system

Abstract

We provide the system. [Solution] A terminal means for receiving size and dimension data entered by the user, A server means that automatically generates patterns based on received size and dimension data, A design method for generating design proposals based on generated pattern data and user preferences and trend information, An instruction generation means for creating sewing machine operation instructions based on the selected design proposal, A control means that transmits sewing machine operation instructions to the sewing machine and automatically starts production, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional handmade production, it is necessary to create a pattern by oneself, which is a major obstacle for beginners. Also, it is a difficult task to devise individual designs and select designs that follow current fashion trends by oneself. Furthermore, the work of accurately sewing the created pattern with a sewing machine is also complicated, taking a long production time and being prone to mistakes. There is a need to solve these problems and provide an environment where anyone can easily enjoy high-quality handmade production.

Means for Solving the Problems

[0005] This invention provides a system that automatically generates patterns based on size and dimension data entered by the user, and further automatically suggests designs based on the user's preferences and trends. Specifically, it solves these problems by providing a server means for automatically generating patterns based on received size and dimension data, a design means for generating design proposals based on the generated patterns and user data, and a control means for creating sewing machine operation instructions and controlling automatic production. As a result, the user can simplify the entire process from input to completion of production and easily create products that suit their preferences.

[0006] A "terminal device" is an electronic device used by a user to input size and dimension data and transmit it to a server.

[0007] A "server means" is a computer system that has the function of automatically generating patterns based on data received from a terminal means.

[0008] The "design method" is a mechanism that generates design proposals based on the generated pattern data and user preferences and trend information.

[0009] "Instruction creation method" refers to the process of creating sewing machine operation instructions based on the selected design proposal.

[0010] A "control mechanism" is a system that communicates with the sewing machine to initiate production in order to carry out sewing machine operation instructions.

[0011] "Optimization means" refers to a method of selecting or adjusting the optimal pattern by comparing the received size and dimension data with a standard pattern database.

[0012] "Communication method" refers to a data communication method used to send generated design proposals to a terminal and to accept design selections from the user. [Brief explanation of the drawing]

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

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

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

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

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

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

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, 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.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system in which a user inputs size and dimension data, and a controlled sewing machine automatically manufactures the product. This system operates in an environment where terminals, servers, and sewing machines are interconnected via a network. Specific embodiments are described below.

[0035] First, the user uses a terminal to input the size and dimensions of the object they want to create (person, pet, small object). The terminal sends this data to the server. The server automatically generates a pattern based on the received data. This pattern generation is performed by an algorithm stored on the server, which selects or adjusts the most suitable pattern by referring to a standard pattern database.

[0036] Next, the server generates multiple design options by combining the generated patterns with the user's past selection history and trend information. These design options are designed to provide the user with attractive choices. The generated design options are sent to the terminal, and the user can select their preferred design from among them.

[0037] Once the user selects a design, the server generates specific sewing machine instructions based on that information. These instructions include details such as the fabric to be used, the cutting order, and the sewing procedure, detailing how the sewing machine will operate accordingly. The terminal sends these instructions to the sewing machine, which then begins production. The sewing machine is automated, and the user only needs to supervise the production process.

[0038] As a concrete example, consider a case where a user wants to create clothes for their pet. The user inputs the pet's length and chest circumference into the terminal. The server receives this information, generates a pattern for the pet, and then creates several design options based on pet fashion trends. The user selects a design, and the sewing machine operates according to the instructions, allowing for easy creation of clothes for the pet. This entire process significantly simplifies the traditional production process, saving time and effort.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user inputs the size and dimensions of the object to be manufactured using a terminal. The entered data is immediately sent to the server.

[0042] Step 2:

[0043] The server analyzes the size and dimension data received from the terminal and executes a pattern generation algorithm to automatically generate a pattern. During this process, it refers to a standard pattern database as needed to create an optimized pattern.

[0044] Step 3:

[0045] Based on the patterns generated by the server, multiple design options are automatically generated, taking into account the user's past design selections and current fashion trend information. The generated design options are then sent to the user's device.

[0046] Step 4:

[0047] The user selects their preferred design from the design options presented on their device. The selected design information is then sent to the server.

[0048] Step 5:

[0049] The server generates specific sewing machine instructions based on the selected design. These instructions include fabric cutting patterns, sewing sequences, and stitch details.

[0050] Step 6:

[0051] The terminal transmits sewing machine operation instructions received from the server to the sewing machine. The sewing machine receives these instructions and starts automatic production, proceeding with the work as specified.

[0052] Step 7:

[0053] The user monitors the production process and makes minor adjustments as needed. Once production is complete, they receive the finished product and terminate their use of the system.

[0054] (Example 1)

[0055] 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."

[0056] Conventional manufacturing processes were time-consuming and laborious, requiring users to input size and dimension data, manually create patterns based on that data, and then manually instruct each step of the necessary manufacturing process. Furthermore, it was difficult to provide optimal designs tailored to user preferences. To solve this problem, the present invention aims to provide a system that automates and streamlines the entire manufacturing process, from pattern generation to production.

[0057] 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.

[0058] In this invention, the server includes a calculation means for automatically generating a pattern using acquired size and dimension information, a design means for creating multiple design options considering the generated pattern and past selection history and trend information, and an instruction creation means for generating operation instructions for an automated manufacturing device based on the design option selected by the user. This enables the user to efficiently generate a pattern and obtain a high-quality product in a short time through the manufacturing process using an automated manufacturing device.

[0059] "Information processing means" refers to a device or system for acquiring size and dimensional information of an object input by a user.

[0060] "Calculation means" refers to a device or system that automatically generates a pattern based on acquired size and dimension information.

[0061] "Design means" refers to a device or system that creates multiple design options by considering generated patterns and past selection history and trend information.

[0062] "Instruction generation means" refers to a device or system that generates operating instructions for an automated manufacturing device based on a design proposal selected by the user.

[0063] "Control means" refers to a device or system for transmitting generated operation instructions to an automated manufacturing device and starting the automated execution of manufacturing.

[0064] An "optimization means" is a device or system that compares acquired size and dimension information with a standard pattern database to select or adjust the optimal pattern.

[0065] "Communication means" refers to a device or system for transmitting multiple design proposals to an information processing device and accepting design selections from the user.

[0066] This invention is a system for automatically manufacturing products based on size and dimension information entered by a user. This system operates in an environment where terminals, servers, and automated manufacturing equipment are interconnected via a network.

[0067] First, the user uses a terminal to input the size and dimensions of the object to be manufactured. This input process involves the user entering the actual measurements of the object into a dedicated application on the terminal. The terminal then organizes the input information and sends it to a server via the network.

[0068] Based on the received size and dimension information, the server automatically generates patterns using an internally installed AI model. During this process, the server compares the pattern against a standard pattern database to select or fine-tune the most suitable pattern. Furthermore, the server references the user's past selection history and the latest trend information to create multiple design options based on the pattern.

[0069] The generated design proposals are then sent to the terminal, and the user can select their preferred design from among them. After the user's selection, the server generates operating instructions for the automated manufacturing machine based on the selected design. These instructions include detailed information such as fabric selection, cutting order, and sewing procedure.

[0070] Finally, the terminal sends the generated operating instructions to the automated manufacturing machine. The automated manufacturing machine follows the received instructions and begins manufacturing. This process is fully automated, and the user only needs to supervise the manufacturing process until the product is completed.

[0071] As a concrete example, consider a scenario where a user makes clothes for their pet. The user inputs the pet's length and chest circumference into a terminal and sends that data to a server. The server automatically generates a pattern for the pet and creates several design options based on the latest pet fashion trends. Once the user selects their desired design, an automated manufacturing device produces the pet's clothes according to that design.

[0072] An example of a prompt for the generating AI model would be: "Based on the data that the pet's body length is 50cm and chest circumference is 40cm, please generate patterns and design proposals based on the latest pet fashion."

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user inputs the size and dimensions of the object to be manufactured using a terminal. Specifically, the user uses a measuring tape or similar tool to input the measured values ​​into a dedicated application on the terminal. This input data becomes the initial input data for the program.

[0076] Step 2:

[0077] The terminal formats the entered size and dimension information and sends it to the server. Specifically, the terminal formats the entered data along with the user ID and sends it to the server via the network. This process converts the data into the format required by the server.

[0078] Step 3:

[0079] The server uses the received size and dimension information to automatically generate patterns using a generation AI model. First, the server refers to a standard pattern database and selects the most suitable pattern based on the input information. If necessary, it applies an algorithm to adjust the dimensions to optimize the pattern and stores the result as an intermediate product.

[0080] Step 4:

[0081] The server considers past selection history and trend information to create multiple design options based on the generated patterns. Specifically, the server uses a machine learning algorithm to suggest design patterns that suit the user's preferences. As an output of this step, multiple design options are generated.

[0082] Step 5:

[0083] The server sends the generated design proposals to the terminal. The transmitted design proposals are converted into a format that can be visually viewed on the user interface. This process allows the user to input their selection of a design proposal.

[0084] Step 6:

[0085] The user selects their preferred design from several options presented on the device. The user's selection information becomes the input data for the next processing step.

[0086] Step 7:

[0087] The server generates operating instructions for the automated sewing machine based on the selected design. These instructions include a detailed production guide, such as the type of fabric to use, the cutting order, and the sewing procedure. These instructions become the prepared output data for production.

[0088] Step 8:

[0089] The terminal transmits the generated operation instructions to the automated manufacturing device. Specifically, the terminal communicates with the device via the network to ensure smooth data transmission.

[0090] Step 9:

[0091] The automated manufacturing equipment begins production according to the received operating instructions. The manufacturing equipment is fully automated and, by operating as instructed, produces high-precision products. The user supervises the manufacturing process and monitors and adjusts the process as needed.

[0092] Through these steps, the system enables rapid and accurate production, saving time and effort.

[0093] (Application Example 1)

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

[0095] Conventional sewing techniques have presented challenges such as difficulty for consumers to easily obtain clothing that fits their body shape, and the production process being complicated and time-consuming. Furthermore, it has been difficult to provide designs that reflect the individual preferences of consumers and current trends. This invention aims to solve these problems and provide a system that allows consumers to easily customize clothing and have it produced while checking the process in real time.

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

[0097] In this invention, the server includes information processing means, calculation means, and design means. This makes it possible to provide a system that enables an automated process for producing customized clothing based on the size and preferences entered by the consumer, along with real-time visual confirmation.

[0098] "Information processing means" refers to a function that receives size and dimension data entered by the user and processes it within the system.

[0099] The "calculation means" is a function that performs calculations to automatically generate a pattern using the received size and dimension data.

[0100] The "design method" refers to a function that generates design proposals based on the generated pattern data, user preferences, and trend information.

[0101] The "instruction generation means" is a function that generates instructions for operating sewing machines and other sewing equipment based on the selected design proposal.

[0102] A "control means" is a function that transmits sewing machine operation instructions to sewing machines and other sewing equipment, and automatically starts the production process.

[0103] "Visualization means" refers to a function that allows users to check the product manufacturing process in real time using a terminal device.

[0104] This invention provides a system that allows users to customize and manufacture products such as clothing. Users input their body size and dimensions using a device such as a smartphone or tablet. This data is transmitted from the device to a server via a network.

[0105] The server processes the received size and dimension data using information processing means and automatically generates patterns using calculation means. This pattern generation uses machine learning algorithms such as Python and R to efficiently generate accurate patterns.

[0106] Furthermore, the server uses design tools to create design proposals based on the generated pattern data and user preferences and trend information. This process utilizes database queries using user history and market data, as well as AI models, to propose designs that reflect current trends.

[0107] The generated design proposals are sent to the terminal, and the user selects one from among them. Based on the design selected by the user, the server creates detailed instructions for operating the sewing machine and sewing equipment through an instruction creation device, and the control device sends these instructions to the sewing machine to start automatic production. This allows the user to actually produce their selected design and enjoy the process, even without any special skills.

[0108] The entire manufacturing process can be monitored in real time by the user through a visualization device on the terminal. This allows users to check the manufacturing status and receive their own unique product.

[0109] As a concrete example, a primary school child and their parent can use a tablet in-store to input their size, choose the latest design, customize their clothing on the spot, and receive the finished product by the time they leave. The generative AI model uses prompts such as the following:

[0110] "Please build a system that generates multiple clothing designs, taking into account the latest trends, based on size data entered by the user, and automatically creates sewing machine control instructions to produce the selected design."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user uses a terminal to input their size and dimension data. The terminal prepares this input data and sends it to the server. The input in this step is the size and dimension data entered by the user into the terminal, and the output is formatted data. The terminal converts the data to a standard communication format and then sends the data to the next step.

[0114] Step 2:

[0115] The server receives size and dimension data transmitted from the terminal. Information processing equipment formats the received data and prepares it for transmission to the calculation equipment. The input for this step is the data transmitted from the terminal, and the output is the formatted data within the server. The server verifies the data integrity and prepares for pattern generation.

