" refers to the linguistic information that the user transcribes or speaks, and is the initial data that the system should process.

[0569] "Requirements extraction" is the process of analyzing the meaning of the user's natural language input to clarify the required functions and conditions.

[0570] "Emotional information" refers to data that identifies a user's emotional state and reflects that state.

[0571] "Design generation means" refers to a function that automatically creates specific design drawings and models based on requirements and sentiment information obtained from the user.

[0572] "Means of providing content" refers to a method of presenting generated designs or models to users and proposing them as content that responds to the users' emotions.

[0573] "Existing patent information" refers to detailed information about patented technologies that are already publicly known, and is used to evaluate novelty and inventiveness.

[0574] This invention realizes a system that processes information entered by users in natural language and provides sentiment-based content. The server uses natural language processing techniques to analyze the natural language input sent from the user through the terminal and extract requirements. This utilizes common natural language processing libraries and APIs (e.g., Python's NLTK and spaCy).

[0575] Next, an algorithm is executed on the server to generate design drawings and models based on the extracted requirements and emotional information. A generative AI model is used for design generation, resulting in designs that reflect subjective elements. This enables interactive content based on emotional information.

[0576] User sentiment information is automatically extracted by a sentiment analysis engine, and recommended content is generated in a way that corresponds to those sentiments. This information processing uses a deep learning-based sentiment recognition model, enabling highly accurate detection of emotions contained in text data.

[0577] For example, if a user types "I'm in a really good mood today, so I want to watch a funny movie" on their smartphone, the system recognizes the emotion of "fun" and prioritizes recommending comedy movies. An example of a prompt would be, "Please recommend a movie that will put me in a good mood."

[0578] Through this system, users can receive personalized suggestions that take their emotions into consideration, enabling them to enjoy a more satisfying user experience.

[0579] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0580] Step 1:

[0581] The user sends natural language input from the terminal to the server. This input includes words that express the user's requests and their emotions at the time. Text-based sentences are used as the input data.

[0582] Step 2:

[0583] The server analyzes the received natural language input and extracts requirements. This process uses natural language processing libraries (e.g., NLTK and spaCy) to extract keywords from the input text. The analysis results in the user's requests and intentions.

[0584] Step 3:

[0585] The server uses an emotion recognition engine to extract user emotion information based on natural language input. By applying a deep learning model, it analyzes the emotions contained in the text and obtains a classification result.

[0586] Step 4:

[0587] The server combines the requirements extracted in step 2 with the emotional information obtained in step 3, and uses design generation tools to generate appropriate design drawings and content models. This generation process uses a generation AI model to generate content suggestions that match the emotions.

[0588] Step 5:

[0589] The generated designs and content are presented to the user via their device. The user can review the proposals and submit feedback. As output, content information tailored to the user's selections is provided.

[0590] Step 6:

[0591] The server receives feedback from users and modifies the design and content as needed. The regenerated content, based on the feedback, is then presented to the user again.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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".

[0609] This invention is a system that accepts ideas from users in natural language, analyzes them, extracts necessary requirements, and then creates a concrete design using a design generation means. Furthermore, this system also has a function to evaluate patentability, allowing users to receive comprehensive support, including legal aspects of the design.

[0610] In implementing this system, users first input their ideas and requirements for the product they want to create using natural language through their devices. For example, a specific desire such as "I want to create original accessories that light up." The server then uses a language analysis algorithm to analyze the input text and extract the requirements and conditions necessary for the design.

[0611] Based on the extraction results, the server launches a design generation program and automatically generates design drawings and models. This design generation method includes the automatic creation of CAD data and 3D models, and can also provide suggestions regarding physical properties and materials to be used. The generated designs are presented to the user via a terminal, allowing them to review the contents.

[0612] Subsequently, the user provides feedback on the presented design and sends requests for further improvements or changes to the server. The server automatically modifies the design based on this feedback, and by repeating this process, it can provide a design that best meets the user's requirements.

[0613] Once the final design is determined, the server uses a patent-specific program to assess the patentability of that design. This program searches existing patent databases to check for similar patents and legal risks. The user is then provided with the patentability assessment results and related advice on their terminal, allowing them to make additional modifications to the design or proceed with preparing a patent application as needed.

