Systems and methods for automated garment design

Abstract

Systems and methods for automated garment design. In one embodiment, a method for designing a garment can be provided. The method can include receiving 2D information associated with at least one side of a garment; generating, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment; generating, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout of the garment; and outputting, via at one user interface, the 3D layout for review by a user.

Description

SYSTEMS AND METHODS FOR AUTOMATED GARMENT DESIGNRELATED APPLICATION

[0001] The present application claims priority to U.S. Provisional Serial No. 63 / 588,072, titled “PLATFORM FOR AUTOMATED GARMENT DESIGN AND DISTRIBUTION”, filed October 5, 2023, the contents of which are incorporated herein by reference.FIELD OF DISCLOSURE

[0002] The present disclosure is related to fashion technology, in particular, systems and methods for automated garment design.BACKGROUND

[0003] The fashion industry is fraught with inefficiencies, ranging from suboptimal workflow designs to overreliance on legacy systems ill-equipped to deal with modem challenges. Such inefficiencies not only result in increased operational costs but also hinder the timely launch of new garment products, which can be particularly problematic in an industry driven by rapidly changing trends.

[0004] Conventional garment design, production, and distribution processes often lack integration between different stages of design and production, such as between the creation, sourcing, construction, and logistics stages of garment design and production. In one example, discrepancies in garment design interpretation can lead to production delays and increased costs. Additionally, determining the source of materials and components may be challenging due to a deficiency in information regarding supplier capabilities and sustainability practices.

[0005] Furthermore, conventional logistics of garment production and distribution, including shipment and storage may result in inefficiencies and inaccuracies in inventory management and financial transactions. Consequently, the conventional approach to garment production and distribution may not adequately adapt to the dynamic demands of the modern market.

[0006] In the realm of conventional garment production, the translation of two-dimensional (2D) designs into three-dimensional (3D) models presents a significant challenge. For instance, accurately conveying the depth and texture inherent in a 2D image into a 3D representation requires sophisticated computational techniques. Consequently, the traditional approach may struggle to achieve a relatively high level of precision in this translation process. This often leads to issues as the traditional approach may not fully capture the designer's intent, potentially compromising the aesthetic and functional integrity of the final garment. Additionally, the selection of materials for these designs often necessitates a comprehensive understanding offabric properties and availability, which conventional processes may be hindered in determining.

[0007] Moreover, the coordination of garment construction and pattern making processes frequently encounters obstacles. As a result, inefficiencies in garment pattern creation can further complicate the production workflow. Furthermore, accurately estimating the costs involved in garment production can pose a challenge. As a result, the conventional methodologies applied in garment production and distribution may fall short of providing an efficient and cohesive framework necessary for addressing the complexities of modern garment design, production, and manufacturing.

[0008] Verifying the supply chain origin in production can be challenging. This may lead to difficulties in confirming the ethical sourcing and environmental impact of materials used in garment production. Additionally, preventing unplanned downtime is crucial for continuous garment production flow, and identifying potential issues before they occur remains a challenge. This can lead to unexpected production halts and increased maintenance costs. Furthermore, conventional methods may not provide the agility needed to implement dynamic pricing strategies that adjust in response to supply chain costs and market demand predictions, potentially impacting profitability and market competitiveness.

[0009] One conventional system and method lacks suitable online or digital tools for a user to generate a 2D or 3D layout of a garment being designed by the user. The lack of suitable tools increases the time and cost of designing the garment.BRIEF SUMMARY OF THE DISCLOSURE

[0010] Embodiments of the present disclosure can relate to systems and methods for garment design. In certain embodiments, systems, methods, and a computer-readable medium for automating garment design can be provided. Certain embodiments of the present disclosure can provide systems, methods, and a computer-readable medium for designing all sides of a garment, and for providing instructions to construct the garment. A “garment” as used herein can be defined as any article that can be worn and / or used by a person or an animal.

[0011] Further, in at least one embodiment, systems, methods, and a computer-readable medium can be provided for designing a garment, wherein based at least in part on generating or receiving a 2D image, such as a 2D digital image of, for example, a pattern, artwork, or a garment, a 3D layout to construct a garment can be generated. Moreover, in at least one embodiment, systems, methods, and a computer-readable medium can be provided for designing a garment, wherein based at least in part on generating or receiving text instructionsassociated with a garment or a 2D or 3D image, such as instructions for a pattern, instructions for artwork, or instructions for a garment, a 3D layout to construct a garment can be generated.

[0012] In any instance, the 3D layout to construct a garment can be, for example, a blueprint for constructing a garment with information associated with constructing the garment including, but not limited to, instructions to adjoin together one or more components of the garment, a selection of a material or textile for the garment, or artwork application information. Based at least in part on the 3D layout, certain embodiments of the present disclosure may execute or otherwise facilitate constructing the garment. Furthermore, certain embodiments of the disclosure may manage or otherwise facilitate shipment, storage, and associated financial transactions related to production and distribution of the garment.

[0013] Certain embodiments of the present disclosure can provide technical solutions to provide conversion of a 2D image of a garment to a 3D layout to construct the garment including a transformation of a 2D image of at least one side of the garment to 2D images of the other sides of the garment. Further, certain embodiments of the present disclosure can provide technical solutions to provide conversion of text instructions and / or text description associated with a garment or a 2D or 3D image to a 3D layout to construct the garment including a transformation of the text information instructions and / or text description to the 3D layout.

[0014] In one embodiment of the present disclosure, a system for garment design can be provided. The system can include a garment design module including a 2D design input interface, a 2D-to-3D design converter, and an artificial intelligence (Al) module. The 2D design input interface can be operable to receive 2D information associated with at least one side of a garment. The 2D-to-3D design converter can be operable to generate, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment. The Al module can be operable to generate, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout of the garment; and output, via at one user interface, the 3D layout for review by a user.

[0015] In at least one aspect of the embodiment, the Al module can be further operable to generate or receive, based at least in part on the 3D layout of the garment, construction information for the garment, wherein the construction information includes one or more of: a blueprint for garment construction, instructions to adjoin together one or more components ofthe garment, a selection of a material or textile for the garment, or artwork application information.

[0016] In at least one aspect of the embodiment, the 3D layout of the garment can include a 3D artwork design; and the Al module can be further operable to generate, based at least in part on the 3D artwork design, 3D artwork application information to apply the 3D artwork design to the garment.

[0017] In at least one aspect of the embodiment, the Al module can be further operable to generate or receive at least one 3D digital fit model (mannequin), wherein the generating a 3D layout of the garment is further based at least in part on the at least one 3D digital fit model (mannequin).

[0018] In at least one aspect of the embodiment, the generating or receiving at least one 3D digital fit model (mannequin) by the Al module further operable to generate at least one 3D digital fit model (mannequin) is based at least in part on at least one of: a body scan, a user input, selection from a data source (library), selection of a size by an Al module, or selection of a size based on a brand.

[0019] In at least one aspect of the embodiment, the Al module can be further operable to receive, at least one user input associated with the 3D layout; and implement, based at least in part on the at least one user input, a change to the 3D layout.

[0020] In at least one aspect of the embodiment, the at least one user input can be based at least in part on at least one of: a fashion trend, user feedback on the 3D layout, garment dimensional information, color, artwork application construction, material type, different size, or artwork on 3D model.

[0021] In at least one aspect of the embodiment, the Al module can be further operable to generate, based at least in part on the 2D information, repeating artwork information; generate, based at least in part on the repeating artwork information, alternate colorways information; generate or receive selected base material information; and generate or receive, based at least in part on the repeating artwork information, alternate colorways information, selected base material information, and 3D layout of the garment, construction information for the garment, wherein the construction information includes at least one of: a blueprint for garment construction, instructions to adjoin together one or more components of the garment, a material selection for the garment, or artwork application information.

[0022] In at least one aspect of the embodiment, the Al module can be further operable to receive, based at least in part on a user input, custom solution information, wherein the customsolution information includes at least one of: customer-specific information, model-specific information, fit model-specific information, body dimension information, body composition information, anatomical information, physiological information, demographic information, ergonomic information, patient-specific information, therapeutic information, diagnostic information, healthcare information, temperature control information, compression information, wound healing information, medical standard information, prosthetics information, safety information, military specification information, athletic information, injury prevention information, compression information, or computing information, wherein the generating, based at least in part on the 2D information associated with the at least one side of a garment, the 2D information associated with one or more other sides of the garment, a 3D layout of the garment by the artificial intelligence module can be further based at least in part on the custom solution information.

[0023] In another embodiment of the present disclosure, a method for designing a garment can be provided. The method can include receiving 2D information associated with at least one side of a garment; generating, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment; generating, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout of the garment; and outputting, via at one user interface, the 3D layout for review by a user.

[0024] In at least one aspect of the embodiment, the method can further include generating or receiving, based at least in part on the 3D layout of the garment, construction information for the garment, wherein the construction information includes at least one of: a blueprint for garment construction, instructions to adjoin together one or more components of the garment, a selection of a material or textile for the garment, or artwork application information.

[0025] In at least one aspect of the embodiment, the 3D layout of the garment can include a 3D artwork design; and the method can further include generating, based at least in part on the 3D artwork design, 3D artwork application information to apply the 3D artwork design to the garment.

[0026] In at least one aspect of the embodiment, the method can further include generating or receiving at least one 3D digital fit model (mannequin), wherein the generating a 3D layout of the garment is further based at least in part on the at least one 3D digital fit model (mannequin).

[0027] In at least one aspect of the embodiment, the generating at least one 3D digital fit model (mannequin) is based at least in part on at least one of: a body scan, a user input, selection froma data source (library), selection of a size by an Al module, or selection of a size based on a brand.

[0028] In at least one aspect of the embodiment, the method can further include receiving, at least one user input associated with the 3D layout; and implementing, based at least in part on the at least one user input, a change to the 3D layout.

[0029] In at least one aspect of the embodiment, the at least one user input can be based at least in part on at least one of: a fashion trend, user feedback on the 3D layout, garment dimensional information, color, artwork application construction, material type, different size, or artwork on 3D model.

[0030] In at least one aspect of the embodiment, the 3D layout can include at least one of: garment fabrication (what material is to create) instructions, garment pattern (cutout) flat layout, artwork layout, or garment construction (sew Al to B2, etc.) instructions.

[0031] In at least one aspect of the embodiment, the method can further include generating, based at least in part on the 2D information, repeating artwork information; generating, based at least in part on the repeating artwork information, alternate colorways information; generating or receiving selected base material information; generating or receiving, based at least in part on the repeating artwork information, alternate colorways information, selected base material information, and 3D layout of the garment, construction information for the garment, wherein the construction information includes at least one of: a blueprint for garment construction, instructions to adjoin together one or more components of the garment, a selection of a material or textile for the garment, or artwork application information.

[0032] In at least one aspect of the embodiment, the method can further include receiving, based at least in part on a user input, custom solution information, wherein the custom solution information includes at least one of: customer-specific information, model-specific information, fit model-specific information, body dimension information, body composition information, anatomical information, physiological information, demographic information, ergonomic information, patient-specific information, therapeutic information, diagnostic information, healthcare information, temperature control information, compression information, wound healing information, medical standard information, prosthetics information, safety information, military specification information, athletic information, injury prevention information, compression information, or computing information, wherein the generating, based at least in part on the 2D information associated with the at least one side ofa garment, the 2D information associated with one or more other sides of the garment, a 3D layout of the garment can be further based at least in part on the custom solution information.

[0033] In another embodiment of the present disclosure, a computer-readable medium with computer-executable instructions stored in a memory device can be provided for designing a garment. The instructions can be operable to receive 2D information associated with at least one side of a garment; generate, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment; generate, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment; generate, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout of the garment; and output, via at one user interface, the 3D layout for review by a user.

[0034] In at least one aspect of the embodiment, the instructions can be further operable to generate or receive, based at least in part on the 3D layout of the garment, construction information for the garment, wherein the construction information includes at least one of: a blueprint for garment construction, instructions to adjoin together one or more components of the garment, a selection of a material or textile for the garment, or artwork application information.

[0035] In at least one aspect of the embodiment, the 3D layout of the garment can include a 3D artwork design; and the instructions can be further operable to generate, based at least in part on the 3D artwork design, 3D artwork application information to apply the 3D artwork design to the garment.

[0036] In at least one aspect of the embodiment, the instructions can be further operable to generate or receive at least one 3D digital fit model (mannequin), wherein the generating a 3D layout of the garment is further based at least in part on the at least one 3D digital fit model (mannequin).

[0037] In at least one aspect of the embodiment, the generating or receiving at least one 3D digital fit model (mannequin) can be further operable to generate at least one 3D digital fit model (mannequin) is based at least in part on at least one of: a body scan, a user input, selection from a data source (library), selection of a size by an Al module, or selection of a size based on a brand.

[0038] In at least one aspect of the embodiment, the instructions can be further operable to receive, at least one user input associated with the 3D layout; and implement, based at least in part on the at least one user input, a change to the 3D layout.

[0039] In at least one aspect of the embodiment, the at least one user input can be based at least in part on at least one of: a fashion trend, user feedback on the 3D layout, garment dimensional information, color, artwork application construction, material type, different size, or artwork on a 3D model.

[0040] In at least one aspect of the embodiment, the instructions can be further operable to generate, based at least in part on the 2D information, repeating artwork information; generate, based at least in part on the repeating artwork information, alternate colorways information; generate or receive selected base material information; and generate or receive, based at least in part on the repeating artwork information, alternate colorways information, selected base material information, and 3D layout of the garment, construction information for the garment, wherein the construction information includes at least one of: a blueprint for garment construction, instructions to adjoin together one or more components of the garment, a material selection for the garment, or artwork application information.

[0041] In at least one aspect of the embodiment, the instructions can be further operable to receive, based at least in part on a user input, custom solution information, wherein the custom solution information includes at least one of: customer-specific information, model-specific information, fit model-specific information, body dimension information, body composition information, anatomical information, physiological information, demographic information, ergonomic information, patient-specific information, therapeutic information, diagnostic information, healthcare information, temperature control information, compression information, wound healing information, medical standard information, prosthetics information, safety information, military specification information, athletic information, injury prevention information, compression information, or computing information, wherein the generating, based at least in part on the 2D information associated with the at least one side of a garment, the 2D information associated with one or more other sides of the garment, a 3D layout of the garment can be further based at least in part on the custom solution information.

