system

Abstract

We provide a system for generating automobile designs. [Solution] The system for generating automobile designs using extracted characteristics is characterized by including means for constructing a database having characteristic data of plants and animals, means for obtaining requirements from a user, means for extracting the characteristics of plants and animals from the database based on the requirements, means for generating automobile designs using the extracted characteristics, and means for providing the generated designs to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By effectively applying the characteristics of animals and plants obtained from nature to automobile design, it is required to promote innovation in automobile design and reduce the time and cost involved in the design process. However, with conventional design methods, it is difficult to systematically utilize various characteristic information obtained from nature, and as a result, there is a problem that the design effect is limited.

Means for Solving the Problems

[0005] This invention provides a system that constructs a database containing characteristic data of plants and animals, extracts characteristics based on user requirements, and generates automobile designs. Specifically, this system manages the characteristics of plants and animals in a database, selects the optimal characteristics according to user requests, and generates designs using AI, thereby realizing innovative automobile designs quickly and efficiently. The generated designs are provided to the user and further optimized based on user feedback, making it possible to improve the accuracy and functionality of the designs.

[0006] "Plant and animal characteristic data" refers to information about the ecology, structure, and physical characteristics of plants and animals, and includes data such as the shape, material properties, and operating principles of specific organisms.

[0007] A "database" is a collection of information that is systematically stored and organized in a way that makes it easily accessible, searchable, and manageable.

[0008] "User requirements" refer to information that describes the specific functions, performance, design, and other conditions and preferences that users desire.

[0009] "Characteristic extraction" refers to the process of selecting characteristics that meet the user's requirements from the characteristic data of plants and animals stored in the database.

[0010] "Automotive design" is a concept that refers to the external appearance and internal configuration of a car, designed with consideration for elements such as shape, structure, and function.

[0011] "Generated designs" refer to new car design proposals generated by AI based on characteristic data of plants and animals.

[0012] "User feedback" refers to information that includes user evaluations and suggestions for improvement regarding the generated design. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a system for generating automobile designs using characteristic data of plants and animals. The system consists of a server, a terminal, and a user.

[0035] First, the server collects detailed data on the characteristics of plants and animals and builds a database of that data. This data includes structural characteristics found in nature, such as the shape and properties of fish scales, or the strength and elasticity of bird wings.

[0036] Next, the user inputs the requirements for the car design through the terminal. This includes specific design goals and functional requirements, such as "I want to improve aerodynamics."

[0037] The server then analyzes these requirements and extracts suitable plant and animal characteristics from the database. Using an AI algorithm, the server selects the optimal characteristics that best match the user's requirements.

[0038] Based on these characteristic selections, the server executes a generation AI to create automobile design proposals. The generated designs are optimized in terms of shape, material selection, energy efficiency, and other aspects, providing an appropriate response to the user's requirements.

[0039] For example, if a user inputs the requirement "I want to reduce air resistance," the server will utilize the streamlined characteristics found in fish scales to design a vehicle structure with superior aerodynamics.

[0040] Finally, the generated design is delivered from the server to the user's terminal. The user can review the design and provide feedback as needed, enabling further optimization. This interactive process results in innovative and practical automotive designs.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects data on the characteristics of plants and animals. This involves organizing and integrating information from various nature observation databases and research data. The information stored in the database provides detailed descriptions of the physical and structural characteristics of specific plants and animals.

[0044] Step 2:

[0045] Users input their automotive design requirements using a terminal. For example, they specify concrete design conditions such as reducing air resistance at a particular speed or improving safety performance.

[0046] Step 3:

[0047] The server receives and analyzes the requirements entered by the user. Next, it extracts relevant plant and animal characteristics from the database and evaluates how these characteristics match the user requirements. This evaluation is performed using a specific algorithm.

[0048] Step 4:

[0049] The server uses the extracted characteristics to run a generative AI algorithm to generate car designs. The algorithm creates an optimal design proposal while considering the characteristics. This design proposal includes shape, material selection, and engineering considerations.

[0050] Step 5:

[0051] The server sends the generated design to the user's terminal. Here, the user reviews the design proposal and determines whether the car design meets their requirements.

[0052] Step 6:

[0053] Users can provide feedback on the design. This feedback may include requests for new improvements or ideas for fine-tuning.

[0054] Step 7:

[0055] The server implements a process of readjusting the design based on user feedback. This results in an optimized design being regenerated and presented to the user again. This cyclical process improves both the accuracy of the design and user satisfaction.

[0056] (Example 1)

[0057] 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."

[0058] Traditional automotive design processes often lack novelty and innovation because they rely primarily on human experience and intuition. Furthermore, they struggle to quickly generate optimal designs that meet functional requirements. Against this backdrop, there is a need for a system that leverages characteristics derived from plants and animals in nature to efficiently generate innovative and functional automotive designs, and that can flexibly optimize them according to user demands.

[0059] 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.

[0060] In this invention, the server includes means for collecting information relating to the characteristics of plants and animals, data storage means for organizing and accumulating the said information, and means for obtaining design goals from users. This makes it possible to efficiently generate novel and functional automobile designs that incorporate characteristics of the natural world.

[0061] "Characteristics of plants and animals" refer to structural or functional features derived from plants and animals that exist in nature.

[0062] "Means of collecting information" refers to a function or device for obtaining data on the characteristics of plants and animals from external sources.

[0063] "Data storage means" refers to a function or device that organizes collected information and records it so that it can be efficiently searched and used later.

[0064] "Means of obtaining design goals from users" refer to interfaces or functions for users to input their requirements and goals regarding the design of the product they desire.

[0065] "Means for automatically generating design proposals" refers to a function or process in which a computer program creates an automobile design plan based on the characteristics of selected plants and animals.

[0066] "Means of providing to users" refers to a function or device that presents the generated design proposals so that users can view and evaluate them.

[0067] "Means for obtaining opinions and improving the design proposal" refers to a function or process for collecting user feedback on the generated design proposal and improving the design based on that information.

[0068] "Means of improvement to meet specific functional requirements" refers to a function or process that adjusts a generated design proposal to meet the requirements of a specific business or function.

[0069] This invention is a system for generating automobile designs using characteristic data of plants and animals, and mainly consists of a server, terminals, and users. The server collects and organizes characteristic data on plants and animals and builds a database. This data is obtained from publicly available literature and databases via the internet. The database includes characteristic information from the natural world, such as the shape of fish scales and the strength of bird wings. The server uses hardware with high-performance data processing capabilities, and is equipped with AI algorithms for data analysis.

[0070] The user inputs specific requirements for the automobile design using a terminal. The terminal receives the design requirements from the user and sends them to the server. This terminal is a typical computer or mobile device and has an intuitive interface that the user can operate.

[0071] The server analyzes the user's requirements and selects the relevant characteristic data from the database. This process utilizes AI algorithms to select the necessary plant and animal characteristics to generate the optimal design. The generated design is automatically created using a generative AI model. This generative AI model runs on advanced design software, providing a design that considers shape optimization and material selection.

[0072] For example, if a user inputs the requirement "I want to reduce air resistance," the server will utilize the streamlined characteristics of a fish to generate an aerodynamically superior design. As an example of a prompt, a user could input "Please design a car body with streamlined characteristics."

[0073] The generated design is sent from the server to the terminal for user review. User feedback is incorporated into the system for further design optimization. This entire process efficiently generates innovative and functional automotive designs that utilize properties from the natural world.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server collects characteristic data of plants and animals from the internet. Specifically, it obtains characteristic information from biological databases and academic papers, organizes this data, and stores it in a database. The input is biological characteristic information obtained from the internet, and the output is structural characteristic information registered in the characteristic database on the server. In this process, the server performs data cleansing and format conversion to ensure the accuracy and consistency of the information.

[0077] Step 2:

[0078] The user uses a terminal to input requirements for automotive design. These inputs include design goals and specifications specified by the user, such as "I want to improve aerodynamics." The information entered on the terminal is sent to a server, and the user's requests are registered on the server as output. The terminal features an intuitive UI, allowing users to easily input the necessary information.

[0079] Step 3:

[0080] The server analyzes the user's requirements and selects suitable plant and animal characteristics from the database. It utilizes AI algorithms to extract characteristic information that best matches the input requirements. In this step, the user's design requirements and characteristic information from the database are used as input, and a list of selected characteristics is obtained as output. The server evaluates the relationship between requirements and characteristics and uses machine learning models to extract appropriate information.

[0081] Step 4:

[0082] The server generates car designs using a generative AI model based on selected characteristics. The input is the selected characteristics, and the output is the generated car design proposal. The generative AI model runs on design software and automatically generates designs that take into account the optimization of shape and materials. The server sends prompts to the generative AI model during this process to automate the design.

[0083] Step 5:

[0084] The server sends the generated design proposal to the user's device. Here, the input is the generated design proposal, and the output is the design proposal displayed on the user's device. The user reviews the design on their device and provides feedback. The server then receives this feedback and uses it to further optimize the design. The device is designed to allow users to submit feedback concisely.

[0085] (Application Example 1)

[0086] 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."

[0087] Conventional mechanical design methods have made it difficult to optimize energy efficiency and mechanical durability by fully utilizing properties derived from nature. In particular, in the design of robots used in factories, there is a need to create designs that can flexibly respond to diverse environments and working conditions, but current technology has not been able to fully achieve this.

[0088] 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.

[0089] In this invention, the server includes an information accumulating means having characteristic information of plants and animals, a means for acquiring design requirements from users, and a means for selecting the characteristics of plants and animals from the information accumulating means based on the design requirements. This makes it possible to efficiently generate designs for mechanical devices that utilize the characteristics of the natural world and to provide optimal designs under diverse conditions.

[0090] "Plant and animal characteristic information" refers to detailed data about the shape, structure, and function of plants and animals that exist in nature.

[0091] "Information gathering means" refers to a device or system for collecting and storing characteristic information of plants and animals, and making it accessible as needed.

[0092] A "user" is a person or group that operates the system and inputs specific design requirements.

[0093] "Design requirements" are specific conditions and goals regarding machinery and equipment as desired by the user, and include requirements related to design and function.

[0094] "Selection method" refers to a processing method or apparatus for extracting optimal characteristic information of plants and animals from information gathering means based on the user's design requirements.

[0095] "Means for generating mechanical device designs" refer to processes or systems that utilize characteristic information of selected plants and animals to concretize the shape and structure of a target mechanical device.

[0096] "Means of provision" refers to the mechanisms and methods for presenting the design of the generated machinery to the user.

[0097] The system that realizes this invention performs a process of generating mechanical device designs using characteristic information of plants and animals. The server collects characteristic information about plants and animals and builds a database as an information aggregation means. This database includes detailed data on shape and structural characteristics obtained from the natural world, such as animal durability.

[0098] Users input design requirements into the server using devices such as smartphones or smart glasses. These requirements include specific design goals aimed at improving particular functions or performance. The server then selects the optimal characteristics of plants and animals from its information aggregation system based on the user's input requirements.

[0099] Based on the selected characteristic information, the server invokes a generation AI model and automatically generates a design for the mechanical device. This design is optimized for the user's desired shape, energy efficiency, durability, and other factors. The generated design is provided to the user through an information transmission method, allowing the user to review the design and provide feedback as needed.

[0100] As a concrete example, a user might input a prompt message such as, "Generate a robot arm design based on the structural characteristics of nature to improve durability when working in high-temperature environments." In response, the server executes the process described above, generates a robot arm design based on the characteristics of appropriate plants and animals, and presents it to the user. This entire process is achieved through high-speed data processing on the server and design generation using an AI model.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The server collects characteristic information on plants and animals and builds a database as a means of information aggregation. The collected data is automatically retrieved using APIs and web crawlers. The input is data on the shape and structural characteristics of plants and animals in the natural world, which is classified, organized, and stored in the database. The output is a database in which characteristic information is systematically stored.

[0104] Step 2:

[0105] The user inputs design requirements using a terminal. These requirements include specific functional and performance targets, such as "improve durability"—specific design goals. The user's requirements are input and sent to the server. The output is the user's design requirements received by the server.

[0106] Step 3:

[0107] The server analyzes the received design requirements and, based on that, selects appropriate plant and animal characteristics from the information aggregation system. Using database queries, it searches for characteristics that match the input design requirements, and an AI algorithm determines the optimal characteristics. The output is information on the selected characteristics.

