system

Abstract

The system according to this embodiment aims to provide a system that allows users to easily create clothing that fits their dog's body shape and incorporates the latest trends in design. [Solution] The system according to the embodiment comprises an input unit, a generation unit, an instruction unit, a transmission unit, a storage unit, a prediction unit, and a design generation unit. The input unit is used by the user to input details of their dog's body shape and dimensions. The generation unit automatically generates an optimal clothing pattern based on the data entered by the input unit. The instruction unit provides cutting and sewing instructions based on the pattern generated by the generation unit. The transmission unit transmits the cutting and sewing instructions given by the instruction unit to the sewing machine. The storage unit saves and shares the design to the cloud. The prediction unit predicts the latest trends using search data and trend data. The design generation unit automatically generates a design that anticipates the latest trends based on the trends predicted by the prediction unit.

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to easily create clothes that fit the body shape of a pet dog and it is difficult to provide designs incorporating the latest trends.

[0005] The system according to the embodiment aims to easily create clothes that fit the body shape of a pet dog and provide designs incorporating the latest trends.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, a generation unit, an instruction unit, a transmission unit, a storage unit, a prediction unit, and a design generation unit. The input unit is used by the user to input details of their dog's body shape and dimensions. The generation unit automatically generates an optimal clothing pattern based on the data entered by the input unit. The instruction unit provides cutting and sewing instructions based on the pattern generated by the generation unit. The transmission unit transmits the cutting and sewing instructions given by the instruction unit to the sewing machine. The storage unit saves and shares the design to the cloud. The prediction unit predicts the latest trends using search data and trend data. The design generation unit automatically generates a design that anticipates the latest trends based on the trends predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can easily create clothing that fits a dog's body shape and offer designs that incorporate the latest trends. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The pet clothing creation system according to an embodiment of the present invention is a system that automatically generates an optimal clothing pattern based on data entered by the user regarding the body shape and dimensions of their dog. The pet clothing creation system uses a generating AI to design the clothing and provides cutting and sewing instructions in real time in conjunction with a sewing machine. The pet clothing creation system also provides a function to save and share designs in the cloud by linking with a mobile service. Furthermore, the pet clothing creation system has been enhanced with a function that predicts the latest trends in pet fashion by utilizing search data and trend data, and automatically generates trend-ahead designs based on that data using the generating AI. For example, the pet clothing creation system allows the user to enter the body shape and dimensions of their dog. The generating AI automatically generates an optimal clothing pattern based on that data. The generating AI analyzes the user's input data and generates a clothing design that perfectly fits the dog's body shape. For example, by entering dimensions such as chest circumference, back length, and neck circumference, the generating AI generates a pattern based on those dimensions. The generated pattern is then cut and sewn in real time in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dogs and incorporate the latest pet fashion trends. Furthermore, sharing information with other pet owners via the cloud fosters community building. For instance, users can upload their designs to the cloud, and other users can download and use them, spreading pet fashion trends. This allows the pet clothing creation system to easily create clothes that perfectly fit their dogs and incorporate the latest pet fashion trends.

[0029] The pet clothing creation system according to this embodiment comprises an input unit, a generation unit, an instruction unit, a transmission unit, a storage unit, a prediction unit, and a design generation unit. The input unit allows the user to input details of their dog's body shape and dimensions. To input details of their dog's body shape and dimensions, the user inputs, for example, dimensions such as chest circumference, back length, and neck circumference. The input unit provides, for example, an interface for the user to input details of their dog's body shape and dimensions. The generation unit automatically generates an optimal clothing pattern based on the data entered by the input unit. The generation unit uses a generation AI to analyze the user's input data and generate a clothing design that perfectly fits the dog's body shape. For example, the generation AI generates an optimal clothing pattern based on the user's input data. The generation unit generates a pattern based on dimensions such as chest circumference, back length, and neck circumference. The generation unit uses a generation AI to analyze the user's input data and generate a clothing design that perfectly fits the dog's body shape. The instruction unit provides cutting and sewing instructions based on the pattern generated by the generation unit. The instruction unit uses generation AI to analyze the cutting and sewing processes and sends the optimal instructions to the sewing machine. For example, the generation AI instructs the instruction unit on the cutting order and sewing stitch pattern. The instruction unit uses generation AI to analyze the cutting and sewing processes and sends the optimal instructions to the sewing machine. The instruction unit uses generation AI to instruct the cutting order and sewing stitch pattern. The transmission unit transmits the cutting and sewing instructions given by the instruction unit to the sewing machine. The transmission unit uses generation AI to analyze the cutting and sewing processes and sends the optimal instructions to the sewing machine. For example, the generation AI instructs the cutting order and sewing stitch pattern. The transmission unit uses generation AI to analyze the cutting and sewing processes and sends the optimal instructions to the sewing machine. The transmission unit uses generation AI to instruct the cutting order and sewing stitch pattern. The storage unit saves and shares the design to the cloud. The storage unit uses a generation AI to save user-created designs to the cloud and share them with other users. For example, the storage unit uploads a user-created design to the cloud, and other users can download and use that design. The storage unit uses a generation AI to save user-created designs to the cloud and share them with other users. The storage unit uploads a user-created design to the cloud, and other users can download and use that design.The prediction unit uses search data and trend data to predict the latest trends. The prediction unit uses generative AI to analyze search data and trend data and learn the latest fashion trends. For example, the prediction unit uses generative AI to analyze search data and trend data and learn the latest fashion trends. The prediction unit uses generative AI to analyze search data and trend data and learn the latest fashion trends. The prediction unit uses generative AI to analyze search data and trend data and learn the latest fashion trends. The design generation unit automatically generates fashion-forward designs based on the trends predicted by the prediction unit. The design generation unit uses generative AI to automatically generate fashion-forward designs based on the trends predicted by the prediction unit. For example, the design generation unit uses generative AI to automatically generate fashion-forward designs based on the trends predicted by the prediction unit. The design generation unit uses generative AI to automatically generate fashion-forward designs based on the trends predicted by the prediction unit. As a result, the pet clothing creation system according to this embodiment allows users to easily create clothing that perfectly fits their dog's body shape and incorporates the latest pet fashion trends.

[0030] The input section allows users to enter details about their dog's body shape and measurements. This includes entering measurements such as chest circumference, back length, and neck circumference. The input section provides an interface for users to input these details. Specifically, it offers an intuitive graphical user interface (GUI) to facilitate easy measurement. The GUI displays diagrams and illustrations of the dog's body shape, allowing users to input measurements for each part accordingly. The input section can also provide guidelines and tutorials demonstrating how to use a measuring tape and key measurement points to assist with measurement. Furthermore, the input section verifies user-entered data in real time, providing feedback to prevent errors and inaccuracies. For example, if entered measurements exceed a general range or contain contradictory data, a warning message is displayed prompting correction. In this way, the input section supports users in accurately and efficiently entering details about their dog's body shape and measurements.

[0031] The generation unit automatically generates the optimal clothing pattern based on the data entered by the input unit. Using a generation AI, the generation unit analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape. Specifically, the generation AI executes an algorithm to generate the optimal pattern based on the user's input dimensions such as chest circumference, back length, and neck circumference. The generation AI learns from past data and existing pattern designs to derive patterns that best suit the dog's body shape. The generated pattern is customized to perfectly fit the dog's body shape, minimizing the use of wasted fabric. Furthermore, the generation unit can also customize the design according to the user's preferences. For example, if the user desires a specific design or decoration, the generation AI can incorporate those elements into the pattern. This allows the generation unit to provide original clothing designs tailored to the user's needs.

[0032] The instruction unit provides cutting and sewing instructions based on the patterns generated by the generation unit. Using generation AI, the instruction unit analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. Specifically, the generation AI analyzes the order in which each part of the pattern should be cut and which stitch patterns should be used for sewing. The generation AI utilizes past data and optimization algorithms to optimize the cutting order and sewing stitch patterns. For example, the generation AI optimizes the pattern placement and determines the cutting order to minimize fabric waste. It also selects the optimal sewing stitch pattern considering strength and aesthetics. Based on these analysis results, the instruction unit sends specific cutting and sewing instructions to the sewing machine. This allows the instruction unit to support the efficient and high-quality production of clothing.

[0033] The transmitter unit sends the cutting and sewing instructions given by the instruction unit to the sewing machine. The transmitter unit uses generational AI to analyze the cutting and sewing processes and send optimal instructions to the sewing machine. Specifically, the transmitter unit sends the cutting sequence and sewing stitch patterns analyzed by the generational AI to the sewing machine, so that the sewing machine automatically operates according to these instructions. The transmitter unit communicates with the sewing machine in real time, monitors the progress of cutting and sewing, and can modify the instructions as needed. For example, it can adjust the cutting and sewing speed and pattern according to the condition of the fabric and the operating status of the sewing machine. In this way, the transmitter unit supports the sewing machine in operating at optimal performance and producing high-quality clothing.

[0034] The storage unit saves and shares designs in the cloud. Using generative AI, the storage unit saves user-created designs to the cloud and shares them with other users. Specifically, the storage unit uploads user-created clothing designs to a cloud server, allowing other users to view and download those designs. Beyond design storage, the storage unit also handles version control and access control. For example, if a user updates a design, the storage unit records the change history, allowing users to revert to previous versions. Furthermore, the storage unit allows users to set the scope of design sharing, making designs publicly available only to specific users or groups. In this way, the storage unit supports the secure and efficient management and sharing of user-created designs with other users.