[0042] One skilled in the art will recognize other aspects, implementations, and embodiments in accordance with the detailed description of the present disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0043] FIG. 1 shows an example schematic diagram depicting an example operating environment for using a system or platform in accordance with one or more example embodiments of the present disclosure;

[0044] FIGs. 2-4 are example flowcharts depicting example methods or processes in accordance with one or more example embodiments of the present disclosure;

[0045] FIGs. 5A-5F illustrate an example user workflow in accordance with one or more example embodiments of the present disclosure;

[0046] FIG. 6A-6G illustrate an example user workflow in accordance with one or more example embodiments of the present disclosure;

[0047] FIG. 7A-7F illustrate an example user workflow in accordance with one or more example embodiments of the present disclosure; and

[0048] FIG. 8 is a block diagram of an example system, machine, or computing device, and computer-readable medium in accordance with one or more example embodiments of the present disclosure.

[0049] The detailed description below is set forth with reference to the accompanying drawings, which are provided for purposes of illustration only and merely depict exemplary embodiments of the disclosure. The use of the same reference numerals indicates similar, but not necessarily the same or identical components. However, different reference numerals may be used to identify similar components as well. Various embodiments may utilize elements or components other than those illustrated in the drawings, and some elements and / or components may not be present in various embodiments.DETAILED DESCRIPTION

[0050] The present disclosure relates to systems and methods for automated garment design. Certain embodiments of the present disclosure can provide systems, methods, and a computer- readable medium for automated garment design. Certain embodiments of the present disclosure can provide systems, methods, and a computer-readable medium for designing a garment. Further, certain embodiments of the present disclosure can provide systems, methods, and a computer-readable medium to design all sides of a garment, and for providing instructions to construct the garment.

[0051] Further, in at least one embodiment, systems, methods, and a computer-readable medium can be provided for designing a garment, wherein based at least in part on generating or receiving a 2D image, such as a 2D digital image of, for example, a pattern, artwork, or a garment, a 3D layout to construct a garment can be generated. Moreover, in at least one embodiment, systems, methods, and a computer-readable medium can be provided for designing a garment, wherein based at least in part on generating or receiving text instructions associated with a 2D or 3D image, such as instructions for a pattern, instructions for artwork,or instructions for a garment, a 3D layout to construct a garment can be generated. Further, in at least one embodiment, systems, methods, and a computer-readable medium can be provided for designing a garment, wherein based at least in part on generating or receiving text description associated with a garment, such as text description for a pattern, text description for artwork, or text description for a garment, a 3D layout to construct a garment can be generated.

[0052] In any instance, a 3D layout to construct a garment can be, for example, a blueprint for constructing a garment with information associated with constructing the garment including, but not limited to, instructions to adjoin together one or more components of the garment, a selection of a material or textile for the garment, or artwork application information. Based at least in part on the 3D layout, certain embodiments of the present disclosure may execute or otherwise facilitate constructing the garment. Furthermore, certain embodiments of the present disclosure may manage or otherwise facilitate shipment, storage, and associated financial transactions related to production and distribution of the garment.

[0053] In one embodiment of the present disclosure, a system for designing a garment can include a garment design module with a 2D design input interface, a 2D-to-3D design converter, and an artificial intelligence (Al) module. The 2D design input interface can be operable to receive 2D information associated with at least one side of a garment. The 2D-to- 3D design converter can be operable to generate, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment. The Al module can be operable to generate, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment; generate, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout to construct the garment; and output, via at one user interface, the 3D layout for review by a user.

[0054] In another example embodiment, a method for designing a garment can be provided. The method can include receiving 2D information associated with at least one side of a garment; generating, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment; generating, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout to construct the garment; and outputting, via at one user interface, the 3D layout for review by a user.

[0055] In yet another example embodiment, a computer-readable medium with computerexecutable instructions for garment design stored in a memory device can be provided. The instructions can be operable to receive 2D information associated with at least one side of a garment; generate, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment; generate, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout of the garment; and output, via at one user interface, the 3D layout for review by a user.

[0056] Embodiments of the present disclosure may include methods, systems, and a computer readable medium including, but not limited to, at least one of the following: a garment design module, an Al module, and a logistics module. The modular architecture can allow for scalability and adaptability of various embodiments to other industries and applications, such that certain embodiments can operate in, for example, a variety of industries, such as the fashion, medical, military, and / or athletics industry. For example, in the medical industry, a garment design module may generate a 3D layout to construct a garment. The garment design module may, based at least in part on a 2D image or text information associated with a garment, or a 2D or 3D image input by a user, generate a 3D layout to construct a medical garment.

[0057] Various operations and features of one or more modules according to embodiments of the present disclosure can include:

[0058] AI-Driven Garment Design. Certain embodiments can use generative Al to automatically convert 2D designs, such as sketches, into 3D layouts, such as detailed 3D models. In certain embodiments, relatively rapid user iterations and modifications can significantly reduce the time from design concept to approval. An Al module can suggest design alterations based on current fashion trends and user feedback, ensuring designs are both innovative and market-relevant.

[0059] Garment Design and Fitting. Certain embodiments can facilitate the creation and modification of garment designs using a user-friendly interface. This can include generating fit mannequins based on 3D body scans or manual inputs, applying 2D designs to these mannequins, and enabling real-time adjustments to optimize fit and aesthetics.

[0060] Material Simulation and Selection. Certain embodiments can integrate a comprehensive database of component properties, enabling a simulation of how different materials will behave in a finished garment. This can help in selecting suitable materials based on the desired characteristics of the garment, such as durability, comfort, and aesthetic appeal. Designparameters can be automatically adjusted to accommodate the specific properties of chosen materials.

[0061] Sustainability Optimization. Certain embodiments can focus on reducing the environmental impact of garment production. Sustainable materials, formulation, and production methods can be selected, fabric usage can be optimized to minimize waste, and data analytics can be used to improve overall resource efficiency.

[0062] Automated Pattern Adjustment. Certain embodiments can automatically adjust patterns for various sizes and material types, using Al to optimize a marker layout for cutting and assembly. This can reduce material waste and ensure that the patterns are production-ready, minimizing the need for manual adjustments during the prototyping or manufacturing stages.

[0063] Customization and Personalization. Certain embodiments can allow a user to customize garments according to his or her preferences directly through a user-friendly interface. This can facilitate adjusting designs in real-time to fit individual user and / or customer measurements and preferences, thus offering a high degree of personalization.

[0064] Advanced Simulation Capabilities. Certain embodiments can use advanced physics engines and / or digital twins to simulate the real-world performance of garments under various conditions. Further, certain embodiments can use one or more geometric and 3D modeling algorithms to generate a 3D layout of a garment. In any instance, these capabilities can facilitate predicting how garments will perform in terms of fit, movement, and comfort, thus allowing for adjustments before the physical production begins.

[0065] Simulation of Garment Performance. Certain embodiments can leverage generative Al to transform initial design concepts into detailed 3D models and / or digital twins that can accurately predict the fit and appearance of garments without the need for physical prototypes. Further, such simulation capabilities can allow for the dynamic testing of garment designs under various conditions. This can include using digital twins and virtual prototyping to assess the functionality, comfort, and compliance of garments with desired industry standards, ensuring that designs are both practical and market-ready before production.

[0066] Medical and Therapeutic Applications. Certain embodiments can facilitate designing and producing therapeutic garments tailored to specific medical needs. This can include integrating patient-specific anatomical data to create custom-fit, therapeutic garments that meet desired medical standards and support patient recovery and comfort.

[0067] Comprehensive Data Collection. Certain embodiments can be configured to collect data from various sources, including user interactions, design modifications, material selections,production metrics, and quality control results. loT (Internet of Things) devices embedded in production equipment provide real-time operational data, while user interfaces track interactions and feedback.

[0068] Data Pre-processing. Certain embodiments can facilitate data pre-processing including cleaning (removing or correcting erroneous data), normalization (scaling input variables to a standard range), and transformation (converting data into a suitable format for analysis). These operations can ensure the relative accuracy and reliability of subsequent analyses.

[0069] Real-Time Monitoring and Feedback. Certain embodiments can incorporate one or more sensors, such as loT sensors, in the production equipment to monitor the manufacturing process in real-time. This can facilitate immediate detection and correction of any deviations from the design specifications, ensuring that a garment can meet certain desired quality standards. Feedback can be used to continuously improve the design and production processes.

[0070] Supply Chain Management. Certain embodiments can facilitate comprehensive management of the garment supply chain, from material sourcing to product delivery. For example, real-time tracking algorithms can monitor the inventory status and produce shipping documentation. This can facilitate vendor management, logistics optimization, production monitoring, and inventory control, all designed to enhance efficiency, reduce costs, and ensure timely delivery of relatively high quality garments.

[0071] Vendor Management and Production Integration. Certain embodiments can provide a relatively seamless connection between the design team, suppliers, and manufacturers. This can facilitate sending precise production specifications directly from the design software to the production facilities, ensuring that all parties have consistent and up-to-date information. This can facilitate tracking supplier performance and compliance, thereby enhancing the reliability of the supply chain.

[0072] Data Analytics and Continuous Improvement. Certain embodiments can collect comprehensive data from every stage of the design and production process. This data can be analyzed to identify trends, predict potential issues, and suggest improvements. Machine learning algorithms can be used to refine performance over time, making relatively smarter and more efficient decisions with each production cycle. Further, the utilization of data analytics can facilitate extracting insights from production and supply chain operations. This can facilitate identifying bottlenecks, predicting potential issues, and recommending improvements, thereby fostering a culture of continuous enhancement and adaptation in garment production processes.

[0073] Descriptive Analytics. Certain embodiments can summarize historical data to identify patterns and trends. For example, this can facilitate analyzing historical production data to identify common bottlenecks or quality issues in a garment production process.

[0074] Diagnostic Analytics. Certain embodiments can utilize certain techniques such as, for example, root cause analysis, to understand the reasons behind certain outcomes. For instance, if a particular type of material consistently leads to production delays, diagnostic analytics can help pinpoint the underlying causes, such as supplier issues or inadequate material handling protocols. Further, certain embodiments may integrate with external ERP (enterprise resource planning) systems through one or more APIs (application programming interfaces), enabling a relatively smooth data flow for accounting and compliance management across the system or platform. In some embodiments, a risk assessment engine may predict supply chain disruptions based on at least one of: historical data or real-time data.

[0075] Predictive Modeling. Certain embodiments can utilize machine learning models to predict future outcomes based on historical data. For example, predictive models can forecast the demand for certain garment styles, predict the optimal quantity of materials needed to minimize waste, or anticipate potential quality issues based on production parameters.

[0076] Model Training and Validation. Certain embodiments can utilize models trained using historical data sets, and their performance can be validated using separate test data. Techniques such as cross-validation can be used to ensure that models are robust and generalize well to new data.

[0077] Real-Time Learning and Adaptation. Certain embodiments can facilitate implementing online learning algorithms that continuously update the models as new data becomes available. This can facilitate adapting to changes in trends, consumer preferences, or production conditions, ensuring that the analytics and predictions remain relevant and accurate. For example, one or more advanced computational geometry and machine learning algorithms can be implemented to analyze and propose processes for automated garment design, production, and distribution. In some embodiments, advanced computational geometry and machine learning algorithms may be implemented for material incompatibility detection, among other implementations. In another example, one or more multi-layer neural networks can be implemented by certain embodiments of the disclosure. A neural network can be implemented to perform trend analysis, cost optimization, and sustainable decision-making, enabling optimized garment design, production, and distribution. The neural network may also provide an immediate snapshot of current industry trends and future projections through the analysis ofmarket data and consumer behavior to implement the automation of garment design, production, and distribution.

[0078] Automated Recommendations. Certain embodiments can facilitate, based on the insights generated from data analytics and machine learning models, automatically generating recommendations for design modifications, process improvements, and resource optimizations. These recommendations can be presented to a user through a user interface for approval or further action. In at least one embodiment, data analytics can be implemented to identify and flag incompatible fabric combinations based on one or more parameters such as, for example, tensile strength, elasticity, and thermal properties. In one example, a thread size selection algorithm can be implemented to generate at least one recommendation based at least in part on an intended use, selected material, and stitching requirements for a desired garment. Further, certain embodiments of the present disclosure can include a learning management system providing one or more tutorials and guidance for using some or all of the modules.

[0079] Feedback Loop. Certain embodiments can facilitate providing a feedback loop where the outcomes of implemented recommendations can be monitored and analyzed to assess their effectiveness. This can be used to further refine the models and strategies, embodying a continuous improvement paradigm.

[0080] Visualization and Reporting. Certain embodiments can facilitate providing advanced visualization tools integrated into a user interface, providing intuitive dashboards and reports that highlight key metrics, trends, and predictions. These visualizations can aid stakeholders and facilitate understanding the data to help make informed decisions.

[0081] Integration of Technologies. Certain embodiments can provide a comprehensive, automated system or platform that seamlessly integrate some or all stages of garment design, production, and distribution into a single system or platform. For example, certain embodiments can facilitate the integration of Al, AR / VR technologies, and predictive analytics positions to assist in one or more operations of garment design, production, and / or distribution. With integrated data analysis, real-time workflow adjustments can be made to streamline one or more operations in garment design and creation, and supply chain logistics.

[0082] Data Integration. Certain embodiments can facilitate integrating some or all collected data into a centralized database. This integration can ensure that data from disparate sources can be standardized and made accessible for analysis. APIs can be used to fetch and update data across different modules, ensuring that some or all components or modules have access to the latest information.

[0083] Integration with External Platforms. Certain embodiments can facilitate compatibility with external fashion design and retail systems or platforms, allowing for seamless integration into existing workflows. This can extend functionality and allow users to effectively leverage other tools, systems and / or platforms.

[0084] FIG. 1 depicts an example schematic diagram depicting an operating environment for using a system or platform in accordance with one or more example embodiments of the present disclosure. According to various embodiments of the present disclosure, a system or platform of the present disclosure can include multiple configurations. By way of non-limiting example, a system or platform for designing a garment may be hosted on, for example, a cloud computing service. In some embodiments, the system or platform may be hosted on a computing device. A user may access the system or platform through a software application and / or hardware device. The software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with the computing device.