[0116] Step 3:

[0117] The server's computing power automatically generates patterns using the formatted data. This process utilizes a database of past patterns and machine learning algorithms. The input for this step is formatted user size data, and the output is the generated pattern data. The server selects the optimal pattern and makes adjustments as needed.

[0118] Step 4:

[0119] The server uses design tools to generate design proposals that take into account pattern data, trend information, and user preferences. The input for this step is pattern data and trend information, and the output is multiple design proposals. The server uses database queries and AI models to select designs that reflect current trends.

[0120] Step 5:

[0121] The server sends design proposals to the terminal, and the user selects their preferred design from among them. The input for this step is the design proposals from the server, and the output is the user's design selection. The terminal visually displays the design proposals and interactively accepts the user's selection.

[0122] Step 6:

[0123] Once the user has selected a design, the server generates sewing machine operation instructions using an instruction creation mechanism. The input for this step is the user's selected design, and the output is specific operation instructions. The server creates detailed instructions, including cutting sequence and sewing methods.

[0124] Step 7:

[0125] The server sends operating instructions to the sewing machine via the control system, automatically starting the production process. The input in this step is the operating instruction, and the output is the trigger for starting production. The sewing machine operates according to the received instructions and produces the product.

[0126] Step 8:

[0127] The user monitors the production process in real time using the terminal's visualization capabilities. The input for this step is information regarding the production status, and the output is real-time information provided to the user. The user can check the progress through the terminal and intervene as needed.

[0128] 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.

[0129] This invention is a system that generates design proposals that reflect the user's preferences and emotions, automatically generates patterns based on those proposals, and performs sewing machine operations. This system operates with the terminal, server, sewing machine, and emotion engine working in coordination with each other.

[0130] First, the user inputs size and dimension data for themselves, their pets, and small objects into the device. The device sends the input data to the server, and at this time, the emotion engine analyzes emotional data such as the user's facial expressions and tone of voice. The emotion engine recognizes the user's emotions in real time as they input and make selections, and reflects the results in the design generation.

[0131] The server automatically generates patterns based on the received size and dimension data. This generation process references a standard pattern database and optimizes the design as needed. Furthermore, the server generates design proposals considering user sentiment analysis data and preference / trend information. The analysis results from the sentiment engine are particularly reflected in the selection of design colors and elements, resulting in suggestions that better align with the user's emotions.

[0132] The generated design proposals are sent to the terminal, where the user reviews them and selects their favorite. Once the selection is complete, the server creates sewing machine instructions based on the chosen design. These instructions include information such as fabric selection and color combinations, and are designed to ensure that the finished product best matches the user's feelings.

[0133] The terminal sends instructions to the sewing machine, which automatically begins production. The user simply watches the production process unfold and eagerly awaits the finished product. For example, if the user wants to make baby clothes, the emotion engine analyzes the user's sense of security and happiness, and based on this, provides design suggestions that make extensive use of gentle colors such as pastels. In this way, the system enhances the quality of the finished product while being attentive to the user's emotions.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user inputs the size and dimensions of the object to be manufactured using a terminal. The terminal then prepares to send this data to the server.

[0137] Step 2:

[0138] The emotion engine is activated on the device and analyzes the user's facial expressions and tone of voice to generate emotion data. This allows the system to determine the user's current emotional state.

[0139] Step 3:

[0140] The device sends emotional data along with size and dimension data to the server.

[0141] Step 4:

[0142] The server uses the size and dimension data it receives to run a pattern generation algorithm. It consults a standard pattern database as needed to generate the optimal pattern.

[0143] Step 5:

[0144] The server automatically generates multiple design options based on emotional data, the user's past design history, and trend information. By utilizing the data from the emotional engine, the colors and shapes of the design options are adjusted to match the user's emotions.

[0145] Step 6:

[0146] The server sends the generated design proposal to the terminal.

[0147] Step 7:

[0148] The user reviews the submitted design proposals on their device and selects the most preferred design. The selection result is then sent to the server.

[0149] Step 8:

[0150] The server generates detailed instructions for sewing machine operation based on the user's selected design. These instructions include cutting patterns, sewing sequences, and artistic adjustments to suit the user's mood.

[0151] Step 9:

[0152] The terminal sends operation instructions from the server to the sewing machine.

[0153] Step 10:

[0154] The sewing machine automatically begins production according to the instructions it receives. The user can monitor the production process and make fine adjustments as needed. The finished product is designed to best fit the user's emotions.

[0155] (Example 2)

[0156] 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".

[0157] Traditional manufacturing processes often fail to adequately reflect user emotions and individual preferences in product design and specifications. There is a need to automatically and efficiently produce individually customized products while considering user sentiment. Furthermore, a smooth workflow from design to manufacturing is essential to enhance user satisfaction.

[0158] 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.

[0159] In this invention, the server includes means for analyzing dimensional information and emotional information, means for presenting automatically generated patterns and design proposals, and means for controlling manufacturing equipment to automatically carry out production. This enables the efficient production of individually customized products that reflect the user's emotions.

[0160] "Terminal means" refers to a device that receives dimensional information and emotional information from the user and analyzes them.

[0161] "Calculation means" refers to a computing device that automatically generates patterns and design proposals based on received dimensional information and sentiment data.

[0162] "Display means" refers to a device that presents generated design proposals to the user and accepts their selection.

[0163] "Instruction generation means" refers to a device for creating specific operating instructions for a manufacturing device based on a selected design proposal.

[0164] "Control means" refers to a device that sends operating instructions to a manufacturing device and automatically starts and controls the manufacturing process.

[0165] "Optimization means" refers to an apparatus or method for creating an optimal pattern by adjusting dimensional information based on standard pattern information.

[0166] "Communication means" refers to a device or method for transmitting generated design proposals to a terminal and receiving selection information from the user.

[0167] This invention is a system that automatically designs and manufactures products that take into account the individual needs and emotional state of the user. This system primarily utilizes terminals, servers, manufacturing equipment, and an emotion analysis engine.

[0168] The user inputs dimensional information about the product they want to manufacture via a terminal. The terminal also features an emotion analysis engine that identifies the user's emotional state based on their facial expressions and tone of voice. This emotion data is also sent to the server.

[0169] The server automatically generates patterns using an AI model based on the received dimensional information. During this process, it refers to a database of standard pattern information and optimizes dimensions as needed. Simultaneously, it uses sentiment analysis data to control the generation of design proposals. This generation method uses prompts such as "a design that expresses a sense of security." This allows the design's colors and style to be adjusted to the user's preferences.

[0170] The generated design proposals are sent to the terminal, where the user reviews and makes a selection. Once the user has made their selection, the server creates operating instructions for the manufacturing equipment based on the selected design. These instructions include specific production conditions, such as fabric selection and color combinations.

[0171] The manufacturing equipment receives operation instructions transmitted from the terminal and automatically starts the production process. Users can monitor this process in real time, ensuring that the finished product fits their emotions and preferences.

[0172] For example, if a user wants to create baby clothes, the device analyzes the user's feelings of security and happiness and generates design proposals based on them. The generating AI model uses the prompt phrase "baby clothes, pastel color design emphasizing security" to make suggestions that match the user's wishes.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The user uses a terminal to input dimensional information of the object to be manufactured. The input data is analyzed by the terminal's built-in emotion analysis engine, which also analyzes facial expressions and voice tone to generate emotion information. The terminal then sends this combined dimensional and emotion information to the server. The input consists of dimensional and emotion information, and the output is the transfer of data to the server.

[0176] Step 2:

[0177] The server uses the received dimensional information to automatically generate patterns using a generation AI model, referencing standard pattern information. Simultaneously, it analyzes the received emotional information and selects design elements based on those emotions. The inputs are dimensional information and emotional information, and the output is an optimized pattern and design proposals. The server provides the generation AI model with the prompt "Design based on user confidence" to create design proposals.

[0178] Step 3:

[0179] The generated design proposals and patterns are sent to the terminal. The terminal presents the user with multiple designs in a visual format. The user selects their preferred design, and this information is sent back to the server. The input is the generated design proposals, and the output is the user's design selection.

[0180] Step 4:

[0181] The server generates operating instructions for the manufacturing equipment based on the selected design. These instructions include fabric characteristics, color combinations, and sewing procedures. The input is the user-selected design, and the output is specific operating instructions. The server transmits these to the manufacturing equipment via a terminal.

[0182] Step 5:

[0183] The terminal transmits operation instructions received from the server to the manufacturing equipment. The manufacturing equipment follows the instructions and automatically starts production. The user can monitor the production process on the terminal. The input is the operation instructions, and the output is the start of the production process. Through this process, the manufacturing equipment completes a product that is highly emotionally resonant.

[0184] (Application Example 2)

[0185] 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".

[0186] Traditional clothing production systems struggled to quickly and accurately propose designs that matched the user's emotions and preferences, and to produce them on the spot. This made it difficult to meet the customer's desire for a more personalized experience and immediate production when they visited a store, posing a challenge in enhancing the individual customer experience.

[0187] 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.

[0188] In this invention, the server includes an information terminal means, a computing device means, and an emotion analysis means. This makes it possible to analyze the user's emotion data in real time, quickly and accurately generate design proposals based on that data, and immediately manufacture the selected design.

[0189] An "information terminal device" is a device used by users to input and receive size and dimension data.

[0190] A "calculation device means" is a device that has the function of automatically generating a pattern based on the input size and dimension data.

[0191] A "design tool" is a device that performs the process of generating design proposals based on generated pattern data and user preferences and emotional information.

[0192] An "emotion analysis device" is a device that provides technology to analyze a user's facial expressions and voice data and generate emotion data in real time.

[0193] A "command generation device" is a device that generates commands to instruct the operation of sewing machines and other sewing equipment according to a selected design.

[0194] "Control means" refers to technology that receives instructions for operating sewing machines, transmits them to the device, and automatically starts the manufacturing process based on those instructions.

[0195] "Means of supplying goods" refers to a method or apparatus for providing manufactured products to customers.

[0196] A "standard pattern database" is a database that stores standard pattern data that is referenced for optimization purposes.

[0197] A "generative AI model" is an artificial intelligence model used for design generation, a technology that generates designs adapted to the user's emotions based on prompt text.

[0198] A "prompt statement" is an instruction given to a generative AI model to prompt it to produce a specific output.

[0199] The system for implementing this invention consists of an information terminal, a computing device, an emotion analysis device, and a generative AI model. The information terminal is a device for the user to input size and dimension data, thereby transmitting basic information to the server. The computing device operates on the server and is responsible for automatically generating patterns based on the received size and dimension data.

[0200] When the server generates patterns based on data entered by the user, it refers to a standard pattern database and performs optimization processing. As part of this process, an emotion analysis system analyzes facial expressions and voice to generate user emotion data. This emotion data is passed as a prompt to the generation AI model, which uses the AI ​​to generate design proposals that match the user's emotions.

[0201] For example, by giving a specific instruction to the AI ​​model as a prompt, such as "Please suggest a pastel-colored design based on the user's feelings of joy," design proposals that match the emotion can be generated.

[0202] Users can review and select generated design proposals via an information terminal. The server creates operating instructions for the sewing machine based on the selected design proposal and sends these instructions to the sewing machine. The sewing machine automatically begins producing clothing or other items, and the final product is delivered to the user via an item distribution system.

[0203] This entire process enables a system that quickly delivers personalized products based on the user's individual emotions and preferences.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user enters size and dimension data using an information terminal. This input data includes the user's body shape and basic information necessary for manufacturing clothing. The terminal sends this information to the server. The input is treated as raw data, and the output to the server is converted to a standard data format.

[0207] Step 2:

[0208] The server automatically generates a pattern using a calculation device based on the received size and dimension data. The data is compared with a standard pattern database and optimized. This results in the output of the most suitable pattern for the user.

[0209] Step 3:

[0210] To analyze user emotions, an emotion analysis system collects the user's facial expressions and voice in real time via an information terminal. The emotion data is converted into emotion indices by an analysis engine and sent to a server. This data is then used in the subsequent design generation process.

[0211] Step 4:

[0212] The server receives emotion data and sends prompt messages to the AI ​​model to reflect this in the generated design proposals. For example, a prompt might say, "Please propose a design based on the user's feelings of joy." The AI ​​model then generates design proposals based on these prompts and outputs them to the terminal.

[0213] Step 5:

[0214] The user reviews and selects a design proposal generated on their device. The selected proposal is then fed back to the server. Based on this information, the server creates operating instructions for the sewing machine. This includes details such as fabric selection and sewing sequence.

[0215] Step 6:

[0216] The server sends operating instructions to the sewing machine, and production starts automatically. The sewing machine follows the received instructions and creates a garment with the exact specified design. The output is the finished garment.