[0035] The prediction unit uses search data and trend data to predict the latest trends. The prediction unit uses generative AI to analyze search data and trend data and learn the latest fashion trends. Specifically, the prediction unit collects search data from the internet and trend data from social media, and analyzes this data to grasp the latest fashion trends. Based on this data, the generative AI predicts future fashion trends and determines the direction of designs to propose to users. For example, the prediction unit analyzes whether specific colors, patterns, or styles are trending and gives instructions to the design generation unit based on that. In this way, the prediction unit supports users in creating clothes that incorporate the latest fashion trends.

[0036] The design generation unit automatically generates trend-ahead designs based on trends predicted by the prediction unit. Specifically, the design generation unit automatically generates dog clothing designs based on trend data provided by the prediction unit. The generation AI analyzes the trend data and generates designs incorporating popular colors, patterns, and styles. For example, the generation AI selects colors and patterns suitable for dog clothing based on the latest fashion trends and generates a design combining them. The design generation unit can also provide individually customized designs, taking into account the user's preferences and past design history. This allows the design generation unit to support users in creating original, trend-ahead clothing.

[0037] The storage unit includes a sharing unit that allows users to upload designs they have created to the cloud and for other users to download and use those designs. The storage unit allows users to upload designs they have created to the cloud and for other users to download and use those designs. The storage unit allows users to upload designs they have created to the cloud and for other users to download and use those designs. This allows users to share information with each other and keep up with the latest pet fashion trends. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can implement the sharing unit using an AI model for uploading user-created designs to the cloud and for other users to download and use those designs.

[0038] The prediction unit includes an identification unit that identifies popular colors and patterns from search data. The prediction unit identifies popular colors and patterns from search data. The prediction unit identifies popular colors and patterns from search data. The prediction unit identifies popular colors and patterns from search data. This allows the system to learn the latest fashion trends and generate designs that anticipate trends. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the identification unit of the prediction unit can be implemented using an AI model for identifying popular colors and patterns from search data.

[0039] The instruction unit includes a stitch instruction unit in which the generating AI instructs the cutting order and sewing stitch patterns. The instruction unit, for example, uses the generating AI to instruct the cutting order and sewing stitch patterns. The instruction unit, for example, uses the generating AI to instruct the cutting order and sewing stitch patterns. The instruction unit, for example, uses the generating AI to instruct the cutting order and sewing stitch patterns. This enables optimal cutting and sewing instructions to be provided to achieve a professional-level finish. Some or all of the above-described processes in the instruction unit may be performed using the generating AI, for example, or without the generating AI. For example, the stitch instruction unit can be implemented using an AI model for the generating AI to instruct the cutting order and sewing stitch patterns.

[0040] The generation unit analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape. The generation unit analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape. The generation unit analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape. The generation unit analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape. This makes it possible to generate clothing designs that perfectly fit the user's dog. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the generation unit can be realized using an AI model in which the generation AI analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape.

[0041] The design generation unit analyzes search data and trend data to learn the latest fashion trends. The design generation unit analyzes search data and trend data to learn the latest fashion trends. The design generation unit analyzes search data and trend data to learn the latest fashion trends. The design generation unit analyzes search data and trend data to learn the latest fashion trends. This makes it possible to automatically generate designs that are ahead of the trends. Some or all of the above processing in the design generation unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the design generation unit can be realized using an AI model in which the generation AI analyzes search data and trend data to learn the latest fashion trends.

[0042] The input unit analyzes the user's past input history and proposes the optimal input method. For example, the input unit automatically displays as candidates detailed information about the user's dog's body shape and dimensions that the user has frequently entered in the past. The input unit prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The input unit predicts and suggests dimension data to be used at a specific time period based on the user's past input history. This allows the input unit to propose the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can be implemented using an AI model to analyze the user's past input history and propose the optimal input method.

[0043] The input unit adjusts the dimensions based on the user's pet's activity data during input. For example, if the user's pet is very active, the input unit adjusts the dimensions to be slightly larger to allow for easier movement. If the user's pet spends most of its time indoors, the input unit adjusts the dimensions with a focus on a good fit. If the user's pet is in its growth phase, the input unit adjusts the dimensions in anticipation of its growth. By adjusting the dimensions based on the pet's activity data, it is possible to create more appropriate clothing. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can be implemented using an AI model for adjusting dimensions based on the user's pet's activity data.

[0044] The input unit prioritizes acquiring relevant dimension data based on the user's geographical location information during input. For example, if the user lives in a cold region, the input unit prioritizes acquiring dimension data suitable for thick materials. If the user lives in a warm region, the input unit prioritizes acquiring dimension data suitable for thin materials. If the user lives in an urban area, the input unit prioritizes acquiring dimension data that emphasizes fashion. This allows for the creation of appropriate clothing by prioritizing the acquisition of relevant dimension data based on the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can be implemented using an AI model for prioritizing the acquisition of relevant dimension data based on the user's geographical location information.

[0045] The input unit analyzes the user's social media activity and obtains relevant dimensional data during input. For example, the input unit estimates the body shape and dimensions from photos of pets shared by the user on social media and supplements the input data. The input unit analyzes pet fashion trends that the user follows and obtains relevant dimensional data. The input unit obtains dimensional data based on information from pet communities that the user participates in. This allows for the creation of appropriate clothing by obtaining relevant dimensional data based on the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can be implemented using an AI model to analyze the user's social media activity and obtain relevant dimensional data.

[0046] The generation unit generates the optimal pattern by referring to the pet's past wearing data during the generation process. For example, the generation unit generates the optimal pattern based on the fit of clothes the pet has worn in the past. The generation unit generates the optimal pattern based on the material of clothes the pet has worn in the past. The generation unit generates the optimal pattern based on the design of clothes the pet has worn in the past. This makes it possible to generate the optimal pattern based on the pet's past wearing data. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can be realized using an AI model for generating the optimal pattern by referring to the pet's past wearing data.

[0047] The generation unit adjusts the material and shape of the pattern according to the pet's activity level during generation. For example, if the pet is active, the generation unit generates a pattern with a material and shape that allows for easy movement. If the pet spends most of its time indoors, the generation unit generates a pattern with a material and shape that prioritizes comfort. If the pet is in its growth stage, the generation unit generates a pattern with a material and shape that anticipates growth. This makes it possible to generate an optimal pattern according to the pet's activity level. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the generation unit can be realized using an AI model for adjusting the material and shape of the pattern according to the pet's activity level.

[0048] The generation unit customizes the pattern based on the pet's age and health condition during the generation process. For example, if the pet is elderly, the generation unit generates a pattern that prioritizes comfort. If the pet is healthy, the generation unit generates a pattern with an active design. If the pet is ill, the generation unit generates a pattern with a design that protects specific body parts. This allows for the generation of an optimal pattern according to the pet's age and health condition. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can be realized using an AI model for customizing patterns based on the pet's age and health condition.

[0049] The generation unit adjusts the pattern design based on the pet owner's lifestyle during the generation process. For example, if the owner is an outdoorsy person, the generation unit generates a pattern with durable materials and design. If the owner is an indoorsy person, the generation unit generates a pattern with comfortable materials and design. If the owner is fashion-conscious, the generation unit generates a pattern that incorporates the latest trends. This allows for the generation of the optimal pattern tailored to the pet owner's lifestyle. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can be realized using an AI model for adjusting the pattern design based on the pet owner's lifestyle.

[0050] The instruction unit adjusts the instructions for cutting and sewing based on changes in the pet's body shape. For example, if the pet is in its growth phase, the instruction unit provides cutting and sewing instructions that anticipate growth. If the pet's weight fluctuates significantly, the instruction unit provides cutting and sewing instructions with an adjustable design. If the pet has a specific illness, the instruction unit provides cutting and sewing instructions that protect that area. This makes it possible to provide optimal cutting and sewing instructions that respond to changes in the pet's body shape. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not. For example, the instruction unit can be implemented using an AI model for adjusting cutting and sewing instructions based on changes in the pet's body shape.

[0051] The instruction unit optimizes the instructions for cutting and sewing according to the characteristics of the material being used. For example, when using stretchable material, the instruction unit provides cutting and sewing instructions with appropriate tension. When using thick material, the instruction unit provides cutting and sewing instructions using appropriate needles and threads. When using delicate material, the instruction unit provides careful cutting and sewing instructions. This makes it possible to provide optimal cutting and sewing instructions according to the characteristics of the material being used. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can be realized using an AI model for optimizing cutting and sewing instructions according to the characteristics of the material being used.

[0052] The instruction unit adjusts the instructions for cutting and sewing based on the pet's activity level. For example, if the pet is active, the instruction unit will provide cutting and sewing instructions for a design that allows for easy movement. If the pet spends most of its time indoors, the instruction unit will provide cutting and sewing instructions that prioritize comfort. If the pet is in its growth stage, the instruction unit will provide cutting and sewing instructions that anticipate its growth. This makes it possible to provide optimal cutting and sewing instructions according to the pet's activity level. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can be implemented using an AI model for adjusting cutting and sewing instructions based on the pet's activity level.