[0108] Step 4:

[0109] The server runs a generating AI model based on the selected characteristic information to generate a design for the machine. The input is the characteristic information selected in step 3, and the AI ​​model processes and calculates the data based on this to generate an optimized design proposal. The output is the generated design for the machine.

[0110] Step 5:

[0111] The server provides the generated design to the user's terminal. For information transmission, an interface is constructed to allow the user to visually confirm the design. The input is the generated design, and the output is the design proposal received by the user.

[0112] Step 6:

[0113] Users can review the provided designs and provide feedback. The input consists of user comments and suggestions for improvement on the generated design, while the output is the feedback information received by the server. This allows for further design improvements.

[0114] 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.

[0115] This invention relates to a system that generates automobile designs using characteristic data of plants and animals, combined with an emotion engine that recognizes user emotions. This system consists of a server, a terminal, a user, and an emotion engine.

[0116] First, the server builds a database that aggregates characteristic data of plants and animals. This database includes a wide range of biological characteristics, such as the aerodynamic properties of bird wing shapes and the water resistance properties of fish scale surface shapes.

[0117] When a user inputs automotive design requirements using a terminal, an emotion engine activates and simultaneously analyzes the user's emotions at the time of input. This emotion analysis is performed by recognizing and quantifying the user's emotions, such as joy, satisfaction, and dissatisfaction, in real time.

[0118] Next, the server receives the user's requirements and the results of the emotion engine's analysis, and proceeds to the process of extracting appropriate plant and animal characteristics from the database. By considering the output of the emotion engine, it becomes possible to prioritize design characteristics that the user feels they want.

[0119] Based on the extracted characteristics, the server uses a generative AI algorithm to create car design proposals. For example, a user with a high "excitement level" in the emotion analysis will be proposed a design incorporating sporty and advanced design elements.

[0120] The completed design proposal is sent from the server to the terminal, where the user reviews the design and sends further feedback via the emotion engine. This feedback includes emotional reactions to the design, and the system uses this to optimize the design.

[0121] In this way, a design feedback system that incorporates user emotions makes it possible to realize automotive designs with high emotional value that go beyond mere functional design.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server collects characteristic data on plants and animals, organizes detailed characteristic information, and builds a database. This is done based on scientific research data on the shape and characteristics of plants and animals.

[0125] Step 2:

[0126] The user inputs the design requirements for the car using a terminal. During this process, an emotion engine analyzes the user's facial expressions and tone of voice, analyzing their emotional state in real time.

[0127] Step 3:

[0128] The emotion engine sends user emotion data to the server. This data includes numerical representations of the user's level of excitement, satisfaction, and stress.

[0129] Step 4:

[0130] The server processes user requirements and emotional data, and extracts relevant plant and animal characteristics from the database. Based on the emotional data, it prioritizes and selects the characteristics that best match the user's emotional state.

[0131] Step 5:

[0132] The server executes a generation AI algorithm based on selected characteristics to generate car designs. During this generation process, the design is adjusted while taking emotional data into consideration.

[0133] Step 6:

[0134] The server sends the generated design to the user's device and presents it to the user. The user can view the design and provide feedback.

[0135] Step 7:

[0136] Users provide feedback through their devices, expressing emotional reactions and opinions on the design. This feedback is then processed again through an emotion engine, which adds emotional data to it.

[0137] Step 8:

[0138] The server re-evaluates and optimizes the design based on user feedback. It then re-considers sentiment data and regenerates the optimized design.

[0139] Step 9:

[0140] The server provides users with an optimized design, ensuring that this improves user satisfaction. Design improvements are continuously made through a user feedback cycle.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] Conventional vehicle design systems have problems with incorporating the characteristics of plants and animals into designs, and with generating designs that adequately consider user emotions. Therefore, there is a need for technology that enhances emotional value and efficiently generates designs that meet the individual needs of users.

[0144] 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.

[0145] In this invention, the server includes means for constructing an information repository that stores characteristic information of plants and animals, means for obtaining conditions from the user, and means for extracting the characteristics of plants and animals from the information repository based on the conditions. This makes it possible to generate vehicle designs that apply the characteristics of plants and animals that reflect the user's emotions.

[0146] "Personal information on plants and animals" refers to specific data and attributes concerning the shape, function, and performance of animals and plants.

[0147] An "information repository" is a database that efficiently stores data and information, and allows for quick access when needed.

[0148] "User requirements" refers to information that includes the user's requests and preferences regarding the design they provide.

[0149] An "emotion analysis engine" is an algorithm or system used to recognize and quantify a user's emotions.

[0150] A "generative AI model" refers to an artificial intelligence algorithm that uses data to generate new designs and ideas.

[0151] This invention is a system for generating vehicle designs using characteristic information of plants and animals, and in particular, it enables the generation of designs that take into account the user's emotions. The system consists of a server, a terminal, a user, and an emotion analysis engine.

[0152] The server constructs an information repository for storing characteristic information about plants and animals. This repository contains characteristic data on the shape and function of living organisms. The server organizes this data by categorizing it according to the shape and performance attributes specific to plants and animals.

[0153] The user inputs vehicle design requirements using a terminal. As the user inputs their design preferences and requirements, an emotion analysis engine built into the terminal simultaneously evaluates the user's emotions in real time. This evaluation utilizes facial recognition technology and voice analysis technology.

[0154] Next, the server receives the user's input conditions and the results of the sentiment analysis engine, and based on that, extracts the most suitable animal and plant characteristics from its database. The extracted characteristics are then used to generate design proposals using a generative AI model. The generative AI model uses prompts such as "Please propose an elegant and dynamic vehicle design" to generate a new design using the characteristic data.

[0155] The generated design proposals are sent from the server to the terminal, where the user reviews them and sends feedback via an emotion analysis engine. For example, the user determines whether the design meets their expectations and communicates that reaction to the system as feedback. The system then uses this feedback to further optimize the design.

[0156] This system enables the creation of vehicle designs with high emotional value that prioritize user emotions and reflect the characteristics of plants and animals.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The server collects characteristic information on plants and animals to build an information repository. Here, data is gathered from scientific databases and research papers, and characteristic information about the shape and performance of plants and animals is classified and organized. The input is biological characteristic data, and the output is a usable database. This database serves as the foundational material for design generation by AI models.

[0160] Step 2:

[0161] The user inputs design requirements using a terminal. This input includes requests regarding the car's style, functions, and color. The input data is sent to the server in text format. The output here is data that conveys the user requirements to the server.

[0162] Step 3:

[0163] The emotion analysis engine built into the device analyzes the emotions the user experiences during input. Using a facial recognition camera and voice sensor, the user's emotions are measured and quantified in real time. In this step, the input is the user's facial expressions and voice, and the output is quantified emotion data.

[0164] Step 4:

[0165] The server receives user conditions and sentiment data and extracts appropriate plant and animal characteristics from its database. The input is user conditions and sentiment data, and the server processes the selection of plant and animal characteristic data through database searches. The output is the characteristic data necessary for design generation.

[0166] Step 5:

[0167] The server generates design proposals using a generative AI model. Here, instructions are given to the AI ​​using prompts based on the characteristic data obtained in the previous step. The input is the prompt "Please propose an elegant and dynamic vehicle design" and characteristic data, and the output is an automatically generated design proposal.

[0168] Step 6:

[0169] The generated design proposal is sent from the server to the terminal. The user reviews the design proposal and provides feedback. The input is the design proposal, and the output is the user's feedback.

[0170] Step 7:

[0171] The server receives user feedback and sentiment ratings to optimize the design proposal. Optimization involves a regeneration process that incorporates feedback to create a design closer to the user's requirements. The input is feedback data, and the output is the optimized design proposal.

[0172] (Application Example 2)

[0173] 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".

[0174] In generating automotive designs, the challenge lies in providing new emotional value that cannot be obtained with conventional design methods by incorporating information on the characteristics of plants and animals while reflecting the emotional state of the user. Furthermore, there is a need to build a system that supports a process of appropriately feeding back the generated designs and optimizing them based on that feedback.

[0175] 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.

[0176] In this invention, the server includes means for constructing an information source having characteristic information of plants and animals, means for obtaining requirements from a user, means for extracting the characteristics of plants and animals from the information source based on the requirements, means for analyzing the user's emotional state, means for improving the design generated based on the emotional state, and means for dynamically changing the vehicle display according to the user's emotional state using a display device for an autonomous vehicle. This makes it possible to generate highly emotionally valuable automobile designs that reflect the user's emotions and to dynamically optimize them.

[0177] "Personal information on plants and animals" refers to information about the physical or chemical properties of plants and animals, such as the aerodynamic characteristics of a bird's wing shape or the water resistance characteristics of a fish's scale surface shape.

[0178] "Information source" refers to a database or similar storage method for storing characteristic information about plants and animals, and for extracting or providing that information as needed.

[0179] "User" refers to an individual or organization that uses the system to submit automotive design requirements and receives the resulting designs.

[0180] "Requirements" refer to information that indicates the conditions and specifications that the user desires for the design, and the direction of the design is determined based on these requirements.

[0181] "Extraction" is the process of selecting and obtaining specific elements or information from a series of pieces of information or data.

[0182] "Emotional state" refers to the user's psychological or emotional condition or reactions, and is analyzed through facial expressions, voice, body movements, etc.

[0183] "Improvement" refers to modifying or adding to existing designs or processes to give them better functionality or features.

[0184] A "display device" refers to hardware such as equipment or screens that provide visual information to the user.

[0185] "Dynamically changing" means that the displayed content and system operation are modified in real time according to the situation and conditions.

[0186] This system employs an approach that generates car designs based on information about the characteristics of plants and animals, and then refines them to reflect the user's emotional state. Specific implementations are described below.

[0187] The server first constructs an information source that stores characteristic information about plants and animals. This information source includes aerodynamic characteristics of bird wing shapes and water resistance characteristics of fish scales. Using this information source, the server extracts the appropriate characteristics of plants and animals based on the requirements obtained from the user.

[0188] Next, the device analyzes the user's emotional state. This emotional state is quantified by analyzing the user's facial expressions and voice in real time using an emotion engine. For example, image analysis is performed using software such as TENSORFLOW®, and if the user is relaxed, design elements that evoke a tranquil forest are extracted.

[0189] Based on the extracted characteristics, the server uses a generative AI model to generate car design proposals. These design proposals are provided to the user, who then provides feedback. This feedback includes emotional responses, and the system uses this to optimize the design, improving the generated design to better reflect the user's emotions.

[0190] For example, if a user is relaxed while with family, the dashboard display will show a calming green-based theme. The generative AI model adjusts the design theme based on the prompt, "Recognize the current emotional state and provide the optimal display theme."

[0191] In this way, a design generation process that combines the natural characteristics of plants and animals with the emotions of users makes it possible to provide automotive designs with new emotional value.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server aggregates characteristic information about plants and animals to build an information source. Various characteristic data about plants and animals are used as input. Based on this data, a database is created and organized so that characteristic information can be efficiently searched. The output is an information source containing the stored characteristic information.

[0195] Step 2:

[0196] The user uses a terminal to input the requirements for the car design. The input consists of data regarding the design specifications and conditions desired by the user. At this stage, the terminal receives the user's input and sends it to the server as requirements data. The output is the requirements data sent to the server.

[0197] Step 3:

[0198] The device analyzes the user's emotional state. The user's facial expressions and voice are captured in real time as input. Software such as TensorFlow is used to quantify the emotional state from this data. The output is the analyzed emotional data.

[0199] Step 4:

[0200] The server receives requirements and sentiment data and extracts appropriate plant and animal characteristics from the information source. User requirements and sentiment data are used as input. Relevant characteristics are extracted from the information source based on both requirements and sentiment and provided to the design process. The output is the extracted plant and animal characteristic data.

[0201] Step 5:

[0202] The server generates car design proposals using a generative AI model based on extracted characteristics. Inputs include characteristic data of plants and animals, and prompt statements (e.g., "Recognize the current emotional state and provide the optimal display theme"). The AI ​​model processes this information and creates design proposals that match user requirements and emotions. The output is the generated design proposal.

[0203] Step 6:

[0204] The user reviews the generated design proposal and submits feedback from their device. The input includes the user's emotional reactions and opinions, which are then transmitted from the device to a server where this feedback is used in the design optimization process. The output is the feedback data sent to the server.