[0217] Step 7:

[0218] Finally, the finished garments are delivered to users through a distribution system. This allows users to receive individually customized products. Product delivery forms a feedback loop, providing data that helps improve future experiences.

[0219] 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.

[0220] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention provides a system in which a user inputs size and dimension data, and a controlled sewing machine automatically manufactures the product. This system operates in an environment where terminals, servers, and sewing machines are interconnected via a network. Specific embodiments are described below.

[0236] First, the user uses a terminal to input the size and dimensions of the object they want to create (person, pet, small object). The terminal sends this data to the server. The server automatically generates a pattern based on the received data. This pattern generation is performed by an algorithm stored on the server, which selects or adjusts the most suitable pattern by referring to a standard pattern database.

[0237] Next, the server generates multiple design options by combining the generated patterns with the user's past selection history and trend information. These design options are designed to provide the user with attractive choices. The generated design options are sent to the terminal, and the user can select their preferred design from among them.

[0238] Once the user selects a design, the server generates specific sewing machine instructions based on that information. These instructions include details such as the fabric to be used, the cutting order, and the sewing procedure, detailing how the sewing machine will operate accordingly. The terminal sends these instructions to the sewing machine, which then begins production. The sewing machine is automated, and the user only needs to supervise the production process.

[0239] As a concrete example, consider a case where a user wants to create clothes for their pet. The user inputs the pet's length and chest circumference into the terminal. The server receives this information, generates a pattern for the pet, and then creates several design options based on pet fashion trends. The user selects a design, and the sewing machine operates according to the instructions, allowing for easy creation of clothes for the pet. This entire process significantly simplifies the traditional production process, saving time and effort.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The user inputs the size and dimensions of the object to be manufactured using a terminal. The entered data is immediately sent to the server.

[0243] Step 2:

[0244] The server analyzes the size and dimension data received from the terminal and executes a pattern generation algorithm to automatically generate a pattern. During this process, it refers to a standard pattern database as needed to create an optimized pattern.

[0245] Step 3:

[0246] Based on the patterns generated by the server, multiple design options are automatically generated, taking into account the user's past design selections and current fashion trend information. The generated design options are then sent to the user's device.

[0247] Step 4:

[0248] The user selects their preferred design from the design options presented on their device. The selected design information is then sent to the server.

[0249] Step 5:

[0250] The server generates specific sewing machine instructions based on the selected design. These instructions include fabric cutting patterns, sewing sequences, and stitch details.

[0251] Step 6:

[0252] The terminal transmits sewing machine operation instructions received from the server to the sewing machine. The sewing machine receives these instructions and starts automatic production, proceeding with the work as specified.

[0253] Step 7:

[0254] The user monitors the production process and makes minor adjustments as needed. Once production is complete, they receive the finished product and terminate their use of the system.

[0255] (Example 1)

[0256] 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."

[0257] Conventional manufacturing processes were time-consuming and laborious, requiring users to input size and dimension data, manually create patterns based on that data, and then manually instruct each step of the necessary manufacturing process. Furthermore, it was difficult to provide optimal designs tailored to user preferences. To solve this problem, the present invention aims to provide a system that automates and streamlines the entire manufacturing process, from pattern generation to production.

[0258] 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.

[0259] In this invention, the server includes a calculation means for automatically generating a pattern using acquired size and dimension information, a design means for creating multiple design options considering the generated pattern and past selection history and trend information, and an instruction creation means for generating operation instructions for an automated manufacturing device based on the design option selected by the user. This enables the user to efficiently generate a pattern and obtain a high-quality product in a short time through the manufacturing process using an automated manufacturing device.

[0260] "Information processing means" refers to a device or system for acquiring size and dimensional information of an object input by a user.

[0261] "Calculation means" refers to a device or system that automatically generates a pattern based on acquired size and dimension information.

[0262] "Design means" refers to a device or system that creates multiple design options by considering generated patterns and past selection history and trend information.

[0263] "Instruction generation means" refers to a device or system that generates operating instructions for an automated manufacturing device based on a design proposal selected by the user.

[0264] "Control means" refers to a device or system for transmitting generated operation instructions to an automated manufacturing device and starting the automated execution of manufacturing.

[0265] An "optimization means" is a device or system that compares acquired size and dimension information with a standard pattern database to select or adjust the optimal pattern.

[0266] "Communication means" refers to a device or system for transmitting multiple design proposals to an information processing device and accepting design selections from the user.

[0267] This invention is a system for automatically manufacturing products based on size and dimension information entered by a user. This system operates in an environment where terminals, servers, and automated manufacturing equipment are interconnected via a network.

[0268] First, the user uses a terminal to input the size and dimensions of the object to be manufactured. This input process involves the user entering the actual measurements of the object into a dedicated application on the terminal. The terminal then organizes the input information and sends it to a server via the network.

[0269] Based on the received size and dimension information, the server automatically generates patterns using an internally installed AI model. During this process, the server compares the pattern against a standard pattern database to select or fine-tune the most suitable pattern. Furthermore, the server references the user's past selection history and the latest trend information to create multiple design options based on the pattern.

[0270] The generated design proposals are then sent to the terminal, and the user can select their preferred design from among them. After the user's selection, the server generates operating instructions for the automated manufacturing machine based on the selected design. These instructions include detailed information such as fabric selection, cutting order, and sewing procedure.

[0271] Finally, the terminal sends the generated operating instructions to the automated manufacturing machine. The automated manufacturing machine follows the received instructions and begins manufacturing. This process is fully automated, and the user only needs to supervise the manufacturing process until the product is completed.

[0272] As a concrete example, consider a scenario where a user makes clothes for their pet. The user inputs the pet's length and chest circumference into a terminal and sends that data to a server. The server automatically generates a pattern for the pet and creates several design options based on the latest pet fashion trends. Once the user selects their desired design, an automated manufacturing device produces the pet's clothes according to that design.

[0273] An example of a prompt for the generating AI model would be: "Based on the data that the pet's body length is 50cm and chest circumference is 40cm, please generate patterns and design proposals based on the latest pet fashion."

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The user inputs the size and dimensions of the object to be manufactured using a terminal. Specifically, the user uses a measuring tape or similar tool to input the measured values ​​into a dedicated application on the terminal. This input data becomes the initial input data for the program.

[0277] Step 2:

[0278] The terminal formats the input size and dimension information and sends it to the server. Specifically, the terminal formats the data input together with the user ID and sends it to the server via the network. Through this process, it is converted into the data format required by the server.

[0279] Step 3:

[0280] The server uses the received size and dimension information to automatically generate a template by leveraging the generative AI model. First, the server refers to the standard template database and selects the most suitable template based on the input information. If necessary, an algorithm for adjusting the dimensions is applied to optimize the template, and the result is retained as an intermediate product.

[0281] Step 4:

[0282] The server creates multiple design proposals based on the generated template, taking into account past selection histories and trend information. As a specific operation, the server uses a machine learning algorithm to propose a design pattern that suits the user's preferences. As the output of this step, multiple design proposals are generated.

[0283] Step 5:

[0284] The server sends the generated design proposals to the terminal. The design proposals to be sent are converted into a format that can be visually confirmed on the user interface. Through this process, the user obtains an input for selecting a design proposal.

[0285] Step 6:

[0286] The user selects a preferred design from the multiple design proposals presented on the terminal. The user's selection information becomes the input data for the next processing step.

[0287] Step 7:

[0288] The server generates operating instructions for the automated sewing machine based on the selected design. These instructions include a detailed production guide, such as the type of fabric to use, the cutting order, and the sewing procedure. These instructions become the prepared output data for production.

[0289] Step 8:

[0290] The terminal transmits the generated operation instructions to the automated manufacturing device. Specifically, the terminal communicates with the device via the network to ensure smooth data transmission.

[0291] Step 9:

[0292] The automated manufacturing equipment begins production according to the received operating instructions. The manufacturing equipment is fully automated and, by operating as instructed, produces high-precision products. The user supervises the manufacturing process and monitors and adjusts the process as needed.

[0293] Through these steps, the system enables rapid and accurate production, saving time and effort.

[0294] (Application Example 1)

[0295] 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."

[0296] Conventional sewing techniques have presented challenges such as difficulty for consumers to easily obtain clothing that fits their body shape, and the production process being complicated and time-consuming. Furthermore, it has been difficult to provide designs that reflect the individual preferences of consumers and current trends. This invention aims to solve these problems and provide a system that allows consumers to easily customize clothing and have it produced while checking the process in real time.

[0297] 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.

[0298] In this invention, the server includes information processing means, calculation means, and design means. This makes it possible to provide a system that enables an automated process for producing customized clothing based on the size and preferences entered by the consumer, along with real-time visual confirmation.

[0299] "Information processing means" refers to a function that receives size and dimension data entered by the user and processes it within the system.

[0300] The "calculation means" is a function that performs calculations to automatically generate a pattern using the received size and dimension data.

[0301] The "design method" refers to a function that generates design proposals based on the generated pattern data, user preferences, and trend information.

[0302] The "instruction generation means" is a function that generates instructions for operating sewing machines and other sewing equipment based on the selected design proposal.

[0303] A "control means" is a function that transmits sewing machine operation instructions to sewing machines and other sewing equipment, and automatically starts the production process.

[0304] "Visualization means" refers to a function that allows users to check the product manufacturing process in real time using a terminal device.

[0305] This invention provides a system that allows users to customize and manufacture products such as clothing. Users input their body size and dimensions using a device such as a smartphone or tablet. This data is transmitted from the device to a server via a network.

[0306] The server processes the received size and dimension data using information processing means and automatically generates a pattern sheet by means of computing means. For this pattern sheet generation, machine learning algorithms such as Python and R are used to efficiently generate accurate pattern sheets.

[0307] Furthermore, the server creates a design proposal based on the generated pattern sheet data and the user's preferences and fashion information by using design means. In this process, database queries and AI models using user history and market data are utilized, and a design reflecting trends is proposed.

[0308] The generated design proposal is sent to the terminal, and the user selects from them. Based on the design selected by the user, the server creates detailed sewing machine and sewing equipment operation instructions through instruction creation means, and the control means sends the instructions to the sewing machine to start automatic production. As a result, the user can actually produce and enjoy the selected design without special techniques.

[0309] The entire production process can be monitored in real time by the user through the visualization means of the terminal. As a result, the user can obtain their own special product while checking the production status.

[0310] As a specific example, a primary school student and their parent can input the size using a tablet at the store, select the latest design, customize the clothes on the spot, and receive a complete product when they go home. The following prompt sentences are used for the generation AI model:

[0311] "Please construct a system that generates multiple clothing designs considering the latest trends based on the size data input by the user and automatically creates sewing machine control instructions that can produce the selected design."

[0312] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0313] Step 1:

[0314] The user uses a terminal to input their size and dimension data. The terminal prepares this input data and sends it to the server. The input in this step is the size and dimension data entered by the user into the terminal, and the output is formatted data. The terminal converts the data to a standard communication format and then sends the data to the next step.

[0315] Step 2:

[0316] The server receives size and dimension data transmitted from the terminal. Information processing equipment formats the received data and prepares it for transmission to the calculation equipment. The input for this step is the data transmitted from the terminal, and the output is the formatted data within the server. The server verifies the data integrity and prepares for pattern generation.

[0317] Step 3:

[0318] The server's computing power automatically generates patterns using the formatted data. This process utilizes a database of past patterns and machine learning algorithms. The input for this step is formatted user size data, and the output is the generated pattern data. The server selects the optimal pattern and makes adjustments as needed.

[0319] Step 4:

[0320] The server uses design tools to generate design proposals that take into account pattern data, trend information, and user preferences. The input for this step is pattern data and trend information, and the output is multiple design proposals. The server uses database queries and AI models to select designs that reflect current trends.

[0321] Step 5:

[0322] The server sends design proposals to the terminal, and the user selects their preferred design from among them. The input for this step is the design proposals from the server, and the output is the user's design selection. The terminal visually displays the design proposals and interactively accepts the user's selection.

[0323] Step 6:

[0324] Once the user has selected a design, the server generates sewing machine operation instructions using an instruction creation mechanism. The input for this step is the user's selected design, and the output is specific operation instructions. The server creates detailed instructions, including cutting sequence and sewing methods.

[0325] Step 7:

[0326] The server sends operating instructions to the sewing machine via the control system, automatically starting the production process. The input in this step is the operating instruction, and the output is the trigger for starting production. The sewing machine operates according to the received instructions and produces the product.

[0327] Step 8:

[0328] The user monitors the production process in real time using the terminal's visualization capabilities. The input for this step is information regarding the production status, and the output is real-time information provided to the user. The user can check the progress through the terminal and intervene as needed.

[0329] 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.

[0330] This invention is a system that generates design proposals that reflect the user's preferences and emotions, automatically generates patterns based on those proposals, and performs sewing machine operations. This system operates with the terminal, server, sewing machine, and emotion engine working in coordination with each other.

