system

Abstract

We provide the system. [Solution] A data acquisition method for obtaining actual animal-based food shape data, An artificial intelligence means having a generative model for generating the three-dimensional shape of alternative ingredients from acquired shape data and similar data, A three-dimensional printing method for forming alternative food ingredients three-dimensionally using plant-derived raw materials based on the generated three-dimensional shape, A data processing means for feeding back evaluation data obtained from consumers to the generation model, A system that includes this.

Description

Technical Field

[0004] , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, due to the increasing environmental load and health consciousness, the demand for foods that replace animal-based ingredients has been increasing. However, existing alternative ingredients have different textures and appearances from actual animal-based ingredients, and there is a limit to improving consumer satisfaction. Therefore, there is a need to produce alternative ingredients with textures and shapes closer to actual animal-based ingredients. Furthermore, flexibility that allows improvement by incorporating consumer feedback is also necessary.

Means for Solving the Problems

[0006] "Actual animal-derived food shape data" refers to digital information about the shape and internal structure of animal-derived food ingredients, and serves as basic data for designing alternative food ingredients.

[0007] A "generative model" is an artificial intelligence algorithm that generates new data patterns from a given dataset, and in this invention, it is used to generate three-dimensional shapes.

[0008] "Artificial intelligence tools" refer to computer programs or systems that use machine learning or deep learning techniques to perform specific tasks, supporting the design and production processes of alternative food ingredients.

[0009] "Three-dimensional printing means" refers to equipment or technology that physically generates three-dimensional structures based on digital data, and in this context, it refers to a function used to manufacture alternative food ingredients using plant-derived raw materials.

[0010] "Plant-derived ingredients" refer to materials formulated based on components obtained from plants, and in this context, they specifically include nutrients used as substitutes for animal-based ingredients.

[0011] "Evaluation data obtained from consumers" refers to information provided by consumers who have actually experienced a product and provided feedback on its taste, texture, appearance, etc., which can be used to improve and optimize the product. [Brief explanation of the drawing]

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

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

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

[0015] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention relates to a system for producing alternative food ingredients using plant-derived raw materials. The system aims to produce alternative food ingredients that have a shape and texture similar to actual animal-based ingredients. Specific embodiments of this invention are described below.

[0034] First, the server acquires shape data of animal-based ingredients. This shape data is obtained using high-resolution 3D scanners and micro-CT scanners. This provides detailed data capturing the external shape of the meat, its internal fibrous structure, and the distribution of individual fat cells.

[0035] Next, a generative model is constructed using the shape data acquired by the server. This model utilizes artificial intelligence techniques, including generative adversarial networks (GANs), and is designed to mimic the shape and texture of actual animal-based food ingredients. Through artificial intelligence, this model continuously learns and optimizes, improving its accuracy based on the feedback received.

[0036] Subsequently, the server uses a generative model to generate a three-dimensional shape of the alternative food. The generated shape data is then physically molded using a three-dimensional printing method. Specifically, a three-dimensional printer is used to print the alternative meat in 3D from plant-based raw materials. Pea protein and other plant-based ingredients are used as raw materials, resulting in a nutritionally balanced product.

[0037] Ultimately, the alternative ingredients are provided to consumers via a device. Users then cook them, experience the taste and texture, and provide feedback. This feedback data is sent to a server and used to improve the entire system.

[0038] Through the above process, the present invention provides consumers with more realistic and customized alternative ingredients and enables continuous product improvement. Through such a system, it is possible to reduce environmental impact and improve food diversity.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server acquires shape data of actual animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. The scanned data includes the external shape of the meat, its internal fibrous structure, and fat distribution. This data is stored in a database within the system and used in subsequent processes.

[0042] Step 2:

[0043] The server uses shape data to train a generative model. Generative adversarial networks (GANs) are used to build an algorithm that mimics the shape and texture of real food ingredients. This improves the model's ability to generate shapes that closely resemble real food ingredients. This process also includes data preprocessing and hyperparameter optimization.

[0044] Step 3:

[0045] The server uses a pre-trained generative model to generate three-dimensional shapes of alternative ingredients according to the user's requests. Specific features of the shape (e.g., fat distribution, meat thickness) are specified, and a three-dimensional model is created using CG software. The generated shape can be viewed through the user interface.

[0046] Step 4:

[0047] The server generates three-dimensional shape data, which is then sent to a 3D printer to physically shape the alternative meat using plant-based materials. The 3D printer follows a programmed procedure, creating the product of the specified shape using a layering method. During this process, the material composition is considered to achieve appropriate strength and elasticity.

[0048] Step 5:

[0049] Users receive substitute ingredients, cook them, and taste them. They then provide feedback on the appearance, texture, and taste of the ingredients via their device. This feedback can be provided, for example, through a dedicated application or web form.

[0050] Step 6:

[0051] The server collects feedback data from users and uses it to improve the generative model. The feedback is analyzed, and the content is reflected in the model adjustments and the generation of new shapes. This improves the accuracy of the next generation process, allowing it to better meet user demands.

[0052] (Example 1)

[0053] 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."

[0054] In the production of alternative edible ingredients that mimic animal products, there are shortcomings in appearance, taste, and texture, making it difficult to sufficiently improve consumer satisfaction. Furthermore, there is the challenge of providing products that cater to different consumer preferences while using environmentally friendly plant-based materials.

[0055] 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.

[0056] In this invention, the server includes data acquisition means for obtaining physical property data of actual animal-based ingredients, intelligent data processing means having a generative model for generating a three-dimensional shape of an alternative edible material from the acquired property data, and three-dimensional processing means for forming the alternative edible material three-dimensionally using plant-derived edible raw materials based on the generated three-dimensional shape. This makes it possible to manufacture high-quality alternative edible materials that meet the diverse needs of consumers.

[0057] "Data acquisition means" refers to an apparatus or method used to accurately acquire physical property data of actual animal-based food ingredients.

[0058] An "intelligent data processing means" is a system that uses an artificial intelligence model to process data in order to generate a three-dimensional shape of an alternative edible material from acquired physical property data.

[0059] "Three-dimensional processing means" refers to an apparatus or technology that physically forms a three-dimensional substitute edible material using plant-derived edible raw materials based on the generated three-dimensional shape.

[0060] A "generative adversarial algorithm" is an artificial intelligence algorithm that aims to generate more realistic data by having different models compete with each other.

[0061] "Plant-derived edible ingredients" refer to natural or processed materials made from plants that are suitable for consumption.

[0062] "Evaluation information" refers to opinions and feedback from consumers regarding their experience using a product and their level of satisfaction with it.

[0063] "Data update methods" refer to techniques and systems for improving the accuracy of generative models based on evaluation information obtained from consumers.

[0064] This invention is a system for efficiently producing alternative edible materials that mimic animal-based ingredients. Specifically, a server is central to the process, handling data acquisition, model generation, 3D modeling, and feedback processing.

[0065] First, the server uses data acquisition methods to obtain physical property data of animal-based ingredients. This can be done using devices such as high-resolution 3D scanners and micro-CT scanners. This allows for detailed information such as the shape of the ingredients, the structure of the fibers, and the distribution of fat.

[0066] Next, the server constructs a generative AI model based on the acquired data through intelligent data processing. This model uses a generative adversarial algorithm to process the data. As a result, a three-dimensional shape that closely resembles actual animal-based food ingredients is generated.

[0067] Subsequently, the terminal uses a three-dimensional processing device to print a three-dimensional alternative edible material using plant-derived edible raw materials based on the generated three-dimensional shape data. For this purpose, a three-dimensional printer can be used, and pea protein or other nutrient-rich materials can be employed.

[0068] Ultimately, users receive the alternative edible ingredients, cook and taste them, and provide feedback on taste and texture. This feedback data is then fed back to the server. The server continuously improves its generative model based on this feedback data. The goal is to provide high-quality alternative edible ingredients that meet the diverse needs of consumers.

[0069] As a concrete example, here is an example of a prompt: "Generate three-dimensional shape data of a plant-based alternative food that mimics the shape and texture of chicken breast."

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server acquires physical property data of animal-based ingredients. To do this, the server operates a high-resolution 3D scanner or micro-CT scanner to scan the ingredients. The input is the actual animal-based ingredient, and the scanner captures its shape and internal structure as digital data. The output is precise three-dimensional shape data. This data will be used for subsequent processing.

[0073] Step 2:

[0074] The server uses the acquired three-dimensional shape data as input to construct a generative AI model using intelligent data processing. Here, a generative adversarial algorithm is employed to generate a model that mimics the detailed shape and texture of animal-based food ingredients. Data processing involves analyzing the input data and optimizing the parameters of the generative model. The output is a highly accurate generative AI model.

[0075] Step 3:

[0076] The server generates three-dimensional shape data of the alternative edible material using a generative AI model. In this step, prompt statements are used to instruct the AI ​​model and calculate the specific three-dimensional shape. The inputs are prompt statements and the generative AI model, and the output is detailed three-dimensional shape data of the alternative edible material.

[0077] Step 4:

[0078] The terminal uses the generated three-dimensional shape data as input to create a three-dimensional alternative edible material using a three-dimensional processing method. A three-dimensional printer is used to print plant-derived edible raw materials in three dimensions. Specifically, a material containing pea protein is loaded into the printer, and the material is layered layer by layer. The output is the completed alternative edible material.

[0079] Step 5:

[0080] Users cook alternative edible ingredients received via their device and evaluate their texture and taste. User input consists of cooking and tasting the ingredients, while output is feedback information about texture and taste. This feedback information is sent to a server and used to improve the generated AI model.

[0081] (Application Example 1)

[0082] 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."

[0083] Traditional methods of producing alternative foods made it difficult for consumers to customize the shape and texture of these foods to their individual preferences, and also made it difficult to reflect consumer demands in real time. As a result, it was not possible to perfectly provide the diverse food experiences that consumers desired, leaving room for improvement in satisfaction with alternative foods.

[0084] 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.

[0085] In this invention, the server includes data acquisition means for obtaining actual animal-based food shape data, artificial intelligence means having a generative model for generating a three-dimensional shape of an alternative food from the acquired shape data and similar data, three-dimensional printing means for forming the alternative food three-dimensionally using plant-derived raw materials based on the generated three-dimensional shape, and user interface means that enables the selection and customization of the alternative food using an augmented reality device. This makes it possible for consumers to customize their preferred shape and texture on the spot and generate an alternative food instantly.

[0086] "Animal-based food shape data" refers to detailed data, including the external shape and internal structure of animal-based food products, acquired using high-resolution 3D scanners or micro-CT scanners.

[0087] A "generative model" is a model that uses artificial intelligence technology to generate the three-dimensional shape of a substitute food ingredient from acquired animal food ingredient shape data.

[0088] "Artificial intelligence methods" refer to methods that utilize AI technology to build generative models and mimic the shape and texture of animal-based ingredients.

[0089] A "three-dimensional printing method" is a device for physically shaping alternative food ingredients from plant-derived raw materials based on the generated three-dimensional shape.

[0090] "Data processing means" refers to digital processing technology used to improve generative models by utilizing evaluation data obtained from consumers.

[0091] An "augmented reality device" is a device that overlays information onto real space, allowing users to visually select and customize alternative ingredients.

[0092] A "user interface means" is an interface that allows consumers to customize the shape and texture of alternative food ingredients.

[0093] To implement this invention, the server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or a micro-CT scanner. This shape data captures the external shape and internal structure of the ingredients in detail. Based on the acquired data, a generative model is constructed on the server. This generative model utilizes artificial intelligence technology, particularly generative adversarial networks, and is designed to precisely mimic the shape and texture of animal-based ingredients.

[0094] A three-dimensional printing device is used based on the three-dimensional shape data obtained from the generative model. Specifically, plant-derived raw materials are supplied to the printer, and alternative food products tailored to consumer preferences are formed in three dimensions. The raw materials used in this process include proteins with a nutritionally superior composition.

[0095] Consumers can visually select and customize alternative ingredients using augmented reality devices, such as smart glasses. These devices function as a user interface, digitizing user requests and sending them to a server. Evaluation data and user feedback are continuously used by data processing systems to optimize the generative model.

[0096] A concrete example is when a consumer chooses vegan ingredients and uses them as a substitute for chicken. Through an augmented reality device, the user can set the texture and thickness and simulate the final cooked state. The generating AI model operates based on the prompt, "Generate a softer, thicker substitute ingredient based on 3D scan data of chicken breast." In this way, customization to meet diverse consumer needs becomes possible on the spot.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server acquires shape data of animal-based food ingredients using a high-resolution 3D scanner and a micro-CT scanner. At this stage, the external shape and internal fiber structure of the food ingredients are captured in detail and stored as digital data within the server. The input is the scanned actual food ingredients, and the output is detailed 3D model data.

[0100] Step 2:

[0101] The server constructs a generative model based on the acquired shape data. This is a process that uses Generative Adversarial Networks (GANs) as part of artificial intelligence technology to learn from the data. The input is the previously obtained 3D model data, and the output is the model parameters for generating the three-dimensional shape of the substitute food item.

[0102] Step 3:

[0103] The user selects and customizes alternative ingredients via an augmented reality device. The user's selections are sent to the server as prompts. For example, the user might send the prompt, "Based on 3D scan data of chicken breast, please generate a softer, thicker alternative ingredient." The input is the user's customization request, and the output is a specific production instruction.

[0104] Step 4:

[0105] The server constructs the three-dimensional shape of the alternative food using a generative model and sends that shape data to a 3D printer. This printer physically forms the alternative food using plant-based raw materials. The input consists of model parameters and user customization instructions, and the output is the alternative food actually produced by the 3D printer.

[0106] Step 5:

[0107] Users receive alternative ingredients generated via their terminal, then cook and taste them. They then send feedback to the server, evaluating the taste and texture. The input is the user's tasting results, and the output is feedback data used for system optimization.