[0053] The instruction unit customizes the cutting and sewing instructions according to the pet owner's sewing skill level. For example, if the owner is a beginner, the instruction unit provides simple and easy-to-understand cutting and sewing instructions. If the owner is an intermediate sewer, the instruction unit provides slightly more complex cutting and sewing instructions. If the owner is an advanced sewer, the instruction unit provides detailed and advanced cutting and sewing instructions. This allows the instruction unit to provide optimal cutting and sewing instructions tailored to the pet owner's sewing skill level. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not. For example, the instruction unit can be implemented using an AI model for customizing cutting and sewing instructions according to the pet owner's sewing skill level.

[0054] The transmitting unit monitors the operating status of the sewing machine in real time during transmission and sends instructions at the optimal timing. For example, if the sewing machine is in operation, the transmitting unit sends cutting and sewing instructions when the operation is finished. If the sewing machine is in standby mode, the transmitting unit sends cutting and sewing instructions immediately. If the sewing machine is undergoing maintenance, the transmitting unit sends cutting and sewing instructions when the maintenance is completed. This allows for the provision of optimal transmission timing according to the operating status of the sewing machine. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can be realized using an AI model for monitoring the operating status of the sewing machine in real time and sending instructions at the optimal timing.

[0055] The transmitting unit provides feedback on the progress of cutting and sewing during transmission and modifies the instructions as necessary. For example, the transmitting unit transmits sewing instructions when cutting is completed. If sewing is in progress, the transmitting unit transmits the next instructions according to the progress. If a problem occurs in the progress of cutting and sewing, the transmitting unit modifies the instructions and retransmits them. This allows for the provision of optimal instructions according to the progress of cutting and sewing. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can be realized using an AI model to provide feedback on the progress of cutting and sewing and modify the instructions as necessary.

[0056] The transmitting unit adjusts the transmission content based on the sewing machine's maintenance information when transmitting. For example, if the sewing machine is undergoing maintenance, the transmitting unit transmits cutting and sewing instructions when the maintenance is completed. If the sewing machine requires maintenance, the transmitting unit transmits cutting and sewing instructions after the maintenance is completed. Based on the sewing machine's maintenance information, the transmitting unit transmits cutting and sewing instructions at the optimal timing. This allows for the provision of optimal transmission content according to the sewing machine's maintenance information. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can be implemented using an AI model for adjusting the transmission content based on the sewing machine's maintenance information.

[0057] The transmitting unit optimizes the transmitted content based on coordination with other devices during transmission. For example, if other devices are in operation, the transmitting unit transmits cutting and sewing instructions when their operation ends. If coordination with other devices is required, the transmitting unit transmits cutting and sewing instructions when the coordination is completed. The transmitting unit transmits cutting and sewing instructions at the optimal timing based on the operating status of other devices. This enables the provision of optimal transmitted content in accordance with coordination with other devices. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can be realized using an AI model for optimizing transmitted content based on coordination with other devices.

[0058] The storage unit performs version control of the design during saving, facilitating comparison with past designs. For example, the storage unit assigns a version number to the design being saved, enabling comparison with past designs. The storage unit records the change history of the design being saved, clearly indicating the differences from past designs. The storage unit performs version control of the design being saved, facilitating comparison with past designs. This makes it easier to compare designs with past designs through design version control. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can be implemented using an AI model for design version control and facilitating comparison with past designs.

[0059] The storage unit automatically generates metadata for the design during saving to improve searchability. The storage unit, for example, assigns tags to the designs to be saved to improve searchability. The storage unit automatically generates metadata for the designs to be saved to improve searchability. The storage unit records information related to the designs to be saved as metadata to improve searchability. As a result, searchability is improved by automatically generating metadata for the designs. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can be implemented using an AI model for automatically generating metadata for designs and improving searchability.

[0060] The storage unit records the usage history of the design when it is saved and uses this information for future design generation. For example, the storage unit records the usage history of the design being saved and uses this information for future design generation. The storage unit records the frequency of use of the design being saved and uses this information for future design generation. The storage unit records the usage status of the design being saved and uses this information for future design generation. In this way, by recording the usage history of the design, it can be used for future design generation. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can be realized using an AI model for recording the usage history of the design and using it for future design generation.

[0061] The saving unit customizes the sharing settings of the design when saving, and shares it only to a specific user group. The saving unit customizes the sharing settings of the design to be saved, and shares it only to a specific user group. The saving unit sets the sharing scope of the design to be saved, and shares it only to a specific user group. The saving unit customizes the sharing settings of the design to be saved, and shares it only to a specific user group. This allows sharing to be limited to a specific user group by customizing the design's sharing settings. Some or all of the above processing in the saving unit may be performed using AI, for example, or not. For example, the saving unit can be implemented using an AI model for customizing the sharing settings of a design and sharing it only to a specific user group.

[0062] The prediction unit improves prediction accuracy based on past trend data during prediction. For example, the prediction unit predicts the latest trend based on past trend data. The prediction unit analyzes past trend data to improve prediction accuracy. The prediction unit refers to past trend data and provides prediction results. This makes it possible to improve prediction accuracy based on past trend data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can be implemented using an AI model to improve prediction accuracy based on past trend data.

[0063] The prediction unit customizes the prediction results based on regional trends during the prediction process. For example, the prediction unit customizes the prediction results based on regional trend data. The prediction unit considers regional trends and provides prediction results. The prediction unit refers to regional trend data and customizes the prediction results. This makes it possible to provide optimal prediction results based on regional trends. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can be implemented using an AI model for customizing prediction results based on regional trends.

[0064] The prediction unit filters trend data based on the type and size of the pet during prediction. For example, the prediction unit filters trend data according to the type of pet. The prediction unit filters trend data according to the size of the pet. The prediction unit filters trend data according to the type and size of the pet. This makes it possible to provide optimal trend data according to the type and size of the pet. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can be implemented using an AI model for filtering trend data based on the type and size of the pet.

[0065] The prediction unit provides prediction results based on seasonal trends during the prediction process. For example, the prediction unit provides prediction results based on seasonal trend data. The prediction unit considers seasonal trends and provides prediction results. The prediction unit refers to seasonal trend data and provides prediction results. This makes it possible to provide optimal prediction results based on seasonal trends. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can be implemented using an AI model for providing prediction results based on seasonal trends.

[0066] The design generation unit generates the optimal design based on past design data during the design generation process. For example, the design generation unit generates the optimal design based on past design data. The design generation unit analyzes past design data and generates a design that suits the user's preferences. The design generation unit references past design data and generates a design that incorporates the latest trends. This makes it possible to generate the optimal design based on past design data. Some or all of the above processes in the design generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the design generation unit can be realized using an AI model for generating the optimal design based on past design data.

[0067] The design generation unit adjusts the functionality of the design based on the pet's activity level during design generation. For example, if the pet is active, the design generation unit generates a design that is easy to move in. If the pet spends most of its time indoors, the design generation unit generates a design that prioritizes comfort. If the pet is in its growth stage, the design generation unit generates a design that anticipates its growth. This makes it possible to provide optimal design functionality according to the pet's activity level. Some or all of the above processing in the design generation unit may be performed using, for example, a generation AI, or not. For example, the design generation unit can be realized using an AI model for adjusting the functionality of the design based on the pet's activity level.

[0068] The design generation unit customizes the design based on the pet owner's lifestyle during the design generation process. For example, if the owner is an outdoorsy person, the design generation unit generates durable materials and designs. If the owner is an indoorsy person, the design generation unit generates materials and designs that prioritize comfort. If the owner is fashion-conscious, the design generation unit generates designs that incorporate the latest trends. This allows for the provision of optimal designs tailored to the pet owner's lifestyle. Some or all of the above-described processes in the design generation unit may be performed using, for example, a generation AI, or not. For example, the design generation unit can be realized using an AI model for customizing designs based on the pet owner's lifestyle.

[0069] The design generation unit selects design materials based on the pet's health condition during design generation. For example, if the pet has allergies, the design generation unit selects allergy-friendly materials. If the pet is elderly, the design generation unit selects materials that prioritize comfort. If the pet is ill, the design generation unit selects materials that protect specific body parts. This allows for the provision of optimal design materials tailored to the pet's health condition. Some or all of the above-described processes in the design generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the design generation unit can be realized using an AI model for selecting design materials based on the pet's health condition.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0072] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0073] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0074] The AI-generated design automatically creates and suggests trendy designs to users. For example, it identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create pet clothing that perfectly fits their dog's body shape and incorporate the latest pet fashion trends. Furthermore, sharing information with other pet owners via the cloud promotes community building. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0075] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0076] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0077] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0078] Users can save their designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their designs to the cloud, and other users can download and use them. A new feature has been added that uses search data and trend data to predict the latest trends in pet fashion, and a generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data to learn the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users. For example, it identifies popular colors and patterns from search data and generates designs based on them. This makes it easy for users to create clothes that fit their dogs perfectly and incorporate the latest pet fashion trends. Furthermore, sharing information with other pet owners through the cloud promotes community building. For example, when users upload their designs to the cloud and other users download and use them, pet fashion trends spread.

[0079] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0080] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0081] The following briefly describes the processing flow for example form 1.