[0614] Thus, the present invention not only quickly and efficiently realizes users' ideas but also comprehensively supports the legal aspects of design, thereby providing users with a valuable product development process.

[0615] The following describes the processing flow.

[0616] Step 1:

[0617] The user inputs their product idea in natural language through the device's interface. This input includes a basic description of the product and the desired characteristics.

[0618] Step 2:

[0619] The server receives user input and analyzes the input using a large-scale language model. From the analyzed data, it extracts necessary design requirements and conditions.

[0620] Step 3:

[0621] The server activates the design generation mechanism based on the extracted requirements. A design-specialized generation AI automatically generates design drawings and models. Information regarding material properties and product functionality is also considered during this process.

[0622] Step 4:

[0623] The server sends the generated design data to the terminal and presents it to the user. This allows the user to visually confirm and evaluate the design.

[0624] Step 5:

[0625] Users can send their opinions and feedback on the presented design from their device to the server. This can include specific suggestions for improvements and additional requests.

[0626] Step 6:

[0627] The server modifies the design data based on user feedback. If necessary, it restarts the design generation system, generates the improved design, and presents it to the user again.

[0628] Step 7:

[0629] Finally, the server launches a patent-specific program to evaluate the patentability of the completed design. It searches existing patent databases and checks for similarities with relevant patents.

[0630] Step 8:

[0631] The server sends the patentability assessment results and advice to the terminal, providing them to the user. Based on this, the user can make decisions about filing a patent application and make further design adjustments.

[0632] (Example 1)

[0633] 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".

[0634] In modern product development processes, it is crucial to quickly and efficiently realize user ideas and address their legal aspects. However, accurately analyzing natural language input from users, extracting necessary design requirements, automatically generating design drawings and 3D models, and evaluating patentability are not easy tasks. There is a need to efficiently integrate these multiple processes, reducing effort while obtaining high-quality results.

[0635] 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.

[0636] In this invention, the server includes a processing unit that analyzes natural language information received from the user and extracts technical requirements; a processing unit that generates structural diagrams and three-dimensional models based on the extracted requirements; a processing unit that automatically modifies the structure based on user feedback; a processing unit that evaluates the patentability of the finally generated structure and compares it with existing legal information; and a device that presents the generated structure and the patentability evaluation results to the user. This enables the efficient realization of the user's ideas and comprehensive product development support, including legal considerations of the design.

[0637] "Information in natural language" refers to information that users input as words or sentences, and is data provided in normal linguistic expression.

[0638] "Technical requirements" refer to the conditions and standards necessary for the design of a product or system, and include specifications regarding specific functions and performance.

[0639] A "processing device" refers to hardware or software that includes mechanisms and programs for processing data from input and generating output.

[0640] A "structural drawing" is a drawing that serves as the basis for a design, and it is a visual representation used to show the shape and arrangement of parts of a product or system.

[0641] A "three-dimensional model" is digital data that represents an object or structure in three-dimensional space, and is created to reproduce the shape and proportions of the real thing.

[0642] "Evaluating patentability" is the process of determining whether something is novel and inventive compared to existing technologies and rights, and it is the act of determining the likelihood of obtaining a patent.

[0643] "Legal information" refers to existing patent databases and data on relevant laws, which are used to verify the patent status and its relevance to technology.

[0644] A "presenting device" refers to a device, including terminals and screens, that visually displays information to the user and enables interaction.

[0645] In this system, users first input product ideas and requests in natural language using their own devices. A concrete example of this input is, "I want to create original accessories that light up."

[0646] Next, the server receives this information and performs detailed linguistic analysis using a generative AI model (e.g., GPT-4). Through this analysis, technical requirements are clarified and the conditions necessary for design are extracted. An example of a prompt message is, "Please extract the necessary design requirements based on the input idea."

[0647] Based on the extracted conditions, the server automatically generates design drawings and 3D models using software such as AutoCAD and Blender. This process also takes into account physical properties and the selection of optimal materials.

[0648] The generated design data is provided to the user via a terminal. The terminal provides the user with a visual interface to review the design and evaluate its various details. The user can, for example, suggest changes to the design's size or design options.