[0108] Step 6:

[0109] The server analyzes the received feedback using data processing tools and continuously improves the generative model to enhance its performance and accuracy. The input is the feedback data, and the output is the improved generative model.

[0110] 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.

[0111] This invention is a system that aims to provide alternative food products that better reflect consumer sentiment, in addition to manufacturing alternative food products using plant-derived raw materials. This system provides alternative food products that have a shape and texture similar to actual animal-based foods, and further has a function to customize them while considering user sentiment. Specific embodiments of this invention are described below.

[0112] First, the server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. The acquired data accurately reflects the external shape and internal structure of the meat and is used as foundational data in subsequent processing. The shape data is stored in a database within the system.

[0113] Next, a generative model is constructed based on the shape data acquired by the server. This generative model is designed to generate algorithms that mimic the shape and texture of actual food ingredients using Generative Adversarial Networks (GANs). The server continuously learns and optimizes the generative model using feedback and evaluation data obtained from the user.

[0114] Furthermore, the system incorporates an emotion engine. The emotion engine analyzes the user's facial expressions, tone of voice, and selected words to recognize the user's emotions. This allows the server to retrieve the user's emotion data and customize the food items according to those emotions. For example, if a user expresses an emotion indicating a preference for a particular food item, it is possible to generate a shape that incorporates that characteristic.

[0115] Next, the server uses a generative model to generate a three-dimensional shape that responds to the user's requests and emotions. The generated shape data is then physically molded using a three-dimensional printing method. Specifically, a three-dimensional printer prints the alternative meat in three dimensions using plant-based raw materials. These raw materials include nutritionally balanced ingredients such as pea protein.

[0116] Ultimately, the user receives the alternative ingredients via a terminal. The user receives the product, cooks and samples it, and provides feedback. The feedback data is collected and analyzed by a server to help improve the next production process.

[0117] Through the above process, the present invention meets the diverse needs of consumers and enables product customization based on emotional data, thereby continuously providing a better dining experience.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. This shape data includes detailed structural information such as the surface shape, internal structure, and fat distribution of the meat. This data is stored in the system's database and serves as foundational data for subsequent processing.

[0121] Step 2:

[0122] The generative model is trained based on the shape data acquired by the server. Here, a generative adversarial network (GAN) is used to learn an algorithm that reproduces the shape and texture of actual meat. This model is important for simulating the realistic appearance and texture of food ingredients.

[0123] Step 3:

[0124] To recognize the user's emotions, the device uses its camera and microphone to analyze the user's facial expressions, voice tone, and selected words. The emotion engine analyzes this data to determine the user's emotional state. This provides crucial input information for determining what characteristics of ingredients to produce.

[0125] Step 4:

[0126] The server utilizes a generative model to generate three-dimensional shape data that responds to user requests and emotions. For example, if a user expresses a preference for rich flavor, the server can create a shape that emphasizes the distribution of fat accordingly. At this point, feedback data obtained from the user is also taken into consideration.

[0127] Step 5:

[0128] The server generates three-dimensional shape data, which is then sent to a 3D printer to print a three-dimensional substitute food using plant-based materials. Based on the set shape, the food is precisely molded using a layering method. This process takes into account the blending of nutritionally balanced materials, such as pea protein.

[0129] Step 6:

[0130] Users receive the completed substitute ingredients via a device and then actually cook and taste them. Users input their evaluations of the ingredients' appearance, texture, and taste, and the device collects this feedback. The user experience is recorded and used to create better products.

[0131] Step 7:

[0132] The server analyzes the collected feedback data and uses it to adjust the generative model. Based on the insights gained from the feedback, the model's generative parameters are updated and reflected in the next product generation. This allows the system to be continuously improved, enabling the production of better alternative ingredients.

[0133] (Example 2)

[0134] 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".

[0135] In recent years, the food industry has seen a growing demand for alternative foods to replace animal-based ingredients. However, conventional technologies have presented challenges in fully meeting consumer preferences because the shape and texture of these alternative foods differ significantly from those of animal-based ingredients. Furthermore, customization based on individual consumer preferences is difficult, often resulting in uniform product offerings. Therefore, there is a need to develop systems that provide realistic alternative foods that closely resemble animal-based ingredients while taking consumer preferences into account.

[0136] 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.

[0137] In this invention, the server includes sensor means for acquiring actual animal-based food shape data, artificial intelligence means for generating a three-dimensional shape of a substitute food from the acquired shape data and similar data, molding means for forming the substitute food three-dimensionally using plant-derived materials based on the generated three-dimensional shape, information processing means for feeding back evaluation data and emotional data acquired from consumers to the artificial intelligence means, and analysis means for generating emotional data by analyzing the consumer's facial expressions, voice, and selected words. This makes it possible to provide realistic substitute foods that take consumer emotions into consideration.

[0138] "Sensing means" refers to a device used to acquire shape data of actual animal-based food ingredients.

[0139] "Artificial intelligence means" refers to technology for generating the three-dimensional shape of a substitute food from acquired shape data and similar data.

[0140] "Molding means" refers to a device for forming a three-dimensional substitute food using plant-derived materials based on the generated three-dimensional shape.

[0141] "Information processing means" refers to a device or program for feeding back evaluation data and emotional data acquired from consumers to artificial intelligence means.

[0142] "Analysis means" refers to a device or program for analyzing a consumer's facial expressions, voice, and chosen words to generate emotional data.

[0143] A "generative adversarial network" refers to a machine learning model in which two networks compete and learn together to generate new data.

[0144] "Plant-derived materials" refer to materials obtained from plants that contain proteins with a nutritional composition taken into consideration.

[0145] This invention is a system for manufacturing alternative foods that have a shape and texture similar to animal-based ingredients, and further customizes the products while taking consumer preferences into consideration. This embodiment is realized primarily through the collaboration of a server, a terminal, and a user, using sensor means, artificial intelligence means, molding means, information processing means, and analysis means.

[0146] The server uses a high-resolution 3D scanner or micro-CT scanner as a sensor to acquire shape data of animal-based ingredients. This shape data is stored in a database in a digital format. Next, the server utilizes generative adversarial networks (GANs) as an artificial intelligence tool to generate three-dimensional shapes of alternative foods from this shape data. In this process, the algorithm designs alternative foods with a realistic appearance and texture based on the obtained shape data.

[0147] The device analyzes the user's facial expressions, voice tone, and selected words as analytical tools, and generates emotional data using an emotion engine. For example, it measures the user's emotions through the camera and microphone and sends emotion-based data to a server. This makes it possible to customize alternative foods based on consumer preferences.

[0148] Ultimately, the server uses a 3D printer as a molding tool to print alternative foods using plant-based materials, such as pea protein. After the molded alternative food is provided to the consumer, the user cooks and evaluates the product. The evaluation data is then fed back to the server and used in the next production process. Through this entire process, consumers can obtain a customized food experience tailored to their individual preferences and emotions.

[0149] For example, if facial expression data analyzes that a user prefers a "steak-like" alternative food, the server uses that information to design an alternative food that closely resembles the shape and taste of steak, employing a generative model.

[0150] An example of a prompt message would be: "Based on the user's emotional data, please 3D print a spicy-flavored steak-like substitute food." In this way, detailed customization based on emotions is possible.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The server uses a high-resolution 3D scanner or micro-CT scanner as a sensor to acquire shape data of actual animal-based ingredients. The animal-based ingredients themselves are prepared as input, and the scanner scans them to obtain shape data as output. This data is saved in a digital format and used in subsequent generation processes.

[0154] Step 2:

[0155] The server constructs a generative model using generative adversarial networks (GANs) based on the acquired shape data. Shape data is given as input, and the GANs process this data to generate a realistic three-dimensional shape algorithm as output. This model is optimized to mimic the appearance and texture of actual food ingredients.

[0156] Step 3:

[0157] The device uses a camera and microphone as analytical tools to collect the user's facial expressions, voice tone, and selected words, generating emotional data. The user's biometric information is provided as input, and the device analyzes it to construct emotional data as output. This information is sent to a server and used for food customization.

[0158] Step 4:

[0159] The server generates a three-dimensional shape of the alternative food using the user's emotional data and generative model. Emotional data and a previously constructed generative model are used as input, and the server matches them to output a three-dimensional shape that reflects specific customizations. Texture and taste are also adjusted to the ideal state at this stage.

[0160] Step 5:

[0161] The server uses a 3D printer as a molding mechanism to physically shape the alternative food based on the generated shape data. Three-dimensional shape data and plant-derived materials are supplied as input, and the printer processes them to produce materialized alternative food as output.

[0162] Step 6:

[0163] Users receive alternative food items provided through a terminal, then cook and taste them. They receive the prepared food as input, go through the cooking process, and receive tasting results and feedback as output. This feedback information is then sent back to the server via the terminal and used to improve future products.

[0164] (Application Example 2)

[0165] 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".

[0166] Modern consumers have diverse needs for plant-based alternative foods and desire that the ingredients offered be customized to their individual preferences and emotions. However, existing alternative foods have fixed textures and shapes, making it difficult to reflect the preferences and emotions of individual consumers. Furthermore, the traditional food service industry lacks sufficient systems for real-time food customization at the point of sale, limiting opportunities to improve the consumer experience.

[0167] 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.

[0168] In this invention, the server includes information acquisition means for acquiring actual animal-based food shape data, intelligent means having a generative model for generating a three-dimensional shape of an alternative food from the acquired shape information and similar information, three-dimensional molding means for forming the alternative food three-dimensionally using plant-derived materials based on the generated three-dimensional shape, information processing means for feeding back evaluation information acquired from consumers to the generative model, and emotion analysis means for recognizing consumer emotions using a wearable terminal and reflecting them in the generative model. This makes it possible to customize alternative foods to meet diverse consumer needs and provide a real-time virtual tasting experience at the point of sale.

[0169] "Information acquisition means" refers to a device or method for collecting shape data of actual animal-based foods.

[0170] A "generative model" is an artificial intelligence algorithm used to generate the three-dimensional shape of a substitute food product based on acquired shape information and similarity information.

[0171] An "intelligent means" is a computer system or software that has the ability to maintain a generative model and process information.

[0172] "Three-dimensional molding means" refers to a technology or apparatus for physically forming substitute food in a three-dimensional manner based on the generated three-dimensional shape.

[0173] "Plant-derived materials" are food ingredients based on components obtained from plants, and are environmentally friendly and have a balanced nutritional profile.

[0174] "Information processing means" refers to a process or device for feeding back evaluation information obtained from consumers to a generation model.

[0175] "Emotion analysis means" refers to a technology or device that uses a wearable device to recognize a consumer's emotions and reflects that information in a generative model.

[0176] A "terminal" is an electronic device that consumers can wear and use for inputting and displaying information.

[0177] In order to implement this invention, it is necessary to construct a system in which a server, terminal, and user cooperate to go through a series of data processing and physical food production processes.

[0178] The server first collects shape information of animal products through information acquisition methods. Specifically, it uses high-resolution scanning technology to obtain detailed three-dimensional shapes of the food. This shape information is stored in a database and used as foundational data to support various generative models. The generative models include generative adversarial networks (GANs), which generate three-dimensional models that mimic the shape and texture in response to user requests and emotions.

[0179] The user wears smart glasses as a terminal and collects emotional data through emotion analysis. This includes facial recognition, voice tone analysis, and analysis of selected words, using emotion recognition software such as Affectiva. The emotional data is transmitted to a server in real time and used there to adjust the parameters of the generative model.

[0180] Based on a three-dimensional model generated by the server, a three-dimensional modeling tool is used to create a three-dimensional substitute food using plant-derived materials. The materials used include components with nutritional value, such as pea protein. The finished food is tasted by the user, and feedback is sent to the server via the terminal to obtain evaluation information. This improves the accuracy of the next generation process.

[0181] For example, if a consumer indicates that they prefer a "juicier texture," the system can generate a customized model suitable for the user using the considered ingredients and present it to the device through a virtual tasting experience.

[0182] As an example of a prompt, use the following text: "If users frequently prefer juicy and tender alternative meats, generate a new model that takes this into account and display it in the AR tasting."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The server uses data acquisition methods to collect shape data of actual animal products. High-resolution 3D scanners and micro-CT scanners are used as input, and the obtained three-dimensional shape data is stored as information. The collected data is stored in a database and used for subsequent data processing by algorithms.

[0186] Step 2:

[0187] The server uses a generative model to generate the three-dimensional shape of a substitute food from collected shape data. The input is shape information of animal products stored in a database, and the output is three-dimensional shape data of the substitute food. Generative AI models, particularly generative adversarial networks (GANs), are used to generate models with highly accurate shape and texture imitation.

[0188] Step 3:

[0189] The user wears smart glasses, which act as a terminal, and collects their own emotional data using emotion analysis technology. Inputs include the user's facial expressions, voice tone, and selected words, while output is informational data analyzing the user's emotions. This analysis is performed in real time by emotion recognition software.

[0190] Step 4:

[0191] The terminal sends the collected sentiment data to the server. The server uses this as additional information to adjust the parameters of the generative model. It receives sentiment data as input and obtains a generative model with adjusted parameters as output. The passenger seat model is updated in response to this prompt.

[0192] Step 5:

[0193] Based on an updated generative model, the server manufactures physical alternative foods using 3D printing technology. The input is adjusted 3D shape data, and the output is the actual alternative food. The printing process uses plant-based materials and is precisely molded with a 3D printer.

[0194] Step 6:

[0195] Users view the alternative food generated on their devices and participate in a virtual tasting experience. Based on their experience, users input evaluation information into their devices and send it to the server. The input consists of feedback from the tasting experience, and the output is improvement data that contributes to optimizing the next generation process.