[0082] Step 1: The input section allows the user to enter details about their dog's body shape and dimensions. For example, it provides an interface for entering dimensions such as chest circumference, back length, and neck circumference. Step 2: The generation unit automatically generates the optimal clothing pattern based on the data entered by the input unit. Using the generation AI, it analyzes the user's input data and generates a clothing design that perfectly fits the dog's body shape. Step 3: The instruction unit issues cutting and sewing instructions based on the pattern generated by the generation unit. Using the generation AI, it analyzes the cutting and sewing processes and sends the optimal instructions to the sewing machine. Step 4: The transmitting unit sends the cutting and sewing instructions given by the instruction unit to the sewing machine. Using generation AI, it analyzes the cutting and sewing processes and sends the optimal instructions to the sewing machine. Step 5: The storage section saves and shares the design to the cloud. Using generation AI, the user's created design is saved to the cloud and shared with other users. Step 6: The prediction unit uses search data and trend data to predict the latest trends. Using generative AI, it analyzes search data and trend data and learns the latest fashion trends. Step 7: The design generation unit automatically generates trend-ahead designs based on the trends predicted by the prediction unit. Using generation AI, it automatically generates trend-ahead designs based on the trends predicted by the prediction unit.

[0083] (Example of form 2) The pet clothing creation system according to an embodiment of the present invention is a system that automatically generates an optimal clothing pattern based on data entered by the user regarding the body shape and dimensions of their dog. The pet clothing creation system uses a generating AI to design the clothing and provides cutting and sewing instructions in real time in conjunction with a sewing machine. The pet clothing creation system also provides a function to save and share designs in the cloud by linking with a mobile service. Furthermore, the pet clothing creation system has been enhanced with a function that predicts the latest trends in pet fashion by utilizing search data and trend data, and automatically generates trend-ahead designs based on that data using the generating AI. For example, the pet clothing creation system allows the user to enter the body shape and dimensions of their dog. The generating AI automatically generates an optimal clothing pattern based on that data. The generating AI analyzes the user's input data and generates a clothing design that perfectly fits the dog's body shape. For example, by entering dimensions such as chest circumference, back length, and neck circumference, the generating AI generates a pattern based on those dimensions. The generated pattern is then cut and sewn in real time in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dogs and incorporate the latest pet fashion trends. Furthermore, sharing information with other pet owners via the cloud fosters community building. For instance, users can upload their designs to the cloud, and other users can download and use them, spreading pet fashion trends. This allows the pet clothing creation system to easily create clothes that perfectly fit their dogs and incorporate the latest pet fashion trends.

[0084] The pet clothing creation system according to this embodiment comprises an input unit, a generation unit, an instruction unit, a transmission unit, a storage unit, a prediction unit, and a design generation unit. The input unit allows the user to input details of their dog's body shape and dimensions. To input details of their dog's body shape and dimensions, the user inputs, for example, dimensions such as chest circumference, back length, and neck circumference. The input unit provides, for example, an interface for the user to input details of their dog's body shape and dimensions. The generation unit automatically generates an optimal clothing pattern based on the data entered by the input unit. The generation unit uses a generation AI to analyze the user's input data and generate a clothing design that perfectly fits the dog's body shape. For example, the generation AI generates an optimal clothing pattern based on the user's input data. The generation unit generates a pattern based on dimensions such as chest circumference, back length, and neck circumference. The generation unit uses a generation AI to analyze the user's input data and generate a clothing design that perfectly fits the dog's body shape. The instruction unit provides cutting and sewing instructions based on the pattern generated by the generation unit. The instruction unit uses generation AI to analyze the cutting and sewing processes and sends the optimal instructions to the sewing machine. For example, the generation AI instructs the instruction unit on the cutting order and sewing stitch pattern. The instruction unit uses generation AI to analyze the cutting and sewing processes and sends the optimal instructions to the sewing machine. The instruction unit uses generation AI to instruct the cutting order and sewing stitch pattern. The transmission unit transmits the cutting and sewing instructions given by the instruction unit to the sewing machine. The transmission unit uses generation AI to analyze the cutting and sewing processes and sends the optimal instructions to the sewing machine. For example, the generation AI instructs the cutting order and sewing stitch pattern. The transmission unit uses generation AI to analyze the cutting and sewing processes and sends the optimal instructions to the sewing machine. The transmission unit uses generation AI to instruct the cutting order and sewing stitch pattern. The storage unit saves and shares the design to the cloud. The storage unit uses a generation AI to save user-created designs to the cloud and share them with other users. For example, the storage unit uploads a user-created design to the cloud, and other users can download and use that design. The storage unit uses a generation AI to save user-created designs to the cloud and share them with other users. The storage unit uploads a user-created design to the cloud, and other users can download and use that design.The prediction unit uses search data and trend data to predict the latest trends. The prediction unit uses generative AI to analyze search data and trend data and learn the latest fashion trends. For example, the prediction unit uses generative AI to analyze search data and trend data and learn the latest fashion trends. The prediction unit uses generative AI to analyze search data and trend data and learn the latest fashion trends. The prediction unit uses generative AI to analyze search data and trend data and learn the latest fashion trends. The design generation unit automatically generates fashion-forward designs based on the trends predicted by the prediction unit. The design generation unit uses generative AI to automatically generate fashion-forward designs based on the trends predicted by the prediction unit. For example, the design generation unit uses generative AI to automatically generate fashion-forward designs based on the trends predicted by the prediction unit. The design generation unit uses generative AI to automatically generate fashion-forward designs based on the trends predicted by the prediction unit. As a result, the pet clothing creation system according to this embodiment allows users to easily create clothing that perfectly fits their dog's body shape and incorporates the latest pet fashion trends.

[0085] The input section allows users to enter details about their dog's body shape and measurements. This includes entering measurements such as chest circumference, back length, and neck circumference. The input section provides an interface for users to input these details. Specifically, it offers an intuitive graphical user interface (GUI) to facilitate easy measurement. The GUI displays diagrams and illustrations of the dog's body shape, allowing users to input measurements for each part accordingly. The input section can also provide guidelines and tutorials demonstrating how to use a measuring tape and key measurement points to assist with measurement. Furthermore, the input section verifies user-entered data in real time, providing feedback to prevent errors and inaccuracies. For example, if entered measurements exceed a general range or contain contradictory data, a warning message is displayed prompting correction. In this way, the input section supports users in accurately and efficiently entering details about their dog's body shape and measurements.

[0086] The generation unit automatically generates the optimal clothing pattern based on the data entered by the input unit. Using a generation AI, the generation unit analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape. Specifically, the generation AI executes an algorithm to generate the optimal pattern based on the user's input dimensions such as chest circumference, back length, and neck circumference. The generation AI learns from past data and existing pattern designs to derive patterns that best suit the dog's body shape. The generated pattern is customized to perfectly fit the dog's body shape, minimizing the use of wasted fabric. Furthermore, the generation unit can also customize the design according to the user's preferences. For example, if the user desires a specific design or decoration, the generation AI can incorporate those elements into the pattern. This allows the generation unit to provide original clothing designs tailored to the user's needs.

[0087] The instruction unit provides cutting and sewing instructions based on the patterns generated by the generation unit. Using generation AI, the instruction unit analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. Specifically, the generation AI analyzes the order in which each part of the pattern should be cut and which stitch patterns should be used for sewing. The generation AI utilizes past data and optimization algorithms to optimize the cutting order and sewing stitch patterns. For example, the generation AI optimizes the pattern placement and determines the cutting order to minimize fabric waste. It also selects the optimal sewing stitch pattern considering strength and aesthetics. Based on these analysis results, the instruction unit sends specific cutting and sewing instructions to the sewing machine. This allows the instruction unit to support the efficient and high-quality production of clothing.

[0088] The transmitter unit sends the cutting and sewing instructions given by the instruction unit to the sewing machine. The transmitter unit uses generational AI to analyze the cutting and sewing processes and send optimal instructions to the sewing machine. Specifically, the transmitter unit sends the cutting sequence and sewing stitch patterns analyzed by the generational AI to the sewing machine, so that the sewing machine automatically operates according to these instructions. The transmitter unit communicates with the sewing machine in real time, monitors the progress of cutting and sewing, and can modify the instructions as needed. For example, it can adjust the cutting and sewing speed and pattern according to the condition of the fabric and the operating status of the sewing machine. In this way, the transmitter unit supports the sewing machine in operating at optimal performance and producing high-quality clothing.

[0089] The storage unit saves and shares designs in the cloud. Using generative AI, the storage unit saves user-created designs to the cloud and shares them with other users. Specifically, the storage unit uploads user-created clothing designs to a cloud server, allowing other users to view and download those designs. Beyond design storage, the storage unit also handles version control and access control. For example, if a user updates a design, the storage unit records the change history, allowing users to revert to previous versions. Furthermore, the storage unit allows users to set the scope of design sharing, making designs publicly available only to specific users or groups. In this way, the storage unit supports the secure and efficient management and sharing of user-created designs with other users.

[0090] The prediction unit uses search data and trend data to predict the latest trends. The prediction unit uses generative AI to analyze search data and trend data and learn the latest fashion trends. Specifically, the prediction unit collects search data from the internet and trend data from social media, and analyzes this data to grasp the latest fashion trends. Based on this data, the generative AI predicts future fashion trends and determines the direction of designs to propose to users. For example, the prediction unit analyzes whether specific colors, patterns, or styles are trending and gives instructions to the design generation unit based on that. In this way, the prediction unit supports users in creating clothes that incorporate the latest fashion trends.