[0205] Step 7:

[0206] The server improves and optimizes the design proposal based on feedback. User feedback data is used as input. By analyzing this feedback and adjusting the design as needed, user satisfaction is further enhanced. The output is the optimized design proposal.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] This invention relates to a system for generating automobile designs using characteristic data of plants and animals. The system consists of a server, a terminal, and a user.

[0224] First, the server collects detailed data on the characteristics of plants and animals and builds a database of that data. This data includes structural characteristics found in nature, such as the shape and properties of fish scales, or the strength and elasticity of bird wings.

[0225] Next, the user inputs the requirements for the car design through the terminal. This includes specific design goals and functional requirements, such as "I want to improve aerodynamics."

[0226] The server then analyzes these requirements and extracts suitable plant and animal characteristics from the database. Using an AI algorithm, the server selects the optimal characteristics that best match the user's requirements.

[0227] Based on these characteristic selections, the server executes a generation AI to create automobile design proposals. The generated designs are optimized in terms of shape, material selection, energy efficiency, and other aspects, providing an appropriate response to the user's requirements.

[0228] For example, if a user inputs the requirement "I want to reduce air resistance," the server will utilize the streamlined characteristics found in fish scales to design a vehicle structure with superior aerodynamics.

[0229] Finally, the generated design is delivered from the server to the user's terminal. The user can review the design and provide feedback as needed, enabling further optimization. This interactive process results in innovative and practical automotive designs.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server collects data on the characteristics of plants and animals. This involves organizing and integrating information from various nature observation databases and research data. The information stored in the database provides detailed descriptions of the physical and structural characteristics of specific plants and animals.

[0233] Step 2:

[0234] Users input their automotive design requirements using a terminal. For example, they specify concrete design conditions such as reducing air resistance at a particular speed or improving safety performance.

[0235] Step 3:

[0236] The server receives and analyzes the requirements entered by the user. Next, it extracts relevant plant and animal characteristics from the database and evaluates how these characteristics match the user requirements. This evaluation is performed using a specific algorithm.

[0237] Step 4:

[0238] The server uses the extracted characteristics to run a generative AI algorithm to generate car designs. The algorithm creates an optimal design proposal while considering the characteristics. This design proposal includes shape, material selection, and engineering considerations.

[0239] Step 5:

[0240] The server sends the generated design to the user's terminal. Here, the user reviews the design proposal and determines whether the car design meets their requirements.

[0241] Step 6:

[0242] Users can provide feedback on the design. This feedback may include requests for new improvements or ideas for fine-tuning.

[0243] Step 7:

[0244] The server implements a process of readjusting the design based on user feedback. This results in an optimized design being regenerated and presented to the user again. This cyclical process improves both the accuracy of the design and user satisfaction.

[0245] (Example 1)

[0246] 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."

[0247] Traditional automotive design processes often lack novelty and innovation because they rely primarily on human experience and intuition. Furthermore, they struggle to quickly generate optimal designs that meet functional requirements. Against this backdrop, there is a need for a system that leverages characteristics derived from plants and animals in nature to efficiently generate innovative and functional automotive designs, and that can flexibly optimize them according to user demands.

[0248] 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.

[0249] In this invention, the server includes means for collecting information relating to the characteristics of plants and animals, data storage means for organizing and accumulating the said information, and means for obtaining design goals from users. This makes it possible to efficiently generate novel and functional automobile designs that incorporate characteristics of the natural world.

[0250] "Characteristics of plants and animals" refer to structural or functional features derived from plants and animals that exist in nature.

[0251] "Means of collecting information" refers to a function or device for obtaining data on the characteristics of plants and animals from external sources.

[0252] "Data storage means" refers to a function or device that organizes collected information and records it so that it can be efficiently searched and used later.

[0253] "Means of obtaining design goals from users" refer to interfaces or functions for users to input their requirements and goals regarding the design of the product they desire.

[0254] "Means for automatically generating design proposals" refers to a function or process in which a computer program creates an automobile design plan based on the characteristics of selected plants and animals.

[0255] "Means of providing to users" refers to a function or device that presents the generated design proposals so that users can view and evaluate them.

[0256] "Means for obtaining opinions and improving the design proposal" refers to a function or process for collecting user feedback on the generated design proposal and improving the design based on that information.

[0257] "Means of improvement to meet specific functional requirements" refers to a function or process that adjusts a generated design proposal to meet the requirements of a specific business or function.

[0258] This invention is a system for generating automobile designs using characteristic data of plants and animals, and mainly consists of a server, terminals, and users. The server collects and organizes characteristic data on plants and animals and builds a database. This data is obtained from publicly available literature and databases via the internet. The database includes characteristic information from the natural world, such as the shape of fish scales and the strength of bird wings. The server uses hardware with high-performance data processing capabilities, and is equipped with AI algorithms for data analysis.

[0259] The user inputs specific requirements for the automobile design using a terminal. The terminal receives the design requirements from the user and sends them to the server. This terminal is a typical computer or mobile device and has an intuitive interface that the user can operate.

[0260] The server analyzes the user's requirements and selects the relevant characteristic data from the database. This process utilizes AI algorithms to select the necessary plant and animal characteristics to generate the optimal design. The generated design is automatically created using a generative AI model. This generative AI model runs on advanced design software, providing a design that considers shape optimization and material selection.

[0261] For example, if a user inputs the requirement "I want to reduce air resistance," the server will utilize the streamlined characteristics of a fish to generate an aerodynamically superior design. As an example of a prompt, a user could input "Please design a car body with streamlined characteristics."

[0262] The generated design is sent from the server to the terminal for user review. User feedback is incorporated into the system for further design optimization. This entire process efficiently generates innovative and functional automotive designs that utilize properties from the natural world.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The server collects characteristic data of plants and animals from the internet. Specifically, it obtains characteristic information from biological databases and academic papers, organizes this data, and stores it in a database. The input is biological characteristic information obtained from the internet, and the output is structural characteristic information registered in the characteristic database on the server. In this process, the server performs data cleansing and format conversion to ensure the accuracy and consistency of the information.

[0266] Step 2:

[0267] The user uses a terminal to input requirements for automotive design. These inputs include design goals and specifications specified by the user, such as "I want to improve aerodynamics." The information entered on the terminal is sent to a server, and the user's requests are registered on the server as output. The terminal features an intuitive UI, allowing users to easily input the necessary information.

[0268] Step 3:

[0269] The server analyzes the user's requirements and selects suitable plant and animal characteristics from the database. It utilizes AI algorithms to extract characteristic information that best matches the input requirements. In this step, the user's design requirements and characteristic information from the database are used as input, and a list of selected characteristics is obtained as output. The server evaluates the relationship between requirements and characteristics and uses machine learning models to extract appropriate information.

[0270] Step 4:

[0271] The server generates car designs using a generative AI model based on selected characteristics. The input is the selected characteristics, and the output is the generated car design proposal. The generative AI model runs on design software and automatically generates designs that take into account the optimization of shape and materials. The server sends prompts to the generative AI model during this process to automate the design.

[0272] Step 5:

[0273] The server sends the generated design proposal to the user's device. Here, the input is the generated design proposal, and the output is the design proposal displayed on the user's device. The user reviews the design on their device and provides feedback. The server then receives this feedback and uses it to further optimize the design. The device is designed to allow users to submit feedback concisely.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] Conventional mechanical design methods have made it difficult to optimize energy efficiency and mechanical durability by fully utilizing properties derived from nature. In particular, in the design of robots used in factories, there is a need to create designs that can flexibly respond to diverse environments and working conditions, but current technology has not been able to fully achieve this.

[0277] 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.

[0278] In this invention, the server includes an information accumulating means having characteristic information of plants and animals, a means for acquiring design requirements from users, and a means for selecting the characteristics of plants and animals from the information accumulating means based on the design requirements. This makes it possible to efficiently generate designs for mechanical devices that utilize the characteristics of the natural world and to provide optimal designs under diverse conditions.

[0279] "Plant and animal characteristic information" refers to detailed data about the shape, structure, and function of plants and animals that exist in nature.

[0280] "Information gathering means" refers to a device or system for collecting and storing characteristic information of plants and animals, and making it accessible as needed.

[0281] A "user" is a person or group that operates the system and inputs specific design requirements.

[0282] "Design requirements" are specific conditions and goals regarding machinery and equipment as desired by the user, and include requirements related to design and function.

[0283] The "means of selection" is a processing method or device for extracting the optimal characteristic information of animals and plants from the information integration means based on the design requirements of the user..

[0284] The "means of generating the design of the mechanical device" is a process or system for specifying the shape and structure of the target mechanical device by using the characteristic information of the selected animals and plants..

[0285] The "means of providing" is a mechanism or method for presenting the generated design of the mechanical device to the user..

[0286] The system that realizes this invention executes a process of generating the design of the mechanical device by using the characteristic information of animals and plants. The server collects the characteristic information related to animals and plants and constructs a database as the information integration means. This database contains the characteristics of the shape and structure obtained from nature, such as detailed data on the durability of animals..

[0287] The user inputs the design requirements to the server by using a terminal such as a smartphone or smart glasses. This includes specific design goals aimed at improving certain functions and performances. The server selects the optimal characteristics of animals and plants based on the requirements input by the user from the information integration means..

[0288] Based on the selected characteristic information, the server calls the generation AI model and automatically generates the design of the mechanical device. This design is optimized in terms of the shape, energy efficiency, durability, etc. that the user desires. The generated design is provided to the user through the information transmission means, and the user can confirm the design and provide feedback if necessary..

[0289] As a concrete example, a user might input a prompt message such as, "Generate a robot arm design based on the structural characteristics of nature to improve durability when working in high-temperature environments." In response, the server executes the process described above, generates a robot arm design based on the characteristics of appropriate plants and animals, and presents it to the user. This entire process is achieved through high-speed data processing on the server and design generation using an AI model.

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The server collects characteristic information on plants and animals and builds a database as a means of information aggregation. The collected data is automatically retrieved using APIs and web crawlers. The input is data on the shape and structural characteristics of plants and animals in the natural world, which is classified, organized, and stored in the database. The output is a database in which characteristic information is systematically stored.

[0293] Step 2:

[0294] The user inputs design requirements using a terminal. These requirements include specific functional and performance targets, such as "improve durability"—specific design goals. The user's requirements are input and sent to the server. The output is the user's design requirements received by the server.

[0295] Step 3:

[0296] The server analyzes the received design requirements and, based on that, selects appropriate plant and animal characteristics from the information aggregation system. Using database queries, it searches for characteristics that match the input design requirements, and an AI algorithm determines the optimal characteristics. The output is information on the selected characteristics.

[0297] Step 4:

[0298] The server runs a generating AI model based on the selected characteristic information to generate a design for the machine. The input is the characteristic information selected in step 3, and the AI ​​model processes and calculates the data based on this to generate an optimized design proposal. The output is the generated design for the machine.

[0299] Step 5:

[0300] The server provides the generated design to the user's terminal. For information transmission, an interface is constructed to allow the user to visually confirm the design. The input is the generated design, and the output is the design proposal received by the user.

[0301] Step 6:

[0302] Users can review the provided designs and provide feedback. The input consists of user comments and suggestions for improvement on the generated design, while the output is the feedback information received by the server. This allows for further design improvements.

[0303] 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.

[0304] This invention relates to a system that generates automobile designs using characteristic data of plants and animals, combined with an emotion engine that recognizes user emotions. This system consists of a server, a terminal, a user, and an emotion engine.

[0305] First, the server builds a database that aggregates characteristic data of plants and animals. This database includes a wide range of biological characteristics, such as the aerodynamic properties of bird wing shapes and the water resistance properties of fish scale surface shapes.

[0306] When the user inputs the requirements for automobile design using a terminal, the emotion engine operates and simultaneously analyzes the emotion of the user at the time of input. This emotion analysis is performed by recognizing and quantifying emotions such as the user's joy, satisfaction, and dissatisfaction in real time.

[0307] Next, the server receives the user's requirements and the analysis results of the emotion engine, and proceeds to the process of extracting appropriate characteristics of animals and plants from the database. By considering the output of the emotion engine, it becomes possible to prioritize the design characteristics that the user feels are desired.