[0196] Step 7:

[0197] The server analyzes user feedback and incorporates it into subsequent food generation algorithms. The input is user feedback, and the output is an improved generation algorithm. This results in more accurate generation processes in subsequent attempts, providing users with a more satisfying experience.

[0198] 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.

[0199] 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 those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] This invention relates to a system for producing alternative food ingredients using plant-derived raw materials. The system aims to produce alternative food ingredients that have a shape and texture similar to actual animal-based ingredients. Specific embodiments of this invention are described below.

[0215] First, the server acquires shape data of animal-based ingredients. This shape data is obtained using high-resolution 3D scanners and micro-CT scanners. This provides detailed data capturing the external shape of the meat, its internal fibrous structure, and the distribution of individual fat cells.

[0216] Next, a generative model is constructed using the shape data acquired by the server. This model utilizes artificial intelligence techniques, including generative adversarial networks (GANs), and is designed to mimic the shape and texture of actual animal-based food ingredients. Through artificial intelligence, this model continuously learns and optimizes, improving its accuracy based on the feedback received.

[0217] Subsequently, the server uses a generative model to generate a three-dimensional shape of the alternative food. The generated shape data is then physically molded using a three-dimensional printing method. Specifically, a three-dimensional printer is used to print the alternative meat in 3D from plant-based raw materials. Pea protein and other plant-based ingredients are used as raw materials, resulting in a nutritionally balanced product.

[0218] Ultimately, the alternative ingredients are provided to consumers via a device. Users then cook them, experience the taste and texture, and provide feedback. This feedback data is sent to a server and used to improve the entire system.

[0219] Through the above process, the present invention provides consumers with more realistic and customized alternative ingredients and enables continuous product improvement. Through such a system, it is possible to reduce environmental impact and improve food diversity.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The server acquires shape data of actual animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. The scanned data includes the external shape of the meat, its internal fibrous structure, and fat distribution. This data is stored in a database within the system and used in subsequent processes.

[0223] Step 2:

[0224] The server uses shape data to train a generative model. Generative adversarial networks (GANs) are used to build an algorithm that mimics the shape and texture of real food ingredients. This improves the model's ability to generate shapes that closely resemble real food ingredients. This process also includes data preprocessing and hyperparameter optimization.

[0225] Step 3:

[0226] The server uses a pre-trained generative model to generate three-dimensional shapes of alternative ingredients according to the user's requests. Specific features of the shape (e.g., fat distribution, meat thickness) are specified, and a three-dimensional model is created using CG software. The generated shape can be viewed through the user interface.

[0227] Step 4:

[0228] The server generates three-dimensional shape data, which is then sent to a 3D printer to physically shape the alternative meat using plant-based materials. The 3D printer follows a programmed procedure, creating the product of the specified shape using a layering method. During this process, the material composition is considered to achieve appropriate strength and elasticity.

[0229] Step 5:

[0230] Users receive substitute ingredients, cook them, and taste them. They then provide feedback on the appearance, texture, and taste of the ingredients via their device. This feedback can be provided, for example, through a dedicated application or web form.

[0231] Step 6:

[0232] The server collects feedback data from users and uses it to improve the generative model. The feedback is analyzed, and the content is reflected in the model adjustments and the generation of new shapes. This improves the accuracy of the next generation process, allowing it to better meet user demands.

[0233] (Example 1)

[0234] 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."

[0235] In the production of alternative edible ingredients that mimic animal products, there are shortcomings in appearance, taste, and texture, making it difficult to sufficiently improve consumer satisfaction. Furthermore, there is the challenge of providing products that cater to different consumer preferences while using environmentally friendly plant-based materials.

[0236] 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.

[0237] In this invention, the server includes data acquisition means for obtaining physical property data of actual animal-based ingredients, intelligent data processing means having a generative model for generating a three-dimensional shape of an alternative edible material from the acquired property data, and three-dimensional processing means for forming the alternative edible material three-dimensionally using plant-derived edible raw materials based on the generated three-dimensional shape. This makes it possible to manufacture high-quality alternative edible materials that meet the diverse needs of consumers.

[0238] "Data acquisition means" refers to an apparatus or method used to accurately acquire physical property data of actual animal-based food ingredients.

[0239] An "intelligent data processing means" is a system that uses an artificial intelligence model to process data in order to generate a three-dimensional shape of an alternative edible material from acquired physical property data.

[0240] "Three-dimensional processing means" refers to an apparatus or technology that physically forms a three-dimensional substitute edible material using plant-derived edible raw materials based on the generated three-dimensional shape.

[0241] A "generative adversarial algorithm" is an artificial intelligence algorithm that aims to generate more realistic data by having different models compete with each other.

[0242] "Plant-derived edible ingredients" refer to natural or processed materials made from plants that are suitable for consumption.

[0243] "Evaluation information" refers to opinions and feedback from consumers regarding their experience using a product and their level of satisfaction with it.

[0244] "Data update methods" refer to techniques and systems for improving the accuracy of generative models based on evaluation information obtained from consumers.

[0245] This invention is a system for efficiently producing alternative edible materials that mimic animal-based ingredients. Specifically, a server is central to the process, handling data acquisition, model generation, 3D modeling, and feedback processing.

[0246] First, the server uses data acquisition methods to obtain physical property data of animal-based ingredients. This can be done using devices such as high-resolution 3D scanners and micro-CT scanners. This allows for detailed information such as the shape of the ingredients, the structure of the fibers, and the distribution of fat.

[0247] Next, the server constructs a generative AI model based on the acquired data through intelligent data processing. This model uses a generative adversarial algorithm to process the data. As a result, a three-dimensional shape that closely resembles actual animal-based food ingredients is generated.

[0248] Subsequently, the terminal uses a three-dimensional processing device to print a three-dimensional alternative edible material using plant-derived edible raw materials based on the generated three-dimensional shape data. For this purpose, a three-dimensional printer can be used, and pea protein or other nutrient-rich materials can be employed.

[0249] Ultimately, users receive the alternative edible ingredients, cook and taste them, and provide feedback on taste and texture. This feedback data is then fed back to the server. The server continuously improves its generative model based on this feedback data. The goal is to provide high-quality alternative edible ingredients that meet the diverse needs of consumers.

[0250] As a concrete example, here is an example of a prompt: "Generate three-dimensional shape data of a plant-based alternative food that mimics the shape and texture of chicken breast."

[0251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0252] Step 1:

[0253] The server acquires physical property data of animal-based ingredients. To do this, the server operates a high-resolution 3D scanner or micro-CT scanner to scan the ingredients. The input is the actual animal-based ingredient, and the scanner captures its shape and internal structure as digital data. The output is precise three-dimensional shape data. This data will be used for subsequent processing.

[0254] Step 2:

[0255] The server uses the acquired three-dimensional shape data as input to construct a generative AI model using intelligent data processing. Here, a generative adversarial algorithm is employed to generate a model that mimics the detailed shape and texture of animal-based food ingredients. Data processing involves analyzing the input data and optimizing the parameters of the generative model. The output is a highly accurate generative AI model.

[0256] Step 3:

[0257] The server generates three-dimensional shape data of the alternative edible material using a generative AI model. In this step, prompt statements are used to instruct the AI ​​model and calculate the specific three-dimensional shape. The inputs are prompt statements and the generative AI model, and the output is detailed three-dimensional shape data of the alternative edible material.

[0258] Step 4:

[0259] The terminal uses the generated three-dimensional shape data as input to create a three-dimensional alternative edible material using a three-dimensional processing method. A three-dimensional printer is used to print plant-derived edible raw materials in three dimensions. Specifically, a material containing pea protein is loaded into the printer, and the material is layered layer by layer. The output is the completed alternative edible material.

[0260] Step 5:

[0261] Users cook alternative edible ingredients received via their device and evaluate their texture and taste. User input consists of cooking and tasting the ingredients, while output is feedback information about texture and taste. This feedback information is sent to a server and used to improve the generated AI model.

[0262] (Application Example 1)

[0263] 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."

[0264] Traditional methods of producing alternative foods made it difficult for consumers to customize the shape and texture of these foods to their individual preferences, and also made it difficult to reflect consumer demands in real time. As a result, it was not possible to perfectly provide the diverse food experiences that consumers desired, leaving room for improvement in satisfaction with alternative foods.

[0265] 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.

[0266] In this invention, the server includes data acquisition means for obtaining actual animal-based food shape data, artificial intelligence means having a generative model for generating a three-dimensional shape of an alternative food from the acquired shape data and similar data, three-dimensional printing means for forming the alternative food three-dimensionally using plant-derived raw materials based on the generated three-dimensional shape, and user interface means that enables the selection and customization of the alternative food using an augmented reality device. This makes it possible for consumers to customize their preferred shape and texture on the spot and generate an alternative food instantly.

[0267] "Animal-based food shape data" refers to detailed data, including the external shape and internal structure of animal-based food products, acquired using high-resolution 3D scanners or micro-CT scanners.

[0268] A "generative model" is a model that uses artificial intelligence technology to generate the three-dimensional shape of a substitute food ingredient from acquired animal food ingredient shape data.

[0269] "Artificial intelligence methods" refer to methods that utilize AI technology to build generative models and mimic the shape and texture of animal-based ingredients.

[0270] A "three-dimensional printing method" is a device for physically shaping alternative food ingredients from plant-derived raw materials based on the generated three-dimensional shape.

[0271] "Data processing means" refers to digital processing technology used to improve generative models by utilizing evaluation data obtained from consumers.

[0272] An "augmented reality device" is a device that overlays information onto real space, allowing users to visually select and customize alternative ingredients.

[0273] A "user interface means" is an interface that allows consumers to customize the shape and texture of alternative food ingredients.

[0274] To implement this invention, the server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or a micro-CT scanner. This shape data captures the external shape and internal structure of the ingredients in detail. Based on the acquired data, a generative model is constructed on the server. This generative model utilizes artificial intelligence technology, particularly generative adversarial networks, and is designed to precisely mimic the shape and texture of animal-based ingredients.

[0275] A three-dimensional printing device is used based on the three-dimensional shape data obtained from the generative model. Specifically, plant-derived raw materials are supplied to the printer, and alternative food products tailored to consumer preferences are formed in three dimensions. The raw materials used in this process include proteins with a nutritionally superior composition.

[0276] Consumers can visually select and customize alternative ingredients using augmented reality devices, such as smart glasses. These devices function as a user interface, digitizing user requests and sending them to a server. Evaluation data and user feedback are continuously used by data processing systems to optimize the generative model.

[0277] A concrete example is when a consumer chooses vegan ingredients and uses them as a substitute for chicken. Through an augmented reality device, the user can set the texture and thickness and simulate the final cooked state. The generating AI model operates based on the prompt, "Generate a softer, thicker substitute ingredient based on 3D scan data of chicken breast." In this way, customization to meet diverse consumer needs becomes possible on the spot.

[0278] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0279] Step 1:

[0280] The server acquires the shape data of animal-derived food ingredients using a high-resolution 3D scanner and a micro-CT scanner. At this stage, the external shape and internal fiber structure of the food ingredients are captured in detail and stored in the server as digital data. The input is the scanned physical food ingredients, and the output obtained is detailed 3D model data.

[0281] Step 2:

[0282] The server constructs a generation model based on the acquired shape data. This is a process of using adversarial generative networks (GANs) as part of artificial intelligence technology to learn the data. The input is the 3D model data obtained previously, and the output is the model parameters for generating the three-dimensional shape of the alternative food ingredient.

[0283] Step 3:

[0284] The user selects and customizes the alternative food ingredient via an augmented reality device. The user's selection operation is sent to the server as a prompt message. For example, the user sends a prompt message such as "Please generate a softer and thicker alternative food ingredient based on the 3D scan data of chicken breast." The input is the user's customization requirement, and the output is a specific production instruction.

[0285] Step 4:

[0286] The server constructs the three-dimensional shape of the alternative food ingredient using the generation model and sends the shape data to a three-dimensional printing device. This device physically forms the alternative food ingredient using plant-derived raw materials. The input is the model parameters and the user's customization instructions, and the output is the actual alternative food ingredient generated from the 3D printer.

[0287] Step 5:

[0288] Users receive alternative ingredients generated via their terminal, then cook and taste them. They then send feedback to the server, evaluating the taste and texture. The input is the user's tasting results, and the output is feedback data used for system optimization.

[0289] Step 6:

[0290] The server analyzes the received feedback using data processing tools and continuously improves the generative model to enhance its performance and accuracy. The input is the feedback data, and the output is the improved generative model.

[0291] 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.

[0292] This invention is a system that aims to provide alternative food products that better reflect consumer sentiment, in addition to manufacturing alternative food products using plant-derived raw materials. This system provides alternative food products that have a shape and texture similar to actual animal-based foods, and further has a function to customize them while considering user sentiment. Specific embodiments of this invention are described below.

[0293] First, the server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. The acquired data accurately reflects the external shape and internal structure of the meat and is used as foundational data in subsequent processing. The shape data is stored in a database within the system.

[0294] Next, a generative model is constructed based on the shape data acquired by the server. This generative model is designed to generate algorithms that mimic the shape and texture of actual food ingredients using Generative Adversarial Networks (GANs). The server continuously learns and optimizes the generative model using feedback and evaluation data obtained from the user.

[0295] Furthermore, the system incorporates an emotion engine. The emotion engine analyzes the user's facial expressions, tone of voice, and selected words to recognize the user's emotions. This allows the server to retrieve the user's emotion data and customize the food items according to those emotions. For example, if a user expresses an emotion indicating a preference for a particular food item, it is possible to generate a shape that incorporates that characteristic.

[0296] Next, the server uses a generative model to generate a three-dimensional shape that responds to the user's requests and emotions. The generated shape data is then physically molded using a three-dimensional printing method. Specifically, a three-dimensional printer prints the alternative meat in three dimensions using plant-based raw materials. These raw materials include nutritionally balanced ingredients such as pea protein.