[0091] The design generation unit automatically generates trend-ahead designs based on trends predicted by the prediction unit. Specifically, the design generation unit automatically generates dog clothing designs based on trend data provided by the prediction unit. The generation AI analyzes the trend data and generates designs incorporating popular colors, patterns, and styles. For example, the generation AI selects colors and patterns suitable for dog clothing based on the latest fashion trends and generates a design combining them. The design generation unit can also provide individually customized designs, taking into account the user's preferences and past design history. This allows the design generation unit to support users in creating original, trend-ahead clothing.

[0092] The storage unit includes a sharing unit that allows users to upload designs they have created to the cloud and for other users to download and use those designs. The storage unit allows users to upload designs they have created to the cloud and for other users to download and use those designs. The storage unit allows users to upload designs they have created to the cloud and for other users to download and use those designs. This allows users to share information with each other and keep up with the latest pet fashion trends. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can implement the sharing unit using an AI model for uploading user-created designs to the cloud and for other users to download and use those designs.

[0093] The prediction unit includes an identification unit that identifies popular colors and patterns from search data. The prediction unit identifies popular colors and patterns from search data. The prediction unit identifies popular colors and patterns from search data. The prediction unit identifies popular colors and patterns from search data. This allows the system to learn the latest fashion trends and generate designs that anticipate trends. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the identification unit of the prediction unit can be implemented using an AI model for identifying popular colors and patterns from search data.

[0094] The instruction unit includes a stitch instruction unit in which the generating AI instructs the cutting order and sewing stitch patterns. The instruction unit, for example, uses the generating AI to instruct the cutting order and sewing stitch patterns. The instruction unit, for example, uses the generating AI to instruct the cutting order and sewing stitch patterns. The instruction unit, for example, uses the generating AI to instruct the cutting order and sewing stitch patterns. This enables optimal cutting and sewing instructions to be provided to achieve a professional-level finish. Some or all of the above-described processes in the instruction unit may be performed using the generating AI, for example, or without the generating AI. For example, the stitch instruction unit can be implemented using an AI model for the generating AI to instruct the cutting order and sewing stitch patterns.

[0095] The generation unit analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape. The generation unit analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape. The generation unit analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape. The generation unit analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape. This makes it possible to generate clothing designs that perfectly fit the user's dog. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the generation unit can be realized using an AI model in which the generation AI analyzes the user's input data and generates clothing designs that perfectly fit the dog's body shape.

[0096] The design generation unit analyzes search data and trend data to learn the latest fashion trends. The design generation unit analyzes search data and trend data to learn the latest fashion trends. The design generation unit analyzes search data and trend data to learn the latest fashion trends. The design generation unit analyzes search data and trend data to learn the latest fashion trends. This makes it possible to automatically generate designs that are ahead of the trends. Some or all of the above processing in the design generation unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the design generation unit can be realized using an AI model in which the generation AI analyzes search data and trend data to learn the latest fashion trends.

[0097] The input unit estimates the user's emotions and adjusts the layout of the input interface based on the estimated emotions. For example, if the user is stressed, the input unit provides a simple and intuitive interface and minimizes the number of input steps. If the user is relaxed, the input unit provides detailed input options and suggests a customizable input method. If the user is in a hurry, the input unit prioritizes voice input, allowing for quick input of details such as the dog's body shape and dimensions. This allows for the provision of an optimal input interface tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can be implemented using an AI model to estimate the user's emotions and adjust the layout of the input interface based on the estimated emotions.

[0098] The input unit analyzes the user's past input history and proposes the optimal input method. For example, the input unit automatically displays as candidates detailed information about the user's dog's body shape and dimensions that the user has frequently entered in the past. The input unit prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The input unit predicts and suggests dimension data to be used at a specific time period based on the user's past input history. This allows the input unit to propose the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can be implemented using an AI model to analyze the user's past input history and propose the optimal input method.

[0099] The input unit adjusts the dimensions based on the user's pet's activity data during input. For example, if the user's pet is very active, the input unit adjusts the dimensions to be slightly larger to allow for easier movement. If the user's pet spends most of its time indoors, the input unit adjusts the dimensions with a focus on a good fit. If the user's pet is in its growth phase, the input unit adjusts the dimensions in anticipation of its growth. By adjusting the dimensions based on the pet's activity data, it is possible to create more appropriate clothing. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can be implemented using an AI model for adjusting dimensions based on the user's pet's activity data.

[0100] The input unit estimates the user's emotions and prioritizes input data based on the estimated emotions. For example, if the user is stressed, the input unit prioritizes inputting important dimensional data. If the user is relaxed, the input unit prioritizes inputting detailed dimensional data. If the user is in a hurry, the input unit prioritizes inputting minimal dimensional data. This enables efficient data entry by prioritizing input data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can be implemented using an AI model to estimate the user's emotions and prioritize input data based on the estimated emotions.

[0101] The input unit prioritizes acquiring relevant dimension data based on the user's geographical location information during input. For example, if the user lives in a cold region, the input unit prioritizes acquiring dimension data suitable for thick materials. If the user lives in a warm region, the input unit prioritizes acquiring dimension data suitable for thin materials. If the user lives in an urban area, the input unit prioritizes acquiring dimension data that emphasizes fashion. This allows for the creation of appropriate clothing by prioritizing the acquisition of relevant dimension data based on the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can be implemented using an AI model for prioritizing the acquisition of relevant dimension data based on the user's geographical location information.

[0102] The input unit analyzes the user's social media activity and obtains relevant dimensional data during input. For example, the input unit estimates the body shape and dimensions from photos of pets shared by the user on social media and supplements the input data. The input unit analyzes pet fashion trends that the user follows and obtains relevant dimensional data. The input unit obtains dimensional data based on information from pet communities that the user participates in. This allows for the creation of appropriate clothing by obtaining relevant dimensional data based on the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can be implemented using an AI model to analyze the user's social media activity and obtain relevant dimensional data.

[0103] The generation unit estimates the user's emotions and adjusts the design of the pattern it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a relaxed pattern design. If the user is in a hurry, the generation unit generates a simple and easy-to-make pattern design. If the user is excited, the generation unit generates a visually stimulating pattern design. This allows for the generation of the optimal pattern design according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can be implemented using an AI model for estimating the user's emotions and adjusting the design of the pattern it generates based on the estimated emotions.

[0104] The generation unit generates the optimal pattern by referring to the pet's past wearing data during the generation process. For example, the generation unit generates the optimal pattern based on the fit of clothes the pet has worn in the past. The generation unit generates the optimal pattern based on the material of clothes the pet has worn in the past. The generation unit generates the optimal pattern based on the design of clothes the pet has worn in the past. This makes it possible to generate the optimal pattern based on the pet's past wearing data. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can be realized using an AI model for generating the optimal pattern by referring to the pet's past wearing data.

[0105] The generation unit adjusts the material and shape of the pattern according to the pet's activity level during generation. For example, if the pet is active, the generation unit generates a pattern with a material and shape that allows for easy movement. If the pet spends most of its time indoors, the generation unit generates a pattern with a material and shape that prioritizes comfort. If the pet is in its growth stage, the generation unit generates a pattern with a material and shape that anticipates growth. This makes it possible to generate an optimal pattern according to the pet's activity level. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the generation unit can be realized using an AI model for adjusting the material and shape of the pattern according to the pet's activity level.

[0106] The generation unit estimates the user's emotions and adjusts the level of detail of the generated pattern based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a detailed pattern. If the user is in a hurry, the generation unit generates a simple and easy-to-make pattern. If the user is excited, the generation unit generates a visually stimulating pattern. This allows for the provision of the optimal level of detail of the pattern according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can be implemented using an AI model for estimating the user's emotions and adjusting the level of detail of the generated pattern based on the estimated emotions.

[0107] The generation unit customizes the pattern based on the pet's age and health condition during the generation process. For example, if the pet is elderly, the generation unit generates a pattern that prioritizes comfort. If the pet is healthy, the generation unit generates a pattern with an active design. If the pet is ill, the generation unit generates a pattern with a design that protects specific body parts. This allows for the generation of an optimal pattern according to the pet's age and health condition. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can be realized using an AI model for customizing patterns based on the pet's age and health condition.

[0108] The generation unit adjusts the pattern design based on the pet owner's lifestyle during the generation process. For example, if the owner is an outdoorsy person, the generation unit generates a pattern with durable materials and design. If the owner is an indoorsy person, the generation unit generates a pattern with comfortable materials and design. If the owner is fashion-conscious, the generation unit generates a pattern that incorporates the latest trends. This allows for the generation of the optimal pattern tailored to the pet owner's lifestyle. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can be realized using an AI model for adjusting the pattern design based on the pet owner's lifestyle.

[0109] The instruction unit estimates the user's emotions and adjusts the order of cutting and sewing instructions based on the estimated emotions. For example, if the user is tense, the instruction unit provides cutting and sewing instructions in a simple and easy-to-understand order. If the user is relaxed, the instruction unit provides detailed cutting and sewing instructions. If the user is in a hurry, the instruction unit provides cutting and sewing instructions in an order that will complete the task in the shortest possible time. This allows for the provision of an optimal order of cutting and sewing instructions that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can be implemented using an AI model for estimating the user's emotions and adjusting the order of cutting and sewing instructions based on the estimated emotions.

[0110] The instruction unit adjusts the instructions for cutting and sewing based on changes in the pet's body shape. For example, if the pet is in its growth phase, the instruction unit provides cutting and sewing instructions that anticipate growth. If the pet's weight fluctuates significantly, the instruction unit provides cutting and sewing instructions with an adjustable design. If the pet has a specific illness, the instruction unit provides cutting and sewing instructions that protect that area. This makes it possible to provide optimal cutting and sewing instructions that respond to changes in the pet's body shape. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not. For example, the instruction unit can be implemented using an AI model for adjusting cutting and sewing instructions based on changes in the pet's body shape.