[0308] Based on the extracted characteristics, the server uses a generative AI algorithm to create a design proposal for the automobile. For example, for a user with a high "excitement level" in the emotion analysis, a design incorporating sporty and advanced design elements is proposed.

[0309] The completed design proposal is sent from the server to the terminal, and the user checks the design and sends further feedback via the emotion engine. This feedback includes an emotional reaction to the design, and the system performs an optimization process for the design based on this.

[0310] In this way, the design feedback system that incorporates the user's emotions enables the realization of an automobile design with high emotional value that goes beyond mere functional design.

[0311] The following explains the processing flow.

[0312] Step 1:

[0313] The server collects characteristics data of animals and plants, organizes detailed characteristic information, and constructs a database. This is done based on scientific research data on the shapes and characteristics of animals and plants.

[0314] Step 2:

[0315] The user inputs the design requirements for the car using a terminal. During this process, an emotion engine analyzes the user's facial expressions and tone of voice, analyzing their emotional state in real time.

[0316] Step 3:

[0317] The emotion engine sends user emotion data to the server. This data includes numerical representations of the user's level of excitement, satisfaction, and stress.

[0318] Step 4:

[0319] The server processes user requirements and emotional data, and extracts relevant plant and animal characteristics from the database. Based on the emotional data, it prioritizes and selects the characteristics that best match the user's emotional state.

[0320] Step 5:

[0321] The server executes a generation AI algorithm based on selected characteristics to generate car designs. During this generation process, the design is adjusted while taking emotional data into consideration.

[0322] Step 6:

[0323] The server sends the generated design to the user's device and presents it to the user. The user can view the design and provide feedback.

[0324] Step 7:

[0325] Users provide feedback through their devices, expressing emotional reactions and opinions on the design. This feedback is then processed again through an emotion engine, which adds emotional data to it.

[0326] Step 8:

[0327] The server re-evaluates and optimizes the design based on user feedback. It then re-considers sentiment data and regenerates the optimized design.

[0328] Step 9:

[0329] The server provides users with an optimized design, ensuring that this improves user satisfaction. Design improvements are continuously made through a user feedback cycle.

[0330] (Example 2)

[0331] 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".

[0332] Conventional vehicle design systems have problems with incorporating the characteristics of plants and animals into designs, and with generating designs that adequately consider user emotions. Therefore, there is a need for technology that enhances emotional value and efficiently generates designs that meet the individual needs of users.

[0333] 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.

[0334] In this invention, the server includes means for constructing an information repository that stores characteristic information of plants and animals, means for obtaining conditions from the user, and means for extracting the characteristics of plants and animals from the information repository based on the conditions. This makes it possible to generate vehicle designs that apply the characteristics of plants and animals that reflect the user's emotions.

[0335] "Personal information on plants and animals" refers to specific data and attributes concerning the shape, function, and performance of animals and plants.

[0336] An "information repository" is a database that efficiently stores data and information, and allows for quick access when needed.

[0337] "User requirements" refers to information that includes the user's requests and preferences regarding the design they provide.

[0338] An "emotion analysis engine" is an algorithm or system used to recognize and quantify a user's emotions.

[0339] A "generative AI model" refers to an artificial intelligence algorithm that uses data to generate new designs and ideas.

[0340] This invention is a system for generating vehicle designs using characteristic information of plants and animals, and in particular, it enables the generation of designs that take into account the user's emotions. The system consists of a server, a terminal, a user, and an emotion analysis engine.

[0341] The server constructs an information repository for storing characteristic information about plants and animals. This repository contains characteristic data on the shape and function of living organisms. The server organizes this data by categorizing it according to the shape and performance attributes specific to plants and animals.

[0342] The user inputs vehicle design requirements using a terminal. As the user inputs their design preferences and requirements, an emotion analysis engine built into the terminal simultaneously evaluates the user's emotions in real time. This evaluation utilizes facial recognition technology and voice analysis technology.

[0343] Next, the server receives the user's input conditions and the results of the sentiment analysis engine, and based on that, extracts the most suitable animal and plant characteristics from its database. The extracted characteristics are then used to generate design proposals using a generative AI model. The generative AI model uses prompts such as "Please propose an elegant and dynamic vehicle design" to generate a new design using the characteristic data.

[0344] The generated design proposals are sent from the server to the terminal, where the user reviews them and sends feedback via an emotion analysis engine. For example, the user determines whether the design meets their expectations and communicates that reaction to the system as feedback. The system then uses this feedback to further optimize the design.

[0345] This system enables the creation of vehicle designs with high emotional value that prioritize user emotions and reflect the characteristics of plants and animals.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The server collects characteristic information on plants and animals to build an information repository. Here, data is gathered from scientific databases and research papers, and characteristic information about the shape and performance of plants and animals is classified and organized. The input is biological characteristic data, and the output is a usable database. This database serves as the foundational material for design generation by AI models.

[0349] Step 2:

[0350] The user inputs design requirements using a terminal. This input includes requests regarding the car's style, functions, and color. The input data is sent to the server in text format. The output here is data that conveys the user requirements to the server.

[0351] Step 3:

[0352] The emotion analysis engine built into the device analyzes the emotions the user experiences during input. Using a facial recognition camera and voice sensor, the user's emotions are measured and quantified in real time. In this step, the input is the user's facial expressions and voice, and the output is quantified emotion data.

[0353] Step 4:

[0354] The server receives user conditions and sentiment data and extracts appropriate plant and animal characteristics from its database. The input is user conditions and sentiment data, and the server processes the selection of plant and animal characteristic data through database searches. The output is the characteristic data necessary for design generation.

[0355] Step 5:

[0356] The server generates design proposals using a generative AI model. Here, instructions are given to the AI ​​using prompts based on the characteristic data obtained in the previous step. The input is the prompt "Please propose an elegant and dynamic vehicle design" and characteristic data, and the output is an automatically generated design proposal.

[0357] Step 6:

[0358] The generated design proposal is sent from the server to the terminal. The user reviews the design proposal and provides feedback. The input is the design proposal, and the output is the user's feedback.

[0359] Step 7:

[0360] The server receives user feedback and sentiment ratings to optimize the design proposal. Optimization involves a regeneration process that incorporates feedback to create a design closer to the user's requirements. The input is feedback data, and the output is the optimized design proposal.

[0361] (Application Example 2)

[0362] 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 as the "terminal".

[0363] In generating automotive designs, the challenge lies in providing new emotional value that cannot be obtained with conventional design methods by incorporating information on the characteristics of plants and animals while reflecting the emotional state of the user. Furthermore, there is a need to build a system that supports a process of appropriately feeding back the generated designs and optimizing them based on that feedback.

[0364] 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.

[0365] In this invention, the server includes means for constructing an information source having characteristic information of plants and animals, means for obtaining requirements from a user, means for extracting the characteristics of plants and animals from the information source based on the requirements, means for analyzing the user's emotional state, means for improving the design generated based on the emotional state, and means for dynamically changing the vehicle display according to the user's emotional state using a display device for an autonomous vehicle. This makes it possible to generate highly emotionally valuable automobile designs that reflect the user's emotions and to dynamically optimize them.

[0366] "Personal information on plants and animals" refers to information about the physical or chemical properties of plants and animals, such as the aerodynamic characteristics of a bird's wing shape or the water resistance characteristics of a fish's scale surface shape.

[0367] "Information source" refers to a database or similar storage method for storing characteristic information about plants and animals, and for extracting or providing that information as needed.

[0368] "User" refers to an individual or organization that uses the system to submit automotive design requirements and receives the resulting designs.

[0369] "Requirements" refer to information that indicates the conditions and specifications that the user desires for the design, and the direction of the design is determined based on these requirements.

[0370] "Extraction" is the process of selecting and obtaining specific elements or information from a series of pieces of information or data.

[0371] "Emotional state" refers to the user's psychological or emotional condition or reactions, and is analyzed through facial expressions, voice, body movements, etc.

[0372] "Improvement" refers to modifying or adding to existing designs or processes to give them better functionality or features.

[0373] A "display device" refers to hardware such as equipment or screens that provide visual information to the user.

[0374] "Dynamically changing" means that the displayed content and system operation are modified in real time according to the situation and conditions.

[0375] This system employs an approach that generates car designs based on information about the characteristics of plants and animals, and then refines them to reflect the user's emotional state. Specific implementations are described below.

[0376] The server first constructs an information source that stores characteristic information about plants and animals. This information source includes aerodynamic characteristics of bird wing shapes and water resistance characteristics of fish scales. Using this information source, the server extracts the appropriate characteristics of plants and animals based on the requirements obtained from the user.

[0377] Next, the device analyzes the user's emotional state. This emotional state is quantified by analyzing the user's facial expressions and voice in real time using an emotion engine. For example, image analysis is performed using software such as TensorFlow, and if the user is relaxed, design elements that evoke a tranquil forest are extracted.

[0378] Based on the extracted characteristics, the server uses a generative AI model to generate car design proposals. These design proposals are provided to the user, who then provides feedback. This feedback includes emotional responses, and the system uses this to optimize the design, improving the generated design to better reflect the user's emotions.

[0379] For example, if a user is relaxed while with family, the dashboard display will show a calming green-based theme. The generative AI model adjusts the design theme based on the prompt, "Recognize the current emotional state and provide the optimal display theme."

[0380] In this way, a design generation process that combines the natural characteristics of plants and animals with the emotions of users makes it possible to provide automotive designs with new emotional value.

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The server aggregates characteristic information about plants and animals to build an information source. Various characteristic data about plants and animals are used as input. Based on this data, a database is created and organized so that characteristic information can be efficiently searched. The output is an information source containing the stored characteristic information.

[0384] Step 2:

[0385] The user uses a terminal to input the requirements for the car design. The input consists of data regarding the design specifications and conditions desired by the user. At this stage, the terminal receives the user's input and sends it to the server as requirements data. The output is the requirements data sent to the server.

[0386] Step 3:

[0387] The device analyzes the user's emotional state. The user's facial expressions and voice are captured in real time as input. Software such as TensorFlow is used to quantify the emotional state from this data. The output is the analyzed emotional data.

[0388] Step 4:

[0389] The server receives requirements and sentiment data and extracts appropriate plant and animal characteristics from the information source. User requirements and sentiment data are used as input. Relevant characteristics are extracted from the information source based on both requirements and sentiment and provided to the design process. The output is the extracted plant and animal characteristic data.

[0390] Step 5:

[0391] The server generates car design proposals using a generative AI model based on extracted characteristics. Inputs include characteristic data of plants and animals, and prompt statements (e.g., "Recognize the current emotional state and provide the optimal display theme"). The AI ​​model processes this information and creates design proposals that match user requirements and emotions. The output is the generated design proposal.

[0392] Step 6:

[0393] The user reviews the generated design proposal and submits feedback from their device. The input includes the user's emotional reactions and opinions, which are then transmitted from the device to a server where this feedback is used in the design optimization process. The output is the feedback data sent to the server.

[0394] Step 7:

[0395] The server improves and optimizes the design proposal based on feedback. User feedback data is used as input. By analyzing this feedback and adjusting the design as needed, user satisfaction is further enhanced. The output is the optimized design proposal.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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".

[0412] This invention relates to a system for generating automobile designs using characteristic data of plants and animals. The system consists of a server, a terminal, and a user.

[0413] First, the server collects detailed data on the characteristics of plants and animals and builds a database of that data. This data includes structural characteristics found in nature, such as the shape and properties of fish scales, or the strength and elasticity of bird wings.

[0414] Next, the user inputs the requirements for the car design through the terminal. This includes specific design goals and functional requirements, such as "I want to improve aerodynamics."

[0415] The server then analyzes these requirements and extracts suitable plant and animal characteristics from the database. Using an AI algorithm, the server selects the optimal characteristics that best match the user's requirements.

[0416] Based on these characteristic selections, the server executes a generation AI to create automobile design proposals. The generated designs are optimized in terms of shape, material selection, energy efficiency, and other aspects, providing an appropriate response to the user's requirements.

[0417] For example, if a user inputs the requirement "I want to reduce air resistance," the server will utilize the streamlined characteristics found in fish scales to design a vehicle structure with superior aerodynamics.