[0649] When a user provides feedback, the server automatically modifies the design based on that feedback and sends the improved version back to the user's device. This process is repeated until the optimal design is determined.

[0650] Finally, the server uses a dedicated program to evaluate patentability and compare it with existing legal information. This evaluation result is also communicated to the user via the terminal, and the user decides whether to modify the design or proceed with filing a patent application based on this information.

[0651] In this way, user ideas are materialized efficiently and structurally, and detailed support regarding legal aspects is also provided. This integrated system enables users to develop products effectively and comprehensively.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] The user inputs their product idea in natural language using their own device. This input is the starting point for the system; for example, they might write, "I want to make original accessories that light up." The entered information is then sent to the server.

[0655] Step 2:

[0656] The server analyzes natural language input received from the user using a generative AI model (e.g., GPT-4). The generative model is given the prompt, "Extract the necessary design requirements based on the input idea." This analysis extracts the technical requirements. The output provides the detailed extracted design requirements.

[0657] Step 3:

[0658] Based on the extracted technical requirements, the server automatically generates design drawings and 3D models using design generation software (e.g., AutoCAD or Blender). The input is the requirements data from step 2, and the output is specific design drawings and 3D models.

[0659] Step 4:

[0660] The generated design is provided to the user via a terminal. The terminal visually displays the generated design data and provides an interface for the user to review the details. The output is a digital design in a format that the user can view.

[0661] Step 5:

[0662] Users provide feedback on the provided design in natural language and send it to the server via their device. For example, they might request a modification such as "I want to change the color to red." This feedback becomes the input to the server.

[0663] Step 6:

[0664] The server analyzes user feedback and modifies the design as needed. The input is feedback information, and an improved version of the design, including configuration updates, is output. The improved design is then sent back to the terminal for user confirmation.

[0665] Step 7:

[0666] The server uses a patent evaluation program to determine the patentability of the finalized design. The design finalized in step 6 is used as input, and the evaluation results, compared with existing legal information, are obtained as output.

[0667] Step 8:

[0668] The patent evaluation results are presented to the user via a terminal. The output includes a decision on whether or not to grant a patent and related advice, which the user can use to revise their design or proceed with preparing their patent application.

[0669] (Application Example 1)

[0670] 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".

[0671] There is a growing need to accelerate and optimize product design in factories. However, conventional methods involve time-consuming requirements extraction, design generation, and patentability assessment in the initial stages of design, and these processes are fragmented, resulting in reduced overall efficiency. Therefore, there is a demand for a system that provides integrated support for each stage of product design, delivers rapid and highly accurate design proposals, and also takes patentability into consideration.

[0672] 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.

[0673] In this invention, the server includes means for analyzing natural language input received from a user and extracting requirements; means for generating design drawings and models based on the extracted requirements; means for evaluating the patentability of the generated design and comparing it with existing patent information; means for automatically creating a structure as a product concept and including variable elements during the design generation process; and means for immediately displaying the results of design generation and patentability evaluation on a mobile terminal to support product design in the factory. This enables users to receive rapid design generation and patentability evaluation based on natural language input.

[0674] "Natural language input" refers to information that uses everyday words and phrases and is not in a mechanically formatted form.

[0675] "Means for extracting requirements" refers to technologies or systems for identifying and extracting the conditions and specifications necessary for design from received information.

[0676] "Design generation means" refers to a technology or method for constructing specific design drawings or models based on extracted requirements.

[0677] "Means for evaluating the patentability of a generated design" refers to technologies or processes for verifying the originality and legal protectability of a design and comparing it with existing patents.

[0678] "Existing patent information" refers to databases and records of patents registered in the past, and is used to verify similar technologies.

[0679] "A means of automatically creating a product's conceptual structure and including variable elements" refers to a technology that automates the basic conceptual design of a product and allows for flexible modification of elements in response to user requirements.

[0680] "Means for immediate display on a mobile device" refers to a technology or method for displaying generated information or evaluation results in real time on the screen of a portable electronic device.

[0681] The system that realizes this application example can perform everything from design generation to patentability evaluation in one go, based on the user's natural language input.

[0682] The server receives natural language input from the user and uses natural language processing libraries such as spaCy and the Google Cloud Natural Language API to extract requirements. This clarifies the specifications and conditions necessary for the design from the input sentence.