[0085] In the embodiment shown in FIG, 1, a suitable environment 100 for a system or platform, method, and computer-readable medium for designing a garment, can be a clientserver network architecture. The environment 100 shown can include one or more client devices 102A-102N, for example, a laptop computer 102 A. Other examples of client devices can include, but are not limited to, a mobile communication device 102B, a computing tablet device 102C, a desktop computer, a wearable computing device, a processor-based device, or a communication device.

[0086] In any instance, the one or more client devices 102A-102N can communicate with at least one server device 104 via at least one network 106. The server device 104 shown can be a processor-based device. The network 106 shown can be a communication network, such as the Internet, a cellular network, a private network, a public network, a series of interconnected networks, a cloud computing network, or any combination thereof.

[0087] As shown in FIG. 1, in the environment 100, a user 108, who may be a designer, can interact with at least one client device 102A-102N. For example, the user 108 can provide one more inputs to a client device, such as 102N, and the client device 102N, based at least in part on the user inputs, can execute or otherwise receive one or more computer-executable instructions from at least one garment design module, such as 110A. As needed, the user 108 and garment design module 110A can communicate with, or may otherwise incorporate, a 2D design input interface 112 A, a 2D-to-3D design converter 114 A, an artificial intelligence (Al)module, such as 116A. The client device 102N can also execute or otherwise receive one or more computer-executable instructions from at least one Al module, such as 116A. In some instances, a client device 102A-102N can communicate via the network 106 with the server 104 to execute or otherwise receive one or more computer-executable instructions from a garment design module, such as HOB, and / or a 2D design input interface 112B, a 2D-to-3D design converter 114B, and an Al module, such as 116B. In other instances, a client device 102A-102N can communicate via at least one network, such as 106, to execute or otherwise receive one or more computer-executable instructions from another remotely located garment design module, such as HON, and / or a 2D design input interface 112N, a 2D-to-3D design converter 114N, and a remotely located Al module, such as 116N.

[0088] In certain embodiments, a garment design module, such as 110N, may refer to hardware and / or software configured to or otherwise operable to perform operations described herein for receiving data associated with a desired garment or garment of interest. In certain embodiments, a garment design module, such as 11 ON, may be interoperable with other modules of the present disclosure. In certain embodiments, a garment design module, such as 110N, may be integrated with or into an Al module, such as 116N, and / or a logistics module, such as 118N, such that the modules may work together to provide the operations and / or functionality described herein. A garment design module, such as 11 ON, may provide the automated garment design, production, and distribution through interaction with one or more modules using parameters that may be associated with a garment to be designed, produced, and / or distributed by the embodiments of the present disclosure.

[0089] In some embodiments, a garment design module, such as 11 ON, may enable garment artwork and garment creation. A garment design module, such as 11 ON, may support vectorbased designs, bitmap images, or other forms of digital storage. In some embodiments, a garment design module, such as 110N, may enable the upload of an existing design. A garment design module, such as 110N, may also enable the creation of new artwork using built-in tools. A garment design module, such as 110N, may enable functions such as color correction, pattern generation, and texture mapping. A garment design module, such as 11 ON, may interact with one or more external libraries that can include predefined design elements.

[0090] In some embodiments, a garment design module, such as 110N, may utilize the external libraries, such as patches, labels, and fonts for garment artwork and creation. A garment design module, such as 11 ON, may also support the importation of design elements from third-party sources. A garment design module, such as 11 ON, may ensure compliance with intellectualproperty laws by flagging potential intellectual property violations during creation of the garment and artwork. A garment design module, such as 11 ON, may enable real-time simulation of how the artwork would appear on various types of materials, shapes, or garment types. A garment design module, such as 1 ION, may also enable the design of a specific region of the garment for artwork application, such as sleeves, collar, or body, facilitating more complex design scenarios. Through machine learning algorithms or predefined logic, a garment design module, such as 1 ION, could suggest optimizations for design elements based on factors such as fabric type, printing technology available, and estimated production cost.

[0091] In some embodiments, a garment design module, such as 11 ON, may enable garment construction. The garment construction may be influenced by the garment artwork and garment design. In some embodiments, once the garment artwork and the garment design are finalized, a garment design module, such as 11 ON, may generate technical instructions associated with garment construction. The garment construction information may include artwork and material selection. A garment design module, such as 11 ON, may automatically generate a set of manufacturing instructions tailored to the selected fabric and design elements determined for the garment construction. These instructions may be based on a database of garment construction techniques accessible to the garment design module, such as 1 ION. The garment construction may be optimized for various material types and machinery available in the supply chain based on the configuration of a garment. A garment design module, such as 1 ION, may also interact with one or more other modules to ensure the necessary materials and components for garment construction. A garment design module, such as 11 ON, may interact with one or more other modules for garment construction to verify components such as zippers or buttons, are in stock or scheduled for delivery to satisfy the requirements associated with garment construction. A garment design module, such as 1 ION, can coordinate with an Al module, such as 116N, for garment construction. A garment design module, such as 1 ION, may also interface with an Al module, such as 116N, to predict potential construction issues.

[0092] A garment design module, such as 11 ON, may utilize the data received relating to the garment and the artwork to analyze the complexity of the garment and the properties of the chosen materials for garment construction. In some embodiments, if a user includes intricate embroidery on a relatively stretchy fabric, the garment design module, such as 11 ON, could flag a potential issue for material puckering and suggest one or more adjustments. Similarly, a garment design module, such as 11 ON, may integrate feedback loops from quality control processes, enabling real-time adjustments to construction parameters if deviations fromexpected quality metrics are detected. Thus, the garment design module, such as 11 ON, can operate in relatively seamless operation with one or more other modules, creating a comprehensive solution for automated garment creation from design to production.

[0093] In some embodiments, a garment design module, such as 110N, may enable the selection of a material for the garment. In other embodiments, a garment design module, such as 1 ION, may extend its functionality to enable the selection of materials for the garment based on the received data relating to the garment and the data generated by one or more other modules. A garment design module, such as 1 ION, may enable material selection based on the specific parameters of the garment. In some embodiments, the material selection may be based on garment artwork and garment construction.

[0094] In some embodiments, a garment design module, such as 11 ON, can provide material selection options filtered by compatibility with a finalized design and construction parameters. In other embodiments, a garment design module, such as 11 ON, may enable a user to select from a range of fabric types based on the parameters associated with the garment. In further embodiments, a garment design module, such as 1 ION, may communicably interact with an Al module, such as 116N, to leverage data analytics or machine learning algorithms. A garment design module, such as 1 ION, may interact with an Al module, such as 116N, to suggest ideal materials based on factors like color saturation needs, structural stability, or artwork complexity. In some embodiments of the present disclosure, a garment design module, such as 11 ON, may identify materials for selection based on the construction of the garment. In yet other embodiments, if a user incorporates delicate embroidery, for instance, a garment design module, such as 11 ON, may recommend relatively thicker, non-stretch materials to minimize issues like puckering, thereby aligning with any prior provided analysis and suggestions.

[0095] A garment design module, such as 110N, may interface with one or more other modules to determine real-time availability or lead times for chosen materials. Accordingly, a garment design module, such as 11 ON, may allow for dynamic adjustments based on the operation of one or more modules as a whole, such that the operation of the garment design module, such as 110N, enables the automation of garment design, production, and distribution. Additionally, a garment design module, such as 110N, may predict how the chosen material may impact the process for the garment construction. Accordingly, the garment design module, such as 110N, can ensure that the material selection is compatible with the automation of the garment design, production, and distribution provided by one or more modules.

[0096] In certain embodiments, an Al module, such as 116N, can include or otherwise utilize a machine learning engine. A machine learning engine can implement or otherwise include various techniques in the field of artificial intelligence that deal with computer-implemented, user-independent processes for solving problems that have variable inputs. In some embodiments, a machine learning engine can train a machine learning model to perform one or more operations. Training a machine learning model uses training data to generate a function that, given one or more inputs to the machine learning model, computes a corresponding output. The output may correspond to a prediction based on prior machine learning. In certain embodiments, the output can include a label, classification, and / or categorization assigned to the provided input(s). The machine learning model can correspond to a learned model for performing the desired operation(s) (e.g., labeling, classifying, and / or categorizing inputs). The machine learning engine may use multiple machine learning engines and / or multiple machine learning models for different purposes.

[0097] In an embodiment, a machine learning engine may use supervised learning, semisupervised learning, unsupervised learning, reinforcement learning, and / or another training method or combination thereof. In supervised learning, labeled training data includes input / output pairs in which each input is labeled with a desired output (e.g., a label, classification, and / or categorization), also referred to as a supervisory signal. In semisupervised learning, some inputs are associated with supervisory signals and other inputs are not associated with supervisory signals. In unsupervised learning, the training data does not include supervisory signals. Reinforcement learning uses a feedback system in which the machine learning engine receives positive and / or negative reinforcement in the process of attempting to solve a particular problem (e.g., to optimize performance in a particular scenario, according to one or more predefined performance criteria). In an embodiment, the machine learning engine can initially use supervised learning to train the machine learning model and can use unsupervised learning to update the machine learning model on an ongoing basis.

[0098] In an embodiment, a machine learning engine may use many different techniques to label, classify, and / or categorize inputs. A machine learning engine may transform inputs into feature vectors that describe one or more properties or features of the inputs. The machine learning engine may label, classify, and / or categorize the inputs based on the feature vectors. Alternatively, or additionally, a machine learning engine may use clustering (also referred to as cluster analysis) to identify commonalities in the inputs. The machine learning engine may group (i.e., cluster) the inputs based on those commonalities. The machine learning engine mayuse hierarchical clustering, k-means clustering, and / or another clustering method or combination thereof. In an embodiment, a machine learning engine can include an artificial neural network. An artificial neural network can include multiple nodes (also referred to as artificial neurons) and edges between nodes. Edges may be associated with corresponding weights that represent the strengths of connections between nodes, which the machine learning engine adjusts as machine learning proceeds. Alternatively, or additionally, a machine learning engine may include or otherwise utilize a support vector machine. A support vector machine represents inputs as vectors. The machine learning engine may label, classify, and / or categorize inputs based on the vectors. Alternatively, or additionally, the machine learning engine may use a naive Bayes classifier to label, classify, and / or categorize inputs. Alternatively, or additionally, given a particular input, a machine learning engine may apply a decision tree to predict an output for the given input. Alternatively, or additionally, a machine learning engine may apply fuzzy logic in situations where labeling, classifying, and / or categorizing an input among a fixed set of mutually exclusive options is impossible or impractical.

[0099] For example, a machine learning engine may receive, as inputs, associated with the platform. The machine learning engine may select one or more actions based on actions that have been successfully undertaken. In certain embodiments, the one or more suggested changes may be selected based on the operating parameters of a garment creation module and / or other modules. In certain embodiments, the one or more suggested actions may be selected based on, for example, one or more of a predicted cost to implement the action, a predicted effectiveness of the action.

[0100] In some embodiments, a machine learning engine may also be used to determine the predicted traits, such as the cost and / or the predicted effectiveness of implementing the action. The machine learning engine may determine the traits of the action based on historical cost data and / or historical data indicating the effectiveness of similar actions. In some embodiments, the traits may be predicted based on historical data from any organization, or from historical data from organizations similar to the received organizational data.

[0101] In other embodiments, a machine learning engine may receive, as inputs, organizational data associated with an organization. The machine learning engine may use the received organization data, together with historical data from other organizations with one or more traits similar to the received organizational data, to predict an organizational size change.

[0102] In an embodiment, as a machine learning engine applies different inputs to an associated machine learning model, the accuracy of the corresponding outputs can be improvedover time. As an example, the machine learning engine may use supervised learning to train a machine learning model. After training the machine learning model, if a subsequent input is identical to an input that was included in labeled training data and the output is identical to the supervisory signal in the training data, then output is certain to be accurate. If an input is different from inputs that were included in labeled training data, then the machine learning engine may generate a corresponding output that is inaccurate or of uncertain accuracy. In addition to producing a particular output for a given input, the machine learning engine may be configured to produce an indicator representing a confidence (or lack thereof) in the accuracy of the output. A confidence indicator may include a numeric score, a Boolean value, and / or any other kind of indicator that corresponds to a confidence (or lack thereof) in the accuracy of the output.

[0103] In certain embodiments, a machine learning engine may be configured to receive data from one or more external data sources. An external data source may refer to hardware and / or software operating independent of the engine. For example, the hardware and / or software of the external data source may be under control of a different entity (e.g., a different company or other kind of organization) than an entity that controls the organizational sustainability performance forecasting, planning, and reporting engine.

[0104] In an embodiment, a machine learning engine may be configured to retrieve data from an external data source by “pulling” the data via an application programming interface (API) of the external data source, using user credentials that a user has provided for that particular external data source. Alternatively, or additionally, an external data source may be configured to “push” data to the engine via an API of an associated interface creation engine, using an access key, password, and / or other kind of credential that a user has supplied to the external data source.

[0105] In one or more embodiments, a machine learning engine engine may include or otherwise utilize a data repository. A data repository may be any type of data storage device including, for instance, a file system, database, collection of tables, or any other storage mechanism, for storing data. Further, a data repository may include multiple different data storage devices. The multiple different data storage devices may or may not be of the same type or located at the same physical site. Further, a data repository may be implemented or may execute on the same computing system as the engine. Alternatively, or additionally, a data repository may be implemented or executed on a computing system separate from the engineand / or the external data source. A data repository may be communicatively coupled to the engine and / or the external data source via a direct connection or via a network.

[0106] In certain embodiments, a data repository may store data collected by the engine (e.g., via the data collection module). Alternatively or additionally, the data repository may store data generated by the platform. Further, a data repository may transmit or otherwise provide access to garment information such as garment specifications, material types, artwork applications, fiber contents, and vendor performance metrics, and data managed by other third party entities that supply raw materials, fabrics, or manufacturing services to the garment design, product, and distribution processes.