[0418] Finally, the generated design is delivered from the server to the user's terminal. The user can review the design and provide feedback as needed, enabling further optimization. This interactive process results in innovative and practical automotive designs.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The server collects data on the characteristics of plants and animals. This involves organizing and integrating information from various nature observation databases and research data. The information stored in the database provides detailed descriptions of the physical and structural characteristics of specific plants and animals.

[0422] Step 2:

[0423] Users input their automotive design requirements using a terminal. For example, they specify concrete design conditions such as reducing air resistance at a particular speed or improving safety performance.

[0424] Step 3:

[0425] The server receives and analyzes the requirements entered by the user. Next, it extracts relevant plant and animal characteristics from the database and evaluates how these characteristics match the user requirements. This evaluation is performed using a specific algorithm.

[0426] Step 4:

[0427] The server uses the extracted characteristics to run a generative AI algorithm to generate car designs. The algorithm creates an optimal design proposal while considering the characteristics. This design proposal includes shape, material selection, and engineering considerations.

[0428] Step 5:

[0429] The server sends the generated design to the user's terminal. Here, the user reviews the design proposal and determines whether the car design meets their requirements.

[0430] Step 6:

[0431] Users can provide feedback on the design. This feedback may include requests for new improvements or ideas for fine-tuning.

[0432] Step 7:

[0433] The server implements a process of readjusting the design based on user feedback. This results in an optimized design being regenerated and presented to the user again. This cyclical process improves both the accuracy of the design and user satisfaction.

[0434] (Example 1)

[0435] 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."

[0436] Traditional automotive design processes often lack novelty and innovation because they rely primarily on human experience and intuition. Furthermore, they struggle to quickly generate optimal designs that meet functional requirements. Against this backdrop, there is a need for a system that leverages characteristics derived from plants and animals in nature to efficiently generate innovative and functional automotive designs, and that can flexibly optimize them according to user demands.

[0437] 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.

[0438] In this invention, the server includes means for collecting information relating to the characteristics of plants and animals, data storage means for organizing and accumulating the said information, and means for obtaining design goals from users. This makes it possible to efficiently generate novel and functional automobile designs that incorporate characteristics of the natural world.

[0439] "Characteristics of plants and animals" refer to structural or functional features derived from plants and animals that exist in nature.

[0440] "Means of collecting information" refers to a function or device for obtaining data on the characteristics of plants and animals from external sources.

[0441] "Data storage means" refers to a function or device that organizes collected information and records it so that it can be efficiently searched and used later.

[0442] "Means of obtaining design goals from users" refer to interfaces or functions for users to input their requirements and goals regarding the design of the product they desire.

[0443] "Means for automatically generating design proposals" refers to a function or process in which a computer program creates an automobile design plan based on the characteristics of selected plants and animals.

[0444] "Means of providing to users" refers to a function or device that presents the generated design proposals so that users can view and evaluate them.

[0445] "Means for obtaining opinions and improving the design proposal" refers to a function or process for collecting user feedback on the generated design proposal and improving the design based on that information.

[0446] "Means of improvement to meet specific functional requirements" refers to a function or process that adjusts a generated design proposal to meet the requirements of a specific business or function.

[0447] This invention is a system for generating automobile designs using characteristic data of plants and animals, and mainly consists of a server, terminals, and users. The server collects and organizes characteristic data on plants and animals and builds a database. This data is obtained from publicly available literature and databases via the internet. The database includes characteristic information from the natural world, such as the shape of fish scales and the strength of bird wings. The server uses hardware with high-performance data processing capabilities, and is equipped with AI algorithms for data analysis.

[0448] The user inputs specific requirements for the automobile design using a terminal. The terminal receives the design requirements from the user and sends them to the server. This terminal is a typical computer or mobile device and has an intuitive interface that the user can operate.

[0449] The server analyzes the user's requirements and selects the relevant characteristic data from the database. This process utilizes AI algorithms to select the necessary plant and animal characteristics to generate the optimal design. The generated design is automatically created using a generative AI model. This generative AI model runs on advanced design software, providing a design that considers shape optimization and material selection.

[0450] For example, if a user inputs the requirement "I want to reduce air resistance," the server will utilize the streamlined characteristics of a fish to generate an aerodynamically superior design. As an example of a prompt, a user could input "Please design a car body with streamlined characteristics."

[0451] The generated design is sent from the server to the terminal for user review. User feedback is incorporated into the system for further design optimization. This entire process efficiently generates innovative and functional automotive designs that utilize properties from the natural world.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The server collects characteristic data of plants and animals from the internet. Specifically, it obtains characteristic information from biological databases and academic papers, organizes this data, and stores it in a database. The input is biological characteristic information obtained from the internet, and the output is structural characteristic information registered in the characteristic database on the server. In this process, the server performs data cleansing and format conversion to ensure the accuracy and consistency of the information.

[0455] Step 2:

[0456] The user uses a terminal to input requirements for automotive design. These inputs include design goals and specifications specified by the user, such as "I want to improve aerodynamics." The information entered on the terminal is sent to a server, and the user's requests are registered on the server as output. The terminal features an intuitive UI, allowing users to easily input the necessary information.

[0457] Step 3:

[0458] The server analyzes the user's requirements and selects suitable plant and animal characteristics from the database. It utilizes AI algorithms to extract characteristic information that best matches the input requirements. In this step, the user's design requirements and characteristic information from the database are used as input, and a list of selected characteristics is obtained as output. The server evaluates the relationship between requirements and characteristics and uses machine learning models to extract appropriate information.

[0459] Step 4:

[0460] The server generates car designs using a generative AI model based on selected characteristics. The input is the selected characteristics, and the output is the generated car design proposal. The generative AI model runs on design software and automatically generates designs that take into account the optimization of shape and materials. The server sends prompts to the generative AI model during this process to automate the design.

[0461] Step 5:

[0462] The server sends the generated design proposal to the user's device. Here, the input is the generated design proposal, and the output is the design proposal displayed on the user's device. The user reviews the design on their device and provides feedback. The server then receives this feedback and uses it to further optimize the design. The device is designed to allow users to submit feedback concisely.

[0463] (Application Example 1)

[0464] 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."

[0465] Conventional mechanical design methods have made it difficult to optimize energy efficiency and mechanical durability by fully utilizing properties derived from nature. In particular, in the design of robots used in factories, there is a need to create designs that can flexibly respond to diverse environments and working conditions, but current technology has not been able to fully achieve this.

[0466] 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.

[0467] In this invention, the server includes an information accumulating means having characteristic information of plants and animals, a means for acquiring design requirements from users, and a means for selecting the characteristics of plants and animals from the information accumulating means based on the design requirements. This makes it possible to efficiently generate designs for mechanical devices that utilize the characteristics of the natural world and to provide optimal designs under diverse conditions.

[0468] "Plant and animal characteristic information" refers to detailed data about the shape, structure, and function of plants and animals that exist in nature.

[0469] "Information gathering means" refers to a device or system for collecting and storing characteristic information of plants and animals, and making it accessible as needed.

[0470] A "user" is a person or group that operates the system and inputs specific design requirements.

[0471] "Design requirements" are specific conditions and goals regarding machinery and equipment as desired by the user, and include requirements related to design and function.

[0472] "Selection method" refers to a processing method or apparatus for extracting optimal characteristic information of plants and animals from information gathering means based on the user's design requirements.

[0473] "Means for generating mechanical device designs" refer to processes or systems that utilize characteristic information of selected plants and animals to concretize the shape and structure of a target mechanical device.

[0474] "Means of provision" refers to the mechanisms and methods for presenting the design of the generated machinery to the user.

[0475] The system that realizes this invention performs a process of generating mechanical device designs using characteristic information of plants and animals. The server collects characteristic information about plants and animals and builds a database as an information aggregation means. This database includes detailed data on shape and structural characteristics obtained from the natural world, such as animal durability.

[0476] Users input design requirements into the server using devices such as smartphones or smart glasses. These requirements include specific design goals aimed at improving particular functions or performance. The server then selects the optimal characteristics of plants and animals from its information aggregation system based on the user's input requirements.

[0477] Based on the selected characteristic information, the server invokes a generation AI model and automatically generates a design for the mechanical device. This design is optimized for the user's desired shape, energy efficiency, durability, and other factors. The generated design is provided to the user through an information transmission method, allowing the user to review the design and provide feedback as needed.

[0478] As a concrete example, a user might input a prompt message such as, "Generate a robot arm design based on the structural characteristics of nature to improve durability when working in high-temperature environments." In response, the server executes the process described above, generates a robot arm design based on the characteristics of appropriate plants and animals, and presents it to the user. This entire process is achieved through high-speed data processing on the server and design generation using an AI model.

[0479] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0480] Step 1:

[0481] The server collects characteristic information on plants and animals and builds a database as a means of information aggregation. The collected data is automatically retrieved using APIs and web crawlers. The input is data on the shape and structural characteristics of plants and animals in the natural world, which is classified, organized, and stored in the database. The output is a database in which characteristic information is systematically stored.

[0482] Step 2:

[0483] The user inputs design requirements using a terminal. These requirements include specific functional and performance targets, such as "improve durability"—specific design goals. The user's requirements are input and sent to the server. The output is the user's design requirements received by the server.

[0484] Step 3:

[0485] The server analyzes the received design requirements and, based on that, selects appropriate plant and animal characteristics from the information aggregation system. Using database queries, it searches for characteristics that match the input design requirements, and an AI algorithm determines the optimal characteristics. The output is information on the selected characteristics.

[0486] Step 4:

[0487] The server runs a generating AI model based on the selected characteristic information to generate a design for the machine. The input is the characteristic information selected in step 3, and the AI ​​model processes and calculates the data based on this to generate an optimized design proposal. The output is the generated design for the machine.

[0488] Step 5:

[0489] The server provides the generated design to the user's terminal. For information transmission, an interface is constructed to allow the user to visually confirm the design. The input is the generated design, and the output is the design proposal received by the user.

[0490] Step 6:

[0491] Users can review the provided designs and provide feedback. The input consists of user comments and suggestions for improvement on the generated design, while the output is the feedback information received by the server. This allows for further design improvements.

[0492] 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.

[0493] This invention relates to a system that generates automobile designs using characteristic data of plants and animals, combined with an emotion engine that recognizes user emotions. This system consists of a server, a terminal, a user, and an emotion engine.

[0494] First, the server builds a database that aggregates characteristic data of plants and animals. This database includes a wide range of biological characteristics, such as the aerodynamic properties of bird wing shapes and the water resistance properties of fish scale surface shapes.

[0495] When a user inputs automotive design requirements using a terminal, an emotion engine activates and simultaneously analyzes the user's emotions at the time of input. This emotion analysis is performed by recognizing and quantifying the user's emotions, such as joy, satisfaction, and dissatisfaction, in real time.

[0496] Next, the server receives the user's requirements and the results of the emotion engine's analysis, and proceeds to the process of extracting appropriate plant and animal characteristics from the database. By considering the output of the emotion engine, it becomes possible to prioritize design characteristics that the user feels they want.

[0497] Based on the extracted characteristics, the server uses a generative AI algorithm to create car design proposals. For example, a user with a high "excitement level" in the emotion analysis will be proposed a design incorporating sporty and advanced design elements.

[0498] The completed design proposal is sent from the server to the terminal, where the user reviews the design and sends further feedback via the emotion engine. This feedback includes emotional reactions to the design, and the system uses this to optimize the design.

[0499] In this way, a design feedback system that incorporates user emotions makes it possible to realize automotive designs with high emotional value that go beyond mere functional design.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The server collects characteristic data on plants and animals, organizes detailed characteristic information, and builds a database. This is done based on scientific research data on the shape and characteristics of plants and animals.

[0503] Step 2:

[0504] The user inputs the design requirements for the car using a terminal. During this process, an emotion engine analyzes the user's facial expressions and tone of voice, analyzing their emotional state in real time.

[0505] Step 3:

[0506] The emotion engine sends user emotion data to the server. This data includes numerical representations of the user's level of excitement, satisfaction, and stress.

[0507] Step 4:

[0508] The server processes user requirements and emotional data, and extracts relevant plant and animal characteristics from the database. Based on the emotional data, it prioritizes and selects the characteristics that best match the user's emotional state.

[0509] Step 5:

[0510] The server executes a generation AI algorithm based on selected characteristics to generate car designs. During this generation process, the design is adjusted while taking emotional data into consideration.