[0297] Ultimately, the user receives the alternative ingredients via a terminal. The user receives the product, cooks and samples it, and provides feedback. The feedback data is collected and analyzed by a server to help improve the next production process.

[0298] Through the above process, the present invention meets the diverse needs of consumers and enables product customization based on emotional data, thereby continuously providing a better dining experience.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. This shape data includes detailed structural information such as the surface shape, internal structure, and fat distribution of the meat. This data is stored in the system's database and serves as foundational data for subsequent processing.

[0302] Step 2:

[0303] Based on the shape data obtained by the server, the generation model is trained. Here, an algorithm for reproducing the actual shape and texture of meat is learned using adversarial generative networks (GANs). This model is important for simulating the realistic appearance and texture of food ingredients.

[0304] Step 3:

[0305] In order for the terminal to recognize the user's emotions, the camera and microphone are used to analyze the user's facial expressions, voice tone, and selected words. The emotion engine analyzes this data and determines the user's emotional state. Thereby, important input information for determining what kind of characteristics of food ingredients to generate is obtained.

[0306] Step 4:

[0307] The server utilizes the generation model to generate three-dimensional shape data according to the user's requests and emotions. For example, when the user shows an emotion of preferring a rich flavor, a shape with an emphasized fat distribution can be created accordingly. At this point, the feedback data obtained from the user is also taken into consideration.

[0308] Step 5:

[0309] The server sends the generated three-dimensional shape data to a 3D printer, and uses plant-derived raw materials to stereoscopically print alternative food ingredients. Based on the set shape, the food ingredients are precisely shaped by the lamination method. In this process, the blending of nutritionally balanced materials such as pea protein is taken into consideration.

[0310] Step 6:

[0311] The user receives the completed alternative food ingredient through the terminal and actually cooks and samples it. The user inputs evaluations regarding the appearance, texture, and taste of the food ingredient, and the terminal collects that feedback . The user experience is recorded and utilized for making better products.

[0312] Step 7:

[0313] The server analyzes the collected feedback data and uses it to adjust the generative model. Based on the insights gained from the feedback, the model's generative parameters are updated and reflected in the next product generation. This allows the system to be continuously improved, enabling the production of better alternative ingredients.

[0314] (Example 2)

[0315] 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".

[0316] In recent years, the food industry has seen a growing demand for alternative foods to replace animal-based ingredients. However, conventional technologies have presented challenges in fully meeting consumer preferences because the shape and texture of these alternative foods differ significantly from those of animal-based ingredients. Furthermore, customization based on individual consumer preferences is difficult, often resulting in uniform product offerings. Therefore, there is a need to develop systems that provide realistic alternative foods that closely resemble animal-based ingredients while taking consumer preferences into account.

[0317] 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.

[0318] In this invention, the server includes sensor means for acquiring actual animal-based food shape data, artificial intelligence means for generating a three-dimensional shape of a substitute food from the acquired shape data and similar data, molding means for forming the substitute food three-dimensionally using plant-derived materials based on the generated three-dimensional shape, information processing means for feeding back evaluation data and emotional data acquired from consumers to the artificial intelligence means, and analysis means for generating emotional data by analyzing the consumer's facial expressions, voice, and selected words. This makes it possible to provide realistic substitute foods that take consumer emotions into consideration.

[0319] "Sensing means" refers to a device used to acquire shape data of actual animal-based food ingredients.

[0320] "Artificial intelligence means" refers to technology for generating the three-dimensional shape of a substitute food from acquired shape data and similar data.

[0321] "Molding means" refers to a device for forming a three-dimensional substitute food using plant-derived materials based on the generated three-dimensional shape.

[0322] "Information processing means" refers to a device or program for feeding back evaluation data and emotional data acquired from consumers to artificial intelligence means.

[0323] "Analysis means" refers to a device or program for analyzing a consumer's facial expressions, voice, and chosen words to generate emotional data.

[0324] A "generative adversarial network" refers to a machine learning model in which two networks compete and learn together to generate new data.

[0325] "Plant-derived materials" refer to materials obtained from plants that contain proteins with a nutritional composition taken into consideration.

[0326] This invention is a system for manufacturing alternative foods that have a shape and texture similar to animal-based ingredients, and further customizes the products while taking consumer preferences into consideration. This embodiment is realized primarily through the collaboration of a server, a terminal, and a user, using sensor means, artificial intelligence means, molding means, information processing means, and analysis means.

[0327] The server uses a high-resolution 3D scanner or micro-CT scanner as a sensor to acquire shape data of animal-based ingredients. This shape data is stored in a database in a digital format. Next, the server utilizes generative adversarial networks (GANs) as an artificial intelligence tool to generate three-dimensional shapes of alternative foods from this shape data. In this process, the algorithm designs alternative foods with a realistic appearance and texture based on the obtained shape data.

[0328] The device analyzes the user's facial expressions, voice tone, and selected words as analytical tools, and generates emotional data using an emotion engine. For example, it measures the user's emotions through the camera and microphone and sends emotion-based data to a server. This makes it possible to customize alternative foods based on consumer preferences.

[0329] Ultimately, the server uses a 3D printer as a molding tool to print alternative foods using plant-based materials, such as pea protein. After the molded alternative food is provided to the consumer, the user cooks and evaluates the product. The evaluation data is then fed back to the server and used in the next production process. Through this entire process, consumers can obtain a customized food experience tailored to their individual preferences and emotions.

[0330] For example, if facial expression data analyzes that a user prefers a "steak-like" alternative food, the server uses that information to design an alternative food that closely resembles the shape and taste of steak, employing a generative model.

[0331] An example of a prompt message would be: "Based on the user's emotional data, please 3D print a spicy-flavored steak-like substitute food." In this way, detailed customization based on emotions is possible.

[0332] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0333] Step 1:

[0334] The server uses a high-resolution 3D scanner or micro-CT scanner as a sensor to acquire shape data of actual animal-based ingredients. The animal-based ingredients themselves are prepared as input, and the scanner scans them to obtain shape data as output. This data is saved in a digital format and used in subsequent generation processes.

[0335] Step 2:

[0336] The server constructs a generative model using generative adversarial networks (GANs) based on the acquired shape data. Shape data is given as input, and the GANs process this data to generate a realistic three-dimensional shape algorithm as output. This model is optimized to mimic the appearance and texture of actual food ingredients.

[0337] Step 3:

[0338] The device uses a camera and microphone as analytical tools to collect the user's facial expressions, voice tone, and selected words, generating emotional data. The user's biometric information is provided as input, and the device analyzes it to construct emotional data as output. This information is sent to a server and used for food customization.

[0339] Step 4:

[0340] The server generates a three-dimensional shape of the alternative food using the user's emotional data and generative model. Emotional data and a previously constructed generative model are used as input, and the server matches them to output a three-dimensional shape that reflects specific customizations. Texture and taste are also adjusted to the ideal state at this stage.

[0341] Step 5:

[0342] The server uses a 3D printer as a molding mechanism to physically shape the alternative food based on the generated shape data. Three-dimensional shape data and plant-derived materials are supplied as input, and the printer processes them to produce materialized alternative food as output.

[0343] Step 6:

[0344] Users receive alternative food items provided through a terminal, then cook and taste them. They receive the prepared food as input, go through the cooking process, and receive tasting results and feedback as output. This feedback information is then sent back to the server via the terminal and used to improve future products.

[0345] (Application Example 2)

[0346] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0347] Modern consumers have diverse needs for plant-based alternative foods and desire that the ingredients offered be customized to their individual preferences and emotions. However, existing alternative foods have fixed textures and shapes, making it difficult to reflect the preferences and emotions of individual consumers. Furthermore, the traditional food service industry lacks sufficient systems for real-time food customization at the point of sale, limiting opportunities to improve the consumer experience.

[0348] 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.

[0349] In this invention, the server includes information acquisition means for acquiring actual animal-based food shape data, intelligent means having a generative model for generating a three-dimensional shape of an alternative food from the acquired shape information and similar information, three-dimensional molding means for forming the alternative food three-dimensionally using plant-derived materials based on the generated three-dimensional shape, information processing means for feeding back evaluation information acquired from consumers to the generative model, and emotion analysis means for recognizing consumer emotions using a wearable terminal and reflecting them in the generative model. This makes it possible to customize alternative foods to meet diverse consumer needs and provide a real-time virtual tasting experience at the point of sale.

[0350] "Information acquisition means" refers to a device or method for collecting shape data of actual animal-based foods.

[0351] A "generative model" is an artificial intelligence algorithm used to generate the three-dimensional shape of a substitute food product based on acquired shape information and similarity information.

[0352] An "intelligent means" is a computer system or software that has the ability to maintain a generative model and process information.

[0353] "Three-dimensional molding means" refers to a technology or apparatus for physically forming substitute food in a three-dimensional manner based on the generated three-dimensional shape.

[0354] "Plant-derived materials" are food ingredients based on components obtained from plants, and are environmentally friendly and have a balanced nutritional profile.

[0355] "Information processing means" refers to a process or device for feeding back evaluation information obtained from consumers to a generation model.

[0356] "Emotion analysis means" refers to a technology or device that uses a wearable device to recognize a consumer's emotions and reflects that information in a generative model.

[0357] A "terminal" is an electronic device that consumers can wear and use for inputting and displaying information.

[0358] In order to implement this invention, it is necessary to construct a system in which a server, terminal, and user cooperate to go through a series of data processing and physical food production processes.

[0359] The server first collects shape information of animal products through information acquisition methods. Specifically, it uses high-resolution scanning technology to obtain detailed three-dimensional shapes of the food. This shape information is stored in a database and used as foundational data to support various generative models. The generative models include generative adversarial networks (GANs), which generate three-dimensional models that mimic the shape and texture in response to user requests and emotions.

[0360] The user wears smart glasses as a terminal and collects emotional data through emotion analysis. This includes facial recognition, voice tone analysis, and analysis of selected words, using emotion recognition software such as Affectiva. The emotional data is transmitted to a server in real time and used there to adjust the parameters of the generative model.

[0361] Based on a three-dimensional model generated by the server, a three-dimensional modeling tool is used to create a three-dimensional substitute food using plant-derived materials. The materials used include components with nutritional value, such as pea protein. The finished food is tasted by the user, and feedback is sent to the server via the terminal to obtain evaluation information. This improves the accuracy of the next generation process.

[0362] For example, if a consumer indicates that they prefer a "juicier texture," the system can generate a customized model suitable for the user using the considered ingredients and present it to the device through a virtual tasting experience.

[0363] As an example of a prompt, use the following text: "If users frequently prefer juicy and tender alternative meats, generate a new model that takes this into account and display it in the AR tasting."

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The server uses data acquisition methods to collect shape data of actual animal products. High-resolution 3D scanners and micro-CT scanners are used as input, and the obtained three-dimensional shape data is stored as information. The collected data is stored in a database and used for subsequent data processing by algorithms.

[0367] Step 2:

[0368] The server uses a generative model to generate the three-dimensional shape of a substitute food from collected shape data. The input is shape information of animal products stored in a database, and the output is three-dimensional shape data of the substitute food. Generative AI models, particularly generative adversarial networks (GANs), are used to generate models with highly accurate shape and texture imitation.

[0369] Step 3:

[0370] The user wears smart glasses, which act as a terminal, and collects their own emotional data using emotion analysis technology. Inputs include the user's facial expressions, voice tone, and selected words, while output is informational data analyzing the user's emotions. This analysis is performed in real time by emotion recognition software.

[0371] Step 4:

[0372] The terminal sends the collected sentiment data to the server. The server uses this as additional information to adjust the parameters of the generative model. It receives sentiment data as input and obtains a generative model with adjusted parameters as output. The passenger seat model is updated in response to this prompt.

[0373] Step 5:

[0374] Based on an updated generative model, the server manufactures physical alternative foods using 3D printing technology. The input is adjusted 3D shape data, and the output is the actual alternative food. The printing process uses plant-based materials and is precisely molded with a 3D printer.

[0375] Step 6:

[0376] Users view the alternative food generated on their devices and participate in a virtual tasting experience. Based on their experience, users input evaluation information into their devices and send it to the server. The input consists of feedback from the tasting experience, and the output is improvement data that contributes to optimizing the next generation process.

[0377] Step 7:

[0378] The server analyzes user feedback and incorporates it into subsequent food generation algorithms. The input is user feedback, and the output is an improved generation algorithm. This results in more accurate generation processes in subsequent attempts, providing users with a more satisfying experience.

[0379] 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.

[0380] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] 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.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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".

[0395] This invention relates to a system for producing alternative food ingredients using plant-derived raw materials. The system aims to produce alternative food ingredients that have a shape and texture similar to actual animal-based ingredients. Specific embodiments of this invention are described below.

[0396] First, the server acquires shape data of animal-based ingredients. This shape data is obtained using high-resolution 3D scanners and micro-CT scanners. This provides detailed data capturing the external shape of the meat, its internal fibrous structure, and the distribution of individual fat cells.

[0397] Next, a generative model is constructed using the shape data acquired by the server. This model utilizes artificial intelligence techniques, including generative adversarial networks (GANs), and is designed to mimic the shape and texture of actual animal-based food ingredients. Through artificial intelligence, this model continuously learns and optimizes, improving its accuracy based on the feedback received.

[0398] Subsequently, the server uses a generative model to generate a three-dimensional shape of the alternative food. The generated shape data is then physically molded using a three-dimensional printing method. Specifically, a three-dimensional printer is used to print the alternative meat in 3D from plant-based raw materials. Pea protein and other plant-based ingredients are used as raw materials, resulting in a nutritionally balanced product.

[0399] Ultimately, the alternative ingredients are provided to consumers via a device. Users then cook them, experience the taste and texture, and provide feedback. This feedback data is sent to a server and used to improve the entire system.