[0107] In one or more embodiments, a user interface can refer to hardware and / or software configured to facilitate communications between a user and the engine. A user interface may be used by a user who accesses an interface (e.g., a dashboard interface) for work and / or personal activities. A user interface may be associated with one or more devices for presenting visual media, such as a display, including a monitor, a television, a projector, and / or the like. A user interface can render user interface elements and can receive inputs via user interface elements. Examples of a suitable user interface can include a graphical user interface (GUI), a command line interface (CLI), a haptic interface, and a voice command interface. Examples of user interface elements can include checkboxes, radio buttons, dropdown lists, list boxes, buttons, toggles, text fields, date and time selectors, command lines, sliders, pages, and forms.

[0108] In other embodiments, different components of a user interface can be specified in different languages. The behavior of one or more user interface elements can be specified in a runtime-generated programming language, such as JavaScript. The content of one or more user interface elements can be specified in a markup language, such as hypertext markup language (HTML) or XML user interface language (XUL). The layout of one or more user interface elements can be specified in a style sheet language, such as cascading style sheets (CSS). Alternatively, the user interface is specified in one or more other languages, such as Java, C, or C++.

[0109] In further embodiments, a garment design module, such as 11 ON, may include a user interface, such as a 2D design input interface, that simplifies garment design and artwork creation. A user interface may also interact with a 2D-to-3D design converter to provide coordinated functionality and operations with a 2D design input interface. In any instance, a user interface may allow users to easily navigate through the various operations of garment design and creation associated with the 2D design input interface and 2D-to-3D designconverter. For example, a 2D design input interface can be designed to accept a wide range of file formats, making it accessible for a user using different software. In another example, a 2D- to-3D design converter can employ advanced algorithms to ensure accurate and realistic conversions of designs, enhancing a user’s ability to visualize a final garment. A user interface can be configured to provide material choices from a database of materials, including detailed information on texture, weight, and sustainability, enabling a user to make informed decisions. A user interface can facilitate automatically adjusting pattern pieces based on the selected materials and 3D model, reducing manual errors and streamlining the production process. A user interface can provide real-time cost estimates, allowing for immediate budget adjustments. A user interface can offer high-resolution visuals, enabling detailed inspection before final approval. A user interface can be tailored to facilitate seamless interaction between a user and functionalities of one or more modules. A user interface can incorporate interactive tutorials and guides, aiding a user unfamiliar with the process. A user interface can support drag-and- drop functionality, simplifying the process of uploading designs. Advanced algorithms in a 2D- to-3D design converter can offer options for manual adjustments, granting a user greater control over the conversion process. A user interface can include a feature for comparing physical properties of materials side by side, assisting in the selection of the most suitable materials for specific garment features. A user interface can allow for the automatic generation of multiple pattern variations, enabling a user to explore different construction options efficiently. A user interface can update dynamically with changes in design or material selections, providing immediate feedback on the financial impact of design decisions. A user interface could allow for interactive feedback sessions, where a user can annotate and request changes directly on the 3D model.

[0110] In further embodiments, a user interface can have an adaptive layout that adjusts according to a user's skill level. This adaptive layout could present simplified options to a novice user while offering advanced features to experienced designers. A user interface can incorporate predictive text and image recognition to expedite the design upload process. Advanced algorithms in a 2D-to-3D design converter may also include real-time error detection, alerting a user to potential issues in the conversion phase. A user interface could feature an automated recommendation system that suggests materials based on the garment's intended use and a user’s preferences. A user interface can integrate with a virtual fitting room, allowing a user to test the fit of pattern pieces on virtual models. A user interface may offer scenario-based projections, enabling a user to explore the financial implications of differentdesign scenarios. A user interface could support collaboration, allowing multiple users to review and comment on designs in real time. A user interface may adapt to various user preferences, offering customization options that enhance the overall design experience. A 2D design input interface could feature automatic format recognition, further simplifying the initial design upload process. Advanced algorithms in a 2D-to-3D design converter may facilitate seamless transitions between different design perspectives, providing a holistic view of the garment. A user interface could prioritize certain materials, aligning with current trends towards sustainability. A user interface might incorporate feedback mechanisms, allowing for iterative refinement based on material performance. A user interface may include options for bulk pricing analysis, beneficial for large-scale production planning. A user interface could offer virtual reality integration, enabling immersive design reviews by a user.

[0111] In still further embodiments, a user interface may be designed to automatically save progress of a user in finalizing design of a garment, preventing data loss and enabling a user to return to his or her work at any stage. This feature could aid a user during relatively long design sessions or when working with complex designs. A 2D design input interface can allow for easy access to a library of predefined templates, speeding up the design process for common garment types. Advanced algorithms in a 2D-to-3D design converter may be optimized for low-resource consumption, ensuring that an associated garment design module runs relatively smoothly on a computing device having limited processing power. A user interface can include a virtual assistant to guide a user through the material selection process, providing insights into the best practices for material usage. A user interface can feature an auto-correction tool that adjusts pattern pieces to automatically correct common drafting errors. A user interface can incorporate tax / duty and shipping calculators, offering a comprehensive view of the total costs involved in bringing a design to production. A user interface can include version control, allowing a user to compare different iterations of his or her designs side by side. A user interface can include accessibility features, such as voice commands and screen readers, making functionality available for a user with disabilities. A 2D design input interface can offer integration with external design platforms, allowing for seamless importation of designs created in other software. Advanced algorithms in a 2D-to-3D design converter can be enhanced with machine learning capabilities to improve accuracy over time as more designs are processed. A user interface can provide real-time updates on material availability, ensuring a user can be informed of any shortages or delays that could impact his or her projects. A user interface can support the integration of electronic textiles and wearable technologies, cateringto the growing interest in smart garments. A user interface can feature a comparison tool that allows designers to assess the cost implications of using different materials or production methods. A user interface can be configured to simulate various lighting conditions, offering a user insights into how garments will look in different environments.

[0112] In yet further embodiments, a user interface may be equipped with a feedback collection mechanism, enabling designers to gather input from end-users or customers directly within a particular module. This mechanism could be invaluable for refining designs based on user preferences or market demands. A 2D design input interface might feature an automatic backup function, ensuring that a user’s work is preserved in the event of unexpected disruptions. Advanced algorithms in a 2D-to-3D design converter may include adaptive mesh refinement techniques, optimizing the detail and accuracy of 3D models without excessive computational load. A user interface could be enhanced with a color matching tool, helping designers to select materials that meet specific color requirements or brand guidelines. A user interface might offer integration with cutting machine software, streamlining the transition from design to production by automatically generating cutting paths for material pieces. A user interface may allow for the inclusion of custom cost factors, such as labor rates or special handling fees, providing a more tailored and accurate cost estimation. A user interface could feature a history tracking tool, documenting all changes made to a design, facilitating audit trails, and design evolution tracking.

[0113] In even further embodiments, a user interface may be configured to support collaborative design efforts, enabling multiple users to work on a single project simultaneously. This feature could foster teamwork and creativity, allowing for the pooling of ideas and expertise. A 2D design input interface might include a feature for real-time design sharing, facilitating instant feedback and discussion among users who may be team members. Advanced algorithms in a 2D-to-3D design converter may support collaborative editing, where changes made by one user are immediately visible to all users or collaborating users. A user interface could offer a consensus-building tool, helping teams of users to democratically select materials and components for their projects. A user interface might allow for role-based access control, ensuring that team members can only modify aspects of the design relevant to their expertise. A user interface may be designed to aggregate inputs from multiple team members, providing a comprehensive overview of the financial aspects of the project. A user interface could include a feature for scheduling virtual design review meetings, complete with agenda setting and action item tracking.

[0114] In even further embodiments, a user interface may leverage contextual help and tooltips to guide designers through complex functionalities, enhancing usability without compromising on the depth of features offered. This guidance could be dynamically adjusted based on the user's actions and preferences, ensuring relevant assistance is always at hand. A 2D design input interface might incorporate an auto-correct feature for common design errors, streamlining the initial design phase and reducing the need for manual corrections later in the process. Advanced algorithms in a 2D-to-3D design converter may provide predictive suggestions for design improvements, leveraging historical data and industry standards to enhance the quality of the final garment design. A user interface could include an interactive comparison matrix, allowing users to visually compare the properties of different materials against their design requirements. A user interface might feature an optimization tool that suggests adjustments to pattern layouts to minimize material waste, supporting cost reduction and sustainability efforts. A user interface may integrate with external financial software, enabling seamless export of cost data for budgeting and financial planning. A user interface could offer a feedback aggregation tool, compiling comments and suggestions from various users for easy review and action.

[0115] In yet further embodiments, a user interface may be complemented by a predictive layout adjustment feature, which automatically reconfigures the workspace according to a user's workflow patterns. This adaptation enhances the efficiency of the design process, minimizing the time spent navigating between different modules. A 2D design input interface could be further enhanced with a feature that automatically suggests design variations, encouraging creativity and innovation by presenting users with alternative perspectives. Advanced algorithms in a 2D-to-3D design converter may include a feature for automatic texture mapping, ensuring that the textures of chosen materials accurately conform to the contours of the 3D model. This capability enhances the realism of the design preview, providing a more accurate representation of the final product. A user interface could be equipped with a real-time cost impact analysis feature, which calculates the financial implications of each material selection. This tool can aid a user in making cost-effective choices without compromising on quality or design intent. A user interface might incorporate a feature for automatic seam allowance calculation, simplifying one of the more tedious aspects of garment design and ensuring consistency across pattern pieces. A user interface may include a feature for environmental impact estimation, allowing users to assess the ecological footprint of their garment designs. This tool can support the growing emphasis on sustainable designpractices within the fashion industry. A user interface could be enhanced with an animation feature, enabling users to simulate the movement of the garment on a digital avatar. This functionality provides valuable insights into the functionality and aesthetic appeal of a garment under real-life conditions. The animation feature may visualize how the fabric drapes and flows, offering a dynamic perspective on the garment's behavior in motion. This advanced visualization aids in identifying potential design issues before the production phase, such as unwanted bunching or stretching, thereby enhancing the overall design quality.

[0116] In still further embodiments, a user interface could be designed to support a wide range of input devices, including touchscreens, styluses, and traditional mouse and keyboard setups, catering to the diverse preferences and needs of designers. This flexibility ensures that a user can work in the manner most comfortable and efficient for them, further streamlining the design process. A 2D design input interface might also feature an intelligent layer management system, automatically organizing design elements into manageable layers. This simplifies the editing process, allowing users to quickly isolate and adjust specific components of their design without disrupting the overall composition. Advanced algorithms in a 2D-to-3D design converter may be capable of interpreting and suggesting design optimizations based on the intended function or solution goal of the garment, such as enhancing flexibility in athletic wear or improving the thermal properties of outdoor clothing. This intelligent functionality supports a user in creating garments that not only meet aesthetic goals but also fulfill specific performance criteria. A user interface could provide direct links to suppliers' databases, enabling a user to check the current availability of materials in real time. This integration streamlines the sourcing process, reducing delays in the production timeline caused by material shortages. A user interface might offer a versioning system that allows a user to save and track different iterations of his or her pattern pieces. This facilitates easy comparison and retrieval of past designs, enhancing the design refinement process. A user interface may be equipped with a scalability analysis feature, assessing how changes in production volume could affect cost per unit. This analysis aids in strategic decision-making regarding production runs, optimizing for both cost efficiency and market demand. A user interface could support augmented reality (AR) previews, enabling users, designers, and stakeholders to visualize the garment in a real-world context. This immersive experience provides a compelling way to evaluate and refine designs before committing to physical prototypes. The AR preview feature may allow for instant adjustments to the design in a virtual environment, offering an interactive and collaborative platform for design validation. This capability not only accelerates the designapproval process but also significantly reduces the need for physical samples, aligning with sustainability goals by minimizing waste. A user interface could incorporate a custom shortcut creation tool, allowing a user to configure his or her workflow shortcuts. This personalization optimizes the user interface for individual productivity, ensuring that frequently used functions are easily accessible. A 2D design input interface might include an automated color correction tool, which adjusts imported designs to match material colors accurately. This tool ensures that the digital representation closely mirrors the intended appearance of the final garment, reducing discrepancies between the design and production stages. Advanced algorithms associated with a 2D-to-3D design converter may offer a feature for simulating various material finishes.

[0117] In any instance, a garment design module, such as 11 ON, may also store metadata related to the design, including user (designer), date, and version history. A garment design module, such as 11 ON, may allow integration with pre-existing systems related to garment production and distribution. A garment design module, such as 11 ON, may export the digital representation in multiple file formats and import previously created designs for modification or improvement. A garment design module, such as 1 ION, may allow for customization, based on customer requirements, of a 3D layout or blueprint to construct a garment. A garment design module, such as 11 ON, may also allow users to view the garment from multiple angles and provide a virtual try-on feature for customers. A garment design module, such as 11 ON, may compare supplier costs and evaluate material sustainability.

[0118] Certain embodiments of the present disclosure can provide technical solutions to provide comprehensive, integrated systems and processes that facilitate seamless coordination between human-interfacing and software-driven components across various modules involved in automating garment design, production, and distribution. Certain embodiments can also optimally balance efficiency, adaptability, and sustainability, while ensuring robustness and scalability to adapt to future challenges and opportunities.

[0119] As shown in FIG. 1, the environment 100 can also include a logistics module that can interact with a garment design module and / or Al module. A logistics module may facilitate displaying and selling garments online or in physical retail stores. A logistics module may also collect customer feedback and, based on sales metrics, adjust the online display or a display in a physical retail store. A logistics module may calculate shipping costs and manage inventory levels based on real-time demand. A logistics module can facilitate integrated data analysis and real-time workflow adjustments to streamline garment creation and supply chain logistics. Inany instance, the various modules of the system or platform can communicate with and among each other to perform the various functionalities and operations associated with each module.

[0120] For example, in some instances, a user, such as 108, can interact with a client device, such as 102N, to communicate with a garment design module, such as 110A, and a logistics module, such as 118A. The client device 102N can, in some instances, execute or otherwise receive one or more computer-executable instructions from at least one logistics module, such as 118A. In some instances, a client device 102A-102N can communicate via the network 106 with the server 104 to execute or otherwise receive one or more computer-executable instructions from a logistics module 118B. In other instances, a client device 102A-102N can communicate via at least one network, such as 106, to execute or otherwise receive one or more computer-executable instructions from another remotely located logistics module, such as 118N.