[0331] First, the user inputs size and dimension data for themselves, their pets, and small objects into the device. The device sends the input data to the server, and at this time, the emotion engine analyzes emotional data such as the user's facial expressions and tone of voice. The emotion engine recognizes the user's emotions in real time as they input and make selections, and reflects the results in the design generation.

[0332] The server automatically generates patterns based on the received size and dimension data. This generation process references a standard pattern database and optimizes the design as needed. Furthermore, the server generates design proposals considering user sentiment analysis data and preference / trend information. The analysis results from the sentiment engine are particularly reflected in the selection of design colors and elements, resulting in suggestions that better align with the user's emotions.

[0333] The generated design proposals are sent to the terminal, where the user reviews them and selects their favorite. Once the selection is complete, the server creates sewing machine instructions based on the chosen design. These instructions include information such as fabric selection and color combinations, and are designed to ensure that the finished product best matches the user's feelings.

[0334] The terminal sends instructions to the sewing machine, which automatically begins production. The user simply watches the production process unfold and eagerly awaits the finished product. For example, if the user wants to make baby clothes, the emotion engine analyzes the user's sense of security and happiness, and based on this, provides design suggestions that make extensive use of gentle colors such as pastels. In this way, the system enhances the quality of the finished product while being attentive to the user's emotions.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] The user inputs the size and dimensions of the object to be manufactured using a terminal. The terminal then prepares to send this data to the server.

[0338] Step 2:

[0339] The emotion engine is activated on the device and analyzes the user's facial expressions and tone of voice to generate emotion data. This allows the system to determine the user's current emotional state.

[0340] Step 3:

[0341] The device sends emotional data along with size and dimension data to the server.

[0342] Step 4:

[0343] The server uses the size and dimension data it receives to run a pattern generation algorithm. It consults a standard pattern database as needed to generate the optimal pattern.

[0344] Step 5:

[0345] The server automatically generates multiple design options based on emotional data, the user's past design history, and trend information. By utilizing the data from the emotional engine, the colors and shapes of the design options are adjusted to match the user's emotions.

[0346] Step 6:

[0347] The server sends the generated design proposal to the terminal.

[0348] Step 7:

[0349] The user reviews the submitted design proposals on their device and selects the most preferred design. The selection result is then sent to the server.

[0350] Step 8:

[0351] The server generates detailed instructions for sewing machine operation based on the user's selected design. These instructions include cutting patterns, sewing sequences, and artistic adjustments to suit the user's mood.

[0352] Step 9:

[0353] The terminal sends operation instructions from the server to the sewing machine.

[0354] Step 10:

[0355] The sewing machine automatically begins production according to the instructions it receives. The user can monitor the production process and make fine adjustments as needed. The finished product is designed to best fit the user's emotions.

[0356] (Example 2)

[0357] 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".

[0358] Traditional manufacturing processes often fail to adequately reflect user emotions and individual preferences in product design and specifications. There is a need to automatically and efficiently produce individually customized products while considering user sentiment. Furthermore, a smooth workflow from design to manufacturing is essential to enhance user satisfaction.

[0359] 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.

[0360] In this invention, the server includes means for analyzing dimensional information and emotional information, means for presenting automatically generated patterns and design proposals, and means for controlling manufacturing equipment to automatically carry out production. This enables the efficient production of individually customized products that reflect the user's emotions.

[0361] "Terminal means" refers to a device that receives dimensional information and emotional information from the user and analyzes them.

[0362] "Calculation means" refers to a computing device that automatically generates patterns and design proposals based on received dimensional information and sentiment data.

[0363] "Display means" refers to a device that presents generated design proposals to the user and accepts their selection.

[0364] "Instruction generation means" refers to a device for creating specific operating instructions for a manufacturing device based on a selected design proposal.

[0365] "Control means" refers to a device that sends operating instructions to a manufacturing device and automatically starts and controls the manufacturing process.

[0366] "Optimization means" refers to an apparatus or method for creating an optimal pattern by adjusting dimensional information based on standard pattern information.

[0367] "Communication means" refers to a device or method for transmitting generated design proposals to a terminal and receiving selection information from the user.

[0368] This invention is a system that automatically designs and manufactures products that take into account the individual needs and emotional state of the user. This system primarily utilizes terminals, servers, manufacturing equipment, and an emotion analysis engine.

[0369] The user inputs dimensional information about the product they want to manufacture via a terminal. The terminal also features an emotion analysis engine that identifies the user's emotional state based on their facial expressions and tone of voice. This emotion data is also sent to the server.

[0370] The server automatically generates patterns using an AI model based on the received dimensional information. During this process, it refers to a database of standard pattern information and optimizes dimensions as needed. Simultaneously, it uses sentiment analysis data to control the generation of design proposals. This generation method uses prompts such as "a design that expresses a sense of security." This allows the design's colors and style to be adjusted to the user's preferences.

[0371] The generated design proposals are sent to the terminal, where the user reviews and makes a selection. Once the user has made their selection, the server creates operating instructions for the manufacturing equipment based on the selected design. These instructions include specific production conditions, such as fabric selection and color combinations.

[0372] The manufacturing equipment receives operation instructions transmitted from the terminal and automatically starts the production process. Users can monitor this process in real time, ensuring that the finished product fits their emotions and preferences.

[0373] For example, if a user wants to create baby clothes, the device analyzes the user's feelings of security and happiness and generates design proposals based on them. The generating AI model uses the prompt phrase "baby clothes, pastel color design emphasizing security" to make suggestions that match the user's wishes.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The user uses a terminal to input dimensional information of the object to be manufactured. The input data is analyzed by the terminal's built-in emotion analysis engine, which also analyzes facial expressions and voice tone to generate emotion information. The terminal then sends this combined dimensional and emotion information to the server. The input consists of dimensional and emotion information, and the output is the transfer of data to the server.

[0377] Step 2:

[0378] The server uses the received dimensional information to automatically generate patterns using a generation AI model, referencing standard pattern information. Simultaneously, it analyzes the received emotional information and selects design elements based on those emotions. The inputs are dimensional information and emotional information, and the output is an optimized pattern and design proposals. The server provides the generation AI model with the prompt "Design based on user confidence" to create design proposals.

[0379] Step 3:

[0380] The generated design proposals and patterns are sent to the terminal. The terminal presents the user with multiple designs in a visual format. The user selects their preferred design, and this information is sent back to the server. The input is the generated design proposals, and the output is the user's design selection.

[0381] Step 4:

[0382] The server generates operating instructions for the manufacturing equipment based on the selected design. These instructions include fabric characteristics, color combinations, and sewing procedures. The input is the user-selected design, and the output is specific operating instructions. The server transmits these to the manufacturing equipment via a terminal.

[0383] Step 5:

[0384] The terminal transmits operation instructions received from the server to the manufacturing equipment. The manufacturing equipment follows the instructions and automatically starts production. The user can monitor the production process on the terminal. The input is the operation instructions, and the output is the start of the production process. Through this process, the manufacturing equipment completes a product that is highly emotionally resonant.

[0385] (Application Example 2)

[0386] 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."

[0387] Traditional clothing production systems struggled to quickly and accurately propose designs that matched the user's emotions and preferences, and to produce them on the spot. This made it difficult to meet the customer's desire for a more personalized experience and immediate production when they visited a store, posing a challenge in enhancing the individual customer experience.

[0388] 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.

[0389] In this invention, the server includes an information terminal means, a computing device means, and an emotion analysis means. This makes it possible to analyze the user's emotion data in real time, quickly and accurately generate design proposals based on that data, and immediately manufacture the selected design.

[0390] An "information terminal device" is a device used by users to input and receive size and dimension data.

[0391] A "calculation device means" is a device that has the function of automatically generating a pattern based on the input size and dimension data.

[0392] A "design tool" is a device that performs the process of generating design proposals based on generated pattern data and user preferences and emotional information.

[0393] An "emotion analysis device" is a device that provides technology to analyze a user's facial expressions and voice data and generate emotion data in real time.

[0394] A "command generation device" is a device that generates commands to instruct the operation of sewing machines and other sewing equipment according to a selected design.

[0395] "Control means" refers to technology that receives instructions for operating sewing machines, transmits them to the device, and automatically starts the manufacturing process based on those instructions.

[0396] "Means of supplying goods" refers to a method or apparatus for providing manufactured products to customers.

[0397] A "standard pattern database" is a database that stores standard pattern data that is referenced for optimization purposes.

[0398] A "generative AI model" is an artificial intelligence model used for design generation, a technology that generates designs adapted to the user's emotions based on prompt text.

[0399] A "prompt statement" is an instruction given to a generative AI model to prompt it to produce a specific output.

[0400] The system for implementing this invention consists of an information terminal, a computing device, an emotion analysis device, and a generative AI model. The information terminal is a device for the user to input size and dimension data, thereby transmitting basic information to the server. The computing device operates on the server and is responsible for automatically generating patterns based on the received size and dimension data.

[0401] When the server generates patterns based on data entered by the user, it refers to a standard pattern database and performs optimization processing. As part of this process, an emotion analysis system analyzes facial expressions and voice to generate user emotion data. This emotion data is passed as a prompt to the generation AI model, which uses the AI ​​to generate design proposals that match the user's emotions.

[0402] For example, by giving a specific instruction to the AI ​​model as a prompt, such as "Please suggest a pastel-colored design based on the user's feelings of joy," design proposals that match the emotion can be generated.

[0403] Users can review and select generated design proposals via an information terminal. The server creates operating instructions for the sewing machine based on the selected design proposal and sends these instructions to the sewing machine. The sewing machine automatically begins producing clothing or other items, and the final product is delivered to the user via an item distribution system.

[0404] This entire process enables a system that quickly delivers personalized products based on the user's individual emotions and preferences.

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] The user enters size and dimension data using an information terminal. This input data includes the user's body shape and basic information necessary for manufacturing clothing. The terminal sends this information to the server. The input is treated as raw data, and the output to the server is converted to a standard data format.

[0408] Step 2:

[0409] The server automatically generates a pattern using a calculation device based on the received size and dimension data. The data is compared with a standard pattern database and optimized. This results in the output of the most suitable pattern for the user.

[0410] Step 3:

[0411] To analyze user emotions, an emotion analysis system collects the user's facial expressions and voice in real time via an information terminal. The emotion data is converted into emotion indices by an analysis engine and sent to a server. This data is then used in the subsequent design generation process.

[0412] Step 4:

[0413] The server receives emotion data and sends prompt messages to the AI ​​model to reflect this in the generated design proposals. For example, a prompt might say, "Please propose a design based on the user's feelings of joy." The AI ​​model then generates design proposals based on these prompts and outputs them to the terminal.

[0414] Step 5:

[0415] The user reviews and selects a design proposal generated on their device. The selected proposal is then fed back to the server. Based on this information, the server creates operating instructions for the sewing machine. This includes details such as fabric selection and sewing sequence.

[0416] Step 6:

[0417] The server sends operating instructions to the sewing machine, and production starts automatically. The sewing machine follows the received instructions and creates a garment with the exact specified design. The output is the finished garment.

[0418] Step 7:

[0419] Finally, the finished garments are delivered to users through a distribution system. This allows users to receive individually customized products. Product delivery forms a feedback loop, providing data that helps improve future experiences.

[0420] 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.

[0421] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0422] 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.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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".

[0436] This invention provides a system in which a user inputs size and dimension data, and a controlled sewing machine automatically manufactures the product. This system operates in an environment where terminals, servers, and sewing machines are interconnected via a network. Specific embodiments are described below.

[0437] First, the user uses a terminal to input the size and dimensions of the object they want to create (person, pet, small object). The terminal sends this data to the server. The server automatically generates a pattern based on the received data. This pattern generation is performed by an algorithm stored on the server, which selects or adjusts the most suitable pattern by referring to a standard pattern database.

[0438] Next, the server generates multiple design options by combining the generated patterns with the user's past selection history and trend information. These design options are designed to provide the user with attractive choices. The generated design options are sent to the terminal, and the user can select their preferred design from among them.

[0439] Once the user selects a design, the server generates specific sewing machine instructions based on that information. These instructions include details such as the fabric to be used, the cutting order, and the sewing procedure, detailing how the sewing machine will operate accordingly. The terminal sends these instructions to the sewing machine, which then begins production. The sewing machine is automated, and the user only needs to supervise the production process.

[0440] As a concrete example, consider a case where a user wants to create clothes for their pet. The user inputs the pet's length and chest circumference into the terminal. The server receives this information, generates a pattern for the pet, and then creates several design options based on pet fashion trends. The user selects a design, and the sewing machine operates according to the instructions, allowing for easy creation of clothes for the pet. This entire process significantly simplifies the traditional production process, saving time and effort.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] The user inputs the size and dimensions of the object to be manufactured using a terminal. The entered data is immediately sent to the server.