[0683] Next, the server uses CAD software and 3D modeling tools, such as the Fusion 360 API, to automatically generate design drawings and models based on the extracted requirements. This generates a concrete design that aligns with the user's desired product concept.

[0684] Furthermore, the server uses the USPTO API and the J-PlatPat database to evaluate the patentability of the generated designs. This is to confirm novelty and legal protectionability by comparing them with existing patent information.

[0685] The terminal displays the results of these design generation and patentability evaluations to the user in real time, allowing the user to instantly review them via their mobile device. This streamlines the new product development process in the factory and allows for simultaneous verification of the originality and marketability of the design.

[0686] As a concrete example, a factory site engineer could use the system with a prompt message such as, "Develop a detailed design proposal for a variable robotic arm aimed at automating a logistics center, and search for relevant patents to confirm its novelty." This prompt would allow the engineer to quickly receive feedback on the design proposal and adjust the design direction on the spot.

[0687] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0688] Step 1:

[0689] The user uses their own device to input an idea for a product they want to create in natural language. The input is in the form of a prompt message, such as, "Develop a detailed design proposal for a variable robotic arm aimed at automating logistics centers, and research relevant patents to confirm its novelty." The input data is sent directly to the server.

[0690] Step 2:

[0691] The server processes the received natural language input and extracts requirements using language analysis algorithms (e.g., spaCy or Google Cloud Natural Language API). It identifies key conditions and specifications from the input data and compiles them as necessary information during the design phase. This results in the extracted requirements.

[0692] Step 3:

[0693] The server generates design drawings and models based on the extracted requirements. Specifically, it uses CAD software and 3D modeling tools (e.g., Fusion 360 API) to automatically create design proposals that meet the requirements. The input is requirements information, and the output is the generated design drawings and 3D models.

[0694] Step 4:

[0695] The server evaluates the patentability of the generated design proposals. Using the USPTO API and the J-PlatPat database, it compares the design against existing patent information to confirm its novelty and legal protectability. The input is the design proposal, and the output is the patentability evaluation result.

[0696] Step 5:

[0697] Design proposals and patentability evaluation results from the server are immediately delivered to the terminal and displayed to the user. The user reviews the results on the mobile device screen and sends additional feedback or modifications to the server as needed. The output is the evaluation result, which is then converted into the next feedback information as input.

[0698] Step 6:

[0699] If new feedback is received, the server will re-run the design and evaluation process and generate an optimal design proposal that reflects the user's requirements. The regenerated design will be the final output.

[0700] 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.

[0701] This invention is a system that, in addition to a function to extract requests from the user's natural language input and generate designs, incorporates an emotion engine that recognizes the user's emotions. The aim of this system is to improve the user experience, and it provides more appropriate design suggestions and feedback based on the user's emotional state.

[0702] Users access the system through a terminal and input ideas they want to realize. At this time, users may include phrases that express emotions, such as "I want to make fun and colorful toys." The server receives this input and not only analyzes the requirements using natural language processing technology, but also recognizes the user's emotions using an emotion engine. For example, if the emotion "fun" is recognized, the server prioritizes selecting design concepts that correspond to that emotion.

[0703] Based on the analyzed requirements and emotional information, the server uses design generation tools to create specific design drawings and models. This generation process utilizes the emotional information obtained by the emotional engine and includes elements that respond to emotions, such as appropriate color schemes and shape selections.

[0704] The generated design is presented to the user via the device, and the user can review its contents. The user's feedback on the presented design is also analyzed by the emotion engine and contributes to improving the design. For example, if the feedback is "I want a more fun design," the server will add more colorful and interactive elements to the design.

[0705] Once the final design is determined, the server uses a patent-specific program to evaluate its patentability and check for similarities with existing patents. This result is sent to the terminal, allowing the user to make decisions regarding the patent application.

[0706] This invention enables users to gain a more satisfying product development experience through designs that reflect their own emotions.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] Users use the terminal interface to input ideas and requests regarding their project in natural language. This input may include specific requests such as "a design with fun colors" or "user-friendly features."