[0121] In any instance, as needed, at least one logistics module, such as 118N, can communicate with one or more manufacturing resources 120, transportation resources 122 and / or retail resources 124. Although the logistics module 118N is shown in FIG. 1 as communicating with the various resources 120-124, one skilled in the art will recognize that any of the other logistics modules 118A, 118B can also communicate with the various resources.

[0122] Manufacturing resources, such as 120, can include, but are not limited to, a garment manufacturer, a supplier for a garment manufacturer, a materials supplier, or a manufacturing- related entity. In one example, a processor-based device, such as a client device and / or a server device, can communicate with a logistics module, such as 118N, to exchange instructions between the logistics module 118N and a desired manufacturing entity associated with the client device and / or server device. In other embodiments, the logistics modules 118A, 118B may communicate with the manufacturing resources 120 to exchange instructions between the respective logistics module 118A, 118B and a desired manufacturing-related entity.

[0123] Transportation resources, such as 122, can include, but are not limited to, a shipping or delivery service, a shipping or delivery platform, or a transportation-related entity. In one example, a processor-based device, such as a client device and / or a server device, can communicate with a logistics module, such as 118N, to exchange instructions between the logistics module 118N and a desired transportation entity associated with the client device and / or server device. In other embodiments, the logistics modules 118A, 118B maycommunicate with the transportation resources 122 to exchange instructions between the respective logistics module 118A, 118B and a desired transportation-related entity.

[0124] Retail resources, such as 124, can include, but are not limited to, an online retail platform, an online retail service, a physical retail store location, or a retail-related entity. In one example, a processor-based device, such as a client device and / or a server device, can communicate with a logistics module, such as 118N, to exchange instructions between the logistics module 118N and a desired retail entity associated with the client device and / or server device. In other embodiments, the logistics modules 118A, 118B may communicate with the retail resources 124 to exchange instructions between the respective logistics module 118A, 118B and a desired retail-related entity.

[0125] In some embodiments, a logistics module, such as 118N, may enable product construction based on data provided by one or more other modules. In other embodiments, a logistics module, such as 118N, may utilize various data sets associated with the platform, such as garment specifications, material preferences, and vendor performance metrics. The data from one or more other modules may inform a logistics module, such as 118N, with the implementation of the automated garment design, production, and distribution. In certain embodiments, a logistics module, such as 118N, may interface with one or more other modules to generate and implement supply-based issues associated with the automated design, production, and distribution of the garment. A logistics module, such as 118N, may transform the information from one or more other modules into supply side components such as manufacturer recommendations based on product type, MOQ / MCQ advisor, and generating an estimated cost sheet. A logistics module, such as 118N, may enable a vendor ordering and tracking system that interfaces with one or more other modules.

[0126] In some embodiments, a logistics module, such as 118N, may enable vendor ordering and tracking that can automate procurement based on real-time availability and cost-efficiency data associated with the automated garment design, production, and distribution by one or more other modules. Additionally, a logistics module, such as 118N, may trigger automated checks on material quality or garment construction at different manufacturing stages according to the operation of one or more other modules. In some embodiments, essential parameters like material availability, lead times, and sustainability certifications could be factored into the logistics module, such as 118N. A logistics module, such as 118N, may also incorporate a risk and alerts generator for identifying and communicating potential supply chain risks. In some embodiments, the parameters identified may impact the design of the garment. The logisticsmodule, such as 118N, may provide design limitations or implement design changes to the garment design module based on data associated with the logistics module.

[0127] In other embodiments, a logistics module, such as 118N, may enable input management. The input management may include the coordination and deployment of inputs associated with one or more other modules. A logistics module, such as 118N, may coordinate and deploy at least one of materials, components, and packaging associated with the garment. A logistics module, such as 118N, may include coordinating and deploying materials, components, and packaging associated with the product in accordance with the configuration of one or more other modules and the data associated with the modules. A logistics module, such as 118N, may serve as an extension of the one or more other modules’ capabilities to guide the automated garment design and the implementation of the production and distribution of the garment. In some embodiments, a logistics module, such as 118N, may coordinate the flow of material associated with the design, production, and distribution of the garment.

[0128] In some embodiments, a logistics module, such as 118N, may centralize and streamline the process for selecting, ordering, and tracking raw materials in accordance with the configuration of a garment. In some embodiments, a logistics module, such as 118N, may ensure a seamless flow from garment design to production to garment distribution. In further embodiments, garment distribution may be distributed to at least one of: a manufacturer, and a vendor. A logistics module, such as 118N, may also provide a packaging solution based on the data associated with the configuration of one or more other modules. In some embodiments, a logistics module, such as 118N, may suggest optimal packaging based on material characteristics, retailer specifications, sustainability concerns, and shipping constraints in accordance with the implementation of the automated garment design, production, and distribution associated with one or more other modules.

[0129] In some embodiments, a logistics module, such as 118N, may be configured to monitor the lifecycle of components associated with the implementation of the garment design, production, and distribution. In some embodiments, a logistics module, such as 118N, may monitor and manage the status and condition of specific product components throughout the supply chain associated with the automated design, production, and distribution of the garment. In other embodiments, a logistics module, such as 118N, may flag any deviations or quality issues impacting design, production, and distribution of a garment.

[0130] In some embodiments, a logistics module, such as 118N, may facilitate input management through the utilization of real-time data to modify the input management. In someembodiments, a logistics module, such as 118N, may modify the input management by incorporating key supply-side metrics and constraints. Accordingly, input management further refines a logistics module, such as 118N, by ensuring efficient and coordinated deployment of all necessary elements, from materials to final packaging, synergizing with one or more modules’ existing data-centric functionalities.

[0131] In some embodiments, a logistics module, such as 118N, may enable the production of the garment based on data associated with one or more other modules. A logistics module, such as 118N, may enable supply chain, planning, logistics, quality control, and feedback to operate in accordance with the automated garment design, production, and distribution provided by one or more modules. A logistics module, such as 118N, may be configured to implement the supply chain, planning, logistics, quality control, and feedback loops, all in alignment with the automated garment design, production, and distribution capabilities to produce the garment for distribution.

[0132] In some embodiments, a logistics module, such as 118N, may utilize algorithmic forecasts when implementing the production of a garment. Production of a garment may be based on design complexity, seasonal demands, and material availability to allocate manufacturing resources effectively in accordance with the requirements of the garment. The garment production facilitated by a logistics module, such as 118N, may be impacted by the implemented distribution of the garment to control shipment scheduling, routing, and inventory management to ensure that garments are distributed in accordance with desired parameters. In some embodiments, a logistics module, such as 118N, may apply quality control processes during the production of a garment. Accordingly, the garment production may be modified in response to real-time adjustments identified by a logistics module, such as 118N.

[0133] In some embodiments, if a design alteration is required, a logistics module, such as 118N, may directly interface with a garment design module, such as 1 ION, to generate design changes to prevent interruption of the garment distribution. A logistics module, such as 118N, may be configured to modify the garment production in response to received data. In other embodiments, a logistics module, such as 118N, may incorporate feedback and statistics associated with vendors regarding garment sell-through rates, consumer reviews, and returns. In further embodiments of a logistics module, such as 118N, garment production can be monitored to fine-tune both production and distribution strategies. Accordingly, a logistics module, such as 118N, may operate in tandem with the design and distribution functionalities, thus harmonizing the entire ecosystem from concept to customer.

[0134] In some embodiments, a logistics module, such as 118N, may enable merchandising and marketing of a garment. In other embodiments, a logistics module, such as 118N, may leverage data harvested by one or more modules to activate precise merchandising strategies and targeted marketing campaigns. In other embodiments, a logistics module, such as 118N, may utilize data associated with the consumer to influence the merchandising and marketing performed by one or more modules.

[0135] In some embodiments, a logistics module, such as 118N, may craft marketing messages that resonate with specific demographics. In some embodiments, a logistics module, such as 118N, may be configured to generate a compatibility score associated with the distribution of a garment. In certain embodiments, a logistics module, such as 118N, may identify retailers with customer profiles that align with the garment's target market based on the analysis from the logistics module. Accordingly, distribution of a garment may be influenced by manufacturing, logistics data, and strategic marketing insights.

[0136] In some embodiments, a logistics module, such as 118N, may also be configured to implement a pricing algorithm. In further embodiments, a logistics module, such as 118N, may be configured to adjust garment prices in real-time based on information from one or more modules. In yet further embodiments, a logistics module, such as 118N, may utilize supplydemand fluctuations, inventory levels, seasonal trends, and any other synchronized data associated with garment design, production, and distribution functionalities. Accordingly, certain embodiments of a logistics module, such as 118N, may generate and implement promotions associated with a garment. In some embodiments, a logistics module, such as 118N, may generate a tailored social media campaign for a garment.

[0137] One skilled in the art will recognize that details with regards to each module are provided herein as they relate to systems or platforms, and methods for automated garment design, production, and distribution. Although modules are disclosed with specific functionality, it should be understood that functionality may be shared between modules, with some functions split between modules, while other functions duplicated by the modules. Furthermore, the name of each module should not be construed as limiting upon the functionality of the module. Moreover, each component disclosed within each module can be considered independently, without the context of the other components within the same module or different modules. Each component may contain functionality defined in other portions of this specification. Each component disclosed for one module may be mixed with thefunctionality of other modules. In the present disclosure, each component can be claimed on its own and / or interchangeably with other components of other modules.

[0138] FIGs. 2-4 are example flowcharts depicting example methods or processes in accordance with one or more example embodiments of the present disclosure. One or more of the methods or processes shown can be implemented in or otherwise performed by, for example, the architecture 100 illustrated in FIG. 1. In certain instances, the methods or processes shown can be implemented in or otherwise performed by, for example, a system, apparatus, or machine, such as the machine 800 illustrated in FIG. 8, and / or by instructions stored in a computer-readable medium.

[0139] As shown, FIG. 2 depicts an example flow diagram for a method 200 in accordance with one or more example embodiments of the disclosure. The method 200 can begin with operation 202, receiving 2D information associated with at least one side of a garment. For example, in one embodiment, a garment design module, such as 11 ON, can receive 2D information associated with at least one side of a garment. In this example, a user such as 108 can interact with a client device, such as 102N, to input 2D information associated with at least one side of a garment. The 2D information associated with at least one side of a garment can be received by the associated garment design module 110A via the respective 2D design input interface 112A. The 2D information can include, but is not limited to, an image, a drawing, text information, and / or any combination thereof.

[0140] In one example, for designing a garment such as an embroidered dress, the following 2D information associated with at least one side of the garment can be input by a user 108 to a garment design module 110A via the respective 2D design input interface 112A associated with a client device 102N. The 2D information can be input in response to a series of prompts or questions generated by the garment design module 110A. For instance, in response to a prompt, “Physical description of model”, the following 2D information associated with a garment such as an embroidered dress can be input by the user 108, “Female runway model, 25 years old, 5’ 10” height, average conditioned body composition”. Further, in response to a prompt, “Garment Size”, the following 2D information can be input by the user 108, “Standard US size 2”. Further, in response to a prompt, “Garment type / fit”, the following 2D information can be input by the user 108, “Long gown with cape, high neckline”. Further, in response to a prompt, “Garment side (front or back)”, the following 2D information can be input by the user 108, “Front view”. Further, in response to a prompt, “Garment length”, the following 2D information can be input by the user 108, “Long flowing cape sleeves, floorlength gown, kimono, hidden side pockets”. Further, in response to a prompt, “Garment color”, the following 2D information can be input by the user 108, “All parts ecru color”. Further, in response to a prompt, “Portion or full view of garment (full or partial body)”, the following 2D information can be input by the user 108, “Full body, head to toe”. Further, in response to a prompt, “Design descriptors (ability to add 2D inspiration)”, the following 2D information can be input by the user 108, “Designer ‘name’ inspiration, macrame & embroidery techniques, weaving techniques, haute couture, intricate, art nouveau”. Further, in response to a prompt, “Material type”, the following 2D information can be input by the user 108, “Sheer lOoz silk organza base, fringe woven with macrame techniques, embroidered to organza base”. Further, in response to a prompt, “Artwork application (if needed, see diagram)”, the following 2D information can be input by the user 108, “Applied to material surface, fringe embroidered & macrame”. Further, in response to a prompt, “Background color”, the following 2D information can be input by the user 108, “Black, dark”. Further, in response to a prompt, “Anatomical solution in detail (if needed)”, the following 2D information can be input by the user 108, “None”.

[0141] In the embodiment shown, a text-to-image conversion tool can be provided within a user interface associated with a client device, such as 102N, that allows a user 108 to input descriptive text about a desired garment design, and using a generative Al model associated with a garment design module, such as 11 ON, and / or an Al module, such as 116N, a corresponding 2D image or design can be generated. In some embodiments, a corresponding 3D image or design can be generated. The descriptive text in this example, can include, but is not limited to, garment size, garment type / fit, garment side front description, garment side back description, garment sides description, garment length, garment color, a selection of a portion or full view of garment (full or partial body), one or more design descriptors, a material type, artwork application, a background color, a physical description of a model, or an anatomical solution in detail, and option to input photo inspiration for any of the above entries.

[0142] In any instance, the text-to-image conversion tool can utilize natural language processing to interpret the descriptive text input by the user 108, and a generative adversarial network (GAN) associated with a garment design module, such as 110N, and / or an Al module, such as 116N, can generate the 2D image or design, or in some instances, 3D image or design. The text-to-image conversion tool may also be integrated with a trend analysis tool associated with a garment design module, such as 110N, and / or an Al module, such as 116N, that scans fashion industry data to identify emerging trends, which can be used as inputs for generatingrelatively trendy and market-relevant garment designs. In some embodiments, an Al-driven design assistant associated with a garment design module, such as 1 ION, and / or an Al module, such as 116N, can utilize one or more machine learning algorithms to suggest design improvements or to generate design alternatives based on current fashion trends and user preferences. In some embodiments, a garment design module, such as 11 ON, and / or an Al module, such as 116N, may include a compatibility analysis tool that evaluates the automatically generated 2D images or designs, or in some instances, 3D images or designs, for factors associated with manufacturing or production of a garment, including but not limited to, fabric type, fiber content, garment complexity, and manufacturing or production techniques.