[0111] The instruction unit optimizes the instructions for cutting and sewing according to the characteristics of the material being used. For example, when using stretchable material, the instruction unit provides cutting and sewing instructions with appropriate tension. When using thick material, the instruction unit provides cutting and sewing instructions using appropriate needles and threads. When using delicate material, the instruction unit provides careful cutting and sewing instructions. This makes it possible to provide optimal cutting and sewing instructions according to the characteristics of the material being used. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can be realized using an AI model for optimizing cutting and sewing instructions according to the characteristics of the material being used.

[0112] The instruction unit estimates the user's emotions and adjusts the level of detail in the cutting and sewing instructions based on the estimated emotions. For example, if the user is tense, the instruction unit provides simple and easy-to-understand cutting and sewing instructions. If the user is relaxed, the instruction unit provides detailed cutting and sewing instructions. If the user is in a hurry, the instruction unit provides cutting and sewing instructions that will be completed in the shortest possible time. This allows for the provision of the optimal level of detail in the cutting and sewing instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can be implemented using an AI model for estimating the user's emotions and adjusting the level of detail in the cutting and sewing instructions based on the estimated emotions.

[0113] The instruction unit adjusts the instructions for cutting and sewing based on the pet's activity level. For example, if the pet is active, the instruction unit will provide cutting and sewing instructions for a design that allows for easy movement. If the pet spends most of its time indoors, the instruction unit will provide cutting and sewing instructions that prioritize comfort. If the pet is in its growth stage, the instruction unit will provide cutting and sewing instructions that anticipate its growth. This makes it possible to provide optimal cutting and sewing instructions according to the pet's activity level. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can be implemented using an AI model for adjusting cutting and sewing instructions based on the pet's activity level.

[0114] The instruction unit customizes the cutting and sewing instructions according to the pet owner's sewing skill level. For example, if the owner is a beginner, the instruction unit provides simple and easy-to-understand cutting and sewing instructions. If the owner is an intermediate sewer, the instruction unit provides slightly more complex cutting and sewing instructions. If the owner is an advanced sewer, the instruction unit provides detailed and advanced cutting and sewing instructions. This allows the instruction unit to provide optimal cutting and sewing instructions tailored to the pet owner's sewing skill level. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not. For example, the instruction unit can be implemented using an AI model for customizing cutting and sewing instructions according to the pet owner's sewing skill level.

[0115] The transmitting unit estimates the user's emotions and adjusts the transmission timing based on the estimated emotions. For example, if the user is relaxed, the transmitting unit immediately sends the cutting and sewing instructions. If the user is in a hurry, the transmitting unit immediately sends the cutting and sewing instructions. If the user is stressed, the transmitting unit sends the cutting and sewing instructions after a short delay. This allows for the provision of optimal transmission timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or not using AI. For example, the transmitting unit can be implemented using an AI model to estimate the user's emotions and adjust the transmission timing based on the estimated emotions.

[0116] The transmitting unit monitors the operating status of the sewing machine in real time during transmission and sends instructions at the optimal timing. For example, if the sewing machine is in operation, the transmitting unit sends cutting and sewing instructions when the operation is finished. If the sewing machine is in standby mode, the transmitting unit sends cutting and sewing instructions immediately. If the sewing machine is undergoing maintenance, the transmitting unit sends cutting and sewing instructions when the maintenance is completed. This allows for the provision of optimal transmission timing according to the operating status of the sewing machine. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can be realized using an AI model for monitoring the operating status of the sewing machine in real time and sending instructions at the optimal timing.

[0117] The transmitting unit provides feedback on the progress of cutting and sewing during transmission and modifies the instructions as necessary. For example, the transmitting unit transmits sewing instructions when cutting is completed. If sewing is in progress, the transmitting unit transmits the next instructions according to the progress. If a problem occurs in the progress of cutting and sewing, the transmitting unit modifies the instructions and retransmits them. This allows for the provision of optimal instructions according to the progress of cutting and sewing. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can be realized using an AI model to provide feedback on the progress of cutting and sewing and modify the instructions as necessary.

[0118] The transmission unit estimates the user's emotions and prioritizes the content to be transmitted based on the estimated emotions. For example, if the user is relaxed, the transmission unit prioritizes sending detailed cutting and sewing instructions. If the user is in a hurry, the transmission unit prioritizes sending important cutting and sewing instructions. If the user is stressed, the transmission unit prioritizes sending concise cutting and sewing instructions. This allows for the provision of optimal content prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmission unit may be performed using AI, for example, or not using AI. For example, the transmission unit can be implemented using an AI model for estimating the user's emotions and prioritizing the content to be transmitted based on the estimated emotions.

[0119] The transmitting unit adjusts the transmission content based on the sewing machine's maintenance information when transmitting. For example, if the sewing machine is undergoing maintenance, the transmitting unit transmits cutting and sewing instructions when the maintenance is completed. If the sewing machine requires maintenance, the transmitting unit transmits cutting and sewing instructions after the maintenance is completed. Based on the sewing machine's maintenance information, the transmitting unit transmits cutting and sewing instructions at the optimal timing. This allows for the provision of optimal transmission content according to the sewing machine's maintenance information. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can be implemented using an AI model for adjusting the transmission content based on the sewing machine's maintenance information.

[0120] The transmitting unit optimizes the transmitted content based on coordination with other devices during transmission. For example, if other devices are in operation, the transmitting unit transmits cutting and sewing instructions when their operation ends. If coordination with other devices is required, the transmitting unit transmits cutting and sewing instructions when the coordination is completed. The transmitting unit transmits cutting and sewing instructions at the optimal timing based on the operating status of other devices. This enables the provision of optimal transmitted content in accordance with coordination with other devices. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can be realized using an AI model for optimizing transmitted content based on coordination with other devices.

[0121] The storage unit estimates the user's emotions and determines the priority of designs to save based on the estimated emotions. For example, if the user is relaxed, the storage unit prioritizes saving detailed designs. If the user is in a hurry, the storage unit prioritizes saving important designs. If the user is stressed, the storage unit prioritizes saving concise designs. This provides an optimal design saving priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can be implemented using an AI model to estimate the user's emotions and determine the priority of designs to save based on the estimated emotions.

[0122] The storage unit performs version control of the design during saving, facilitating comparison with past designs. For example, the storage unit assigns a version number to the design being saved, enabling comparison with past designs. The storage unit records the change history of the design being saved, clearly indicating the differences from past designs. The storage unit performs version control of the design being saved, facilitating comparison with past designs. This makes it easier to compare designs with past designs through design version control. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can be implemented using an AI model for design version control and facilitating comparison with past designs.

[0123] The storage unit automatically generates metadata for the design during saving to improve searchability. The storage unit, for example, assigns tags to the designs to be saved to improve searchability. The storage unit automatically generates metadata for the designs to be saved to improve searchability. The storage unit records information related to the designs to be saved as metadata to improve searchability. As a result, searchability is improved by automatically generating metadata for the designs. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can be implemented using an AI model for automatically generating metadata for designs and improving searchability.

[0124] The storage unit estimates the user's emotions and tags the designs to be saved based on the estimated emotions. For example, if the user is relaxed, the storage unit performs detailed tagging. If the user is in a hurry, the storage unit prioritizes important tagging. If the user is stressed, the storage unit performs concise tagging. This allows for optimal design tagging tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can be implemented using an AI model for estimating user emotions and tagging designs to be saved based on the estimated emotions.

[0125] The storage unit records the usage history of the design when it is saved and uses this information for future design generation. For example, the storage unit records the usage history of the design being saved and uses this information for future design generation. The storage unit records the frequency of use of the design being saved and uses this information for future design generation. The storage unit records the usage status of the design being saved and uses this information for future design generation. In this way, by recording the usage history of the design, it can be used for future design generation. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can be realized using an AI model for recording the usage history of the design and using it for future design generation.

[0126] The saving unit customizes the sharing settings of the design when saving, and shares it only to a specific user group. The saving unit customizes the sharing settings of the design to be saved, and shares it only to a specific user group. The saving unit sets the sharing scope of the design to be saved, and shares it only to a specific user group. The saving unit customizes the sharing settings of the design to be saved, and shares it only to a specific user group. This allows sharing to be limited to a specific user group by customizing the design's sharing settings. Some or all of the above processing in the saving unit may be performed using AI, for example, or not. For example, the saving unit can be implemented using an AI model for customizing the sharing settings of a design and sharing it only to a specific user group.

[0127] The prediction unit estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. For example, if the user is relaxed, the prediction unit displays detailed prediction results. If the user is in a hurry, the prediction unit displays concise prediction results. If the user is stressed, the prediction unit displays visually easy-to-understand prediction results. This allows for the provision of an optimal method for displaying prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can be implemented using an AI model for estimating the user's emotions and adjusting how the prediction results are displayed based on the estimated emotions.

[0128] The prediction unit improves prediction accuracy based on past trend data during prediction. For example, the prediction unit predicts the latest trend based on past trend data. The prediction unit analyzes past trend data to improve prediction accuracy. The prediction unit refers to past trend data and provides prediction results. This makes it possible to improve prediction accuracy based on past trend data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can be implemented using an AI model to improve prediction accuracy based on past trend data.