[0710] Step 2:

[0711] The server analyzes the user's input. First, it uses natural language processing techniques to extract the requirements and conditions necessary for the design. Simultaneously, it activates an emotion engine to recognize the user's emotional state and analyze emotions such as "enjoyment" and "comfort" from the input.

[0712] Step 3:

[0713] Based on the analyzed requirements and emotional information, the server automatically generates an initial design using design generation tools. At this stage, colors and shapes that resonate with the user's emotions are selected, and for example, a "colorful and friendly" design might be proposed.

[0714] Step 4:

[0715] The generated initial design is sent to the device and presented visually to the user. The user can review the presented design and provide feedback for further optimization based on the emotions they experience.

[0716] Step 5:

[0717] When a user submits feedback, the server analyzes its content again using the emotion engine. Based on the emotional expression in the feedback, such as "I want more colors," the server improves the design, adjusts the design generation method, and creates a new design.

[0718] Step 6:

[0719] When the final design matches the user's intent, the server uses a patent-specific program to evaluate the patentability of the design. By comparing it with existing patent information, it verifies the novelty and originality of the design.

[0720] Step 7:

[0721] The patentability evaluation results are transmitted to the terminal and presented to the user. Based on these results, the user can decide whether to file a patent application or whether further design adjustments are necessary.

[0722] Through the above process, users can materialize their ideas by going through a design process that reflects their own emotions.

[0723] (Example 2)

[0724] 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".

[0725] Conventional design generation systems could generate designs based on user requirements, but they did not take into account the user's emotional state. This made it difficult to provide designs that reflected the emotions users desired, resulting in limited improvements to the user experience. Furthermore, there was room for improvement in the process of effectively incorporating user feedback.

[0726] 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.

[0727] In this invention, the server includes information processing means for analyzing natural language input received from a user and extracting requirements and emotional states; generation means for generating design drawings and models based on the extracted requirements and emotional states; and evaluation means for evaluating the patentability of the generated design and comparing it with existing patent information. This makes it possible to provide designs that reflect the user's emotions and to improve the design while taking feedback into consideration.

[0728] "Information processing means" refers to technologies for analyzing natural language input from users and extracting their requests and emotional states.

[0729] "Generative means" refers to the technology of creating design drawings and models based on extracted requirements and emotional states.

[0730] "Evaluation means" refers to a technology that evaluates the patentability of a generated design and compares it with existing patent information.

[0731] This invention relates to a system that generates designs that take into account emotional states based on natural language input from a user. This system is implemented through the following steps.

[0732] The server receives natural language data entered by the user through their terminal. This input data is processed using AI technology. Specifically, a common generative AI model (e.g., GPT-3) is used for natural language processing to extract requests and intentions from the input.

[0733] The extracted data is analyzed using an emotion recognition engine (e.g., an emotion analysis API) to determine the user's emotional state. This allows us to identify the emotional direction of the design the user desires.

[0734] Next, the server uses the obtained requirements and sentiment data to execute the design generation process. During this process, design generation tools (e.g., creative generation tools) are used to determine specific design elements such as shapes and color patterns.

[0735] The generated design results are presented to the user via the device. The user can provide feedback on this presented design, and this feedback is also analyzed based on an emotion engine, with the feedback data used to revise the design.

[0736] Ultimately, the server performs a patentability assessment. This process verifies how similar the generated design is to existing patents and whether it meets the requirements for patenting. This assessment uses evaluation tools that refer to patent databases.

[0737] This system allows users to experience highly satisfying designs that reflect their own emotions, and also enables them to develop these designs further and establish criteria for filing patent applications.

[0738] For example, a prompt might be used such as, "I want to create a new toy design. It should be bright, fun, and colorful." This allows the user to receive design suggestions that meet their expectations and are tailored to their specific needs.

[0739] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0740] Step 1:

[0741] Users input their design requests in natural language through their device. This input is sent to the server in the form of prompts. The input includes phrases that indicate the user's wishes and ideas. For example, "I want to make fun and colorful toys."

[0742] Step 2:

[0743] The server analyzes the received natural language input and extracts the user's requirements. This analysis uses a generative AI model. The input is the prompt text from Step 1, which is processed and converted into a specific data format to extract the requirements. Specifically, the AI ​​model processes the text and recognizes keywords such as "fun" and "colorful." A list of requirements is generated as output.