[0143] In the example of the embodiment above, a generative Al model associated with a garment design module, such as 11 ON, and / or an Al module, such as 116N, can receive the descriptive text and / or text instructions, and based at least in part on the descriptive text and / or text instructions, generate a corresponding 2D image or design, or in some instances, a 3D image or design. In any instance, the text-to-image conversion tool can utilize natural language processing to interpret the descriptive text and / or text instructions input by the user 108, and a generative adversarial network (GAN) associated with a garment design module, such as 1 ION, and / or an Al module, such as 116N, can generate the 2D image or design, or in some instances, 3D image or design.

[0144] In one aspect of the embodiment, the garment design module 110A can be operable to, based at least in part on some or all of the 2D information associated with at least one side of the garment, generate multiple versions of a 2D image for the at least one side of the garment. The garment design module 110A can permit the user 108 to update one or more responses to a prompt or descriptors if needed. In this manner, the user 108 can select a particular area of a garment to adjust the design of the garment. The use of a text to image generative Al tool with one or more machine learning capabilities can facilitate the generation of multiple versions of a 2D image for one side of the garment.

[0145] In one aspect of the embodiment, when a garment is intended for an anatomical solution, the garment design module 110A can be operable to, utilize custom solution information such as body composition information, anatomical information, and / or physiological information to generate multiple versions of a 2D image for one side of the garment.

[0146] Operation 202 can be followed by operation 204, generating, based at least in part on the 2D information associated with the at least one side of a garment, 2D information associatedwith one or more other sides of the garment. For example, in one embodiment, a garment design module, such as 110N, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate, based at least in part on the 2D information associated with the at least one side of a garment, 2D information associated with one or more other sides of the garment.

[0147] In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can process the 2D information associated with the at least one side of a garment, such as one or more of the responses received from the user 108 via the respective 2D design input interface 112 A, and generate 2D information associated with one or more other sides of the garment. The 2D-to-3D design converter 114A and / or associated Al module 116N can utilize one or more geometric and / or 3D modeling algorithms to generate 2D information associated with one or more other sides of the garment. Each design of each respective other side of the garment can include edge information needed to adjoin adjacent sides of the garment together including adjoining the initial one side of the garment to adjacent sides of the garment. The edge information can include information to align adjacent sides of the garment together for garment shape and construction, artwork application, material, and model. In any instance, the 2D information associated with one or more other sides of the garment can include a photorealistic 2D design for one or more sides of the garment.

[0148] Operation 204 can be followed by operation 206, generating, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout to construct the garment. For example, in one embodiment, a garment design module, such as 110N, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout to construct the garment. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can process the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, and uses a combination of one or more geometric and / or 3D modeling algorithms to generate a 3D layout to construct the garment. In one example, based at least in part on 2D information associated with the at least one side of a garment, the garment design module 110N can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate or otherwise create a 2D design for an opposing side of the garment. The garmentdesign module 1 ION can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N may generate or otherwise create one or more versions of the opposing side of the garment, and modify specific areas of the opposing side of the garment as needed. The garment design module 110N can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N may provide one or more versions that allow a front side and an opposing or back side of a garment to be adjoined in a 3D garment, accounting for some or all design characteristics such as garment construction, design aesthetic, fit, artwork, and knit / woven construction. The garment design module 1 ION can output a 3D layout to construct at least one version of the garment.

[0149] In the embodiment shown, a 3D layout can be, for example, a blueprint for constructing a garment with information associated with constructing the garment can include, but not limited to, instructions to adjoin together one or more components of the garment, a selection of a material or textile for the garment, or artwork application information.

[0150] In the embodiment shown, a generative Al model associated with an Al module, such as 116N, may be trained on a dataset including one or more historical garment designs and, in some instances, user feedback, enabling the Al model to learn design preferences and trends over time and to improve the accuracy and relevance of the designs the Al model generates, and may be capable of generating multiple design variations from a single text input, providing a user with a range of design options to choose from or further customize.

[0151] In any instance, the garment design module 1 ION may apply the 3D layout to a virtual model to simulate application of the artwork on a particular material. The garment design module 1 ION may also include a feedback loop mechanism that collects feedback from a user regarding the automatically generated 2D designs, and use this feedback to continuously train and refine the generative Al model, thus enhancing the design capabilities of the Al model.

[0152] Further, in the embodiment shown, a preview of the 2D image or design, or in some instances, 3D image or design, can be generated by garment design module, such as 11 ON, and / or Al module, such as 116N, and / or as it would appear on a virtual garment, allowing users to visualize and adjust the final garment with the 2D image or design before proceeding to subsequent operations, such as pattern making and / or production. In certain embodiments, a garment design module, such as 110N, and / or Al module, such as 116N, can include a design tool, wherein once the sides are adjoined, a user 108 has the option to adjust areas directly in the 3D image or design. In certain embodiments, a garment design module, such as 11 ON, and / or Al module, such as 116N, can include a collaborative design tool that can permitmultiple users to simultaneously work on the same 2D image or design, or in some instances, 3D image or design, with changes made by any user being visible in real-time to all other users.

[0153] Further, in the embodiment shown, one or more external design libraries or data storage devices can be accessed by a user, such as 108, interacting with a garment design module, such as 11 ON, and / or an Al module, such as 116N, allowing a user to import pre-existing or professionally designed templates or elements directly into their 2D images or designs, or in some instances, 3D images or designs.

[0154] In some embodiments, initially, a user interface associated with the client device 102N may be configured to allow a user to import 2D information, such as an existing 2D image or design, or to generate 2D information, such as a 2D image or design, directly within the user interface. A design editor application program may be incorporated within the user interface, or executed by a client device, such as 102N, to provide tools for drawing, texturing, and color selection to facilitate direct creation and modification of 2D images or designs. The storage and retrieval of both imported and created 2D images or designs in a remote digital library or other data storage device may be accessible via the user interface. The user interface may also include functionality for a user to drag and drop design elements onto the 2D image or design, supporting intuitive layout and composition adjustments. A feature extraction tool may be included with the user interface and configured to automatically analyze imported 2D images or designs to identify certain design elements such as lines, shapes, and color zones. A suggestion engine may also be included with the user interface and configured to propose design modifications based on the analysis, aimed at enhancing aesthetic appeal or manufacturability. In other embodiments, the design editor application program may include an auto-correction tool that automatically adjusts design elements to meet predefined design standards or production requirements, such as symmetry, pattern continuity, and color contrast. Further, a compatibility checking tool can be provided by the user interface to assess the suitability of the 2D images or designs for various fabric types and to provide feedback to a user through the user interface, and / or an optimization tool within the design editor application program can be provided that suggests adjustments to the 2D images or designs to optimize them for specific fabric characteristics, such as stretchability, opacity, and texture.

[0155] In certain embodiments, a user can employ a conventional software solution, such as Digital Fashion Pro, Adobe Illustrator / Photoshop, Corel Draw, Browzwear, OptiTex, CAD, or Seamly, to design a 2D digital image. The 2D digital image can be uploaded to a garment design module, such as 110N, for further processing. In other embodiments, a user can createa 2D digital image directly within a garment design module, such as 110N. Upon generation or upload of a 2D digital image, a generative Al model, such as ChatGPTv.4, Photoshop Beta, Midjourney, or Draw Things, associated with a garment design module, such as 1 ION, and / or Al module, such as 116N, may process the 2D digital image to generate a 3D layout to construct a garment. The generative Al model may use one or more advanced algorithms to transform the 2D image into a 3D layout to construct a garment, such as a blueprint. In this example, a 3D layout to construct a garment can include, but is not limited to, selection of a particular material or textile application, or selection of a particular artwork application technique to apply artwork to a material or textile. The generative Al model may evaluate various combinations of materials, and artwork application techniques to determine a suitable 3D layout to construct a garment. For example, in one embodiment, a 3D layout can include selection or determination of a particular artwork application technique associated with the generation or upload of a 2D digital image. The selection of determination of a particular artwork application technique may be influenced by the transformation of the artwork from a 2D digital image to the artwork for a 3D layout. An artwork application technique may include, but are not limited to, embroidery, print, embellishment, lace, seamless knit, or knit / woven jacquards. In some embodiments, the garment design module, such as 1 ION, and / or Al module, such as 116N, may be configured to evaluate one or more artwork application techniques at least based on a selection by a user, such as 108. Once a suitable artwork application technique is selected or determined for a 3D layout, the garment design module 11 ON may apply the artwork onto a selected material or textile. The selected material or textile may be associated with the evaluation of the 2D digital image. The selected material may be associated with the artwork for a 3D layout associated with the transformation of the 2D digital image. The garment design module, such as 11 ON, and / or Al module, such as 116N, may provide instructions for applying the artwork onto the selected material, such as a textile. The garment design module, such as 11 ON, and / or Al module, such as 116N, may provide one or more instructions to construct a garment as a pattern. Applying the artwork for a 3D layout to the selected material, such as a textile, may be fully automated and optimized for quality and efficiency based on the configuration of the garment design module, such as 1 ION. In some embodiments, the garment design module, such as 1 ION, and / or Al module, such as 116N, can integrate the artwork into an existing pattern layout for a 3D layout to construct a garment. This feature allows for seamless incorporation of the artwork into, for example, pre-designed textile products.

[0156] Accordingly, in certain embodiments, a garment design module, such as 110N, and / or Al module, such as 116N, may be particularly beneficial for industries that have intricate artwork designs and applications on textiles or certain materials. Various embodiments of the present disclosure may benefit any number of industries including fashion, athletics, footwear, and medical device design and implementation. As a non-limiting example, a user such as a fashion designer can utilize the garment design module, such as 1 ION, and / or Al module, such as 116N, to transition from a 2D digital image to artwork for a 3D layout, and determining a suitable artwork application technique for a garment. In another non-limiting example, the garment design module, such as 11 ON, and / or Al module, such as 116N, can be adapted to design custom artwork for medical devices, such as prosthetic limb covers.

[0157] In certain embodiments, the garment design module 110N may display, via a user interface associated with client device 104N, a simulated 3D engineered output for review and modification by a user, such as 108. The garment design module 110N may include an enhancement tool configured to refine the automatically generated 2D designs by adjusting elements such as line sharpness, color saturation, and element proportions to better suit production requirements. The garment design module 11 ON may also include a feature that allows a user, such as 108, to manually tweak or modify the automatically generated 2D designs using the design editor, combining the efficiency of automated design generation with the flexibility of manual adjustments. The garment design module 1 ION may also include a feature that allows a user, such as 108, to adjust selected areas directly in the 3D image or design. The garment design module 110N may finalize the simulated 3D engineered output based on user input received through the user interface. The garment design module 110N may be configured to automatically generate detailed design documentation, including unique identifiers, material specifications and production instructions, based on the finalized 2D designs. The garment design module 110N may also be configured to automatically document the design process for the automatically generated 2D designs, including the original text input, design iterations, and final design choices, facilitating traceability and design rationale explanation. The garment design module 110N may also be configured to recommend sources and / or application methods.

[0158] In any instance, a garment design module, such as 110N, and / or an Al module, such as 116N, can generate multiple versions of a 3D layout to construct a garment. In each version, a user, such as 108, can update respective text descriptors as needed and / or select a specific area or portion of a 3D layout to adjust a respective design for a particular version.

[0159] In certain instances, for anatomical solutions, a garment design module, such as 1 ION, and / or an Al module, such as 116N, can receive information such as body composition and / or anatomical / physiological data to generate a 3D layout for constructing a garment. The garment design module, such as 1 ION, and / or an Al module, such as 116N, can, based at least in part on average human anatomical measurements and data input by the user, generate a recommendation. In any instance, after reviewing and editing one or more 3D layouts for constructing a garment, a user, such as 108, can store or otherwise save a desired 3D layout for constructing garment, in a data storage device associated with a client device, such as 102N, and / or a server device, such as 104.

[0160] In another aspect of an embodiment, a garment design module can transform a 2D image into a 3D engineered artwork using one or more geometric and. / or 3D modeling algorithms and a generative Al model associated with an Al module, such as 116N. In this example, a garment design module may receive, via a user interface, a 2D image. The user interface may be configured to allow a user, such as 108, to import an existing 2D image or design, or to create a 2D image or design directly within a garment design module, such as 110N. The garment design module, such as 110N, may process the received 2D image through a generative Al model associated with an Al module, such as 116N, to extrapolate depth and texture information. The generative Al model may utilize open source as well as machine learning techniques including convolutional neural networks (CNNs) to analyze and interpret the 2D image. The generative Al model may also be trained on a dataset including diverse 2D images across various industries to enhance its accuracy and versatility in generating a 3D model for an associated 3D engineered artwork. The processing may include the use of depth sensing algorithms to accurately interpret the z-dimension from the 2D image. The garment design module, such as 11 ON, may generate a 3D model of an associated 3D engineered artwork based on the extrapolated information. The garment design module, such as 11 ON, may be configured to apply material properties to the 3D model based on predefined material databases to simulate realistic physical characteristics. The garment design module, such as 110N, may output the 3D model for further processing or display to a user, such as 108. The 3D model may be further refined by user inputs allowing modification of texture, color, and other aesthetic properties through the user interface. The garment design module, such as 110N, may integrate the 3D model into a virtual environment for virtual fitting or visualization purposes. The generative Al model may be configured to automatically suggest modifications to the 3D model and may be optimized for manufacturability based on industry standards. The3D model may be stored in a digital library accessible to a user, such as 108, for future modifications or reuse in different projects. The garment design module, such as 11 ON, may generate a simulation report that evaluates the performance of the 3D model under various simulated conditions. The garment design module, such as 11 ON, may apply open source and machine learning algorithms to refine the edges and contours of the 3D model based on comparative analysis with a database of similar artworks. The generative Al model may be configured to analyze textural details and depth cues within the 2D image to enhance the realism of the 3D model and / or 3D engineered artwork and incorporate user feedback to iteratively adjust the 3D model and / or 3D engineered artwork until it meets predefined aesthetic or functional criteria. The garment design module, such as 11 ON, may use a neural network trained specifically on a dataset comprising various artistic styles to ensure that the 3D model and / or 3D engineered artwork adheres to a desired artistic style. The generative Al model may be further configured to simulate different lighting conditions on the 3D model and / or 3D engineered artwork to predict its appearance under various environmental settings, and / or to utilize advanced pattern recognition to automatically identify and replicate complex patterns and motifs from the 2D image into the 3D model and / or 3D engineered artwork. The garment design module, such as 11 ON, may generate a materials list required to physically create the 3D engineered artwork based on the properties and dimensions of the associated 3D model. To this end, the generative Al model may be adapted to work with specific types of materials, including but not limited to textiles, leather, metals, and ceramics, to optimize the 3D model and / or 3D engineered artwork for material-specific properties. The garment design module, such as 1 ION, may have the capability to export the 3D model and / or 3D engineered artwork in various file formats compatible with 3D printing, CNC machining, or other manufacturing technologies. In summary, the garment design module, such as 11 ON, can generate a garment as well as associated components and artwork layouts, for example, seamless knitting, fabric, print, crochet, macrame, embroidery, bead, trim, and / or sweaters. A garment may include any article that can be worn by a person or an animal. The garment design module, such as 1 ION, may also be utilized for any design on an article, such as furniture, automotive components, etc. In further aspects, the garment design module, such as 11 ON, can determine a suitable layout, thread size, stitch construction, such as, for embroidery and beading; create a repeating or engineered artwork pattern; and / or determine if treatment is to be applied by hand or needs specific machinery. In still further aspects, a generated 3D model and / or 3D engineered artwork may be based at least in part on a range of motion and / or activity, such as, for example, running,a medical benefit, yoga, travel bag, ergonomics, etc. In even further aspects, for 3D artwork, after initial design is created, a garment design module, such as 1 ION, can operate in reverse. For example, an alternative application can be selected, and the garment design module, such as 1 ION, can create a new photorealistic image of a new application along with an associated 3D layout including instructions.