[0444] Step 2:

[0445] The server analyzes the size and dimension data received from the terminal and executes a pattern generation algorithm to automatically generate a pattern. During this process, it refers to a standard pattern database as needed to create an optimized pattern.

[0446] Step 3:

[0447] Based on the patterns generated by the server, multiple design options are automatically generated, taking into account the user's past design selections and current fashion trend information. The generated design options are then sent to the user's device.

[0448] Step 4:

[0449] The user selects their preferred design from the design options presented on their device. The selected design information is then sent to the server.

[0450] Step 5:

[0451] The server generates specific sewing machine instructions based on the selected design. These instructions include fabric cutting patterns, sewing sequences, and stitch details.

[0452] Step 6:

[0453] The terminal transmits sewing machine operation instructions received from the server to the sewing machine. The sewing machine receives these instructions and starts automatic production, proceeding with the work as specified.

[0454] Step 7:

[0455] The user monitors the production process and makes minor adjustments as needed. Once production is complete, they receive the finished product and terminate their use of the system.

[0456] (Example 1)

[0457] 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."

[0458] Conventional manufacturing processes were time-consuming and laborious, requiring users to input size and dimension data, manually create patterns based on that data, and then manually instruct each step of the necessary manufacturing process. Furthermore, it was difficult to provide optimal designs tailored to user preferences. To solve this problem, the present invention aims to provide a system that automates and streamlines the entire manufacturing process, from pattern generation to production.

[0459] 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.

[0460] In this invention, the server includes a calculation means for automatically generating a pattern using acquired size and dimension information, a design means for creating multiple design options considering the generated pattern and past selection history and trend information, and an instruction creation means for generating operation instructions for an automated manufacturing device based on the design option selected by the user. This enables the user to efficiently generate a pattern and obtain a high-quality product in a short time through the manufacturing process using an automated manufacturing device.

[0461] "Information processing means" refers to a device or system for acquiring size and dimensional information of an object input by a user.

[0462] "Calculation means" refers to a device or system that automatically generates a pattern based on acquired size and dimension information.

[0463] "Design means" refers to a device or system that creates multiple design options by considering generated patterns and past selection history and trend information.

[0464] "Instruction generation means" refers to a device or system that generates operating instructions for an automated manufacturing device based on a design proposal selected by the user.

[0465] "Control means" refers to a device or system for transmitting generated operation instructions to an automated manufacturing device and starting the automated execution of manufacturing.

[0466] An "optimization means" is a device or system that compares acquired size and dimension information with a standard pattern database to select or adjust the optimal pattern.

[0467] "Communication means" refers to a device or system for transmitting multiple design proposals to an information processing device and accepting design selections from the user.

[0468] This invention is a system for automatically manufacturing products based on size and dimension information entered by a user. This system operates in an environment where terminals, servers, and automated manufacturing equipment are interconnected via a network.

[0469] First, the user uses a terminal to input the size and dimensions of the object to be manufactured. This input process involves the user entering the actual measurements of the object into a dedicated application on the terminal. The terminal then organizes the input information and sends it to a server via the network.

[0470] Based on the received size and dimension information, the server automatically generates patterns using an internally installed AI model. During this process, the server compares the pattern against a standard pattern database to select or fine-tune the most suitable pattern. Furthermore, the server references the user's past selection history and the latest trend information to create multiple design options based on the pattern.

[0471] The generated design proposals are then sent to the terminal, and the user can select their preferred design from among them. After the user's selection, the server generates operating instructions for the automated manufacturing machine based on the selected design. These instructions include detailed information such as fabric selection, cutting order, and sewing procedure.

[0472] Finally, the terminal sends the generated operating instructions to the automated manufacturing machine. The automated manufacturing machine follows the received instructions and begins manufacturing. This process is fully automated, and the user only needs to supervise the manufacturing process until the product is completed.

[0473] As a concrete example, consider a scenario where a user makes clothes for their pet. The user inputs the pet's length and chest circumference into a terminal and sends that data to a server. The server automatically generates a pattern for the pet and creates several design options based on the latest pet fashion trends. Once the user selects their desired design, an automated manufacturing device produces the pet's clothes according to that design.

[0474] An example of a prompt for the generating AI model would be: "Based on the data that the pet's body length is 50cm and chest circumference is 40cm, please generate patterns and design proposals based on the latest pet fashion."

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The user inputs the size and dimensions of the object to be manufactured using a terminal. Specifically, the user uses a measuring tape or similar tool to input the measured values ​​into a dedicated application on the terminal. This input data becomes the initial input data for the program.

[0478] Step 2:

[0479] The terminal formats the entered size and dimension information and sends it to the server. Specifically, the terminal formats the entered data along with the user ID and sends it to the server via the network. This process converts the data into the format required by the server.

[0480] Step 3:

[0481] The server uses the received size and dimension information to automatically generate patterns using a generation AI model. First, the server refers to a standard pattern database and selects the most suitable pattern based on the input information. If necessary, it applies an algorithm to adjust the dimensions to optimize the pattern and stores the result as an intermediate product.

[0482] Step 4:

[0483] The server considers past selection history and trend information to create multiple design options based on the generated patterns. Specifically, the server uses a machine learning algorithm to suggest design patterns that suit the user's preferences. As an output of this step, multiple design options are generated.

[0484] Step 5:

[0485] The server sends the generated design proposals to the terminal. The transmitted design proposals are converted into a format that can be visually viewed on the user interface. This process allows the user to input their selection of a design proposal.

[0486] Step 6:

[0487] The user selects their preferred design from several options presented on the device. The user's selection information becomes the input data for the next processing step.

[0488] Step 7:

[0489] The server generates operating instructions for the automated sewing machine based on the selected design. These instructions include a detailed production guide, such as the type of fabric to use, the cutting order, and the sewing procedure. These instructions become the prepared output data for production.

[0490] Step 8:

[0491] The terminal transmits the generated operation instructions to the automated manufacturing device. Specifically, the terminal communicates with the device via the network to ensure smooth data transmission.

[0492] Step 9:

[0493] The automated manufacturing equipment begins production according to the received operating instructions. The manufacturing equipment is fully automated and, by operating as instructed, produces high-precision products. The user supervises the manufacturing process and monitors and adjusts the process as needed.

[0494] Through these steps, the system enables rapid and accurate production, saving time and effort.

[0495] (Application Example 1)

[0496] 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."

[0497] Conventional sewing techniques have presented challenges such as difficulty for consumers to easily obtain clothing that fits their body shape, and the production process being complicated and time-consuming. Furthermore, it has been difficult to provide designs that reflect the individual preferences of consumers and current trends. This invention aims to solve these problems and provide a system that allows consumers to easily customize clothing and have it produced while checking the process in real time.

[0498] 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.

[0499] In this invention, the server includes information processing means, calculation means, and design means. This makes it possible to provide a system that enables an automated process for producing customized clothing based on the size and preferences entered by the consumer, along with real-time visual confirmation.

[0500] "Information processing means" refers to a function that receives size and dimension data entered by the user and processes it within the system.

[0501] The "calculation means" is a function that performs calculations to automatically generate a pattern using the received size and dimension data.

[0502] The "design method" refers to a function that generates design proposals based on the generated pattern data, user preferences, and trend information.

[0503] The "instruction generation means" is a function that generates instructions for operating sewing machines and other sewing equipment based on the selected design proposal.

[0504] A "control means" is a function that transmits sewing machine operation instructions to sewing machines and other sewing equipment, and automatically starts the production process.

[0505] "Visualization means" refers to a function that allows users to check the product manufacturing process in real time using a terminal device.

[0506] This invention provides a system that allows users to customize and manufacture products such as clothing. Users input their body size and dimensions using a device such as a smartphone or tablet. This data is transmitted from the device to a server via a network.

[0507] The server processes the received size and dimension data using information processing means and automatically generates patterns using calculation means. This pattern generation uses machine learning algorithms such as Python and R to efficiently generate accurate patterns.

[0508] Furthermore, the server uses design tools to create design proposals based on the generated pattern data and user preferences and trend information. This process utilizes database queries using user history and market data, as well as AI models, to propose designs that reflect current trends.

[0509] The generated design proposals are sent to the terminal, and the user selects one from among them. Based on the design selected by the user, the server creates detailed instructions for operating the sewing machine and sewing equipment through an instruction creation device, and the control device sends these instructions to the sewing machine to start automatic production. This allows the user to actually produce their selected design and enjoy the process, even without any special skills.

[0510] The entire manufacturing process can be monitored in real time by the user through a visualization device on the terminal. This allows users to check the manufacturing status and receive their own unique product.

[0511] As a concrete example, a primary school child and their parent can use a tablet in-store to input their size, choose the latest design, customize their clothing on the spot, and receive the finished product by the time they leave. The generative AI model uses prompts such as the following:

[0512] "Please build a system that generates multiple clothing designs, taking into account the latest trends, based on size data entered by the user, and automatically creates sewing machine control instructions to produce the selected design."

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The user uses a terminal to input their size and dimension data. The terminal prepares this input data and sends it to the server. The input in this step is the size and dimension data entered by the user into the terminal, and the output is formatted data. The terminal converts the data to a standard communication format and then sends the data to the next step.

[0516] Step 2:

[0517] The server receives size and dimension data transmitted from the terminal. Information processing equipment formats the received data and prepares it for transmission to the calculation equipment. The input for this step is the data transmitted from the terminal, and the output is the formatted data within the server. The server verifies the data integrity and prepares for pattern generation.

[0518] Step 3:

[0519] The server's computing power automatically generates patterns using the formatted data. This process utilizes a database of past patterns and machine learning algorithms. The input for this step is formatted user size data, and the output is the generated pattern data. The server selects the optimal pattern and makes adjustments as needed.

[0520] Step 4:

[0521] The server uses design tools to generate design proposals that take into account pattern data, trend information, and user preferences. The input for this step is pattern data and trend information, and the output is multiple design proposals. The server uses database queries and AI models to select designs that reflect current trends.

[0522] Step 5:

[0523] The server sends design proposals to the terminal, and the user selects their preferred design from among them. The input for this step is the design proposals from the server, and the output is the user's design selection. The terminal visually displays the design proposals and interactively accepts the user's selection.

[0524] Step 6:

[0525] Once the user has selected a design, the server generates sewing machine operation instructions using an instruction creation mechanism. The input for this step is the user's selected design, and the output is specific operation instructions. The server creates detailed instructions, including cutting sequence and sewing methods.

[0526] Step 7:

[0527] The server sends operating instructions to the sewing machine via the control system, automatically starting the production process. The input in this step is the operating instruction, and the output is the trigger for starting production. The sewing machine operates according to the received instructions and produces the product.

[0528] Step 8:

[0529] The user monitors the production process in real time using the terminal's visualization capabilities. The input for this step is information regarding the production status, and the output is real-time information provided to the user. The user can check the progress through the terminal and intervene as needed.

[0530] 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.

[0531] This invention is a system that generates design proposals that reflect the user's preferences and emotions, automatically generates patterns based on those proposals, and performs sewing machine operations. This system operates with the terminal, server, sewing machine, and emotion engine working in coordination with each other.

[0532] First, the user inputs size and dimension data for themselves, their pets, and small objects into the device. The device sends the input data to the server, and at this time, the emotion engine analyzes emotional data such as the user's facial expressions and tone of voice. The emotion engine recognizes the user's emotions in real time as they input and make selections, and reflects the results in the design generation.

[0533] The server automatically generates patterns based on the received size and dimension data. This generation process references a standard pattern database and optimizes the design as needed. Furthermore, the server generates design proposals considering user sentiment analysis data and preference / trend information. The analysis results from the sentiment engine are particularly reflected in the selection of design colors and elements, resulting in suggestions that better align with the user's emotions.

[0534] The generated design proposals are sent to the terminal, where the user reviews them and selects their favorite. Once the selection is complete, the server creates sewing machine instructions based on the chosen design. These instructions include information such as fabric selection and color combinations, and are designed to ensure that the finished product best matches the user's feelings.

[0535] The terminal sends instructions to the sewing machine, which automatically begins production. The user simply watches the production process unfold and eagerly awaits the finished product. For example, if the user wants to make baby clothes, the emotion engine analyzes the user's sense of security and happiness, and based on this, provides design suggestions that make extensive use of gentle colors such as pastels. In this way, the system enhances the quality of the finished product while being attentive to the user's emotions.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The user inputs the size and dimensions of the object to be manufactured using a terminal. The terminal then prepares to send this data to the server.

[0539] Step 2:

[0540] The emotion engine is activated on the device and analyzes the user's facial expressions and tone of voice to generate emotion data. This allows the system to determine the user's current emotional state.

[0541] Step 3:

[0542] The device sends emotional data along with size and dimension data to the server.