[0129] The prediction unit customizes the prediction results based on regional trends during the prediction process. For example, the prediction unit customizes the prediction results based on regional trend data. The prediction unit considers regional trends and provides prediction results. The prediction unit refers to regional trend data and customizes the prediction results. This makes it possible to provide optimal prediction results based on regional trends. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can be implemented using an AI model for customizing prediction results based on regional trends.

[0130] The prediction unit estimates the user's emotions and adjusts the importance of the prediction results based on the estimated emotions. For example, if the user is relaxed, the prediction unit provides detailed prediction results. If the user is in a hurry, the prediction unit prioritizes providing important prediction results. If the user is stressed, the prediction unit provides concise prediction results. This allows for the provision of optimal importance of prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can be implemented using an AI model for estimating the user's emotions and adjusting the importance of the prediction results based on the estimated emotions.

[0131] The prediction unit filters trend data based on the type and size of the pet during prediction. For example, the prediction unit filters trend data according to the type of pet. The prediction unit filters trend data according to the size of the pet. The prediction unit filters trend data according to the type and size of the pet. This makes it possible to provide optimal trend data according to the type and size of the pet. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can be implemented using an AI model for filtering trend data based on the type and size of the pet.

[0132] The prediction unit provides prediction results based on seasonal trends during the prediction process. For example, the prediction unit provides prediction results based on seasonal trend data. The prediction unit considers seasonal trends and provides prediction results. The prediction unit refers to seasonal trend data and provides prediction results. This makes it possible to provide optimal prediction results based on seasonal trends. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can be implemented using an AI model for providing prediction results based on seasonal trends.

[0133] The design generation unit estimates the user's emotions and adjusts the style of the design it generates based on the estimated emotions. For example, if the user is relaxed, the design generation unit generates a relaxed style of design. If the user is in a hurry, the design generation unit generates a simple and easy-to-use style of design. If the user is excited, the design generation unit generates a visually stimulating style of design. This allows for the provision of an optimal design style that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the design generation unit may be performed using a generative AI, for example, or without a generative AI. For example, the design generation unit can be implemented using an AI model for estimating the user's emotions and adjusting the style of the design generated based on the estimated emotions.

[0134] The design generation unit generates the optimal design based on past design data during the design generation process. For example, the design generation unit generates the optimal design based on past design data. The design generation unit analyzes past design data and generates a design that suits the user's preferences. The design generation unit references past design data and generates a design that incorporates the latest trends. This makes it possible to generate the optimal design based on past design data. Some or all of the above processes in the design generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the design generation unit can be realized using an AI model for generating the optimal design based on past design data.

[0135] The design generation unit adjusts the functionality of the design based on the pet's activity level during design generation. For example, if the pet is active, the design generation unit generates a design that is easy to move in. If the pet spends most of its time indoors, the design generation unit generates a design that prioritizes comfort. If the pet is in its growth stage, the design generation unit generates a design that anticipates its growth. This makes it possible to provide optimal design functionality according to the pet's activity level. Some or all of the above processing in the design generation unit may be performed using, for example, a generation AI, or not. For example, the design generation unit can be realized using an AI model for adjusting the functionality of the design based on the pet's activity level.

[0136] The design generation unit estimates the user's emotions and adjusts the colors and patterns of the generated design based on the estimated emotions. For example, if the user is relaxed, the design generation unit generates a design with calming colors and patterns. If the user is in a hurry, the design generation unit generates a design with simple, highly visible colors and patterns. If the user is excited, the design generation unit generates a design with visually stimulating colors and patterns. This allows for the provision of optimal design colors and patterns according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design generation unit may be performed using a generative AI, or not. For example, the design generation unit can be implemented using an AI model for estimating the user's emotions and adjusting the colors and patterns of the generated design based on the estimated emotions.

[0137] The design generation unit customizes the design based on the pet owner's lifestyle during the design generation process. For example, if the owner is an outdoorsy person, the design generation unit generates durable materials and designs. If the owner is an indoorsy person, the design generation unit generates materials and designs that prioritize comfort. If the owner is fashion-conscious, the design generation unit generates designs that incorporate the latest trends. This allows for the provision of optimal designs tailored to the pet owner's lifestyle. Some or all of the above-described processes in the design generation unit may be performed using, for example, a generation AI, or not. For example, the design generation unit can be realized using an AI model for customizing designs based on the pet owner's lifestyle.

[0138] The design generation unit selects design materials based on the pet's health condition during design generation. For example, if the pet has allergies, the design generation unit selects allergy-friendly materials. If the pet is elderly, the design generation unit selects materials that prioritize comfort. If the pet is ill, the design generation unit selects materials that protect specific body parts. This allows for the provision of optimal design materials tailored to the pet's health condition. Some or all of the above-described processes in the design generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the design generation unit can be realized using an AI model for selecting design materials based on the pet's health condition.

[0139] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0140] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0141] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0142] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0143] The AI-generated design automatically creates and suggests trendy designs to users. For example, it identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create pet clothing that perfectly fits their dog's body shape and incorporate the latest pet fashion trends. Furthermore, sharing information with other pet owners via the cloud promotes community building. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0144] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0145] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0146] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0147] Users can save their designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their designs to the cloud, and other users can download and use them. A new feature has been added that uses search data and trend data to predict the latest trends in pet fashion, and a generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data to learn the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users. For example, it identifies popular colors and patterns from search data and generates designs based on them. This makes it easy for users to create clothes that fit their dogs perfectly and incorporate the latest pet fashion trends. Furthermore, sharing information with other pet owners through the cloud promotes community building. For example, when users upload their designs to the cloud and other users download and use them, pet fashion trends spread.

[0148] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0149] The pet clothing creation system automatically generates the optimal clothing pattern based on the user's input of their dog's body shape and dimensions. The system's AI generates clothing designs and provides real-time cutting and sewing instructions in conjunction with a sewing machine. Furthermore, the system integrates with mobile services, offering the ability to save and share designs in the cloud. Additionally, the system utilizes search and trend data to predict the latest trends in pet fashion, and the AI ​​automatically generates trend-ahead designs based on this data. For example, the user inputs their dog's body shape and dimensions. The AI ​​then automatically generates the optimal clothing pattern based on this data. The AI ​​analyzes the user's input data to generate a clothing design that perfectly fits the dog's body. For instance, by inputting measurements such as chest circumference, back length, and neck circumference, the AI ​​generates a pattern based on these measurements. The generated pattern then provides real-time cutting and sewing instructions in conjunction with a sewing machine. The generation AI analyzes the cutting and sewing processes and sends optimal instructions to the sewing machine. This allows users to easily create high-quality clothing. For example, the generation AI can instruct on the cutting order and sewing stitch patterns to achieve a professional-level finish. Furthermore, the pet clothing creation system integrates with mobile services and provides the ability to save and share designs in the cloud. Users can save their created designs to the cloud and share them with other pet owners. This allows users to share information and keep up with the latest pet fashion trends. For example, users can upload their created designs to the cloud, and other users can download and use those designs. Newly added is a function that uses search data and trend data to predict the latest trends in pet fashion, and the generation AI automatically generates trend-ahead designs based on that data. The generation AI analyzes search data and trend data and learns the latest fashion trends. As a result, the generation AI automatically generates trend-ahead designs and suggests them to users.For example, the system identifies popular colors and patterns from search data and generates designs based on them. This allows users to easily create clothes that perfectly fit their dog's body shape and incorporate the latest pet fashion trends. It also promotes community building by allowing users to share information with other pet owners through the cloud. For instance, users can upload their designs to the cloud, and other users can download and use those designs, spreading pet fashion trends.

[0150] The following briefly describes the processing flow for example form 2.

[0151] Step 1: The input section allows the user to enter details about their dog's body shape and dimensions. For example, it provides an interface for entering dimensions such as chest circumference, back length, and neck circumference. Step 2: The generation unit automatically generates the optimal clothing pattern based on the data entered by the input unit. Using the generation AI, it analyzes the user's input data and generates a clothing design that perfectly fits the dog's body shape. Step 3: The instruction unit issues cutting and sewing instructions based on the pattern generated by the generation unit. Using the generation AI, it analyzes the cutting and sewing processes and sends the optimal instructions to the sewing machine. Step 4: The transmitting unit sends the cutting and sewing instructions given by the instruction unit to the sewing machine. Using generation AI, it analyzes the cutting and sewing processes and sends the optimal instructions to the sewing machine. Step 5: The storage section saves and shares the design to the cloud. Using generation AI, the user's created design is saved to the cloud and shared with other users. Step 6: The prediction unit uses search data and trend data to predict the latest trends. Using generative AI, it analyzes search data and trend data and learns the latest fashion trends. Step 7: The design generation unit automatically generates trend-ahead designs based on the trends predicted by the prediction unit. Using generation AI, it automatically generates trend-ahead designs based on the trends predicted by the prediction unit.

[0152] 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.

[0153] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0154] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the input unit, generation unit, instruction unit, transmission unit, storage unit, prediction unit, and design generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit provides an interface for the user to input details of their dog's body shape and dimensions using the receiving device 38 of the smart device 14. The generation unit, using the identification processing unit 290 of the data processing unit 12, analyzes the user's input data and generates a clothing design that perfectly fits the dog's body shape. The instruction unit provides cutting and sewing instructions based on the pattern generated by the generation unit, and the transmission unit sends these instructions to the sewing machine. The storage unit saves and shares the design to the cloud, and the prediction unit analyzes search data and trend data to predict the latest trends. The design generation unit automatically generates a fashion-forward design based on the trends predicted by the prediction unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0157] 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.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0159] 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.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0161] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] 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.