[0161] Operation 206 can be followed by operation 208, generating or receiving at least one 3D digital fit model (mannequin), wherein the generating a 3D layout of the garment is further based at least in part on the at least one 3D digital fit model (mannequin). In this embodiment, a 3D digital fit (mannequin) is defined herein as digital representation of a human body. For example, in this embodiment, a garment design module, such as 1 ION, can provide a user, such as 108, at least one option to generate at least one 3D digital fit model (mannequin). Alternatively, in this embodiment, a garment design module, such as 1 ION, can provide a user, such as 108, an option to input data associated with at least one 3D digital fit model (mannequin). For example, in one embodiment, a garment design module, such as 110N can provide a user interface for a user, such as 108, to upload a digital image or file, or otherwise input a predefined set of data associated with a 3D digital fit model (mannequin) to the garment design module, such as 110N. For instance, a predefined set of data can include some or all of the following: a gender; a set of data associated with a full body or a part of a body; dimensions of a body or part of a body; a model type selected from photorealistic, invisible form, or plain mannequin; and a selected color for the mannequin. In certain instances, dimensions of a body or part of a body can be manually input by a user via the user interface; can be generated by a garment design module, such as 110N; or can be derived from or otherwise discerned from a body scan, image, or an augmented reality application or technology. In certain instances, one or more text descriptors can be input by a user, and the garment design module, such as 110N, using a text-to-image technology, can, based on some or all of the text descriptors, generate a photorealistic model type for selection by the user. In other instances, when presented with one or more versions, a user such as 108, can select and edit certain some or all of a 3D digital fit model (mannequin), by either inputting one or more text descriptors or by manually inputting other data to modify at least one version of the 3D digital fit model (mannequin).

[0162] In any instance, in this embodiment, a garment design module, such as 110N, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate a 3D layout of the garment further based at least in part on the at least one 3D digital fit model (mannequin). In this example, the associated 2D-to-3D design converter 114A and / orassociated Al module 116N can process the at least one 3D digital fit model (mannequin), and utilize some or all of the data associated with the at least one 3D digital fit model (mannequin) to generate a 3D layout of the garment. For example, data associated with a 3D digital fit model (mannequin) can include data associated with a full body or a part of a body, and dimensions of a body or part of a body.

[0163] In one aspect of an embodiment, a garment design module, such as 1 ION, may generate or receive a digital fit model (mannequin) based on manually entered input data and / or imported 3D body scans. In further aspects, creating a digital fit model (mannequin) can include receiving, via a user interface, input data including at least one of gender, age, height, weight, and garment or shoe sizes; and generating a digital fit model (mannequin) based on the received input data. The digital fit model (mannequin) may be stored in a digital library with a unique identifier. The digital fit model (mannequin) may be displayed on a user interface for further garment design processes. The digital fit model (mannequin) may be 3D and / or generated using a generative Al model associated with an Al module, such as 116N. In some aspects, the digital fit model (mannequin) may be generated based on one or more of the following: body parameters, garment purpose (i.e., if for an anatomical solution enter activity / description and define body type), gender, age, height, weight, garment and / or shoe sizes, manually entered dimensions, or a generated to derived average size in a data set. In further aspects, the digital fit model (mannequin) may be assigned a name and / or description and unique identifier for each created or modified digital fit model (mannequin), facilitating easy tracking and retrieval from the digital library. Further, the garment design module, such as 1 ION, can facilitate adjusting the dimensions of the digital fit model (mannequin) based on additional user inputs or average size data provided by the garment design module. The user may select to proceed with applying the 2D design on a digital fit model (mannequin) and / or create a photorealistic fit model (mannequin) using one or more 2D designs. In some aspects, the garment design module, such as 11 ON, may apply a 2D design on a selected digital fit model (mannequin). In further aspects, the garment design module, such as 1 ION, may include one or more of selecting, via a user interface, a previously created fit mannequin from a digital library; receiving, via the user interface, a 2D garment design; applying the 2D garment design onto the selected digital fit model (mannequin); generating multiple design versions of the garment with refresh and / or edit functions; and / or enabling a user to select or modify the design versions through the user interface. For example, the garment design module, such as 110N, may automatically design based on mobility (i.e., arms raised, long stride walk, bend over,etc.). The user, such as 108, may select to start with a front or back of a garment. The user, such as 108, may select a single garment or matching set (i.e., a suit). The user, such as 108, may select garment type, color info, description, influence for text to design creation. The user, such as 108, can choose to focus on a portion of the body (e.g., shoes). The user, such as 108, may import, add material, add component types, and add specifications. The garment design module, such as 110N, may generate multiple garment design versions with refresh and / or edit functions. The user, such as 108, may select one or design selections or modify one or more design selections. To apply artwork, an image may be imported, or descriptive text entered to generate various versions with refresh and / or edit functions. The user, such as 108, may select and / or save artwork and garment design to proceed to 2D photorealistic fit model (mannequin) creation. In further aspects, the artwork and garment designs may be assigned a name and / or description and unique identifier, facilitating easy tracking and retrieval from the digital library. In other aspects, the garment design module, such as 110N, may apply or use a 2D design on a photorealistic fit model (mannequin). In further aspects, the garment design module, such as 110N, may facilitate one or more of: receiving, via a user interface, a selection of a photorealistic fit model (mannequin) from a digital library; applying, by a processor, a 2D design onto the selected photorealistic fit model (mannequin); generating, by the processor, multiple photorealistic fit model (mannequin) versions with the applied 2D design; providing refresh and / or edit functions to modify the design on the photorealistic fit model (mannequin); and / or enabling a user to save the selected design for further processing or production. The garment design module, such as 11 ON, may facilitate blending the garment creation with the selected photorealistic fit model (mannequin) to follow the model's body contours and create a new photorealistic fit model (mannequin) based on the adjusted design. The user, such as 108, may have the option to build a photorealistic fit model (mannequin) library before 2D designs are initiated. An image import of model or add text descriptors may be used to create selection (for example, generates 6 options with refresh and / or edit tool). The garment design module, such as 110N, may generate several photorealistic fit model (mannequin) versions with refresh and / or edit functions. The user, such as 108, may then select or modify model selection. A blend garment creation operation with selected digital fit model (mannequin) may be included, for example, an option to follow model body and create a new digital fit model (mannequin). A unique identifier along with description may be assigned and a photorealistic fit model (mannequin) is created. If using an imported 2D image with a model, the garment design module, such as 11 ON, may generate multiple versions for selection, and mix garment stylesfrom a digital library on a digital fit model (mannequin) to create outfits and / or looks. In further aspects, created designs may be used in 3D garment creation. Thus, in various aspects, the garment design module, such as 11 ON, can create 2D design utilizing the user interface or import an image (i.e., ability to create an image in separate software and import the image). In further aspects, a generative Al model associated with an Al module, such as 116N, can create the image design, keyword descriptors can be used to build multiple design versions, while revisions also can provide multiple modified versions. In further aspects, the garment design module, such as 1 ION, can also build historical data libraries to build future products (i.e., 2D design, a digital fit model (mannequin), photorealistic fit model (mannequin), 2D artwork, 3D artwork, patterns, materials, styles, etc.).

[0164] In some embodiments, beginning with an upload of a 2D design to a garment design module, such as 1 ION, a user importing or linking a 2D design of previously created artwork as well as other files that contain artwork data, can utilize the garment design module 1 ION to create a design for another side of garment, and generate multiple 3D versions of the artwork for the garment. A garment design module, such as 1 ION, may support a range of file formats to accommodate designs created in any number of conventional software applications commonly used for digital design and digital images. This import functionality can serve as the foundation for the subsequent artwork manipulation and enhancement operations. In one example, a garment design module, such as 1 ION, can provide multiple generated 3D versions of the artwork for selection. The garment design module, such as 1 ION, may be configured to alter the generation process based on feedback to provide additional 3D versions of the artwork. The 3D artwork can be manipulated and modified to match the specifications associated with at least one of: user requirements, material requirements, or design requirements. In one embodiment, a garment design module, such as 1 ION, and / or an Al module, such as 116N, can independently or collectively generate an array of 3D versions based at least in part on the 2D artwork in response to the imported data. This generation process may utilize any number of machine learning algorithms trained to convert 2D designs into 3D models, taking into account various factors such as depth perception, shadowing, and texture. A garment design module, such as 1 ION, and / or an Al module, such as 116N, may continuously update these algorithms to improve the quality and accuracy of the 3D models generated by the garment design module, such as 1 ION, and / or Al module, such as 116N.

[0165] In any instance, after the 3D versions are generated, a garment design module, such as 11 ON, and / or an Al module, such as 116N, may enable the selection of a design forimplementation. In some embodiments, the selection may be made by a user. A garment design module, such as 11 ON, and / or an Al module, such as 116N, may present the generated 3D models to a user through a user interface. The user selection can be utilized as a blueprint for subsequent operations in an engineered artwork creation process. In certain embodiments, a garment design module, such as 11 ON, and / or an Al module, such as 116N, may also save multiple versions of the design. In some embodiments, the multiple versions of the design may allow for easy iteration and modification of the 3D model as the process progresses.

[0166] In any instance, a user interface may enable real-time adjustments and modifications by a user to the generated 3D model. In some embodiments, a user can interact with the 3D models to adjust various parameters, such as depth, angle, and lighting to achieve the desired design. A garment design module, such as 1 ION, and / or Al module, such as 116N, may also offer template-based adjustments. The template-based adjustments may allow a user to quickly apply pre-defined sets of modifications to the generated 3D models.

[0167] In some embodiments, a garment design module, such as 1 ION, and / or Al module, such as 116N, may be configured to select a 3D design based on a parameter associated with the 2D image or other requirements.

[0168] In some embodiments, a user can enter standard body specifications to ensure the artwork is appropriately sized and shaped for the intended application. The garment design module, such as 11 ON, may be configured to alter the 3D design based on the standard body specifications. The garment design module, such as 1 ION, may enable the selection of the 3D design to be modified based on provided standard body specifications. The garment design module, such as 1 ION, may associate the selected artwork with a material. The garment design module, such as 1 ION, may link the selected artwork to a material. The material's specifications can influence the final artwork layout. The garment design module, such as 1 ION, may update the 3D design based on the material. The final artwork layout may be associated with at least one of the selections of the 3D artwork of the user and the material associated with the 3D artwork. In some embodiments, the garment design module, such as 11 ON, may confirm the material selection.

[0169] As a non-limiting example, a user can approve the 3D artwork with the applied body and fabric specifications. The garment design module, such as 1 ION, may utilize the finalized artwork layout to determine the application method of the artwork to the material. In some embodiments, the artwork may be applied to the material surface. The artwork may be appliedto the surface of the material. The garment design module, such as 110N, may enable the selection of an application method for the artwork.

[0170] As a non-limiting example, a garment design module, such as 1 ION, may allow for the section of the artwork using a method such as embroidery or screen printing. In some embodiments, the conversion of the 2D design to the 3D engineered or repeated artwork may be automated once the alternative side of the garment is designed. In certain embodiments, a garment design module, such as 1 ION, can provide one or more versions that allow the front and back to be adjoined in a 3D garment (for all aspects such as garment construction, design aesthetic, fit, artwork, and knit / woven construction).

[0171] As a non-limiting example, a garment design module, such as 1 ION, may be configured to receive additional data related to standard body specifications. The data relating to the body specification could include measurements, ratios, or other geometric parameters that help in tailoring the artwork layout to specific applications. In some embodiments, a garment design module, such as 11 ON, may utilize algorithms to map these body specifications onto the 3D artwork, thereby generating an optimized layout. In some embodiments, a garment design module, such as 11 ON, may enable the modification of the layout of the 3D artwork on the body specification. A garment design module, such as 11 ON, may provide an option to link the selected 3D artwork with a material choice. In some embodiments, a garment design module, such as 1 ION, may host a database of material specifications, including but not limited to, texture, tensile strength, and color retention properties. A garment design module, such as 11 ON, may receive a link corresponding to a selected material for the artwork. A garment design module, such as 11 ON, may adjust the 3D layout as needed based on the selected material based on characteristics of the chosen material. In response to linking the material and fine-tuning the artwork layout, a garment design module, such as 1 ION, may receive approval for the 3D artwork with all relevant specifications applied. Approval for the 3D artwork may be captured through a variety of user interface elements, such as buttons, checkboxes, or digital signatures.