[0543] Step 4:

[0544] The server uses the size and dimension data it receives to run a pattern generation algorithm. It consults a standard pattern database as needed to generate the optimal pattern.

[0545] Step 5:

[0546] The server automatically generates multiple design options based on emotional data, the user's past design history, and trend information. By utilizing the data from the emotional engine, the colors and shapes of the design options are adjusted to match the user's emotions.

[0547] Step 6:

[0548] The server sends the generated design proposal to the terminal.

[0549] Step 7:

[0550] The user reviews the submitted design proposals on their device and selects the most preferred design. The selection result is then sent to the server.

[0551] Step 8:

[0552] The server generates detailed instructions for sewing machine operation based on the user's selected design. These instructions include cutting patterns, sewing sequences, and artistic adjustments to suit the user's mood.

[0553] Step 9:

[0554] The terminal sends operation instructions from the server to the sewing machine.

[0555] Step 10:

[0556] The sewing machine automatically begins production according to the instructions it receives. The user can monitor the production process and make fine adjustments as needed. The finished product is designed to best fit the user's emotions.

[0557] (Example 2)

[0558] 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."

[0559] Traditional manufacturing processes often fail to adequately reflect user emotions and individual preferences in product design and specifications. There is a need to automatically and efficiently produce individually customized products while considering user sentiment. Furthermore, a smooth workflow from design to manufacturing is essential to enhance user satisfaction.

[0560] 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.

[0561] In this invention, the server includes means for analyzing dimensional information and emotional information, means for presenting automatically generated patterns and design proposals, and means for controlling manufacturing equipment to automatically carry out production. This enables the efficient production of individually customized products that reflect the user's emotions.

[0562] "Terminal means" refers to a device that receives dimensional information and emotional information from the user and analyzes them.

[0563] "Calculation means" refers to a computing device that automatically generates patterns and design proposals based on received dimensional information and sentiment data.

[0564] "Display means" refers to a device that presents generated design proposals to the user and accepts their selection.

[0565] "Instruction generation means" refers to a device for creating specific operating instructions for a manufacturing device based on a selected design proposal.

[0566] "Control means" refers to a device that sends operating instructions to a manufacturing device and automatically starts and controls the manufacturing process.

[0567] "Optimization means" refers to an apparatus or method for creating an optimal pattern by adjusting dimensional information based on standard pattern information.

[0568] "Communication means" refers to a device or method for transmitting generated design proposals to a terminal and receiving selection information from the user.

[0569] This invention is a system that automatically designs and manufactures products that take into account the individual needs and emotional state of the user. This system primarily utilizes terminals, servers, manufacturing equipment, and an emotion analysis engine.

[0570] The user inputs dimensional information about the product they want to manufacture via a terminal. The terminal also features an emotion analysis engine that identifies the user's emotional state based on their facial expressions and tone of voice. This emotion data is also sent to the server.

[0571] The server automatically generates patterns using an AI model based on the received dimensional information. During this process, it refers to a database of standard pattern information and optimizes dimensions as needed. Simultaneously, it uses sentiment analysis data to control the generation of design proposals. This generation method uses prompts such as "a design that expresses a sense of security." This allows the design's colors and style to be adjusted to the user's preferences.

[0572] The generated design proposals are sent to the terminal, where the user reviews and makes a selection. Once the user has made their selection, the server creates operating instructions for the manufacturing equipment based on the selected design. These instructions include specific production conditions, such as fabric selection and color combinations.

[0573] The manufacturing equipment receives operation instructions transmitted from the terminal and automatically starts the production process. Users can monitor this process in real time, ensuring that the finished product fits their emotions and preferences.

[0574] For example, if a user wants to create baby clothes, the device analyzes the user's feelings of security and happiness and generates design proposals based on them. The generating AI model uses the prompt phrase "baby clothes, pastel color design emphasizing security" to make suggestions that match the user's wishes.

[0575] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0576] Step 1:

[0577] The user uses a terminal to input dimensional information of the object to be manufactured. The input data is analyzed by the terminal's built-in emotion analysis engine, which also analyzes facial expressions and voice tone to generate emotion information. The terminal then sends this combined dimensional and emotion information to the server. The input consists of dimensional and emotion information, and the output is the transfer of data to the server.

[0578] Step 2:

[0579] The server uses the received dimensional information to automatically generate patterns using a generation AI model, referencing standard pattern information. Simultaneously, it analyzes the received emotional information and selects design elements based on those emotions. The inputs are dimensional information and emotional information, and the output is an optimized pattern and design proposals. The server provides the generation AI model with the prompt "Design based on user confidence" to create design proposals.

[0580] Step 3:

[0581] The generated design proposals and patterns are sent to the terminal. The terminal presents the user with multiple designs in a visual format. The user selects their preferred design, and this information is sent back to the server. The input is the generated design proposals, and the output is the user's design selection.

[0582] Step 4:

[0583] The server generates operating instructions for the manufacturing equipment based on the selected design. These instructions include fabric characteristics, color combinations, and sewing procedures. The input is the user-selected design, and the output is specific operating instructions. The server transmits these to the manufacturing equipment via a terminal.

[0584] Step 5:

[0585] The terminal transmits operation instructions received from the server to the manufacturing equipment. The manufacturing equipment follows the instructions and automatically starts production. The user can monitor the production process on the terminal. The input is the operation instructions, and the output is the start of the production process. Through this process, the manufacturing equipment completes a product that is highly emotionally resonant.

[0586] (Application Example 2)

[0587] 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."

[0588] Traditional clothing production systems struggled to quickly and accurately propose designs that matched the user's emotions and preferences, and to produce them on the spot. This made it difficult to meet the customer's desire for a more personalized experience and immediate production when they visited a store, posing a challenge in enhancing the individual customer experience.

[0589] 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.

[0590] In this invention, the server includes an information terminal means, a computing device means, and an emotion analysis means. This makes it possible to analyze the user's emotion data in real time, quickly and accurately generate design proposals based on that data, and immediately manufacture the selected design.

[0591] An "information terminal device" is a device used by users to input and receive size and dimension data.

[0592] A "calculation device means" is a device that has the function of automatically generating a pattern based on the input size and dimension data.

[0593] A "design tool" is a device that performs the process of generating design proposals based on generated pattern data and user preferences and emotional information.

[0594] An "emotion analysis device" is a device that provides technology to analyze a user's facial expressions and voice data and generate emotion data in real time.

[0595] A "command generation device" is a device that generates commands to instruct the operation of sewing machines and other sewing equipment according to a selected design.

[0596] "Control means" refers to technology that receives instructions for operating sewing machines, transmits them to the device, and automatically starts the manufacturing process based on those instructions.

[0597] "Means of supplying goods" refers to a method or apparatus for providing manufactured products to customers.

[0598] A "standard pattern database" is a database that stores standard pattern data that is referenced for optimization purposes.

[0599] A "generative AI model" is an artificial intelligence model used for design generation, a technology that generates designs adapted to the user's emotions based on prompt text.

[0600] A "prompt statement" is an instruction given to a generative AI model to prompt it to produce a specific output.

[0601] The system for implementing this invention consists of an information terminal, a computing device, an emotion analysis device, and a generative AI model. The information terminal is a device for the user to input size and dimension data, thereby transmitting basic information to the server. The computing device operates on the server and is responsible for automatically generating patterns based on the received size and dimension data.

[0602] When the server generates patterns based on data entered by the user, it refers to a standard pattern database and performs optimization processing. As part of this process, an emotion analysis system analyzes facial expressions and voice to generate user emotion data. This emotion data is passed as a prompt to the generation AI model, which uses the AI ​​to generate design proposals that match the user's emotions.

[0603] For example, by giving a specific instruction to the AI ​​model as a prompt, such as "Please suggest a pastel-colored design based on the user's feelings of joy," design proposals that match the emotion can be generated.

[0604] Users can review and select generated design proposals via an information terminal. The server creates operating instructions for the sewing machine based on the selected design proposal and sends these instructions to the sewing machine. The sewing machine automatically begins producing clothing or other items, and the final product is delivered to the user via an item distribution system.

[0605] This entire process enables a system that quickly delivers personalized products based on the user's individual emotions and preferences.

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] The user enters size and dimension data using an information terminal. This input data includes the user's body shape and basic information necessary for manufacturing clothing. The terminal sends this information to the server. The input is treated as raw data, and the output to the server is converted to a standard data format.

[0609] Step 2:

[0610] The server automatically generates a pattern using a calculation device based on the received size and dimension data. The data is compared with a standard pattern database and optimized. This results in the output of the most suitable pattern for the user.

[0611] Step 3:

[0612] To analyze user emotions, an emotion analysis system collects the user's facial expressions and voice in real time via an information terminal. The emotion data is converted into emotion indices by an analysis engine and sent to a server. This data is then used in the subsequent design generation process.

[0613] Step 4:

[0614] The server receives emotion data and sends prompt messages to the AI ​​model to reflect this in the generated design proposals. For example, a prompt might say, "Please propose a design based on the user's feelings of joy." The AI ​​model then generates design proposals based on these prompts and outputs them to the terminal.

[0615] Step 5:

[0616] The user reviews and selects a design proposal generated on their device. The selected proposal is then fed back to the server. Based on this information, the server creates operating instructions for the sewing machine. This includes details such as fabric selection and sewing sequence.

[0617] Step 6:

[0618] The server sends operating instructions to the sewing machine, and production starts automatically. The sewing machine follows the received instructions and creates a garment with the exact specified design. The output is the finished garment.

[0619] Step 7:

[0620] Finally, the finished garments are delivered to users through a distribution system. This allows users to receive individually customized products. Product delivery forms a feedback loop, providing data that helps improve future experiences.

[0621] 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.

[0622] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0623] 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.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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".

[0638] This invention provides a system in which a user inputs size and dimension data, and a controlled sewing machine automatically manufactures the product. This system operates in an environment where terminals, servers, and sewing machines are interconnected via a network. Specific embodiments are described below.

[0639] First, the user uses a terminal to input the size and dimensions of the object they want to create (person, pet, small object). The terminal sends this data to the server. The server automatically generates a pattern based on the received data. This pattern generation is performed by an algorithm stored on the server, which selects or adjusts the most suitable pattern by referring to a standard pattern database.

[0640] Next, the server generates multiple design options by combining the generated patterns with the user's past selection history and trend information. These design options are designed to provide the user with attractive choices. The generated design options are sent to the terminal, and the user can select their preferred design from among them.

[0641] Once the user selects a design, the server generates specific sewing machine instructions based on that information. These instructions include details such as the fabric to be used, the cutting order, and the sewing procedure, detailing how the sewing machine will operate accordingly. The terminal sends these instructions to the sewing machine, which then begins production. The sewing machine is automated, and the user only needs to supervise the production process.

[0642] As a concrete example, consider a case where a user wants to create clothes for their pet. The user inputs the pet's length and chest circumference into the terminal. The server receives this information, generates a pattern for the pet, and then creates several design options based on pet fashion trends. The user selects a design, and the sewing machine operates according to the instructions, allowing for easy creation of clothes for the pet. This entire process significantly simplifies the traditional production process, saving time and effort.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The user inputs the size and dimensions of the object to be manufactured using a terminal. The entered data is immediately sent to the server.

[0646] Step 2:

[0647] The server analyzes the size and dimension data received from the terminal and executes a pattern generation algorithm to automatically generate a pattern. During this process, it refers to a standard pattern database as needed to create an optimized pattern.

[0648] Step 3:

[0649] Based on the patterns generated by the server, multiple design options are automatically generated, taking into account the user's past design selections and current fashion trend information. The generated design options are then sent to the user's device.

[0650] Step 4:

[0651] The user selects their preferred design from the design options presented on their device. The selected design information is then sent to the server.

[0652] Step 5:

[0653] The server generates specific sewing machine instructions based on the selected design. These instructions include fabric cutting patterns, sewing sequences, and stitch details.

[0654] Step 6:

[0655] The terminal transmits sewing machine operation instructions received from the server to the sewing machine. The sewing machine receives these instructions and starts automatic production, proceeding with the work as specified.

[0656] Step 7:

[0657] The user monitors the production process and makes minor adjustments as needed. Once production is complete, they receive the finished product and terminate their use of the system.

[0658] (Example 1)

[0659] 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".

[0660] Conventional manufacturing processes were time-consuming and laborious, requiring users to input size and dimension data, manually create patterns based on that data, and then manually instruct each step of the necessary manufacturing process. Furthermore, it was difficult to provide optimal designs tailored to user preferences. To solve this problem, the present invention aims to provide a system that automates and streamlines the entire manufacturing process, from pattern generation to production.

[0661] 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.