[0400] Through the above process, the present invention provides consumers with more realistic and customized alternative ingredients and enables continuous product improvement. Through such a system, it is possible to reduce environmental impact and improve food diversity.

[0401] The following describes the processing flow.

[0402] Step 1:

[0403] The server acquires shape data of actual animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. The scanned data includes the external shape of the meat, its internal fibrous structure, and fat distribution. This data is stored in a database within the system and used in subsequent processes.

[0404] Step 2:

[0405] The server uses shape data to train a generative model. Generative adversarial networks (GANs) are used to build an algorithm that mimics the shape and texture of real food ingredients. This improves the model's ability to generate shapes that closely resemble real food ingredients. This process also includes data preprocessing and hyperparameter optimization.

[0406] Step 3:

[0407] The server uses a pre-trained generative model to generate three-dimensional shapes of alternative ingredients according to the user's requests. Specific features of the shape (e.g., fat distribution, meat thickness) are specified, and a three-dimensional model is created using CG software. The generated shape can be viewed through the user interface.

[0408] Step 4:

[0409] The server generates three-dimensional shape data, which is then sent to a 3D printer to physically shape the alternative meat using plant-based materials. The 3D printer follows a programmed procedure, creating the product of the specified shape using a layering method. During this process, the material composition is considered to achieve appropriate strength and elasticity.

[0410] Step 5:

[0411] Users receive substitute ingredients, cook them, and taste them. They then provide feedback on the appearance, texture, and taste of the ingredients via their device. This feedback can be provided, for example, through a dedicated application or web form.

[0412] Step 6:

[0413] The server collects feedback data from users and uses it to improve the generative model. The feedback is analyzed, and the content is reflected in the model adjustments and the generation of new shapes. This improves the accuracy of the next generation process, allowing it to better meet user demands.

[0414] (Example 1)

[0415] 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."

[0416] In the production of alternative edible ingredients that mimic animal products, there are shortcomings in appearance, taste, and texture, making it difficult to sufficiently improve consumer satisfaction. Furthermore, there is the challenge of providing products that cater to different consumer preferences while using environmentally friendly plant-based materials.

[0417] 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.

[0418] In this invention, the server includes data acquisition means for obtaining physical property data of actual animal-based ingredients, intelligent data processing means having a generative model for generating a three-dimensional shape of an alternative edible material from the acquired property data, and three-dimensional processing means for forming the alternative edible material three-dimensionally using plant-derived edible raw materials based on the generated three-dimensional shape. This makes it possible to manufacture high-quality alternative edible materials that meet the diverse needs of consumers.

[0419] "Data acquisition means" refers to an apparatus or method used to accurately acquire physical property data of actual animal-based food ingredients.

[0420] An "intelligent data processing means" is a system that uses an artificial intelligence model to process data in order to generate a three-dimensional shape of an alternative edible material from acquired physical property data.

[0421] "Three-dimensional processing means" refers to an apparatus or technology that physically forms a three-dimensional substitute edible material using plant-derived edible raw materials based on the generated three-dimensional shape.

[0422] A "generative adversarial algorithm" is an artificial intelligence algorithm that aims to generate more realistic data by having different models compete with each other.

[0423] "Plant-derived edible ingredients" refer to natural or processed materials made from plants that are suitable for consumption.

[0424] "Evaluation information" refers to opinions and feedback from consumers regarding their experience using a product and their level of satisfaction with it.

[0425] "Data update methods" refer to techniques and systems for improving the accuracy of generative models based on evaluation information obtained from consumers.

[0426] This invention is a system for efficiently producing alternative edible materials that mimic animal-based ingredients. Specifically, a server is central to the process, handling data acquisition, model generation, 3D modeling, and feedback processing.

[0427] First, the server uses data acquisition methods to obtain physical property data of animal-based ingredients. This can be done using devices such as high-resolution 3D scanners and micro-CT scanners. This allows for detailed information such as the shape of the ingredients, the structure of the fibers, and the distribution of fat.

[0428] Next, the server constructs a generative AI model based on the acquired data through intelligent data processing. This model uses a generative adversarial algorithm to process the data. As a result, a three-dimensional shape that closely resembles actual animal-based food ingredients is generated.

[0429] Subsequently, the terminal uses a three-dimensional processing device to print a three-dimensional alternative edible material using plant-derived edible raw materials based on the generated three-dimensional shape data. For this purpose, a three-dimensional printer can be used, and pea protein or other nutrient-rich materials can be employed.

[0430] Ultimately, users receive the alternative edible ingredients, cook and taste them, and provide feedback on taste and texture. This feedback data is then fed back to the server. The server continuously improves its generative model based on this feedback data. The goal is to provide high-quality alternative edible ingredients that meet the diverse needs of consumers.

[0431] As a concrete example, here is an example of a prompt: "Generate three-dimensional shape data of a plant-based alternative food that mimics the shape and texture of chicken breast."

[0432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0433] Step 1:

[0434] The server acquires physical property data of animal-based ingredients. To do this, the server operates a high-resolution 3D scanner or micro-CT scanner to scan the ingredients. The input is the actual animal-based ingredient, and the scanner captures its shape and internal structure as digital data. The output is precise three-dimensional shape data. This data will be used for subsequent processing.

[0435] Step 2:

[0436] The server uses the acquired three-dimensional shape data as input to construct a generative AI model using intelligent data processing. Here, a generative adversarial algorithm is employed to generate a model that mimics the detailed shape and texture of animal-based food ingredients. Data processing involves analyzing the input data and optimizing the parameters of the generative model. The output is a highly accurate generative AI model.

[0437] Step 3:

[0438] The server generates three-dimensional shape data of the alternative edible material using a generative AI model. In this step, prompt statements are used to instruct the AI ​​model and calculate the specific three-dimensional shape. The inputs are prompt statements and the generative AI model, and the output is detailed three-dimensional shape data of the alternative edible material.

[0439] Step 4:

[0440] The terminal uses the generated three-dimensional shape data as input to create a three-dimensional alternative edible material using a three-dimensional processing method. A three-dimensional printer is used to print plant-derived edible raw materials in three dimensions. Specifically, a material containing pea protein is loaded into the printer, and the material is layered layer by layer. The output is the completed alternative edible material.

[0441] Step 5:

[0442] Users cook alternative edible ingredients received via their device and evaluate their texture and taste. User input consists of cooking and tasting the ingredients, while output is feedback information about texture and taste. This feedback information is sent to a server and used to improve the generated AI model.

[0443] (Application Example 1)

[0444] 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."

[0445] Traditional methods of producing alternative foods made it difficult for consumers to customize the shape and texture of these foods to their individual preferences, and also made it difficult to reflect consumer demands in real time. As a result, it was not possible to perfectly provide the diverse food experiences that consumers desired, leaving room for improvement in satisfaction with alternative foods.

[0446] 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.

[0447] In this invention, the server includes data acquisition means for obtaining actual animal-based food shape data, artificial intelligence means having a generative model for generating a three-dimensional shape of an alternative food from the acquired shape data and similar data, three-dimensional printing means for forming the alternative food three-dimensionally using plant-derived raw materials based on the generated three-dimensional shape, and user interface means that enables the selection and customization of the alternative food using an augmented reality device. This makes it possible for consumers to customize their preferred shape and texture on the spot and generate an alternative food instantly.

[0448] "Animal-based food shape data" refers to detailed data, including the external shape and internal structure of animal-based food products, acquired using high-resolution 3D scanners or micro-CT scanners.

[0449] A "generative model" is a model that uses artificial intelligence technology to generate the three-dimensional shape of a substitute food ingredient from acquired animal food ingredient shape data.

[0450] "Artificial intelligence methods" refer to methods that utilize AI technology to build generative models and mimic the shape and texture of animal-based ingredients.

[0451] A "three-dimensional printing method" is a device for physically shaping alternative food ingredients from plant-derived raw materials based on the generated three-dimensional shape.

[0452] "Data processing means" refers to digital processing technology used to improve generative models by utilizing evaluation data obtained from consumers.

[0453] An "augmented reality device" is a device that overlays information onto real space, allowing users to visually select and customize alternative ingredients.

[0454] A "user interface means" is an interface that allows consumers to customize the shape and texture of alternative food ingredients.

[0455] To implement this invention, the server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or a micro-CT scanner. This shape data captures the external shape and internal structure of the ingredients in detail. Based on the acquired data, a generative model is constructed on the server. This generative model utilizes artificial intelligence technology, particularly generative adversarial networks, and is designed to precisely mimic the shape and texture of animal-based ingredients.

[0456] A three-dimensional printing device is used based on the three-dimensional shape data obtained from the generative model. Specifically, plant-derived raw materials are supplied to the printer, and alternative food products tailored to consumer preferences are formed in three dimensions. The raw materials used in this process include proteins with a nutritionally superior composition.

[0457] Consumers can visually select and customize alternative ingredients using augmented reality devices, such as smart glasses. These devices function as a user interface, digitizing user requests and sending them to a server. Evaluation data and user feedback are continuously used by data processing systems to optimize the generative model.

[0458] A concrete example is when a consumer chooses vegan ingredients and uses them as a substitute for chicken. Through an augmented reality device, the user can set the texture and thickness and simulate the final cooked state. The generating AI model operates based on the prompt, "Generate a softer, thicker substitute ingredient based on 3D scan data of chicken breast." In this way, customization to meet diverse consumer needs becomes possible on the spot.

[0459] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0460] Step 1:

[0461] The server acquires shape data of animal-based food ingredients using a high-resolution 3D scanner and a micro-CT scanner. At this stage, the external shape and internal fiber structure of the food ingredients are captured in detail and stored as digital data within the server. The input is the scanned actual food ingredients, and the output is detailed 3D model data.

[0462] Step 2:

[0463] The server constructs a generative model based on the acquired shape data. This is a process that uses Generative Adversarial Networks (GANs) as part of artificial intelligence technology to learn from the data. The input is the previously obtained 3D model data, and the output is the model parameters for generating the three-dimensional shape of the substitute food item.

[0464] Step 3:

[0465] The user selects and customizes alternative ingredients via an augmented reality device. The user's selections are sent to the server as prompts. For example, the user might send the prompt, "Based on 3D scan data of chicken breast, please generate a softer, thicker alternative ingredient." The input is the user's customization request, and the output is a specific production instruction.

[0466] Step 4:

[0467] The server constructs the three-dimensional shape of the alternative food using a generative model and sends that shape data to a 3D printer. This printer physically forms the alternative food using plant-based raw materials. The input consists of model parameters and user customization instructions, and the output is the alternative food actually produced by the 3D printer.

[0468] Step 5:

[0469] Users receive alternative ingredients generated via their terminal, then cook and taste them. They then send feedback to the server, evaluating the taste and texture. The input is the user's tasting results, and the output is feedback data used for system optimization.

[0470] Step 6:

[0471] The server analyzes the received feedback using data processing tools and continuously improves the generative model to enhance its performance and accuracy. The input is the feedback data, and the output is the improved generative model.

[0472] 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.

[0473] This invention is a system that aims to provide alternative food products that better reflect consumer sentiment, in addition to manufacturing alternative food products using plant-derived raw materials. This system provides alternative food products that have a shape and texture similar to actual animal-based foods, and further has a function to customize them while considering user sentiment. Specific embodiments of this invention are described below.

[0474] First, the server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. The acquired data accurately reflects the external shape and internal structure of the meat and is used as foundational data in subsequent processing. The shape data is stored in a database within the system.

[0475] Next, a generative model is constructed based on the shape data acquired by the server. This generative model is designed to generate algorithms that mimic the shape and texture of actual food ingredients using Generative Adversarial Networks (GANs). The server continuously learns and optimizes the generative model using feedback and evaluation data obtained from the user.

[0476] Furthermore, the system incorporates an emotion engine. The emotion engine analyzes the user's facial expressions, tone of voice, and selected words to recognize the user's emotions. This allows the server to retrieve the user's emotion data and customize the food items according to those emotions. For example, if a user expresses an emotion indicating a preference for a particular food item, it is possible to generate a shape that incorporates that characteristic.

[0477] Next, the server uses a generative model to generate a three-dimensional shape that responds to the user's requests and emotions. The generated shape data is then physically molded using a three-dimensional printing method. Specifically, a three-dimensional printer prints the alternative meat in three dimensions using plant-based raw materials. These raw materials include nutritionally balanced ingredients such as pea protein.

[0478] Ultimately, the user receives the alternative ingredients via a terminal. The user receives the product, cooks and samples it, and provides feedback. The feedback data is collected and analyzed by a server to help improve the next production process.

[0479] Through the above process, the present invention meets the diverse needs of consumers and enables product customization based on emotional data, thereby continuously providing a better dining experience.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. This shape data includes detailed structural information such as the surface shape, internal structure, and fat distribution of the meat. This data is stored in the system's database and serves as foundational data for subsequent processing.

[0483] Step 2:

[0484] The generative model is trained based on the shape data acquired by the server. Here, a generative adversarial network (GAN) is used to learn an algorithm that reproduces the shape and texture of actual meat. This model is important for simulating the realistic appearance and texture of food ingredients.

[0485] Step 3:

[0486] To recognize the user's emotions, the device uses its camera and microphone to analyze the user's facial expressions, voice tone, and selected words. The emotion engine analyzes this data to determine the user's emotional state. This provides crucial input information for determining what characteristics of ingredients to produce.

[0487] Step 4:

[0488] The server utilizes a generative model to generate three-dimensional shape data that responds to user requests and emotions. For example, if a user expresses a preference for rich flavor, the server can create a shape that emphasizes the distribution of fat accordingly. At this point, feedback data obtained from the user is also taken into consideration.

[0489] Step 5:

[0490] The server generates three-dimensional shape data, which is then sent to a 3D printer to print a three-dimensional substitute food using plant-based materials. Based on the set shape, the food is precisely molded using a layering method. This process takes into account the blending of nutritionally balanced materials, such as pea protein.