[0744] Step 3:

[0745] The server uses an emotion recognition engine to recognize the user's emotional state from their input. The input is the prompt text from Step 1. This is analyzed by the emotion recognition algorithm to identify the user's emotional state. As output, the server obtains emotion-based data, such as "happy." A specific action involves generating an emotion vector.

[0746] Step 4:

[0747] The server generates design drawings and models using design generation tools based on the extracted requirements and sentiment information. The requirements list and sentiment data generated in steps 2 and 3 are used as input. For data processing, the design generation tool is used to determine design elements such as color and shape. Initial design drawings and 3D models are generated as output.

[0748] Step 5:

[0749] The generated design is presented to the user via a terminal. The user visually reviews the design and inputs their satisfaction rating and suggestions for improvement. The input includes specific opinions accompanied by user feedback. Specifically, a view of the design is displayed on the screen, and a format is provided for the user to add comments.

[0750] Step 6:

[0751] The server receives user feedback and analyzes it again using the sentiment engine. The input is the feedback obtained in step 5. Based on this data, the design is modified. The output is the generation of improved design drawings or models. Specifically, an optimization process may be implemented to incorporate the feedback into the requirements.

[0752] Step 7:

[0753] The server performs a patentability assessment on the final design. The design data finalized in step 6 is used as input. The evaluation tool compares the design with existing patent databases to check for similarity. The output is the patentability assessment result, which is reported to the user. Specifically, a patent evaluation tool is used, and the agreement rate is measured using a comparison algorithm.

[0754] (Application Example 2)

[0755] 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".

[0756] This solution addresses the challenge of users not receiving content recommendations tailored to their individual emotional states when selecting content. It also addresses the difficulty of designing and selecting content that takes user emotions into account.

[0757] 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.

[0758] In this invention, the server includes means for analyzing natural language input received from the user and extracting requirements; design generation means for generating design drawings and models based on the extracted requirements and user sentiment information; and means for providing the generated design and content based on the user's sentiment as content. This makes it possible to suggest content that responds to the user's sentiment, thereby improving the user experience.

[0759] "Natural language received from the user" refers to the linguistic information that the user transcribes or speaks, and is the initial data that the system should process.

[0760] "Requirements extraction" is the process of analyzing the meaning of the user's natural language input to clarify the required functions and conditions.

[0761] "Emotional information" refers to data that identifies a user's emotional state and reflects that state.

[0762] "Design generation means" refers to a function that automatically creates specific design drawings and models based on requirements and sentiment information obtained from the user.

[0763] "Means of providing content" refers to a method of presenting generated designs or models to users and proposing them as content that responds to the users' emotions.

[0764] "Existing patent information" refers to detailed information about patented technologies that are already publicly known, and is used to evaluate novelty and inventiveness.

[0765] This invention realizes a system that processes information entered by users in natural language and provides sentiment-based content. The server uses natural language processing techniques to analyze the natural language input sent from the user through the terminal and extract requirements. This utilizes common natural language processing libraries and APIs (e.g., Python's NLTK and spaCy).

[0766] Next, an algorithm is executed on the server to generate design drawings and models based on the extracted requirements and emotional information. A generative AI model is used for design generation, resulting in designs that reflect subjective elements. This enables interactive content based on emotional information.

[0767] User sentiment information is automatically extracted by a sentiment analysis engine, and recommended content is generated in a way that corresponds to those sentiments. This information processing uses a deep learning-based sentiment recognition model, enabling highly accurate detection of emotions contained in text data.

[0768] For example, if a user types "I'm in a really good mood today, so I want to watch a funny movie" on their smartphone, the system recognizes the emotion of "fun" and prioritizes recommending comedy movies. An example of a prompt would be, "Please recommend a movie that will put me in a good mood."

[0769] Through this system, users can receive personalized suggestions that take their emotions into consideration, enabling them to enjoy a more satisfying user experience.

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The user sends natural language input from the terminal to the server. This input includes words that express the user's requests and their emotions at the time. Text-based sentences are used as the input data.

[0773] Step 2:

[0774] The server analyzes the received natural language input and extracts requirements. This process uses natural language processing libraries (e.g., NLTK and spaCy) to extract keywords from the input text. The analysis results in the user's requests and intentions.