[0662] In this invention, the server includes a calculation means for automatically generating a pattern using acquired size and dimension information, a design means for creating multiple design options considering the generated pattern and past selection history and trend information, and an instruction creation means for generating operation instructions for an automated manufacturing device based on the design option selected by the user. This enables the user to efficiently generate a pattern and obtain a high-quality product in a short time through the manufacturing process using an automated manufacturing device.

[0663] "Information processing means" refers to a device or system for acquiring size and dimensional information of an object input by a user.

[0664] "Calculation means" refers to a device or system that automatically generates a pattern based on acquired size and dimension information.

[0665] "Design means" refers to a device or system that creates multiple design options by considering generated patterns and past selection history and trend information.

[0666] "Instruction generation means" refers to a device or system that generates operating instructions for an automated manufacturing device based on a design proposal selected by the user.

[0667] "Control means" refers to a device or system for transmitting generated operation instructions to an automated manufacturing device and starting the automated execution of manufacturing.

[0668] An "optimization means" is a device or system that compares acquired size and dimension information with a standard pattern database to select or adjust the optimal pattern.

[0669] "Communication means" refers to a device or system for transmitting multiple design proposals to an information processing device and accepting design selections from the user.

[0670] This invention is a system for automatically manufacturing products based on size and dimension information entered by a user. This system operates in an environment where terminals, servers, and automated manufacturing equipment are interconnected via a network.

[0671] First, the user uses a terminal to input the size and dimensions of the object to be manufactured. This input process involves the user entering the actual measurements of the object into a dedicated application on the terminal. The terminal then organizes the input information and sends it to a server via the network.

[0672] Based on the received size and dimension information, the server automatically generates patterns using an internally installed AI model. During this process, the server compares the pattern against a standard pattern database to select or fine-tune the most suitable pattern. Furthermore, the server references the user's past selection history and the latest trend information to create multiple design options based on the pattern.

[0673] The generated design proposals are then sent to the terminal, and the user can select their preferred design from among them. After the user's selection, the server generates operating instructions for the automated manufacturing machine based on the selected design. These instructions include detailed information such as fabric selection, cutting order, and sewing procedure.

[0674] Finally, the terminal sends the generated operating instructions to the automated manufacturing machine. The automated manufacturing machine follows the received instructions and begins manufacturing. This process is fully automated, and the user only needs to supervise the manufacturing process until the product is completed.

[0675] As a concrete example, consider a scenario where a user makes clothes for their pet. The user inputs the pet's length and chest circumference into a terminal and sends that data to a server. The server automatically generates a pattern for the pet and creates several design options based on the latest pet fashion trends. Once the user selects their desired design, an automated manufacturing device produces the pet's clothes according to that design.

[0676] An example of a prompt for the generating AI model would be: "Based on the data that the pet's body length is 50cm and chest circumference is 40cm, please generate patterns and design proposals based on the latest pet fashion."

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] The user inputs the size and dimensions of the object to be manufactured using a terminal. Specifically, the user uses a measuring tape or similar tool to input the measured values ​​into a dedicated application on the terminal. This input data becomes the initial input data for the program.

[0680] Step 2:

[0681] The terminal formats the entered size and dimension information and sends it to the server. Specifically, the terminal formats the entered data along with the user ID and sends it to the server via the network. This process converts the data into the format required by the server.

[0682] Step 3:

[0683] The server uses the received size and dimension information to automatically generate patterns using a generation AI model. First, the server refers to a standard pattern database and selects the most suitable pattern based on the input information. If necessary, it applies an algorithm to adjust the dimensions to optimize the pattern and stores the result as an intermediate product.

[0684] Step 4:

[0685] The server considers past selection history and trend information to create multiple design options based on the generated patterns. Specifically, the server uses a machine learning algorithm to suggest design patterns that suit the user's preferences. As an output of this step, multiple design options are generated.

[0686] Step 5:

[0687] The server sends the generated design proposals to the terminal. The transmitted design proposals are converted into a format that can be visually viewed on the user interface. This process allows the user to input their selection of a design proposal.

[0688] Step 6:

[0689] The user selects their preferred design from several options presented on the device. The user's selection information becomes the input data for the next processing step.

[0690] Step 7:

[0691] The server generates operating instructions for the automated sewing machine based on the selected design. These instructions include a detailed production guide, such as the type of fabric to use, the cutting order, and the sewing procedure. These instructions become the prepared output data for production.

[0692] Step 8:

[0693] The terminal transmits the generated operation instructions to the automated manufacturing device. Specifically, the terminal communicates with the device via the network to ensure smooth data transmission.

[0694] Step 9:

[0695] The automated manufacturing equipment begins production according to the received operating instructions. The manufacturing equipment is fully automated and, by operating as instructed, produces high-precision products. The user supervises the manufacturing process and monitors and adjusts the process as needed.

[0696] Through these steps, the system enables rapid and accurate production, saving time and effort.

[0697] (Application Example 1)

[0698] 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".

[0699] Conventional sewing techniques have presented challenges such as difficulty for consumers to easily obtain clothing that fits their body shape, and the production process being complicated and time-consuming. Furthermore, it has been difficult to provide designs that reflect the individual preferences of consumers and current trends. This invention aims to solve these problems and provide a system that allows consumers to easily customize clothing and have it produced while checking the process in real time.

[0700] 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.

[0701] In this invention, the server includes information processing means, calculation means, and design means. This makes it possible to provide a system that enables an automated process for producing customized clothing based on the size and preferences entered by the consumer, along with real-time visual confirmation.

[0702] "Information processing means" refers to a function that receives size and dimension data entered by the user and processes it within the system.

[0703] The "calculation means" is a function that performs calculations to automatically generate a pattern using the received size and dimension data.

[0704] The "design method" refers to a function that generates design proposals based on the generated pattern data, user preferences, and trend information.

[0705] The "instruction generation means" is a function that generates instructions for operating sewing machines and other sewing equipment based on the selected design proposal.

[0706] A "control means" is a function that transmits sewing machine operation instructions to sewing machines and other sewing equipment, and automatically starts the production process.

[0707] "Visualization means" refers to a function that allows users to check the product manufacturing process in real time using a terminal device.

[0708] This invention provides a system that allows users to customize and manufacture products such as clothing. Users input their body size and dimensions using a device such as a smartphone or tablet. This data is transmitted from the device to a server via a network.

[0709] The server processes the received size and dimension data using information processing means and automatically generates patterns using calculation means. This pattern generation uses machine learning algorithms such as Python and R to efficiently generate accurate patterns.

[0710] Furthermore, the server uses design tools to create design proposals based on the generated pattern data and user preferences and trend information. This process utilizes database queries using user history and market data, as well as AI models, to propose designs that reflect current trends.

[0711] The generated design proposals are sent to the terminal, and the user selects one from among them. Based on the design selected by the user, the server creates detailed instructions for operating the sewing machine and sewing equipment through an instruction creation device, and the control device sends these instructions to the sewing machine to start automatic production. This allows the user to actually produce their selected design and enjoy the process, even without any special skills.

[0712] The entire manufacturing process can be monitored in real time by the user through a visualization device on the terminal. This allows users to check the manufacturing status and receive their own unique product.

[0713] As a concrete example, a primary school child and their parent can use a tablet in-store to input their size, choose the latest design, customize their clothing on the spot, and receive the finished product by the time they leave. The generative AI model uses prompts such as the following:

[0714] "Please build a system that generates multiple clothing designs, taking into account the latest trends, based on size data entered by the user, and automatically creates sewing machine control instructions to produce the selected design."

[0715] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0716] Step 1:

[0717] The user uses a terminal to input their size and dimension data. The terminal prepares this input data and sends it to the server. The input in this step is the size and dimension data entered by the user into the terminal, and the output is formatted data. The terminal converts the data to a standard communication format and then sends the data to the next step.

[0718] Step 2:

[0719] The server receives size and dimension data transmitted from the terminal. Information processing equipment formats the received data and prepares it for transmission to the calculation equipment. The input for this step is the data transmitted from the terminal, and the output is the formatted data within the server. The server verifies the data integrity and prepares for pattern generation.

[0720] Step 3:

[0721] The server's computing power automatically generates patterns using the formatted data. This process utilizes a database of past patterns and machine learning algorithms. The input for this step is formatted user size data, and the output is the generated pattern data. The server selects the optimal pattern and makes adjustments as needed.

[0722] Step 4:

[0723] The server uses design tools to generate design proposals that take into account pattern data, trend information, and user preferences. The input for this step is pattern data and trend information, and the output is multiple design proposals. The server uses database queries and AI models to select designs that reflect current trends.

[0724] Step 5:

[0725] The server sends design proposals to the terminal, and the user selects their preferred design from among them. The input for this step is the design proposals from the server, and the output is the user's design selection. The terminal visually displays the design proposals and interactively accepts the user's selection.

[0726] Step 6:

[0727] Once the user has selected a design, the server generates sewing machine operation instructions using an instruction creation mechanism. The input for this step is the user's selected design, and the output is specific operation instructions. The server creates detailed instructions, including cutting sequence and sewing methods.

[0728] Step 7:

[0729] The server sends operating instructions to the sewing machine via the control system, automatically starting the production process. The input in this step is the operating instruction, and the output is the trigger for starting production. The sewing machine operates according to the received instructions and produces the product.

[0730] Step 8:

[0731] The user monitors the production process in real time using the terminal's visualization capabilities. The input for this step is information regarding the production status, and the output is real-time information provided to the user. The user can check the progress through the terminal and intervene as needed.

[0732] 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.

[0733] This invention is a system that generates design proposals that reflect the user's preferences and emotions, automatically generates patterns based on those proposals, and performs sewing machine operations. This system operates with the terminal, server, sewing machine, and emotion engine working in coordination with each other.

[0734] First, the user inputs size and dimension data for themselves, their pets, and small objects into the device. The device sends the input data to the server, and at this time, the emotion engine analyzes emotional data such as the user's facial expressions and tone of voice. The emotion engine recognizes the user's emotions in real time as they input and make selections, and reflects the results in the design generation.

[0735] The server automatically generates patterns based on the received size and dimension data. This generation process references a standard pattern database and optimizes the design as needed. Furthermore, the server generates design proposals considering user sentiment analysis data and preference / trend information. The analysis results from the sentiment engine are particularly reflected in the selection of design colors and elements, resulting in suggestions that better align with the user's emotions.

[0736] The generated design proposals are sent to the terminal, where the user reviews them and selects their favorite. Once the selection is complete, the server creates sewing machine instructions based on the chosen design. These instructions include information such as fabric selection and color combinations, and are designed to ensure that the finished product best matches the user's feelings.

[0737] The terminal sends instructions to the sewing machine, which automatically begins production. The user simply watches the production process unfold and eagerly awaits the finished product. For example, if the user wants to make baby clothes, the emotion engine analyzes the user's sense of security and happiness, and based on this, provides design suggestions that make extensive use of gentle colors such as pastels. In this way, the system enhances the quality of the finished product while being attentive to the user's emotions.

[0738] The following describes the processing flow.

[0739] Step 1:

[0740] The user inputs the size and dimensions of the object to be manufactured using a terminal. The terminal then prepares to send this data to the server.

[0741] Step 2:

[0742] The emotion engine is activated on the device and analyzes the user's facial expressions and tone of voice to generate emotion data. This allows the system to determine the user's current emotional state.

[0743] Step 3:

[0744] The device sends emotional data along with size and dimension data to the server.

[0745] Step 4:

[0746] The server uses the size and dimension data it receives to run a pattern generation algorithm. It consults a standard pattern database as needed to generate the optimal pattern.

[0747] Step 5:

[0748] The server automatically generates multiple design options based on emotional data, the user's past design history, and trend information. By utilizing the data from the emotional engine, the colors and shapes of the design options are adjusted to match the user's emotions.

[0749] Step 6:

[0750] The server sends the generated design proposal to the terminal.

[0751] Step 7:

[0752] The user reviews the submitted design proposals on their device and selects the most preferred design. The selection result is then sent to the server.

[0753] Step 8:

[0754] The server generates detailed instructions for sewing machine operation based on the user's selected design. These instructions include cutting patterns, sewing sequences, and artistic adjustments to suit the user's mood.

[0755] Step 9:

[0756] The terminal sends operation instructions from the server to the sewing machine.

[0757] Step 10:

[0758] The sewing machine automatically begins production according to the instructions it receives. The user can monitor the production process and make fine adjustments as needed. The finished product is designed to best fit the user's emotions.

[0759] (Example 2)

[0760] 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".

[0761] Traditional manufacturing processes often fail to adequately reflect user emotions and individual preferences in product design and specifications. There is a need to automatically and efficiently produce individually customized products while considering user sentiment. Furthermore, a smooth workflow from design to manufacturing is essential to enhance user satisfaction.

[0762] 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.

[0763] In this invention, the server includes means for analyzing dimensional information and emotional information, means for presenting automatically generated patterns and design proposals, and means for controlling manufacturing equipment to automatically carry out production. This enables the efficient production of individually customized products that reflect the user's emotions.