[0491] Step 6:

[0492] Users receive the completed substitute ingredients via a device and then actually cook and taste them. Users input their evaluations of the ingredients' appearance, texture, and taste, and the device collects this feedback. The user experience is recorded and used to create better products.

[0493] Step 7:

[0494] The server analyzes the collected feedback data and uses it to adjust the generative model. Based on the insights gained from the feedback, the model's generative parameters are updated and reflected in the next product generation. This allows the system to be continuously improved, enabling the production of better alternative ingredients.

[0495] (Example 2)

[0496] 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."

[0497] In recent years, the food industry has seen a growing demand for alternative foods to replace animal-based ingredients. However, conventional technologies have presented challenges in fully meeting consumer preferences because the shape and texture of these alternative foods differ significantly from those of animal-based ingredients. Furthermore, customization based on individual consumer preferences is difficult, often resulting in uniform product offerings. Therefore, there is a need to develop systems that provide realistic alternative foods that closely resemble animal-based ingredients while taking consumer preferences into account.

[0498] 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.

[0499] In this invention, the server includes sensor means for acquiring actual animal-based food shape data, artificial intelligence means for generating a three-dimensional shape of a substitute food from the acquired shape data and similar data, molding means for forming the substitute food three-dimensionally using plant-derived materials based on the generated three-dimensional shape, information processing means for feeding back evaluation data and emotional data acquired from consumers to the artificial intelligence means, and analysis means for generating emotional data by analyzing the consumer's facial expressions, voice, and selected words. This makes it possible to provide realistic substitute foods that take consumer emotions into consideration.

[0500] "Sensing means" refers to a device used to acquire shape data of actual animal-based food ingredients.

[0501] "Artificial intelligence means" refers to technology for generating the three-dimensional shape of a substitute food from acquired shape data and similar data.

[0502] "Molding means" refers to a device for forming a three-dimensional substitute food using plant-derived materials based on the generated three-dimensional shape.

[0503] "Information processing means" refers to a device or program for feeding back evaluation data and emotional data acquired from consumers to artificial intelligence means.

[0504] "Analysis means" refers to a device or program for analyzing a consumer's facial expressions, voice, and chosen words to generate emotional data.

[0505] A "generative adversarial network" refers to a machine learning model in which two networks compete and learn together to generate new data.

[0506] "Plant-derived materials" refer to materials obtained from plants that contain proteins with a nutritional composition taken into consideration.

[0507] This invention is a system for manufacturing alternative foods that have a shape and texture similar to animal-based ingredients, and further customizes the products while taking consumer preferences into consideration. This embodiment is realized primarily through the collaboration of a server, a terminal, and a user, using sensor means, artificial intelligence means, molding means, information processing means, and analysis means.

[0508] The server uses a high-resolution 3D scanner or micro-CT scanner as a sensor to acquire shape data of animal-based ingredients. This shape data is stored in a database in a digital format. Next, the server utilizes generative adversarial networks (GANs) as an artificial intelligence tool to generate three-dimensional shapes of alternative foods from this shape data. In this process, the algorithm designs alternative foods with a realistic appearance and texture based on the obtained shape data.

[0509] The device analyzes the user's facial expressions, voice tone, and selected words as analytical tools, and generates emotional data using an emotion engine. For example, it measures the user's emotions through the camera and microphone and sends emotion-based data to a server. This makes it possible to customize alternative foods based on consumer preferences.

[0510] Ultimately, the server uses a 3D printer as a molding tool to print alternative foods using plant-based materials, such as pea protein. After the molded alternative food is provided to the consumer, the user cooks and evaluates the product. The evaluation data is then fed back to the server and used in the next production process. Through this entire process, consumers can obtain a customized food experience tailored to their individual preferences and emotions.

[0511] For example, if facial expression data analyzes that a user prefers a "steak-like" alternative food, the server uses that information to design an alternative food that closely resembles the shape and taste of steak, employing a generative model.

[0512] An example of a prompt message would be: "Based on the user's emotional data, please 3D print a spicy-flavored steak-like substitute food." In this way, detailed customization based on emotions is possible.

[0513] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0514] Step 1:

[0515] The server uses a high-resolution 3D scanner or micro-CT scanner as a sensor to acquire shape data of actual animal-based ingredients. The animal-based ingredients themselves are prepared as input, and the scanner scans them to obtain shape data as output. This data is saved in a digital format and used in subsequent generation processes.

[0516] Step 2:

[0517] The server constructs a generative model using generative adversarial networks (GANs) based on the acquired shape data. Shape data is given as input, and the GANs process this data to generate a realistic three-dimensional shape algorithm as output. This model is optimized to mimic the appearance and texture of actual food ingredients.

[0518] Step 3:

[0519] The device uses a camera and microphone as analytical tools to collect the user's facial expressions, voice tone, and selected words, generating emotional data. The user's biometric information is provided as input, and the device analyzes it to construct emotional data as output. This information is sent to a server and used for food customization.

[0520] Step 4:

[0521] The server generates a three-dimensional shape of the alternative food using the user's emotional data and generative model. Emotional data and a previously constructed generative model are used as input, and the server matches them to output a three-dimensional shape that reflects specific customizations. Texture and taste are also adjusted to the ideal state at this stage.

[0522] Step 5:

[0523] The server uses a 3D printer as a molding mechanism to physically shape the alternative food based on the generated shape data. Three-dimensional shape data and plant-derived materials are supplied as input, and the printer processes them to produce materialized alternative food as output.

[0524] Step 6:

[0525] Users receive alternative food items provided through a terminal, then cook and taste them. They receive the prepared food as input, go through the cooking process, and receive tasting results and feedback as output. This feedback information is then sent back to the server via the terminal and used to improve future products.

[0526] (Application Example 2)

[0527] 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."

[0528] Modern consumers have diverse needs for plant-based alternative foods and desire that the ingredients offered be customized to their individual preferences and emotions. However, existing alternative foods have fixed textures and shapes, making it difficult to reflect the preferences and emotions of individual consumers. Furthermore, the traditional food service industry lacks sufficient systems for real-time food customization at the point of sale, limiting opportunities to improve the consumer experience.

[0529] 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.

[0530] In this invention, the server includes information acquisition means for acquiring actual animal-based food shape data, intelligent means having a generative model for generating a three-dimensional shape of an alternative food from the acquired shape information and similar information, three-dimensional molding means for forming the alternative food three-dimensionally using plant-derived materials based on the generated three-dimensional shape, information processing means for feeding back evaluation information acquired from consumers to the generative model, and emotion analysis means for recognizing consumer emotions using a wearable terminal and reflecting them in the generative model. This makes it possible to customize alternative foods to meet diverse consumer needs and provide a real-time virtual tasting experience at the point of sale.

[0531] "Information acquisition means" refers to a device or method for collecting shape data of actual animal-based foods.

[0532] A "generative model" is an artificial intelligence algorithm used to generate the three-dimensional shape of a substitute food product based on acquired shape information and similarity information.

[0533] An "intelligent means" is a computer system or software that has the ability to maintain a generative model and process information.

[0534] "Three-dimensional molding means" refers to a technology or apparatus for physically forming substitute food in a three-dimensional manner based on the generated three-dimensional shape.

[0535] "Plant-derived materials" are food ingredients based on components obtained from plants, and are environmentally friendly and have a balanced nutritional profile.

[0536] "Information processing means" refers to a process or device for feeding back evaluation information obtained from consumers to a generation model.

[0537] "Emotion analysis means" refers to a technology or device that uses a wearable device to recognize a consumer's emotions and reflects that information in a generative model.

[0538] A "terminal" is an electronic device that consumers can wear and use for inputting and displaying information.

[0539] In order to implement this invention, it is necessary to construct a system in which a server, terminal, and user cooperate to go through a series of data processing and physical food production processes.

[0540] The server first collects shape information of animal products through information acquisition methods. Specifically, it uses high-resolution scanning technology to obtain detailed three-dimensional shapes of the food. This shape information is stored in a database and used as foundational data to support various generative models. The generative models include generative adversarial networks (GANs), which generate three-dimensional models that mimic the shape and texture in response to user requests and emotions.

[0541] The user wears smart glasses as a terminal and collects emotional data through emotion analysis. This includes facial recognition, voice tone analysis, and analysis of selected words, using emotion recognition software such as Affectiva. The emotional data is transmitted to a server in real time and used there to adjust the parameters of the generative model.

[0542] Based on a three-dimensional model generated by the server, a three-dimensional modeling tool is used to create a three-dimensional substitute food using plant-derived materials. The materials used include components with nutritional value, such as pea protein. The finished food is tasted by the user, and feedback is sent to the server via the terminal to obtain evaluation information. This improves the accuracy of the next generation process.

[0543] For example, if a consumer indicates that they prefer a "juicier texture," the system can generate a customized model suitable for the user using the considered ingredients and present it to the device through a virtual tasting experience.

[0544] As an example of a prompt, use the following text: "If users frequently prefer juicy and tender alternative meats, generate a new model that takes this into account and display it in the AR tasting."

[0545] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0546] Step 1:

[0547] The server uses data acquisition methods to collect shape data of actual animal products. High-resolution 3D scanners and micro-CT scanners are used as input, and the obtained three-dimensional shape data is stored as information. The collected data is stored in a database and used for subsequent data processing by algorithms.

[0548] Step 2:

[0549] The server uses a generative model to generate the three-dimensional shape of a substitute food from collected shape data. The input is shape information of animal products stored in a database, and the output is three-dimensional shape data of the substitute food. Generative AI models, particularly generative adversarial networks (GANs), are used to generate models with highly accurate shape and texture imitation.

[0550] Step 3:

[0551] The user wears smart glasses, which act as a terminal, and collects their own emotional data using emotion analysis technology. Inputs include the user's facial expressions, voice tone, and selected words, while output is informational data analyzing the user's emotions. This analysis is performed in real time by emotion recognition software.

[0552] Step 4:

[0553] The terminal sends the collected sentiment data to the server. The server uses this as additional information to adjust the parameters of the generative model. It receives sentiment data as input and obtains a generative model with adjusted parameters as output. The passenger seat model is updated in response to this prompt.

[0554] Step 5:

[0555] Based on an updated generative model, the server manufactures physical alternative foods using 3D printing technology. The input is adjusted 3D shape data, and the output is the actual alternative food. The printing process uses plant-based materials and is precisely molded with a 3D printer.

[0556] Step 6:

[0557] Users view the alternative food generated on their devices and participate in a virtual tasting experience. Based on their experience, users input evaluation information into their devices and send it to the server. The input consists of feedback from the tasting experience, and the output is improvement data that contributes to optimizing the next generation process.

[0558] Step 7:

[0559] The server analyzes user feedback and incorporates it into subsequent food generation algorithms. The input is user feedback, and the output is an improved generation algorithm. This results in more accurate generation processes in subsequent attempts, providing users with a more satisfying experience.

[0560] 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.

[0561] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0562] 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.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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".

[0577] This invention relates to a system for producing alternative food ingredients using plant-derived raw materials. The system aims to produce alternative food ingredients that have a shape and texture similar to actual animal-based ingredients. Specific embodiments of this invention are described below.

[0578] First, the server acquires shape data of animal-based ingredients. This shape data is obtained using high-resolution 3D scanners and micro-CT scanners. This provides detailed data capturing the external shape of the meat, its internal fibrous structure, and the distribution of individual fat cells.

[0579] Next, a generative model is constructed using the shape data acquired by the server. This model utilizes artificial intelligence techniques, including generative adversarial networks (GANs), and is designed to mimic the shape and texture of actual animal-based food ingredients. Through artificial intelligence, this model continuously learns and optimizes, improving its accuracy based on the feedback received.

[0580] Subsequently, the server uses a generative model to generate a three-dimensional shape of the alternative food. The generated shape data is then physically molded using a three-dimensional printing method. Specifically, a three-dimensional printer is used to print the alternative meat in 3D from plant-based raw materials. Pea protein and other plant-based ingredients are used as raw materials, resulting in a nutritionally balanced product.

[0581] Ultimately, the alternative ingredients are provided to consumers via a device. Users then cook them, experience the taste and texture, and provide feedback. This feedback data is sent to a server and used to improve the entire system.

[0582] Through the above process, the present invention provides consumers with more realistic and customized alternative ingredients and enables continuous product improvement. Through such a system, it is possible to reduce environmental impact and improve food diversity.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The server acquires shape data of actual animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. The scanned data includes the external shape of the meat, its internal fibrous structure, and fat distribution. This data is stored in a database within the system and used in subsequent processes.

[0586] Step 2:

[0587] The server uses shape data to train a generative model. Generative adversarial networks (GANs) are used to build an algorithm that mimics the shape and texture of real food ingredients. This improves the model's ability to generate shapes that closely resemble real food ingredients. This process also includes data preprocessing and hyperparameter optimization.

[0588] Step 3:

[0589] The server uses a pre-trained generative model to generate three-dimensional shapes of alternative ingredients according to the user's requests. Specific features of the shape (e.g., fat distribution, meat thickness) are specified, and a three-dimensional model is created using CG software. The generated shape can be viewed through the user interface.

[0590] Step 4:

[0591] The server generates three-dimensional shape data, which is then sent to a 3D printer to physically shape the alternative meat using plant-based materials. The 3D printer follows a programmed procedure, creating the product of the specified shape using a layering method. During this process, the material composition is considered to achieve appropriate strength and elasticity.

[0592] Step 5:

[0593] Users receive substitute ingredients, cook them, and taste them. They then provide feedback on the appearance, texture, and taste of the ingredients via their device. This feedback can be provided, for example, through a dedicated application or web form.