[0163] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the input unit, generation unit, instruction unit, transmission unit, storage unit, prediction unit, and design generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit provides an interface for the user to voice input details of their dog's body shape and dimensions using the microphone 238 of the smart glasses 214. The generation unit, using the identification processing unit 290 of the data processing unit 12, analyzes the user's input data and generates a clothing design that perfectly fits the dog's body shape. The instruction unit gives cutting and sewing instructions based on the pattern generated by the generation unit, and the transmission unit sends these instructions to the sewing machine. The storage unit saves and shares the design to the cloud, and the prediction unit analyzes search data and trend data to predict the latest trends. The design generation unit automatically generates fashion-forward designs based on the trends predicted by the prediction unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0173] 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.

[0174] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0175] 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.

[0176] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0177] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0178] 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.

[0179] 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.

[0180] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0181] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0182] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0183] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0186] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0187] Each of the multiple elements described above, including the input unit, generation unit, instruction unit, transmission unit, storage unit, prediction unit, and design generation unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the input unit provides an interface for the user to voice input details of their dog's body shape and dimensions using the microphone 238 of the headset terminal 314. The generation unit, using the identification processing unit 290 of the data processing unit 12, analyzes the user's input data and generates a clothing design that perfectly fits the dog's body shape. The instruction unit provides cutting and sewing instructions based on the pattern generated by the generation unit, and the transmission unit sends these instructions to the sewing machine. The storage unit saves and shares the design to the cloud, and the prediction unit analyzes search data and trend data to predict the latest trends. The design generation unit automatically generates fashion-forward designs based on the trends predicted by the prediction unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0188] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0189] 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.

[0190] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0191] 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.

[0192] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0193] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0194] 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.

[0195] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0196] 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.

[0197] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0198] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0199] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0200] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0201] 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.

[0202] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0203] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0204] Each of the multiple elements described above, including the input unit, generation unit, instruction unit, transmission unit, storage unit, prediction unit, and design generation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the input unit provides an interface for the user to voice input details of their dog's body shape and dimensions using the microphone 238 of the robot 414. The generation unit, using the identification processing unit 290 of the data processing unit 12, analyzes the user's input data and generates a clothing design that perfectly fits the dog's body shape. The instruction unit gives cutting and sewing instructions based on the pattern generated by the generation unit, and the transmission unit sends these instructions to the sewing machine. The storage unit saves and shares the design to the cloud, and the prediction unit analyzes search data and trend data to predict the latest trends. The design generation unit automatically generates fashion-forward designs based on the trends predicted by the prediction unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0205] 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.

[0206] Figure 9 shows the 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.

[0207] 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.

[0208] 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.

[0209] 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, and motorcycles, 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 based, for example, 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.

[0210] 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."

[0211] 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.

[0212] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0221] 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 other things 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.

[0222] 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.

[0223] (Note 1) An input section where the user enters details of their dog's body shape and dimensions, A generation unit that automatically generates an optimal clothing pattern based on the data input by the aforementioned input unit, An instruction unit provides cutting and sewing instructions based on the pattern generated by the generation unit, A transmitting unit that transmits the cutting and sewing instructions given by the instruction unit to the sewing machine, A storage unit for saving and sharing designs in the cloud, A prediction unit that uses search data and trend data to predict the latest trends, The system includes a design generation unit that automatically generates trend-ahead designs based on the trends predicted by the prediction unit. A system characterized by the following features. (Note 2) The aforementioned storage unit is It includes a sharing section where users can upload their created designs to the cloud, and other users can download and use those designs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The prediction unit, It includes a special unit that identifies popular colors and patterns from search data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The indicator unit is, The system includes a stitch instruction unit where the generating AI instructs the cutting order and sewing stitch patterns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The system analyzes user input data to generate clothing designs that perfectly fit the dog's body shape. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned design generation unit, Analyze search data and trend data to learn the latest fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is It estimates the user's emotions and adjusts the layout of the input interface based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is During input, the dimensions are corrected based on the user's pet's activity data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is It estimates the user's emotions and prioritizes input data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is During input, relevant dimension data is prioritized based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned input unit is During input, the system analyzes the user's social media activity and retrieves relevant dimension data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the design of the patterns generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During the generation process, the optimal pattern is generated by referencing the pet's past wearing data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the pattern material and shape are adjusted according to the pet's activity level. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the level of detail of the generated patterns based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the pattern is customized based on the pet's age and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the pattern design is adjusted based on the pet owner's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 19) The indicator unit is, The system estimates the user's emotions and adjusts the order of cutting and sewing instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The indicator unit is, When giving cutting and sewing instructions, adjust the instructions based on variations in the pet's body shape. The system described in Appendix 1, characterized by the features described herein. (Note 21) The indicator unit is, When giving cutting and sewing instructions, optimize the instructions according to the characteristics of the material being used. The system described in Appendix 1, characterized by the features described herein. (Note 22) The indicator unit is, The system estimates the user's emotions and adjusts the level of detail in the cutting and sewing instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The indicator unit is, When giving cutting and sewing instructions, adjust the instructions based on the pet's activity level. The system described in Appendix 1, characterized by the features described herein. (Note 24) The indicator unit is, When giving cutting and sewing instructions, customize the instructions according to the pet owner's sewing skills. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned transmitting unit It estimates the user's emotions and adjusts the sending timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned transmitting unit During transmission, the machine's operating status is monitored in real time, and instructions are sent at the optimal timing. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned transmitting unit When submitting, the system will provide feedback on the progress of cutting and sewing, and modify the instructions as needed. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned transmitting unit It estimates the user's emotions and prioritizes the content to send based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned transmitting unit When sending, the content of the transmission will be adjusted based on the sewing machine's maintenance information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned transmitting unit When sending, the content to be sent is optimized based on coordination with other devices. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned storage unit is We estimate the user's emotions and determine the design priorities to save based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned storage unit is When saving, version control of the design is implemented, making it easy to compare with previous designs. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned storage unit is When saving, design metadata is automatically generated to improve searchability. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned storage unit is It estimates the user's emotions and tags the design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned storage unit is When saving, the design's usage history is recorded and used for future design generation. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned storage unit is When saving, customize the design's sharing settings and limit sharing to specific user groups. The system described in Appendix 1, characterized by the features described herein. (Note 37) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The prediction unit, When making predictions, improve prediction accuracy based on historical trend data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The prediction unit, At the time of prediction, customize the prediction result based on the trend for each region The system according to appended note 1, characterized in that it does so (Appended note 40) The prediction unit Estimate the user's emotion and adjust the importance of the prediction result based on the estimated user's emotion The system according to appended note 1, characterized in that it does so (Appended note 41) The prediction unit At the time of prediction, filter the trend data based on the type and size of the pet The system according to appended note 1, characterized in that it does so (Appended note 42) The prediction unit At the time of prediction, provide the prediction result based on the trend for each season The system according to appended note 1, characterized in that it does so (Appended note 43) The design generation unit Estimate the user's emotion and adjust the style of the design generated based on the estimated user's emotion The system according to appended note 1, characterized in that it does so (Appended note 44) The design generation unit At the time of design generation, generate an optimal design based on past design data The system according to appended note 1, characterized in that it does so (Appended note 45) The design generation unit At the time of design generation, adjust the functionality of the design based on the activity level of the pet The system according to appended note 1, characterized in that it does so (Appended note 46) The design generation unit Estimate the user's emotion and adjust the color and pattern of the design generated based on the estimated user's emotion The system according to appended note 1, characterized in that it does so (Appended note 47) The design generation unit During design generation, the design is customized based on the pet owner's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned design generation unit, When generating the design, the design materials are selected based on the pet's health condition. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0224] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section where the user enters details about their dog's body shape and dimensions, A generation unit that automatically generates an optimal clothing pattern based on the data input by the aforementioned input unit, An instruction unit provides cutting and sewing instructions based on the pattern generated by the generation unit, A transmitting unit that transmits the cutting and sewing instructions given by the instruction unit to the sewing machine, A storage unit for saving and sharing designs in the cloud, A prediction unit that uses search data and trend data to predict the latest trends, The system includes a design generation unit that automatically generates trend-ahead designs based on the trends predicted by the prediction unit. A system characterized by the following features.

2. The aforementioned storage unit is It includes a sharing section where users can upload their created designs to the cloud, and other users can download and use those designs. The system according to feature 1.

3. The prediction unit, It includes a special unit that identifies popular colors and patterns from search data. The system according to feature 1.

4. The indicator unit is, The generating AI includes a stitch instruction unit that instructs the cutting order and sewing stitch patterns. The system according to feature 1.

5. The generating unit is The system analyzes user input data to generate clothing designs that perfectly fit the dog's body shape. The system according to feature 1.

6. The aforementioned design generation unit, Analyze search data and trend data to learn the latest fashion trends. The system according to feature 1.

7. The aforementioned input unit is It estimates the user's emotions and adjusts the layout of the input interface based on the estimated emotions. The system according to feature 1.

8. The aforementioned input unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

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