[0764] "Terminal means" refers to a device that receives dimensional information and emotional information from the user and analyzes them.

[0765] "Calculation means" refers to a computing device that automatically generates patterns and design proposals based on received dimensional information and sentiment data.

[0766] "Display means" refers to a device that presents generated design proposals to the user and accepts their selection.

[0767] "Instruction generation means" refers to a device for creating specific operating instructions for a manufacturing device based on a selected design proposal.

[0768] "Control means" refers to a device that sends operating instructions to a manufacturing device and automatically starts and controls the manufacturing process.

[0769] "Optimization means" refers to an apparatus or method for creating an optimal pattern by adjusting dimensional information based on standard pattern information.

[0770] "Communication means" refers to a device or method for transmitting generated design proposals to a terminal and receiving selection information from the user.

[0771] This invention is a system that automatically designs and manufactures products that take into account the individual needs and emotional state of the user. This system primarily utilizes terminals, servers, manufacturing equipment, and an emotion analysis engine.

[0772] The user inputs dimensional information about the product they want to manufacture via a terminal. The terminal also features an emotion analysis engine that identifies the user's emotional state based on their facial expressions and tone of voice. This emotion data is also sent to the server.

[0773] The server automatically generates patterns using an AI model based on the received dimensional information. During this process, it refers to a database of standard pattern information and optimizes dimensions as needed. Simultaneously, it uses sentiment analysis data to control the generation of design proposals. This generation method uses prompts such as "a design that expresses a sense of security." This allows the design's colors and style to be adjusted to the user's preferences.

[0774] The generated design proposals are sent to the terminal, where the user reviews and makes a selection. Once the user has made their selection, the server creates operating instructions for the manufacturing equipment based on the selected design. These instructions include specific production conditions, such as fabric selection and color combinations.

[0775] The manufacturing equipment receives operation instructions transmitted from the terminal and automatically starts the production process. Users can monitor this process in real time, ensuring that the finished product fits their emotions and preferences.

[0776] For example, if a user wants to create baby clothes, the device analyzes the user's feelings of security and happiness and generates design proposals based on them. The generating AI model uses the prompt phrase "baby clothes, pastel color design emphasizing security" to make suggestions that match the user's wishes.

[0777] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0778] Step 1:

[0779] The user uses a terminal to input dimensional information of the object to be manufactured. The input data is analyzed by the terminal's built-in emotion analysis engine, which also analyzes facial expressions and voice tone to generate emotion information. The terminal then sends this combined dimensional and emotion information to the server. The input consists of dimensional and emotion information, and the output is the transfer of data to the server.

[0780] Step 2:

[0781] The server uses the received dimensional information to automatically generate patterns using a generation AI model, referencing standard pattern information. Simultaneously, it analyzes the received emotional information and selects design elements based on those emotions. The inputs are dimensional information and emotional information, and the output is an optimized pattern and design proposals. The server provides the generation AI model with the prompt "Design based on user confidence" to create design proposals.

[0782] Step 3:

[0783] The generated design proposals and patterns are sent to the terminal. The terminal presents the user with multiple designs in a visual format. The user selects their preferred design, and this information is sent back to the server. The input is the generated design proposals, and the output is the user's design selection.

[0784] Step 4:

[0785] The server generates operating instructions for the manufacturing equipment based on the selected design. These instructions include fabric characteristics, color combinations, and sewing procedures. The input is the user-selected design, and the output is specific operating instructions. The server transmits these to the manufacturing equipment via a terminal.

[0786] Step 5:

[0787] The terminal transmits operation instructions received from the server to the manufacturing equipment. The manufacturing equipment follows the instructions and automatically starts production. The user can monitor the production process on the terminal. The input is the operation instructions, and the output is the start of the production process. Through this process, the manufacturing equipment completes a product that is highly emotionally resonant.

[0788] (Application Example 2)

[0789] 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".

[0790] Traditional clothing production systems struggled to quickly and accurately propose designs that matched the user's emotions and preferences, and to produce them on the spot. This made it difficult to meet the customer's desire for a more personalized experience and immediate production when they visited a store, posing a challenge in enhancing the individual customer experience.

[0791] 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.

[0792] In this invention, the server includes an information terminal means, a computing device means, and an emotion analysis means. This makes it possible to analyze the user's emotion data in real time, quickly and accurately generate design proposals based on that data, and immediately manufacture the selected design.

[0793] An "information terminal device" is a device used by users to input and receive size and dimension data.

[0794] A "calculation device means" is a device that has the function of automatically generating a pattern based on the input size and dimension data.

[0795] A "design tool" is a device that performs the process of generating design proposals based on generated pattern data and user preferences and emotional information.

[0796] An "emotion analysis device" is a device that provides technology to analyze a user's facial expressions and voice data and generate emotion data in real time.

[0797] A "command generation device" is a device that generates commands to instruct the operation of sewing machines and other sewing equipment according to a selected design.

[0798] "Control means" refers to technology that receives instructions for operating sewing machines, transmits them to the device, and automatically starts the manufacturing process based on those instructions.

[0799] "Means of supplying goods" refers to a method or apparatus for providing manufactured products to customers.

[0800] A "standard pattern database" is a database that stores standard pattern data that is referenced for optimization purposes.

[0801] A "generative AI model" is an artificial intelligence model used for design generation, a technology that generates designs adapted to the user's emotions based on prompt text.

[0802] A "prompt statement" is an instruction given to a generative AI model to prompt it to produce a specific output.

[0803] The system for implementing this invention consists of an information terminal, a computing device, an emotion analysis device, and a generative AI model. The information terminal is a device for the user to input size and dimension data, thereby transmitting basic information to the server. The computing device operates on the server and is responsible for automatically generating patterns based on the received size and dimension data.

[0804] When the server generates patterns based on data entered by the user, it refers to a standard pattern database and performs optimization processing. As part of this process, an emotion analysis system analyzes facial expressions and voice to generate user emotion data. This emotion data is passed as a prompt to the generation AI model, which uses the AI ​​to generate design proposals that match the user's emotions.

[0805] For example, by giving a specific instruction to the AI ​​model as a prompt, such as "Please suggest a pastel-colored design based on the user's feelings of joy," design proposals that match the emotion can be generated.

[0806] Users can review and select generated design proposals via an information terminal. The server creates operating instructions for the sewing machine based on the selected design proposal and sends these instructions to the sewing machine. The sewing machine automatically begins producing clothing or other items, and the final product is delivered to the user via an item distribution system.

[0807] This entire process enables a system that quickly delivers personalized products based on the user's individual emotions and preferences.

[0808] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0809] Step 1:

[0810] The user enters size and dimension data using an information terminal. This input data includes the user's body shape and basic information necessary for manufacturing clothing. The terminal sends this information to the server. The input is treated as raw data, and the output to the server is converted to a standard data format.

[0811] Step 2:

[0812] The server automatically generates a pattern using a calculation device based on the received size and dimension data. The data is compared with a standard pattern database and optimized. This results in the output of the most suitable pattern for the user.

[0813] Step 3:

[0814] To analyze user emotions, an emotion analysis system collects the user's facial expressions and voice in real time via an information terminal. The emotion data is converted into emotion indices by an analysis engine and sent to a server. This data is then used in the subsequent design generation process.

[0815] Step 4:

[0816] The server receives emotion data and sends prompt messages to the AI ​​model to reflect this in the generated design proposals. For example, a prompt might say, "Please propose a design based on the user's feelings of joy." The AI ​​model then generates design proposals based on these prompts and outputs them to the terminal.

[0817] Step 5:

[0818] The user reviews and selects a design proposal generated on their device. The selected proposal is then fed back to the server. Based on this information, the server creates operating instructions for the sewing machine. This includes details such as fabric selection and sewing sequence.

[0819] Step 6:

[0820] The server sends operating instructions to the sewing machine, and production starts automatically. The sewing machine follows the received instructions and creates a garment with the exact specified design. The output is the finished garment.

[0821] Step 7:

[0822] Finally, the finished garments are delivered to users through a distribution system. This allows users to receive individually customized products. Product delivery forms a feedback loop, providing data that helps improve future experiences.

[0823] 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.

[0824] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0830] 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.

[0831] 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."

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] The following is further disclosed regarding the embodiments described above.

[0845] (Claim 1)

[0846] A terminal means for receiving size and dimension data entered by the user,

[0847] A server means that automatically generates patterns based on received size and dimension data,

[0848] A design method for generating design proposals based on generated pattern data and user preferences and trend information,

[0849] An instruction generation means for creating sewing machine operation instructions based on the selected design proposal,

[0850] A control means that transmits sewing machine operation instructions to the sewing machine and automatically starts production,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, further comprising an optimization means for optimizing received size and dimension data by comparing it with a standard pattern database.

[0854] (Claim 3)

[0855] The system according to claim 1, comprising a communication means for transmitting generated design proposals to a terminal and accepting design selections from the user.

[0856] "Example 1"

[0857] (Claim 1)

[0858] Information processing means for acquiring the size and dimension information of an object entered by the user,

[0859] A calculation means for automatically generating a pattern using acquired size and dimension information,

[0860] A design method for creating multiple design options by considering the generated patterns and past selection history and trend information,

[0861] Instruction generation means for generating instructions for operating an automated manufacturing device based on a design proposal selected by the user,

[0862] A control means that transmits the generated operation instructions to the automated manufacturing device and starts automated manufacturing,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, further comprising an optimization means for selecting or adjusting the optimal pattern by comparing acquired size and dimension information with a standard pattern database.

[0866] (Claim 3)

[0867] The system according to claim 1, comprising a communication means for transmitting multiple design proposals to an information processing device and accepting design selection by a user.

[0868] "Application Example 1"

[0869] (Claim 1)

[0870] Information processing means for receiving size and dimension data entered by the user,

[0871] A calculation means for automatically generating a pattern based on received size and dimension data,

[0872] A design method for generating design proposals based on generated pattern data and user preferences and trend information,

[0873] An instruction creation means for creating sewing machine operation instructions based on the selected design proposal,

[0874] A control means that transmits sewing machine operation instructions to the sewing machine and automatically starts production,

[0875] A visualization method that allows users to input data using a terminal device and check the product manufacturing process in real time,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, further comprising an optimization means for optimizing received size and dimension data by comparing it with a standard pattern database.

[0879] (Claim 3)

[0880] The system according to claim 1, comprising a communication means for transmitting generated design proposals to an information terminal and accepting design selections from the user.

[0881] "Example 2 of combining an emotion engine"

[0882] (Claim 1)

[0883] A terminal means that receives dimensional information and emotional information entered by the user and performs emotional analysis,

[0884] A calculation means for automatically generating patterns and design proposals based on received dimensional information and emotion data,

[0885] A display means that shows the generated design proposals and accepts selections from the user,

[0886] An instruction generation means for generating operating instructions for a manufacturing device based on a selected design proposal,

[0887] A control means that transmits operating instructions for the manufacturing equipment to a control device and automatically starts manufacturing,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising an optimization means for optimizing received dimensional information by comparing it with standard pattern information.

[0891] (Claim 3)

[0892] The system according to claim 1, comprising communication means for transmitting generated design proposals and accepting design selections from the user.

[0893] "Application example 2 when combining with an emotional engine"

[0894] (Claim 1)

[0895] An information terminal means for receiving size and dimension data entered by the user,

[0896] A calculation device means for automatically generating a pattern based on received size and dimension data,

[0897] A design method for generating design proposals based on generated pattern data and an engine that analyzes user preferences and emotions,

[0898] An emotion analysis means that analyzes the customer's facial expressions and voice and generates emotion data,

[0899] An instruction creation means for creating sewing machine operation instructions based on a selected design proposal,

[0900] A control means that transmits sewing machine operation instructions to the sewing machine and automatically starts production,

[0901] A means of supplying goods that provide the manufactured deliverables,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, further comprising an optimization means for optimizing received size and dimension data by comparing it with a standard pattern database.

[0905] (Claim 3)

[0906] The system according to claim 1, comprising a communication means for transmitting generated design proposals to an information terminal and accepting design selections from the user, and further generating prompt text for a generation AI model based on emotional data to generate a design that is more in line with the user's emotions. [Explanation of Symbols]

[0907] 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 terminal means for receiving size and dimension data entered by the user, A server means that automatically generates patterns based on received size and dimension data, A design method for generating design proposals based on generated pattern data and user preferences and trend information, An instruction generation means for creating sewing machine operation instructions based on the selected design proposal, A control means that transmits sewing machine operation instructions to the sewing machine and automatically starts production, A system that includes this.

2. The system according to claim 1, further comprising an optimization means for optimizing received size and dimension data by comparing it with a standard pattern database.

3. The system according to claim 1, comprising a communication means for transmitting generated design proposals to a terminal and accepting design selections from the user.

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