[0775] Step 3:

[0776] The server uses an emotion recognition engine to extract user emotion information based on natural language input. By applying a deep learning model, it analyzes the emotions contained in the text and obtains a classification result.

[0777] Step 4:

[0778] The server combines the requirements extracted in step 2 with the emotional information obtained in step 3, and uses design generation tools to generate appropriate design drawings and content models. This generation process uses a generation AI model to generate content suggestions that match the emotions.

[0779] Step 5:

[0780] The generated designs and content are presented to the user via their device. The user can review the proposals and submit feedback. As output, content information tailored to the user's selections is provided.

[0781] Step 6:

[0782] The server receives feedback from users and modifies the design and content as needed. The regenerated content, based on the feedback, is then presented to the user again.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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."

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0804] The following is further disclosed regarding the embodiments described above.

[0805] (Claim 1)

[0806] A means of analyzing natural language input received from the user and extracting requirements,

[0807] A design generation means that generates design drawings and models based on extracted requirements,

[0808] A means for evaluating the patentability of the generated design and comparing it with existing patent information,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, which automatically modifies the design based on user feedback.

[0812] (Claim 3)

[0813] The system according to claim 1, which presents the generated design and patent evaluation results to the user.

[0814] "Example 1"

[0815] (Claim 1)

[0816] A processing unit that analyzes natural language information received from a user and extracts technical requirements,

[0817] A processing device that generates structural diagrams and three-dimensional models based on extracted requirements,

[0818] A processing unit that automatically modifies the structure based on user feedback,

[0819] A processing device that evaluates the patentability of the finally generated structure and compares it with existing legal information,

[0820] A device that presents the generated structure and patentability evaluation results to the user,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, which performs natural language analysis and extraction of technical requirements using a generative AI model.

[0824] (Claim 3)

[0825] The system according to claim 1, which efficiently clarifies technical requirements by generating prompt statements and inputting them into a generation AI model.

[0826] "Application Example 1"

[0827] (Claim 1)

[0828] A means of analyzing natural language input received from the user and extracting requirements,

[0829] A design generation means that generates design drawings and models based on extracted requirements,

[0830] A means for evaluating the patentability of the generated design and comparing it with existing patent information,

[0831] In the design generation process, a means of automatically creating the structure as a product concept and including variable elements,

[0832] To support product design within the factory, a means of immediately displaying the results of design generation and patentability evaluation on a mobile device,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, which automatically modifies the design based on user feedback.

[0836] (Claim 3)

[0837] The system according to claim 1, which presents the generated design and patent evaluation results to the user.

[0838] "Example 2 of combining an emotion engine"

[0839] (Claim 1)

[0840] An information processing means that analyzes natural language input received from a user and extracts requirements and emotional states,

[0841] A generation means for generating design drawings and models based on extracted requirements and emotional states,

[0842] An evaluation means for evaluating the patentability of the generated design and comparing it with existing patent information,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, which automatically modifies the design based on user feedback and sentiment analysis.

[0846] (Claim 3)

[0847] The system according to claim 1, which displays the generated design and patent evaluation results to the user.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A device that analyzes natural language input received from a user and extracts requirements,

[0851] A design generation device that generates design drawings and models based on extracted requirements and user sentiment information,

[0852] A device that provides content based on generated designs and user emotions,

[0853] A device for evaluating the patentability of a generated design and comparing it with existing patent information,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, which automatically modifies the design based on user feedback and sentiment information.

[0857] (Claim 3)

[0858] The system according to claim 1, having a function to present the generated design and patent evaluation results to the user. [Explanation of Symbols]

[0859] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing natural language input received from the user and extracting requirements, A design generation means that generates design drawings and models based on extracted requirements, A means for evaluating the patentability of the generated design and comparing it with existing patent information, In the design generation process, a means of automatically creating the structure as a product concept and including variable elements, To support product design within the factory, a means of immediately displaying the results of design generation and patentability evaluation on a mobile device, A system that includes this.

2. The system according to claim 1, which automatically modifies the design based on user feedback.

3. The system according to claim 1, which presents the generated design and patent evaluation results to the user.

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