[0511] Step 6:

[0512] The server sends the generated design to the user's device and presents it to the user. The user can view the design and provide feedback.

[0513] Step 7:

[0514] Users provide feedback through their devices, expressing emotional reactions and opinions on the design. This feedback is then processed again through an emotion engine, which adds emotional data to it.

[0515] Step 8:

[0516] The server re-evaluates and optimizes the design based on user feedback. It then re-considers sentiment data and regenerates the optimized design.

[0517] Step 9:

[0518] The server provides users with an optimized design, ensuring that this improves user satisfaction. Design improvements are continuously made through a user feedback cycle.

[0519] (Example 2)

[0520] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0521] Conventional vehicle design systems have problems with incorporating the characteristics of plants and animals into designs, and with generating designs that adequately consider user emotions. Therefore, there is a need for technology that enhances emotional value and efficiently generates designs that meet the individual needs of users.

[0522] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0523] In this invention, the server includes means for constructing an information repository that stores characteristic information of plants and animals, means for obtaining conditions from the user, and means for extracting the characteristics of plants and animals from the information repository based on the conditions. This makes it possible to generate vehicle designs that apply the characteristics of plants and animals that reflect the user's emotions.

[0524] "Personal information on plants and animals" refers to specific data and attributes concerning the shape, function, and performance of animals and plants.

[0525] An "information repository" is a database that efficiently stores data and information, and allows for quick access when needed.

[0526] "User requirements" refers to information that includes the user's requests and preferences regarding the design they provide.

[0527] An "emotion analysis engine" is an algorithm or system used to recognize and quantify a user's emotions.

[0528] A "generative AI model" refers to an artificial intelligence algorithm that uses data to generate new designs and ideas.

[0529] This invention is a system for generating vehicle designs using characteristic information of plants and animals, and in particular, it enables the generation of designs that take into account the user's emotions. The system consists of a server, a terminal, a user, and an emotion analysis engine.

[0530] The server constructs an information repository for storing characteristic information about plants and animals. This repository contains characteristic data on the shape and function of living organisms. The server organizes this data by categorizing it according to the shape and performance attributes specific to plants and animals.

[0531] The user inputs vehicle design requirements using a terminal. As the user inputs their design preferences and requirements, an emotion analysis engine built into the terminal simultaneously evaluates the user's emotions in real time. This evaluation utilizes facial recognition technology and voice analysis technology.

[0532] Next, the server receives the user's input conditions and the results of the sentiment analysis engine, and based on that, extracts the most suitable animal and plant characteristics from its database. The extracted characteristics are then used to generate design proposals using a generative AI model. The generative AI model uses prompts such as "Please propose an elegant and dynamic vehicle design" to generate a new design using the characteristic data.

[0533] The generated design proposals are sent from the server to the terminal, where the user reviews them and sends feedback via an emotion analysis engine. For example, the user determines whether the design meets their expectations and communicates that reaction to the system as feedback. The system then uses this feedback to further optimize the design.

[0534] This system enables the creation of vehicle designs with high emotional value that prioritize user emotions and reflect the characteristics of plants and animals.

[0535] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0536] Step 1:

[0537] The server collects characteristic information on plants and animals to build an information repository. Here, data is gathered from scientific databases and research papers, and characteristic information about the shape and performance of plants and animals is classified and organized. The input is biological characteristic data, and the output is a usable database. This database serves as the foundational material for design generation by AI models.

[0538] Step 2:

[0539] The user inputs design requirements using a terminal. This input includes requests regarding the car's style, functions, and color. The input data is sent to the server in text format. The output here is data that conveys the user requirements to the server.

[0540] Step 3:

[0541] The emotion analysis engine built into the device analyzes the emotions the user experiences during input. Using a facial recognition camera and voice sensor, the user's emotions are measured and quantified in real time. In this step, the input is the user's facial expressions and voice, and the output is quantified emotion data.

[0542] Step 4:

[0543] The server receives user conditions and sentiment data and extracts appropriate plant and animal characteristics from its database. The input is user conditions and sentiment data, and the server processes the selection of plant and animal characteristic data through database searches. The output is the characteristic data necessary for design generation.

[0544] Step 5:

[0545] The server generates design proposals using a generative AI model. Here, instructions are given to the AI ​​using prompts based on the characteristic data obtained in the previous step. The input is the prompt "Please propose an elegant and dynamic vehicle design" and characteristic data, and the output is an automatically generated design proposal.

[0546] Step 6:

[0547] The generated design proposal is sent from the server to the terminal. The user reviews the design proposal and provides feedback. The input is the design proposal, and the output is the user's feedback.

[0548] Step 7:

[0549] The server receives user feedback and sentiment ratings to optimize the design proposal. Optimization involves a regeneration process that incorporates feedback to create a design closer to the user's requirements. The input is feedback data, and the output is the optimized design proposal.

[0550] (Application Example 2)

[0551] 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."

[0552] In generating automotive designs, the challenge lies in providing new emotional value that cannot be obtained with conventional design methods by incorporating information on the characteristics of plants and animals while reflecting the emotional state of the user. Furthermore, there is a need to build a system that supports a process of appropriately feeding back the generated designs and optimizing them based on that feedback.

[0553] 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.

[0554] In this invention, the server includes means for constructing an information source having characteristic information of plants and animals, means for obtaining requirements from a user, means for extracting the characteristics of plants and animals from the information source based on the requirements, means for analyzing the user's emotional state, means for improving the design generated based on the emotional state, and means for dynamically changing the vehicle display according to the user's emotional state using a display device for an autonomous vehicle. This makes it possible to generate highly emotionally valuable automobile designs that reflect the user's emotions and to dynamically optimize them.

[0555] "Personal information on plants and animals" refers to information about the physical or chemical properties of plants and animals, such as the aerodynamic characteristics of a bird's wing shape or the water resistance characteristics of a fish's scale surface shape.

[0556] "Information source" refers to a database or similar storage method for storing characteristic information about plants and animals, and for extracting or providing that information as needed.

[0557] "User" refers to an individual or organization that uses the system to submit automotive design requirements and receives the resulting designs.

[0558] "Requirements" refer to information that indicates the conditions and specifications that the user desires for the design, and the direction of the design is determined based on these requirements.

[0559] "Extraction" is the process of selecting and obtaining specific elements or information from a series of pieces of information or data.

[0560] "Emotional state" refers to the user's psychological or emotional condition or reactions, and is analyzed through facial expressions, voice, body movements, etc.

[0561] "Improvement" refers to modifying or adding to existing designs or processes to give them better functionality or features.

[0562] A "display device" refers to hardware such as equipment or screens that provide visual information to the user.

[0563] "Dynamically changing" means that the displayed content and system operation are modified in real time according to the situation and conditions.

[0564] This system employs an approach that generates car designs based on information about the characteristics of plants and animals, and then refines them to reflect the user's emotional state. Specific implementations are described below.

[0565] The server first constructs an information source that stores characteristic information about plants and animals. This information source includes aerodynamic characteristics of bird wing shapes and water resistance characteristics of fish scales. Using this information source, the server extracts the appropriate characteristics of plants and animals based on the requirements obtained from the user.

[0566] Next, the device analyzes the user's emotional state. This emotional state is quantified by analyzing the user's facial expressions and voice in real time using an emotion engine. For example, image analysis is performed using software such as TensorFlow, and if the user is relaxed, design elements that evoke a tranquil forest are extracted.

[0567] Based on the extracted characteristics, the server uses a generative AI model to generate car design proposals. These design proposals are provided to the user, who then provides feedback. This feedback includes emotional responses, and the system uses this to optimize the design, improving the generated design to better reflect the user's emotions.

[0568] For example, if a user is relaxed while with family, the dashboard display will show a calming green-based theme. The generative AI model adjusts the design theme based on the prompt, "Recognize the current emotional state and provide the optimal display theme."

[0569] In this way, a design generation process that combines the natural characteristics of plants and animals with the emotions of users makes it possible to provide automotive designs with new emotional value.

[0570] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0571] Step 1:

[0572] The server aggregates characteristic information about plants and animals to build an information source. Various characteristic data about plants and animals are used as input. Based on this data, a database is created and organized so that characteristic information can be efficiently searched. The output is an information source containing the stored characteristic information.

[0573] Step 2:

[0574] The user uses a terminal to input the requirements for the car design. The input consists of data regarding the design specifications and conditions desired by the user. At this stage, the terminal receives the user's input and sends it to the server as requirements data. The output is the requirements data sent to the server.

[0575] Step 3:

[0576] The device analyzes the user's emotional state. The user's facial expressions and voice are captured in real time as input. Software such as TensorFlow is used to quantify the emotional state from this data. The output is the analyzed emotional data.

[0577] Step 4:

[0578] The server receives requirements and sentiment data and extracts appropriate plant and animal characteristics from the information source. User requirements and sentiment data are used as input. Relevant characteristics are extracted from the information source based on both requirements and sentiment and provided to the design process. The output is the extracted plant and animal characteristic data.

[0579] Step 5:

[0580] The server generates car design proposals using a generative AI model based on extracted characteristics. Inputs include characteristic data of plants and animals, and prompt statements (e.g., "Recognize the current emotional state and provide the optimal display theme"). The AI ​​model processes this information and creates design proposals that match user requirements and emotions. The output is the generated design proposal.

[0581] Step 6:

[0582] The user reviews the generated design proposal and submits feedback from their device. The input includes the user's emotional reactions and opinions, which are then transmitted from the device to a server where this feedback is used in the design optimization process. The output is the feedback data sent to the server.

[0583] Step 7:

[0584] The server improves and optimizes the design proposal based on feedback. User feedback data is used as input. By analyzing this feedback and adjusting the design as needed, user satisfaction is further enhanced. The output is the optimized design proposal.

[0585] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0586] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include 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.

[0587] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0588] [Fourth Embodiment]

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

[0590] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0591] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0592] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0593] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0594] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0595] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0596] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0597] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0598] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0599] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0600] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0601] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0602] This invention relates to a system for generating automobile designs using characteristic data of plants and animals. The system consists of a server, a terminal, and a user.

[0603] First, the server collects detailed data on the characteristics of plants and animals and builds a database of that data. This data includes structural characteristics found in nature, such as the shape and properties of fish scales, or the strength and elasticity of bird wings.

[0604] Next, the user inputs the requirements for the car design through the terminal. This includes specific design goals and functional requirements, such as "I want to improve aerodynamics."

[0605] The server then analyzes these requirements and extracts suitable plant and animal characteristics from the database. Using an AI algorithm, the server selects the optimal characteristics that best match the user's requirements.

[0606] Based on these characteristic selections, the server executes a generation AI to create automobile design proposals. The generated designs are optimized in terms of shape, material selection, energy efficiency, and other aspects, providing an appropriate response to the user's requirements.

[0607] For example, if a user inputs the requirement "I want to reduce air resistance," the server will utilize the streamlined characteristics found in fish scales to design a vehicle structure with superior aerodynamics.

[0608] Finally, the generated design is delivered from the server to the user's terminal. The user can review the design and provide feedback as needed, enabling further optimization. This interactive process results in innovative and practical automotive designs.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] The server collects data on the characteristics of plants and animals. This involves organizing and integrating information from various nature observation databases and research data. The information stored in the database provides detailed descriptions of the physical and structural characteristics of specific plants and animals.

[0612] Step 2:

[0613] Users input their automotive design requirements using a terminal. For example, they specify concrete design conditions such as reducing air resistance at a particular speed or improving safety performance.

[0614] Step 3:

[0615] The server receives and analyzes the requirements entered by the user. Next, it extracts relevant plant and animal characteristics from the database and evaluates how these characteristics match the user requirements. This evaluation is performed using a specific algorithm.

[0616] Step 4:

[0617] The server uses the extracted characteristics to run a generative AI algorithm to generate car designs. The algorithm creates an optimal design proposal while considering the characteristics. This design proposal includes shape, material selection, and engineering considerations.

[0618] Step 5:

[0619] The server sends the generated design to the user's terminal. Here, the user reviews the design proposal and determines whether the car design meets their requirements.

[0620] Step 6:

[0621] Users can provide feedback on the design. This feedback may include requests for new improvements or ideas for fine-tuning.