[0594] Step 6:

[0595] The server collects feedback data from users and uses it to improve the generative model. The feedback is analyzed, and the content is reflected in the model adjustments and the generation of new shapes. This improves the accuracy of the next generation process, allowing it to better meet user demands.

[0596] (Example 1)

[0597] 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".

[0598] In the production of alternative edible ingredients that mimic animal products, there are shortcomings in appearance, taste, and texture, making it difficult to sufficiently improve consumer satisfaction. Furthermore, there is the challenge of providing products that cater to different consumer preferences while using environmentally friendly plant-based materials.

[0599] 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.

[0600] In this invention, the server includes data acquisition means for obtaining physical property data of actual animal-based ingredients, intelligent data processing means having a generative model for generating a three-dimensional shape of an alternative edible material from the acquired property data, and three-dimensional processing means for forming the alternative edible material three-dimensionally using plant-derived edible raw materials based on the generated three-dimensional shape. This makes it possible to manufacture high-quality alternative edible materials that meet the diverse needs of consumers.

[0601] "Data acquisition means" refers to an apparatus or method used to accurately acquire physical property data of actual animal-based food ingredients.

[0602] An "intelligent data processing means" is a system that uses an artificial intelligence model to process data in order to generate a three-dimensional shape of an alternative edible material from acquired physical property data.

[0603] "Three-dimensional processing means" refers to an apparatus or technology that physically forms a three-dimensional substitute edible material using plant-derived edible raw materials based on the generated three-dimensional shape.

[0604] A "generative adversarial algorithm" is an artificial intelligence algorithm that aims to generate more realistic data by having different models compete with each other.

[0605] "Plant-derived edible ingredients" refer to natural or processed materials made from plants that are suitable for consumption.

[0606] "Evaluation information" refers to opinions and feedback from consumers regarding their experience using a product and their level of satisfaction with it.

[0607] "Data update methods" refer to techniques and systems for improving the accuracy of generative models based on evaluation information obtained from consumers.

[0608] This invention is a system for efficiently producing alternative edible materials that mimic animal-based ingredients. Specifically, a server is central to the process, handling data acquisition, model generation, 3D modeling, and feedback processing.

[0609] First, the server uses data acquisition methods to obtain physical property data of animal-based ingredients. This can be done using devices such as high-resolution 3D scanners and micro-CT scanners. This allows for detailed information such as the shape of the ingredients, the structure of the fibers, and the distribution of fat.

[0610] Next, the server constructs a generative AI model based on the acquired data through intelligent data processing. This model uses a generative adversarial algorithm to process the data. As a result, a three-dimensional shape that closely resembles actual animal-based food ingredients is generated.

[0611] Subsequently, the terminal uses a three-dimensional processing device to print a three-dimensional alternative edible material using plant-derived edible raw materials based on the generated three-dimensional shape data. For this purpose, a three-dimensional printer can be used, and pea protein or other nutrient-rich materials can be employed.

[0612] Ultimately, users receive the alternative edible ingredients, cook and taste them, and provide feedback on taste and texture. This feedback data is then fed back to the server. The server continuously improves its generative model based on this feedback data. The goal is to provide high-quality alternative edible ingredients that meet the diverse needs of consumers.

[0613] As a concrete example, here is an example of a prompt: "Generate three-dimensional shape data of a plant-based alternative food that mimics the shape and texture of chicken breast."

[0614] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0615] Step 1:

[0616] The server acquires physical property data of animal-based ingredients. To do this, the server operates a high-resolution 3D scanner or micro-CT scanner to scan the ingredients. The input is the actual animal-based ingredient, and the scanner captures its shape and internal structure as digital data. The output is precise three-dimensional shape data. This data will be used for subsequent processing.

[0617] Step 2:

[0618] The server uses the acquired three-dimensional shape data as input to construct a generative AI model using intelligent data processing. Here, a generative adversarial algorithm is employed to generate a model that mimics the detailed shape and texture of animal-based food ingredients. Data processing involves analyzing the input data and optimizing the parameters of the generative model. The output is a highly accurate generative AI model.

[0619] Step 3:

[0620] The server generates three-dimensional shape data of the alternative edible material using a generative AI model. In this step, prompt statements are used to instruct the AI ​​model and calculate the specific three-dimensional shape. The inputs are prompt statements and the generative AI model, and the output is detailed three-dimensional shape data of the alternative edible material.

[0621] Step 4:

[0622] The terminal uses the generated three-dimensional shape data as input to create a three-dimensional alternative edible material using a three-dimensional processing method. A three-dimensional printer is used to print plant-derived edible raw materials in three dimensions. Specifically, a material containing pea protein is loaded into the printer, and the material is layered layer by layer. The output is the completed alternative edible material.

[0623] Step 5:

[0624] Users cook alternative edible ingredients received via their device and evaluate their texture and taste. User input consists of cooking and tasting the ingredients, while output is feedback information about texture and taste. This feedback information is sent to a server and used to improve the generated AI model.

[0625] (Application Example 1)

[0626] 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".

[0627] Traditional methods of producing alternative foods made it difficult for consumers to customize the shape and texture of these foods to their individual preferences, and also made it difficult to reflect consumer demands in real time. As a result, it was not possible to perfectly provide the diverse food experiences that consumers desired, leaving room for improvement in satisfaction with alternative foods.

[0628] 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.

[0629] In this invention, the server includes data acquisition means for obtaining actual animal-based food shape data, artificial intelligence means having a generative model for generating a three-dimensional shape of an alternative food from the acquired shape data and similar data, three-dimensional printing means for forming the alternative food three-dimensionally using plant-derived raw materials based on the generated three-dimensional shape, and user interface means that enables the selection and customization of the alternative food using an augmented reality device. This makes it possible for consumers to customize their preferred shape and texture on the spot and generate an alternative food instantly.

[0630] "Animal-based food shape data" refers to detailed data, including the external shape and internal structure of animal-based food products, acquired using high-resolution 3D scanners or micro-CT scanners.

[0631] A "generative model" is a model that uses artificial intelligence technology to generate the three-dimensional shape of a substitute food ingredient from acquired animal food ingredient shape data.

[0632] "Artificial intelligence methods" refer to methods that utilize AI technology to build generative models and mimic the shape and texture of animal-based ingredients.

[0633] A "three-dimensional printing method" is a device for physically shaping alternative food ingredients from plant-derived raw materials based on the generated three-dimensional shape.

[0634] "Data processing means" refers to digital processing technology used to improve generative models by utilizing evaluation data obtained from consumers.

[0635] An "augmented reality device" is a device that overlays information onto real space, allowing users to visually select and customize alternative ingredients.

[0636] A "user interface means" is an interface that allows consumers to customize the shape and texture of alternative food ingredients.

[0637] To implement this invention, the server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or a micro-CT scanner. This shape data captures the external shape and internal structure of the ingredients in detail. Based on the acquired data, a generative model is constructed on the server. This generative model utilizes artificial intelligence technology, particularly generative adversarial networks, and is designed to precisely mimic the shape and texture of animal-based ingredients.

[0638] A three-dimensional printing device is used based on the three-dimensional shape data obtained from the generative model. Specifically, plant-derived raw materials are supplied to the printer, and alternative food products tailored to consumer preferences are formed in three dimensions. The raw materials used in this process include proteins with a nutritionally superior composition.

[0639] Consumers can visually select and customize alternative ingredients using augmented reality devices, such as smart glasses. These devices function as a user interface, digitizing user requests and sending them to a server. Evaluation data and user feedback are continuously used by data processing systems to optimize the generative model.

[0640] A concrete example is when a consumer chooses vegan ingredients and uses them as a substitute for chicken. Through an augmented reality device, the user can set the texture and thickness and simulate the final cooked state. The generating AI model operates based on the prompt, "Generate a softer, thicker substitute ingredient based on 3D scan data of chicken breast." In this way, customization to meet diverse consumer needs becomes possible on the spot.

[0641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0642] Step 1:

[0643] The server acquires shape data of animal-based food ingredients using a high-resolution 3D scanner and a micro-CT scanner. At this stage, the external shape and internal fiber structure of the food ingredients are captured in detail and stored as digital data within the server. The input is the scanned actual food ingredients, and the output is detailed 3D model data.

[0644] Step 2:

[0645] The server constructs a generative model based on the acquired shape data. This is a process that uses Generative Adversarial Networks (GANs) as part of artificial intelligence technology to learn from the data. The input is the previously obtained 3D model data, and the output is the model parameters for generating the three-dimensional shape of the substitute food item.

[0646] Step 3:

[0647] The user selects and customizes alternative ingredients via an augmented reality device. The user's selections are sent to the server as prompts. For example, the user might send the prompt, "Based on 3D scan data of chicken breast, please generate a softer, thicker alternative ingredient." The input is the user's customization request, and the output is a specific production instruction.

[0648] Step 4:

[0649] The server constructs the three-dimensional shape of the alternative food using a generative model and sends that shape data to a 3D printer. This printer physically forms the alternative food using plant-based raw materials. The input consists of model parameters and user customization instructions, and the output is the alternative food actually produced by the 3D printer.

[0650] Step 5:

[0651] Users receive alternative ingredients generated via their terminal, then cook and taste them. They then send feedback to the server, evaluating the taste and texture. The input is the user's tasting results, and the output is feedback data used for system optimization.

[0652] Step 6:

[0653] The server analyzes the received feedback using data processing tools and continuously improves the generative model to enhance its performance and accuracy. The input is the feedback data, and the output is the improved generative model.

[0654] 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.

[0655] This invention is a system that aims to provide alternative food products that better reflect consumer sentiment, in addition to manufacturing alternative food products using plant-derived raw materials. This system provides alternative food products that have a shape and texture similar to actual animal-based foods, and further has a function to customize them while considering user sentiment. Specific embodiments of this invention are described below.

[0656] First, the server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. The acquired data accurately reflects the external shape and internal structure of the meat and is used as foundational data in subsequent processing. The shape data is stored in a database within the system.

[0657] Next, a generative model is constructed based on the shape data acquired by the server. This generative model is designed to generate algorithms that mimic the shape and texture of actual food ingredients using Generative Adversarial Networks (GANs). The server continuously learns and optimizes the generative model using feedback and evaluation data obtained from the user.

[0658] Furthermore, the system incorporates an emotion engine. The emotion engine analyzes the user's facial expressions, tone of voice, and selected words to recognize the user's emotions. This allows the server to retrieve the user's emotion data and customize the food items according to those emotions. For example, if a user expresses an emotion indicating a preference for a particular food item, it is possible to generate a shape that incorporates that characteristic.

[0659] Next, the server uses a generative model to generate a three-dimensional shape that responds to the user's requests and emotions. The generated shape data is then physically molded using a three-dimensional printing method. Specifically, a three-dimensional printer prints the alternative meat in three dimensions using plant-based raw materials. These raw materials include nutritionally balanced ingredients such as pea protein.

[0660] Ultimately, the user receives the alternative ingredients via a terminal. The user receives the product, cooks and samples it, and provides feedback. The feedback data is collected and analyzed by a server to help improve the next production process.

[0661] Through the above process, the present invention meets the diverse needs of consumers and enables product customization based on emotional data, thereby continuously providing a better dining experience.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The server acquires shape data of animal-based ingredients using a high-resolution 3D scanner or micro-CT scanner. This shape data includes detailed structural information such as the surface shape, internal structure, and fat distribution of the meat. This data is stored in the system's database and serves as foundational data for subsequent processing.

[0665] Step 2:

[0666] The generative model is trained based on the shape data acquired by the server. Here, a generative adversarial network (GAN) is used to learn an algorithm that reproduces the shape and texture of actual meat. This model is important for simulating the realistic appearance and texture of food ingredients.

[0667] Step 3:

[0668] To recognize the user's emotions, the device uses its camera and microphone to analyze the user's facial expressions, voice tone, and selected words. The emotion engine analyzes this data to determine the user's emotional state. This provides crucial input information for determining what characteristics of ingredients to produce.

[0669] Step 4:

[0670] The server utilizes a generative model to generate three-dimensional shape data that responds to user requests and emotions. For example, if a user expresses a preference for rich flavor, the server can create a shape that emphasizes the distribution of fat accordingly. At this point, feedback data obtained from the user is also taken into consideration.

[0671] Step 5:

[0672] The server generates three-dimensional shape data, which is then sent to a 3D printer to print a three-dimensional substitute food using plant-based materials. Based on the set shape, the food is precisely molded using a layering method. This process takes into account the blending of nutritionally balanced materials, such as pea protein.

[0673] Step 6:

[0674] Users receive the completed substitute ingredients via a device and then actually cook and taste them. Users input their evaluations of the ingredients' appearance, texture, and taste, and the device collects this feedback. The user experience is recorded and used to create better products.

[0675] Step 7:

[0676] The server analyzes the collected feedback data and uses it to adjust the generative model. Based on the insights gained from the feedback, the model's generative parameters are updated and reflected in the next product generation. This allows the system to be continuously improved, enabling the production of better alternative ingredients.

[0677] (Example 2)

[0678] 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".

[0679] In recent years, the food industry has seen a growing demand for alternative foods to replace animal-based ingredients. However, conventional technologies have presented challenges in fully meeting consumer preferences because the shape and texture of these alternative foods differ significantly from those of animal-based ingredients. Furthermore, customization based on individual consumer preferences is difficult, often resulting in uniform product offerings. Therefore, there is a need to develop systems that provide realistic alternative foods that closely resemble animal-based ingredients while taking consumer preferences into account.

[0680] 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.

[0681] In this invention, the server includes sensor means for acquiring actual animal-based food shape data, artificial intelligence means for generating a three-dimensional shape of a substitute food from the acquired shape data and similar data, molding means for forming the substitute food three-dimensionally using plant-derived materials based on the generated three-dimensional shape, information processing means for feeding back evaluation data and emotional data acquired from consumers to the artificial intelligence means, and analysis means for generating emotional data by analyzing the consumer's facial expressions, voice, and selected words. This makes it possible to provide realistic substitute foods that take consumer emotions into consideration.