[0622] Step 7:

[0623] The server implements a process of readjusting the design based on user feedback. This results in an optimized design being regenerated and presented to the user again. This cyclical process improves both the accuracy of the design and user satisfaction.

[0624] (Example 1)

[0625] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0626] Traditional automotive design processes often lack novelty and innovation because they rely primarily on human experience and intuition. Furthermore, they struggle to quickly generate optimal designs that meet functional requirements. Against this backdrop, there is a need for a system that leverages characteristics derived from plants and animals in nature to efficiently generate innovative and functional automotive designs, and that can flexibly optimize them according to user demands.

[0627] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0628] In this invention, the server includes means for collecting information relating to the characteristics of plants and animals, data storage means for organizing and accumulating the said information, and means for obtaining design goals from users. This makes it possible to efficiently generate novel and functional automobile designs that incorporate characteristics of the natural world.

[0629] "Characteristics of plants and animals" refer to structural or functional features derived from plants and animals that exist in nature.

[0630] "Means of collecting information" refers to a function or device for obtaining data on the characteristics of plants and animals from external sources.

[0631] "Data storage means" refers to a function or device that organizes collected information and records it so that it can be efficiently searched and used later.

[0632] "Means of obtaining design goals from users" refer to interfaces or functions for users to input their requirements and goals regarding the design of the product they desire.

[0633] "Means for automatically generating design proposals" refers to a function or process in which a computer program creates an automobile design plan based on the characteristics of selected plants and animals.

[0634] "Means of providing to users" refers to a function or device that presents the generated design proposals so that users can view and evaluate them.

[0635] "Means for obtaining opinions and improving the design proposal" refers to a function or process for collecting user feedback on the generated design proposal and improving the design based on that information.

[0636] "Means of improvement to meet specific functional requirements" refers to a function or process that adjusts a generated design proposal to meet the requirements of a specific business or function.

[0637] This invention is a system for generating automobile designs using characteristic data of plants and animals, and mainly consists of a server, terminals, and users. The server collects and organizes characteristic data on plants and animals and builds a database. This data is obtained from publicly available literature and databases via the internet. The database includes characteristic information from the natural world, such as the shape of fish scales and the strength of bird wings. The server uses hardware with high-performance data processing capabilities, and is equipped with AI algorithms for data analysis.

[0638] The user inputs specific requirements for the automobile design using a terminal. The terminal receives the design requirements from the user and sends them to the server. This terminal is a typical computer or mobile device and has an intuitive interface that the user can operate.

[0639] The server analyzes the user's requirements and selects the relevant characteristic data from the database. This process utilizes AI algorithms to select the necessary plant and animal characteristics to generate the optimal design. The generated design is automatically created using a generative AI model. This generative AI model runs on advanced design software, providing a design that considers shape optimization and material selection.

[0640] For example, if a user inputs the requirement "I want to reduce air resistance," the server will utilize the streamlined characteristics of a fish to generate an aerodynamically superior design. As an example of a prompt, a user could input "Please design a car body with streamlined characteristics."

[0641] The generated design is sent from the server to the terminal for user review. User feedback is incorporated into the system for further design optimization. This entire process efficiently generates innovative and functional automotive designs that utilize properties from the natural world.

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] The server collects characteristic data of plants and animals from the internet. Specifically, it obtains characteristic information from biological databases and academic papers, organizes this data, and stores it in a database. The input is biological characteristic information obtained from the internet, and the output is structural characteristic information registered in the characteristic database on the server. In this process, the server performs data cleansing and format conversion to ensure the accuracy and consistency of the information.

[0645] Step 2:

[0646] The user uses a terminal to input requirements for automotive design. These inputs include design goals and specifications specified by the user, such as "I want to improve aerodynamics." The information entered on the terminal is sent to a server, and the user's requests are registered on the server as output. The terminal features an intuitive UI, allowing users to easily input the necessary information.

[0647] Step 3:

[0648] The server analyzes the user's requirements and selects suitable plant and animal characteristics from the database. It utilizes AI algorithms to extract characteristic information that best matches the input requirements. In this step, the user's design requirements and characteristic information from the database are used as input, and a list of selected characteristics is obtained as output. The server evaluates the relationship between requirements and characteristics and uses machine learning models to extract appropriate information.

[0649] Step 4:

[0650] The server generates car designs using a generative AI model based on selected characteristics. The input is the selected characteristics, and the output is the generated car design proposal. The generative AI model runs on design software and automatically generates designs that take into account the optimization of shape and materials. The server sends prompts to the generative AI model during this process to automate the design.

[0651] Step 5:

[0652] The server sends the generated design proposal to the user's device. Here, the input is the generated design proposal, and the output is the design proposal displayed on the user's device. The user reviews the design on their device and provides feedback. The server then receives this feedback and uses it to further optimize the design. The device is designed to allow users to submit feedback concisely.

[0653] (Application Example 1)

[0654] 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".

[0655] Conventional mechanical design methods have made it difficult to optimize energy efficiency and mechanical durability by fully utilizing properties derived from nature. In particular, in the design of robots used in factories, there is a need to create designs that can flexibly respond to diverse environments and working conditions, but current technology has not been able to fully achieve this.

[0656] 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.

[0657] In this invention, the server includes an information accumulating means having characteristic information of plants and animals, a means for acquiring design requirements from users, and a means for selecting the characteristics of plants and animals from the information accumulating means based on the design requirements. This makes it possible to efficiently generate designs for mechanical devices that utilize the characteristics of the natural world and to provide optimal designs under diverse conditions.

[0658] "Plant and animal characteristic information" refers to detailed data about the shape, structure, and function of plants and animals that exist in nature.

[0659] "Information gathering means" refers to a device or system for collecting and storing characteristic information of plants and animals, and making it accessible as needed.

[0660] A "user" is a person or group that operates the system and inputs specific design requirements.

[0661] "Design requirements" are specific conditions and goals regarding machinery and equipment as desired by the user, and include requirements related to design and function.

[0662] "Selection method" refers to a processing method or apparatus for extracting optimal characteristic information of plants and animals from information gathering means based on the user's design requirements.

[0663] "Means for generating mechanical device designs" refer to processes or systems that utilize characteristic information of selected plants and animals to concretize the shape and structure of a target mechanical device.

[0664] "Means of provision" refers to the mechanisms and methods for presenting the design of the generated machinery to the user.

[0665] The system that realizes this invention performs a process of generating mechanical device designs using characteristic information of plants and animals. The server collects characteristic information about plants and animals and builds a database as an information aggregation means. This database includes detailed data on shape and structural characteristics obtained from the natural world, such as animal durability.

[0666] Users input design requirements into the server using devices such as smartphones or smart glasses. These requirements include specific design goals aimed at improving particular functions or performance. The server then selects the optimal characteristics of plants and animals from its information aggregation system based on the user's input requirements.

[0667] Based on the selected characteristic information, the server invokes a generation AI model and automatically generates a design for the mechanical device. This design is optimized for the user's desired shape, energy efficiency, durability, and other factors. The generated design is provided to the user through an information transmission method, allowing the user to review the design and provide feedback as needed.

[0668] As a concrete example, a user might input a prompt message such as, "Generate a robot arm design based on the structural characteristics of nature to improve durability when working in high-temperature environments." In response, the server executes the process described above, generates a robot arm design based on the characteristics of appropriate plants and animals, and presents it to the user. This entire process is achieved through high-speed data processing on the server and design generation using an AI model.

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] The server collects characteristic information on plants and animals and builds a database as a means of information aggregation. The collected data is automatically retrieved using APIs and web crawlers. The input is data on the shape and structural characteristics of plants and animals in the natural world, which is classified, organized, and stored in the database. The output is a database in which characteristic information is systematically stored.

[0672] Step 2:

[0673] The user inputs design requirements using a terminal. These requirements include specific functional and performance targets, such as "improve durability"—specific design goals. The user's requirements are input and sent to the server. The output is the user's design requirements received by the server.

[0674] Step 3:

[0675] The server analyzes the received design requirements and, based on that, selects appropriate plant and animal characteristics from the information aggregation system. Using database queries, it searches for characteristics that match the input design requirements, and an AI algorithm determines the optimal characteristics. The output is information on the selected characteristics.

[0676] Step 4:

[0677] The server runs a generating AI model based on the selected characteristic information to generate a design for the machine. The input is the characteristic information selected in step 3, and the AI ​​model processes and calculates the data based on this to generate an optimized design proposal. The output is the generated design for the machine.

[0678] Step 5:

[0679] The server provides the generated design to the user's terminal. For information transmission, an interface is constructed to allow the user to visually confirm the design. The input is the generated design, and the output is the design proposal received by the user.

[0680] Step 6:

[0681] Users can review the provided designs and provide feedback. The input consists of user comments and suggestions for improvement on the generated design, while the output is the feedback information received by the server. This allows for further design improvements.

[0682] 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.

[0683] This invention relates to a system that generates automobile designs using characteristic data of plants and animals, combined with an emotion engine that recognizes user emotions. This system consists of a server, a terminal, a user, and an emotion engine.

[0684] First, the server builds a database that aggregates characteristic data of plants and animals. This database includes a wide range of biological characteristics, such as the aerodynamic properties of bird wing shapes and the water resistance properties of fish scale surface shapes.

[0685] When a user inputs automotive design requirements using a terminal, an emotion engine activates and simultaneously analyzes the user's emotions at the time of input. This emotion analysis is performed by recognizing and quantifying the user's emotions, such as joy, satisfaction, and dissatisfaction, in real time.

[0686] Next, the server receives the user's requirements and the results of the emotion engine's analysis, and proceeds to the process of extracting appropriate plant and animal characteristics from the database. By considering the output of the emotion engine, it becomes possible to prioritize design characteristics that the user feels they want.

[0687] Based on the extracted characteristics, the server uses a generative AI algorithm to create car design proposals. For example, a user with a high "excitement level" in the emotion analysis will be proposed a design incorporating sporty and advanced design elements.

[0688] The completed design proposal is sent from the server to the terminal, where the user reviews the design and sends further feedback via the emotion engine. This feedback includes emotional reactions to the design, and the system uses this to optimize the design.

[0689] In this way, a design feedback system that incorporates user emotions makes it possible to realize automotive designs with high emotional value that go beyond mere functional design.

[0690] The following describes the processing flow.

[0691] Step 1:

[0692] The server collects characteristic data on plants and animals, organizes detailed characteristic information, and builds a database. This is done based on scientific research data on the shape and characteristics of plants and animals.

[0693] Step 2:

[0694] The user inputs the design requirements for the car using a terminal. During this process, an emotion engine analyzes the user's facial expressions and tone of voice, analyzing their emotional state in real time.

[0695] Step 3:

[0696] The emotion engine sends user emotion data to the server. This data includes numerical representations of the user's level of excitement, satisfaction, and stress.

[0697] Step 4:

[0698] The server processes user requirements and emotional data, and extracts relevant plant and animal characteristics from the database. Based on the emotional data, it prioritizes and selects the characteristics that best match the user's emotional state.

[0699] Step 5:

[0700] The server executes a generation AI algorithm based on selected characteristics to generate car designs. During this generation process, the design is adjusted while taking emotional data into consideration.

[0701] Step 6:

[0702] The server sends the generated design to the user's device and presents it to the user. The user can view the design and provide feedback.

[0703] Step 7:

[0704] Users provide feedback through their devices, expressing emotional reactions and opinions on the design. This feedback is then processed again through an emotion engine, which adds emotional data to it.

[0705] Step 8:

[0706] The server re-evaluates and optimizes the design based on user feedback. It then re-considers sentiment data and regenerates the optimized design.

[0707] Step 9:

[0708] The server provides users with an optimized design, ensuring that this improves user satisfaction. Design improvements are continuously made through a user feedback cycle.

[0709] (Example 2)

[0710] 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".

[0711] Conventional vehicle design systems have problems with incorporating the characteristics of plants and animals into designs, and with generating designs that adequately consider user emotions. Therefore, there is a need for technology that enhances emotional value and efficiently generates designs that meet the individual needs of users.

[0712] 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.

[0713] In this invention, the server includes means for constructing an information repository that stores characteristic information of plants and animals, means for obtaining conditions from the user, and means for extracting the characteristics of plants and animals from the information repository based on the conditions. This makes it possible to generate vehicle designs that apply the characteristics of plants and animals that reflect the user's emotions.

[0714] "Personal information on plants and animals" refers to specific data and attributes concerning the shape, function, and performance of animals and plants.

[0715] An "information repository" is a database that efficiently stores data and information, and allows for quick access when needed.

[0716] "User requirements" refers to information that includes the user's requests and preferences regarding the design they provide.

[0717] An "emotion analysis engine" is an algorithm or system used to recognize and quantify a user's emotions.

[0718] A "generative AI model" refers to an artificial intelligence algorithm that uses data to generate new designs and ideas.

[0719] This invention is a system for generating vehicle designs using characteristic information of plants and animals, and in particular, it enables the generation of designs that take into account the user's emotions. The system consists of a server, a terminal, a user, and an emotion analysis engine.

[0720] The server constructs an information repository for storing characteristic information about plants and animals. This repository contains characteristic data on the shape and function of living organisms. The server organizes this data by categorizing it according to the shape and performance attributes specific to plants and animals.

[0721] The user inputs vehicle design requirements using a terminal. As the user inputs their design preferences and requirements, an emotion analysis engine built into the terminal simultaneously evaluates the user's emotions in real time. This evaluation utilizes facial recognition technology and voice analysis technology.

[0722] Next, the server receives the user's input conditions and the results of the sentiment analysis engine, and based on that, extracts the most suitable animal and plant characteristics from its database. The extracted characteristics are then used to generate design proposals using a generative AI model. The generative AI model uses prompts such as "Please propose an elegant and dynamic vehicle design" to generate a new design using the characteristic data.

[0723] The generated design proposals are sent from the server to the terminal, where the user reviews them and sends feedback via an emotion analysis engine. For example, the user determines whether the design meets their expectations and communicates that reaction to the system as feedback. The system then uses this feedback to further optimize the design.

[0724] This system enables the creation of vehicle designs with high emotional value that prioritize user emotions and reflect the characteristics of plants and animals.

[0725] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0726] Step 1:

[0727] The server collects characteristic information on plants and animals to build an information repository. Here, data is gathered from scientific databases and research papers, and characteristic information about the shape and performance of plants and animals is classified and organized. The input is biological characteristic data, and the output is a usable database. This database serves as the foundational material for design generation by AI models.

[0728] Step 2:

[0729] The user inputs design requirements using a terminal. This input includes requests regarding the car's style, functions, and color. The input data is sent to the server in text format. The output here is data that conveys the user requirements to the server.

[0730] Step 3:

[0731] The emotion analysis engine built into the device analyzes the emotions the user experiences during input. Using a facial recognition camera and voice sensor, the user's emotions are measured and quantified in real time. In this step, the input is the user's facial expressions and voice, and the output is quantified emotion data.

[0732] Step 4:

[0733] The server receives user conditions and sentiment data and extracts appropriate plant and animal characteristics from its database. The input is user conditions and sentiment data, and the server processes the selection of plant and animal characteristic data through database searches. The output is the characteristic data necessary for design generation.

[0734] Step 5:

[0735] The server generates design proposals using a generative AI model. Here, instructions are given to the AI ​​using prompts based on the characteristic data obtained in the previous step. The input is the prompt "Please propose an elegant and dynamic vehicle design" and characteristic data, and the output is an automatically generated design proposal.

[0736] Step 6:

[0737] The generated design proposal is sent from the server to the terminal. The user reviews the design proposal and provides feedback. The input is the design proposal, and the output is the user's feedback.

[0738] Step 7:

[0739] The server receives user feedback and sentiment ratings to optimize the design proposal. Optimization involves a regeneration process that incorporates feedback to create a design closer to the user's requirements. The input is feedback data, and the output is the optimized design proposal.

[0740] (Application Example 2)

[0741] 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".

[0742] In generating automotive designs, the challenge lies in providing new emotional value that cannot be obtained with conventional design methods by incorporating information on the characteristics of plants and animals while reflecting the emotional state of the user. Furthermore, there is a need to build a system that supports a process of appropriately feeding back the generated designs and optimizing them based on that feedback.

[0743] 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.

[0744] In this invention, the server includes means for constructing an information source having characteristic information of plants and animals, means for obtaining requirements from a user, means for extracting the characteristics of plants and animals from the information source based on the requirements, means for analyzing the user's emotional state, means for improving the design generated based on the emotional state, and means for dynamically changing the vehicle display according to the user's emotional state using a display device for an autonomous vehicle. This makes it possible to generate highly emotionally valuable automobile designs that reflect the user's emotions and to dynamically optimize them.

[0745] "Personal information on plants and animals" refers to information about the physical or chemical properties of plants and animals, such as the aerodynamic characteristics of a bird's wing shape or the water resistance characteristics of a fish's scale surface shape.

[0746] "Information source" refers to a database or similar storage method for storing characteristic information about plants and animals, and for extracting or providing that information as needed.

[0747] "User" refers to an individual or organization that uses the system to submit automotive design requirements and receives the resulting designs.

[0748] "Requirements" refer to information that indicates the conditions and specifications that the user desires for the design, and the direction of the design is determined based on these requirements.

[0749] "Extraction" is the process of selecting and obtaining specific elements or information from a series of pieces of information or data.

[0750] "Emotional state" refers to the user's psychological or emotional condition or reactions, and is analyzed through facial expressions, voice, body movements, etc.

[0751] "Improvement" refers to modifying or adding to existing designs or processes to give them better functionality or features.

[0752] A "display device" refers to hardware such as equipment or screens that provide visual information to the user.

[0753] "Dynamically changing" means that the displayed content and system operation are modified in real time according to the situation and conditions.

[0754] This system employs an approach that generates car designs based on information about the characteristics of plants and animals, and then refines them to reflect the user's emotional state. Specific implementations are described below.

[0755] The server first constructs an information source that stores characteristic information about plants and animals. This information source includes aerodynamic characteristics of bird wing shapes and water resistance characteristics of fish scales. Using this information source, the server extracts the appropriate characteristics of plants and animals based on the requirements obtained from the user.

[0756] Next, the device analyzes the user's emotional state. This emotional state is quantified by analyzing the user's facial expressions and voice in real time using an emotion engine. For example, image analysis is performed using software such as TensorFlow, and if the user is relaxed, design elements that evoke a tranquil forest are extracted.

[0757] Based on the extracted characteristics, the server uses a generative AI model to generate car design proposals. These design proposals are provided to the user, who then provides feedback. This feedback includes emotional responses, and the system uses this to optimize the design, improving the generated design to better reflect the user's emotions.

[0758] For example, if a user is relaxed while with family, the dashboard display will show a calming green-based theme. The generative AI model adjusts the design theme based on the prompt, "Recognize the current emotional state and provide the optimal display theme."

[0759] In this way, a design generation process that combines the natural characteristics of plants and animals with the emotions of users makes it possible to provide automotive designs with new emotional value.

[0760] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0761] Step 1:

[0762] The server aggregates characteristic information about plants and animals to build an information source. Various characteristic data about plants and animals are used as input. Based on this data, a database is created and organized so that characteristic information can be efficiently searched. The output is an information source containing the stored characteristic information.

[0763] Step 2:

[0764] The user uses a terminal to input the requirements for the car design. The input consists of data regarding the design specifications and conditions desired by the user. At this stage, the terminal receives the user's input and sends it to the server as requirements data. The output is the requirements data sent to the server.

[0765] Step 3:

[0766] The device analyzes the user's emotional state. The user's facial expressions and voice are captured in real time as input. Software such as TensorFlow is used to quantify the emotional state from this data. The output is the analyzed emotional data.

[0767] Step 4:

[0768] The server receives requirements and sentiment data and extracts appropriate plant and animal characteristics from the information source. User requirements and sentiment data are used as input. Relevant characteristics are extracted from the information source based on both requirements and sentiment and provided to the design process. The output is the extracted plant and animal characteristic data.

[0769] Step 5:

[0770] The server generates car design proposals using a generative AI model based on extracted characteristics. Inputs include characteristic data of plants and animals, and prompt statements (e.g., "Recognize the current emotional state and provide the optimal display theme"). The AI ​​model processes this information and creates design proposals that match user requirements and emotions. The output is the generated design proposal.

[0771] Step 6:

[0772] The user reviews the generated design proposal and submits feedback from their device. The input includes the user's emotional reactions and opinions, which are then transmitted from the device to a server where this feedback is used in the design optimization process. The output is the feedback data sent to the server.

[0773] Step 7:

[0774] The server improves and optimizes the design proposal based on feedback. User feedback data is used as input. By analyzing this feedback and adjusting the design as needed, user satisfaction is further enhanced. The output is the optimized design proposal.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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."

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0796] The following is further disclosed regarding the embodiments described above.

[0797] (Claim 1)

[0798] A means for constructing a database containing characteristic data of plants and animals,

[0799] Means of obtaining requirements from users,

[0800] A means for extracting the characteristics of plants and animals from the database based on the aforementioned requirements,

[0801] A means of generating an automobile design using extracted characteristics,

[0802] A means of providing the generated design to the user,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for optimizing a design generated based on user feedback.

[0806] (Claim 3)

[0807] The system according to claim 1, comprising means for improving at least a portion of the generated design to have a specific function.

[0808] "Example 1"

[0809] (Claim 1)

[0810] Means for collecting information on the characteristics of plants and animals,

[0811] A data storage means for organizing and accumulating the aforementioned information,

[0812] Means of obtaining design goals from users,

[0813] A means for analyzing the aforementioned design objectives and selecting suitable characteristics of plants and animals from the data storage means,

[0814] A means for automatically generating design proposals based on selected characteristics,

[0815] A means of providing the aforementioned design proposal to the user,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising means for obtaining user feedback on a generated design proposal and for improving the design proposal.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising means for improving the generated design proposal to satisfy specific functional requirements.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] Information accumulating means that have characteristic information of plants and animals,

[0824] Means of obtaining design requirements from users,

[0825] A means for selecting the characteristics of plants and animals from the information accumulating means based on the aforementioned design requirements,

[0826] Means for generating a mechanical device design using selected characteristics,

[0827] A means of providing the generated design to the user,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, further comprising means for improving the design generated based on user evaluations.

[0831] (Claim 3)

[0832] The system according to claim 1, comprising means for modifying at least a portion of the generated design to have a specific operational function.

[0833] "Example 2 of combining an emotion engine"

[0834] (Claim 1)

[0835] A means of constructing an information repository that stores characteristic information on plants and animals,

[0836] Means of obtaining conditions from users,

[0837] A means for extracting the characteristics of plants and animals from the information repository based on the aforementioned conditions,

[0838] A means of generating a vehicle design using extracted characteristics,

[0839] A means of presenting the generated design to the user,

[0840] A means of evaluating a user's emotions using an emotion analysis engine,

[0841] A means for adjusting the design generation based on the aforementioned emotional evaluation results,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for optimizing the design generated based on user feedback and sentiment evaluation.

[0845] (Claim 3)

[0846] The system according to claim 1, comprising means for enhancing at least a portion of the generated design to have a specific function.

[0847] "Application example 2 when combining with an emotional engine"

[0848] (Claim 1)

[0849] Means for constructing an information source containing characteristic information of plants and animals,

[0850] Means of obtaining requirements from users,

[0851] A means for extracting the characteristics of plants and animals from the information source based on the aforementioned requirements,

[0852] A means for generating a vehicle design using extracted characteristics,

[0853] A means of analyzing the emotional state of users,

[0854] Means for improving the design generated based on the aforementioned emotional state,

[0855] A means of providing the generated design to the user,

[0856] Means by which at least a portion of the generated design is improved to have specific attributes,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, further comprising means for optimizing a design generated based on user responses.

[0860] (Claim 3)

[0861] The system according to claim 1, comprising a means for dynamically changing the vehicle display according to the emotional state of the user, using a display device for an autonomous vehicle. [Explanation of symbols]

[0862] 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 for constructing a database containing characteristic data of plants and animals, Means of obtaining requirements from users, A means for extracting the characteristics of plants and animals from the database based on the aforementioned requirements, A means of generating an automobile design using extracted characteristics, A means of providing the generated design to the user, A system that includes this.

2. The system according to claim 1, further comprising means for optimizing a design generated based on user feedback.

3. The system according to claim 1, comprising means for improving at least a portion of the generated design to have a specific function.

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