[0682] "Sensing means" refers to a device used to acquire shape data of actual animal-based food ingredients.

[0683] "Artificial intelligence means" refers to technology for generating the three-dimensional shape of a substitute food from acquired shape data and similar data.

[0684] "Molding means" refers to a device for forming a three-dimensional substitute food using plant-derived materials based on the generated three-dimensional shape.

[0685] "Information processing means" refers to a device or program for feeding back evaluation data and emotional data acquired from consumers to artificial intelligence means.

[0686] "Analysis means" refers to a device or program for analyzing a consumer's facial expressions, voice, and chosen words to generate emotional data.

[0687] A "generative adversarial network" refers to a machine learning model in which two networks compete and learn together to generate new data.

[0688] "Plant-derived materials" refer to materials obtained from plants that contain proteins with a nutritional composition taken into consideration.

[0689] This invention is a system for manufacturing alternative foods that have a shape and texture similar to animal-based ingredients, and further customizes the products while taking consumer preferences into consideration. This embodiment is realized primarily through the collaboration of a server, a terminal, and a user, using sensor means, artificial intelligence means, molding means, information processing means, and analysis means.

[0690] The server uses a high-resolution 3D scanner or micro-CT scanner as a sensor to acquire shape data of animal-based ingredients. This shape data is stored in a database in a digital format. Next, the server utilizes generative adversarial networks (GANs) as an artificial intelligence tool to generate three-dimensional shapes of alternative foods from this shape data. In this process, the algorithm designs alternative foods with a realistic appearance and texture based on the obtained shape data.

[0691] The device analyzes the user's facial expressions, voice tone, and selected words as analytical tools, and generates emotional data using an emotion engine. For example, it measures the user's emotions through the camera and microphone and sends emotion-based data to a server. This makes it possible to customize alternative foods based on consumer preferences.

[0692] Ultimately, the server uses a 3D printer as a molding tool to print alternative foods using plant-based materials, such as pea protein. After the molded alternative food is provided to the consumer, the user cooks and evaluates the product. The evaluation data is then fed back to the server and used in the next production process. Through this entire process, consumers can obtain a customized food experience tailored to their individual preferences and emotions.

[0693] For example, if facial expression data analyzes that a user prefers a "steak-like" alternative food, the server uses that information to design an alternative food that closely resembles the shape and taste of steak, employing a generative model.

[0694] An example of a prompt message would be: "Based on the user's emotional data, please 3D print a spicy-flavored steak-like substitute food." In this way, detailed customization based on emotions is possible.

[0695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0696] Step 1:

[0697] The server uses a high-resolution 3D scanner or micro-CT scanner as a sensor to acquire shape data of actual animal-based ingredients. The animal-based ingredients themselves are prepared as input, and the scanner scans them to obtain shape data as output. This data is saved in a digital format and used in subsequent generation processes.

[0698] Step 2:

[0699] The server constructs a generative model using generative adversarial networks (GANs) based on the acquired shape data. Shape data is given as input, and the GANs process this data to generate a realistic three-dimensional shape algorithm as output. This model is optimized to mimic the appearance and texture of actual food ingredients.

[0700] Step 3:

[0701] The device uses a camera and microphone as analytical tools to collect the user's facial expressions, voice tone, and selected words, generating emotional data. The user's biometric information is provided as input, and the device analyzes it to construct emotional data as output. This information is sent to a server and used for food customization.

[0702] Step 4:

[0703] The server generates a three-dimensional shape of the alternative food using the user's emotional data and generative model. Emotional data and a previously constructed generative model are used as input, and the server matches them to output a three-dimensional shape that reflects specific customizations. Texture and taste are also adjusted to the ideal state at this stage.

[0704] Step 5:

[0705] The server uses a 3D printer as a molding mechanism to physically shape the alternative food based on the generated shape data. Three-dimensional shape data and plant-derived materials are supplied as input, and the printer processes them to produce materialized alternative food as output.

[0706] Step 6:

[0707] Users receive alternative food items provided through a terminal, then cook and taste them. They receive the prepared food as input, go through the cooking process, and receive tasting results and feedback as output. This feedback information is then sent back to the server via the terminal and used to improve future products.

[0708] (Application Example 2)

[0709] 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".

[0710] Modern consumers have diverse needs for plant-based alternative foods and desire that the ingredients offered be customized to their individual preferences and emotions. However, existing alternative foods have fixed textures and shapes, making it difficult to reflect the preferences and emotions of individual consumers. Furthermore, the traditional food service industry lacks sufficient systems for real-time food customization at the point of sale, limiting opportunities to improve the consumer experience.

[0711] 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.

[0712] In this invention, the server includes information acquisition means for acquiring actual animal-based food shape data, intelligent means having a generative model for generating a three-dimensional shape of an alternative food from the acquired shape information and similar information, three-dimensional molding means for forming the alternative food three-dimensionally using plant-derived materials based on the generated three-dimensional shape, information processing means for feeding back evaluation information acquired from consumers to the generative model, and emotion analysis means for recognizing consumer emotions using a wearable terminal and reflecting them in the generative model. This makes it possible to customize alternative foods to meet diverse consumer needs and provide a real-time virtual tasting experience at the point of sale.

[0713] "Information acquisition means" refers to a device or method for collecting shape data of actual animal-based foods.

[0714] A "generative model" is an artificial intelligence algorithm used to generate the three-dimensional shape of a substitute food product based on acquired shape information and similarity information.

[0715] An "intelligent means" is a computer system or software that has the ability to maintain a generative model and process information.

[0716] "Three-dimensional molding means" refers to a technology or apparatus for physically forming substitute food in a three-dimensional manner based on the generated three-dimensional shape.

[0717] "Plant-derived materials" are food ingredients based on components obtained from plants, and are environmentally friendly and have a balanced nutritional profile.

[0718] "Information processing means" refers to a process or device for feeding back evaluation information obtained from consumers to a generation model.

[0719] "Emotion analysis means" refers to a technology or device that uses a wearable device to recognize a consumer's emotions and reflects that information in a generative model.

[0720] A "terminal" is an electronic device that consumers can wear and use for inputting and displaying information.

[0721] In order to implement this invention, it is necessary to construct a system in which a server, terminal, and user cooperate to go through a series of data processing and physical food production processes.

[0722] The server first collects shape information of animal products through information acquisition methods. Specifically, it uses high-resolution scanning technology to obtain detailed three-dimensional shapes of the food. This shape information is stored in a database and used as foundational data to support various generative models. The generative models include generative adversarial networks (GANs), which generate three-dimensional models that mimic the shape and texture in response to user requests and emotions.

[0723] The user wears smart glasses as a terminal and collects emotional data through emotion analysis. This includes facial recognition, voice tone analysis, and analysis of selected words, using emotion recognition software such as Affectiva. The emotional data is transmitted to a server in real time and used there to adjust the parameters of the generative model.

[0724] Based on a three-dimensional model generated by the server, a three-dimensional modeling tool is used to create a three-dimensional substitute food using plant-derived materials. The materials used include components with nutritional value, such as pea protein. The finished food is tasted by the user, and feedback is sent to the server via the terminal to obtain evaluation information. This improves the accuracy of the next generation process.

[0725] For example, if a consumer indicates that they prefer a "juicier texture," the system can generate a customized model suitable for the user using the considered ingredients and present it to the device through a virtual tasting experience.

[0726] As an example of a prompt, use the following text: "If users frequently prefer juicy and tender alternative meats, generate a new model that takes this into account and display it in the AR tasting."

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] The server uses data acquisition methods to collect shape data of actual animal products. High-resolution 3D scanners and micro-CT scanners are used as input, and the obtained three-dimensional shape data is stored as information. The collected data is stored in a database and used for subsequent data processing by algorithms.

[0730] Step 2:

[0731] The server uses a generative model to generate the three-dimensional shape of a substitute food from collected shape data. The input is shape information of animal products stored in a database, and the output is three-dimensional shape data of the substitute food. Generative AI models, particularly generative adversarial networks (GANs), are used to generate models with highly accurate shape and texture imitation.

[0732] Step 3:

[0733] The user wears smart glasses, which act as a terminal, and collects their own emotional data using emotion analysis technology. Inputs include the user's facial expressions, voice tone, and selected words, while output is informational data analyzing the user's emotions. This analysis is performed in real time by emotion recognition software.

[0734] Step 4:

[0735] The terminal sends the collected sentiment data to the server. The server uses this as additional information to adjust the parameters of the generative model. It receives sentiment data as input and obtains a generative model with adjusted parameters as output. The passenger seat model is updated in response to this prompt.

[0736] Step 5:

[0737] Based on an updated generative model, the server manufactures physical alternative foods using 3D printing technology. The input is adjusted 3D shape data, and the output is the actual alternative food. The printing process uses plant-based materials and is precisely molded with a 3D printer.

[0738] Step 6:

[0739] Users view the alternative food generated on their devices and participate in a virtual tasting experience. Based on their experience, users input evaluation information into their devices and send it to the server. The input consists of feedback from the tasting experience, and the output is improvement data that contributes to optimizing the next generation process.

[0740] Step 7:

[0741] The server analyzes user feedback and incorporates it into subsequent food generation algorithms. The input is user feedback, and the output is an improved generation algorithm. This results in more accurate generation processes in subsequent attempts, providing users with a more satisfying experience.

[0742] 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.

[0743] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0744] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0745] 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.

[0746] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0747] 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.

[0748] The inside of the Emotion Map 400 represents what's in your mind, while the outside represents what you're doing. Therefore, the further you go out the 400-coordinate scale, the more visible your emotions become (the more they manifest in your actions).

[0749] 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.

[0750] 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."

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] A data acquisition method for obtaining actual animal-based food shape data,

[0766] An artificial intelligence means having a generative model for generating the three-dimensional shape of alternative ingredients from acquired shape data and similar data,

[0767] A three-dimensional printing method for forming alternative food ingredients three-dimensionally using plant-derived raw materials based on the generated three-dimensional shape,

[0768] A data processing means for feeding back evaluation data obtained from consumers to the generation model,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, wherein the generative model uses a generative adversarial network.

[0772] (Claim 3)

[0773] The system according to claim 1, wherein the plant-derived raw material contains protein with a nutritional composition taken into consideration.

[0774] "Example 1"

[0775] (Claim 1)

[0776] A data acquisition method for obtaining physical property data of actual animal-based food ingredients,

[0777] An intelligent data processing means having a generative model for generating the three-dimensional shape of an alternative edible material from acquired characteristic data,

[0778] A three-dimensional processing method for forming a substitute edible material in three dimensions using plant-derived edible raw materials based on the generated three-dimensional shape,

[0779] A data update means for reflecting evaluation information obtained from consumers into the generation model,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, wherein the generative model uses an adversarial generative algorithm.

[0783] (Claim 3)

[0784] The system according to claim 1, wherein the plant-derived edible raw material contains protein that takes nutritional requirements into consideration.

[0785] "Application Example 1"

[0786] (Claim 1)

[0787] A data acquisition method for obtaining actual animal-based food shape data,

[0788] An artificial intelligence means having a generative model for generating the three-dimensional shape of alternative ingredients from acquired shape data and similar data,

[0789] A three-dimensional printing method for forming alternative food ingredients three-dimensionally using plant-derived raw materials based on the generated three-dimensional shape,

[0790] A data processing means for feeding back evaluation data obtained from consumers to the generation model,

[0791] A user interface means that enables the selection and customization of alternative ingredients using an augmented reality device,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, wherein the generative model uses a generative adversarial network.

[0795] (Claim 3)

[0796] The system according to claim 1, wherein the plant-derived raw material contains protein with a nutritional composition taken into consideration.

[0797] "Example 2 of combining an emotion engine"

[0798] (Claim 1)

[0799] A sensor means for acquiring actual animal-based food shape data,

[0800] An artificial intelligence means for generating the three-dimensional shape of a substitute food from acquired shape data and similar data,

[0801] A molding means for forming a three-dimensional alternative food using plant-derived materials based on the generated three-dimensional shape,

[0802] Information processing means for feeding back evaluation data and emotional data obtained from consumers to the artificial intelligence means,

[0803] An analytical means for generating emotional data by analyzing consumers' facial expressions, voice, and chosen words,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the artificial intelligence means uses a generative adversarial network.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein the plant-derived material contains protein with nutritional composition taken into consideration.

[0809] "Application example 2 when combining with an emotional engine"

[0810] (Claim 1)

[0811] A means for acquiring information to obtain actual animal food shape data,

[0812] An intelligent means having a generative model for generating the three-dimensional shape of a substitute food from acquired shape information and similar information,

[0813] A three-dimensional molding means for forming a three-dimensional alternative food using plant-derived materials based on the generated three-dimensional shape,

[0814] Information processing means for feeding back evaluation information obtained from consumers to the generation model,

[0815] A means of sentiment analysis for recognizing consumer emotions using a wearable device and reflecting them in a generative model,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the generative model uses a generative adversarial network and takes into account virtual trial information presented on a wearable terminal.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the plant-derived material contains protein with nutritional composition taken into consideration. [Explanation of symbols]

[0821] 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 data acquisition method for obtaining actual animal-based food shape data, An artificial intelligence means having a generative model for generating the three-dimensional shape of alternative ingredients from acquired shape data and similar data, A three-dimensional printing method for forming alternative food ingredients three-dimensionally using plant-derived raw materials based on the generated three-dimensional shape, A data processing means for feeding back evaluation data obtained from consumers to the generation model, A system that includes this.

2. The system according to claim 1, wherein the generative model uses a generative adversarial network.

3. The system according to claim 1, wherein the plant-derived raw material contains protein with a nutritional composition taken into consideration.

Images