[0172] Accordingly, a garment design module, such as 11 ON, may be configured with body specifications and material selections prior to receiving the 2D design. The garment design module, such as 110N, may be configured with pre-set conditions such that the 3D artwork can be automatically provided once the 2D design has been provided. Accordingly, A garment design module, such as 11 ON, may automatically convert the approved 2D design into its 3D engineered artwork versions. A garment design module, such as 11 ON, may enable themodification of the 3D artwork. In some embodiments, a garment design module, such as 1 ION, and / or an Al module, such as 116N, may enable users to apply various prompts to further modify the 3D artwork. A garment design module, such as 11 ON, and / or an Al module, such as 116N, may provide an updated version of the 3D artwork in response to the prompts for further modifications to the 3D artwork. In some embodiments, the prompts may be associated with the modification of the 3D artwork. The prompt may be associated with the application of the 3D artwork to the material. The prompt may be associated with the material that can have the 3D artwork applied to it. The garment design module, such as 11 ON, and / or an Al module, such as 116N, may process the received prompts and provide an updated version of the design. In some embodiments, the garment design module, such as 11 ON, and / or an Al module, such as 116N, can intuitively suggest application methods based on the material and design specifications. The garment design module, such as 110N, and / or an Al module, such as 116N, may enable expert review. The garment design module, such as 1 ION, and / or an Al module, such as 116N, may provide an expert that is available for consultation to review the design and provide feedback. The feedback from the expert may be processed by the garment design module, such as 11 ON, and / or an Al module, such as 116N, and implemented into the engineered artwork creation. Once the artwork creation has been finalized, the garment design module, such as 1 ION, and / or an Al module, such as 116N, may proceed to application of the artwork. In some embodiments, once the design of the artwork gains approval, the artwork can be applied to a chosen material using a selected application. The garment design module, such as 110N, and / or an Al module, such as 116N, can also allow running versions and modifications of the artwork application.

[0173] In another example, a 3D layout can include instructions for artwork application on various materials. In this embodiment, selected artwork may be prepared for application to a particular material. The artwork may be resized based on the material for application. In some embodiments, the artwork can be resized and cropped to fit the specific dimensions of the garment. In some embodiments, a garment design module, such as 11 ON, may perform a file conversion before applying the artwork to the material. In some embodiments, the artwork can be converted into the appropriate file format for a garment design module, such as 11 ON, to apply the artwork to the material. In some embodiments, a garment design module, such as 110N, may convert the file into a common file format such as JPEG, PNG, and / or PDF. In some embodiments, vector-based formats like EPS or SVG can be converted to raster-based formats.

[0174] In some embodiments, a garment design module, such as 11 ON, may be configured to receive a selection of an application method. In some embodiments, a garment design module, such as 11 ON, may recommend an application method. A garment design module, such as 1 ION, may determine an optimal application method based on the artwork type, material, and desired visual impact. As a non-limiting example, a garment design module, such as 110N, may offer various methods for applying the artwork, including: embroidery, print, screen printing, and digital printing, seamless knit, knit / woven, jacquards, and other garment construction methods. A garment design module, such as 1 ION, may apply the artwork to the material based on the selected method. Accordingly, the artwork application may vary based on the selected method. A garment design module, such as 1 ION, may proceed to constructing the garment with the applied artwork.

[0175] Consistent with one or more embodiments, following the approval of the 3D artwork, garment design module, such as 1 ION, can transition to applying the 3D artwork to a garment. In some embodiments, a garment design module, such as 1 ION, may receive a selection for an application method for transferring the 3D artwork onto the selected material. A garment design module, such as 11 ON, may provide one or more options for applying the 3D artwork to the selected material such as embroidery, print, screen printing, seamless knit, knit / woven jacquards, or other advanced techniques such as laser etching and the like. The application method may be influenced by various factors, including the type of material chosen, the complexity of the artwork, and the intended use of the final product.

[0176] Upon receiving the selection for the application method, a garment design module, such as 11 ON, may proceed to convert the approved 3D artwork back into a series of 2D designs suitable for the chosen application method. A garment design module, such as 11 ON, may convert the artwork into the design using algorithms that map 3D models into 2D planes while preserving the visual integrity and dimensions of the original design. A garment design module, such as 11 ON, may tailor the specific requirements of different application methods to ensure high fidelity in the final product. A garment design module, such as 11 ON, may enable the modification of the 2D version of the engineered artwork. In some embodiments, a garment design module, such as 1 ION, may present the user with a user interface, such as a dashboard, where he or she can apply various prompts to modify the 2D versions of the engineered artwork. A garment design module, such as 1 ION, may receive the modification as a prompt. The prompts may include adjustments for scale, rotation, or color variations. In some embodiments, a garment design module, such as 110N, may also offer preset templates forcommon modifications, expediting the user's design process. A garment design module, such as 1 ION, may interact with an Al module, such as 116N, to intuitively recommend application methods for the artwork. A garment design module, such as 1 ION, and / or Al module, such as 116N, may recommend application methods based on, but not limited to, the chosen material's specifications, the intricacies of the artwork design, and data from previous successful applications. A garment design module, such as 1 ION, and / or Al module, such as 116N, may then receive a final approval for the 2D designs. Subsequently, the approved designs are processed for application to the chosen material by a garment design module, such as 11 ON, using the selected method.

[0177] In accordance with one or more embodiments, upon completion of the artwork application, a garment design module, such as 11 ON, may proceed with execution of the engineered artwork based on the selection of the application method. As a non-limiting example, if the artwork is applied through embroidery, a garment design module, such as 1 ION, may generate a comprehensive set of instructions detailing the appropriate placement for the design. Instructions provided by a garment design module, such as 11 ON, and / or Al module, such as 116N, may include coordinates, stitching patterns, and thread tension settings, among other parameters for execution of the engineered artwork. In some embodiments, a garment design module, such as 1 ION, and / or Al module, such as 116N, may utilize machine-readable codes, such as QR codes, to streamline the set-up process for embroidery machines executing the engineered artwork. In embodiments of the present disclosure where printing is the selected application method, garment design module, such as 11 ON, and / or Al module, such as 116N, can create a screen for the design. This could involve a garment design module, such as 1 ION, and / or Al module, such as 116N, providing specifications for print application machinery, color screen sharing, and recommending specific inks that are best suited for the chosen material, thereby ensuring durability. Once the engineered artwork has been successfully applied, a garment design module, such as 1 ION, and / or Al module, such as 116N, may execute a checklist for finishing the final garment. The checklist may depend on the nature of the final garment. In some embodiments, a garment design module, such as 11 ON, and / or Al module, such as 116N, may integrate with external databases to pull in industry-standard finishing procedures. Upon the completion of all finishing operations, a garment design module, such as 11 ON, and / or Al module, such as 116N, may prompt the user to confirm that the product is finalized and ready for use or sale.

[0178] In one aspect of the embodiment, a 3D digital fit model (mannequin) can be based on manually entered input data and / or imported 3D body scans. For example, generating a 3D digital fit model (mannequin) can include receiving, via a user interface, input data including at least one of gender, age, height, weight, and garment or shoe sizes; and generating the 3D digital fit model (mannequin) based on the received input data. A 3D digital fit model (mannequin) may be stored in a digital library with a unique identifier. A 3D digital fit model (mannequin) may be displayed on a user interface for subsequent garment design iterations and / or instances. A 3D digital fit model (mannequin) may be 3D and / or generated using a generative Al model. A 3D digital fit model (mannequin) may be generated based on one or more of the following body parameters: garment purpose (i.e., if for an anatomical solution enter activity and / or description and define body type), gender, age, height, weight, garment and / or shoe sizes, manually entered dimensions (or platform can create from average size and / or dimensions). A 3D digital fit model (mannequin) may be assigned a name and / or description and unique identifier for each created or modified digital fit model (mannequin), facilitating relatively easy tracking and retrieval from the digital library.

[0179] In one aspect of the embodiment, a garment design module can adjust the dimensions of the 3D digital fit model (mannequin) based on additional user inputs or average size data provided by the system. The user may select to proceed with applying the 2D image or design on a 3D digital fit model (mannequin) and / or create a photorealistic fit model using one or more 2D images or designs.

[0180] In one aspect of the embodiment, a garment design module can apply a 2D image or design on a selected 3D digital fit model (mannequin). The garment design module can also facilitate one or more of: selecting, via a user interface, a previously created fit mannequin from a digital library; receiving, via the user interface, a 2D garment design; applying the 2D garment design onto the selected 3D digital fit model (mannequin); generating multiple design versions of the garment with refresh and edit functions; and / or enabling a user to select or modify the design versions through the user interface. For example, the garment design module may automatically design based on mobility (i.e., arms raised, long stride walk, bend over, etc.). The user may select to start with the front or back of a garment. The user may select a single garment or matching set (i.e., a suit). The user may select garment type, color info, description, influence for text to design creation. The user can choose to focus on a portion of the body (e.g., shoes). The user may import, add material, component types and specifications. The garment design module may generate multiple garment design versions with refresh and / oredit functions. The user may select one or design selections or modify one or more design selections. To apply artwork, an image may be imported or descriptive text entered to generate various versions with refresh and / or edit functions. The user may select and / or save artwork and garment design to proceed to generate a 2D photorealistic fit model (mannequin). In some instances, the artwork and garment designs may be assigned a name and / or description and unique identifier, facilitating relatively easy tracking and retrieval from the digital library.

[0181] In other aspects, a garment design module may apply or use a 2D design on a photorealistic fit model (mannequin). The garment design module can also facilitate one or more of: receiving, via a user interface, a selection of a photorealistic fit model (mannequin) from a digital library; applying, by a processor, a 2D garment design onto the selected photorealistic fit model (mannequin); generating, by the garment design module, such as 1 ION, multiple photorealistic model versions with the applied 2D design; providing refresh and edit functions to modify the design on the photorealistic fit model (mannequin); and / or enabling a user to save the selected design for further processing or production.

[0182] In other aspects, a garment design module may further include blending the garment creation with the selected photorealistic fit model to follow the model's body contours and create a new fit mannequin based on the adjusted design. The user may have the option to build a photorealistic fit model (mannequin) library before 2D designs are initiated. An image import of model or add text descriptors may be used to create selection (generates, for instance, 6 options with refresh and / or edit tool). The garment design module may generate several photorealistic model versions with refresh and / or edit functions. The user may select or modify the model selection. A blend garment creation operation with selected fit model (mannequin) may be included (i.e., option to follow model body and create new fit mannequin). A unique identifier along with description may be assigned and a photorealistic fit model (mannequin) is created. If an imported 2D image is used with a selected fit model (mannequin), the garment design module may generate versions for selection, and mix garment styles from library on fit model to create outfits and / or looks. In further aspects, created designs may be used in 3D garment creation.

[0183] In one aspect of the embodiment, the generating at least one 3D digital fit model (mannequin) can be based at least in part on at least one of: a body scan, a user input, selection from a data source (library), selection of a size by an Al module, or selection of a size based on a brand. For example, in one embodiment, a garment design module, such as 1 ION, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate a3D digital fit model (mannequin) based at least in part on at least one of: a body scan, a user input, selection from a data source (library), selection of a size by an Al module, or selection of a size based on a brand. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can process a body scan, a user input, selection from a data source (library), selection of a size by an Al module, or selection of a size based on a brand, to generate a 3D digital fit model (mannequin).

[0184] In one aspect of the embodiment, a user can input a text description to generate at least one 3D digital fit model (mannequin). For example, a user, such as 108, can input text description associated with one or more of gender, height and / or weight, dimensions, physical description (if photorealistic fit model), an option to add a 3D scan, an option to select a 3D digital fit model (mannequin) from a digital library or other data storage device, an option to use a model from a 2D digital design. In any instance, a garment design module, such as 1 ION, and / or an Al module, such as 116N, can utilize the text description to generate at least one 3D digital fit model (mannequin).

[0185] Operation 208 can be followed by operation 210, outputting, via at one user interface, the 3D layout for user review. For example, in one embodiment, a garment design module, such as 110N, can output, via a user interface, the 3D layout for user review. In this example, a garment design module, such as 11 ON, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate an output to a client device, such as 102N, for the user 108 to view and / or interact with the 3D layout. The 3D layout can, for example, include a garment fabrication (what material is to create) instructions, garment pattern (cutout) flat layout, artwork layout, or garment construction (sew Al to B2, etc.) instructions.

[0186] Operation 210 can be followed by operation 212, generating or receiving, based at least in part on the 3D layout of the garment, construction information for the garment, wherein the construction information can include at least one of instructions to adjoin together one or more components of the garment, a material selection for the garment, or artwork application information. For example, in one embodiment, a garment design module, such as 110N, can generate or receive, based at least in part on the 3D layout of the garment, construction information for the garment. In this example, a garment design module, such as 110N, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate or receive, based at least in part on the 3D layout of the garment, construction information for the garment. The construction information can, for example, include instructions to adjoin together one or more components of the garment, a material selection for the garment, orartwork application information. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can process the 3D layout of the garment, to generate construction information for the garment.

[0187] Operation 212 can be followed by operation 214, generating, based at least in part on a 3D artwork design, 3D artwork application information to apply the 3D artwork design to the garment. For example, in one embodiment, a garment design module, such as 110N, can generate, based at least in part on a 3D artwork design, 3D artwork application information to apply the 3D artwork design to the garment. In this example, a garment design module, such as 1 ION, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate, based at least in part on a 3D artwork design, 3D artwork application information to apply the 3D artwork design to the garment. The 3D artwork application information can, for example, include instructions to apply the 3D artwork design to one or more components of the garment. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can process the 3D artwork design, and implement a generative Al algorithm that can include open source modeling and / or machine learning techniques to merge the 3D artwork design and the garment to generate 3D artwork application information to apply the 3D artwork design to the garment.

[0188] Operation 214 can be followed by operation 216, receiving, at least one user input associated with the 3D layout. For example, in one embodiment, a garment design module, such as 1 ION, can receive at least one user input associated with the 3D layout. In this example, a garment design module, such as 11 ON, can, via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, receive at least one user input from user 108 through interaction with a client device, such as 102N. The user input can, for example, be based at least in part on at least one of: a fashion trend, user feedback on the 3D layout, garment dimensional information, color, artwork application construction, material type, different size, or artwork on 3D model.

[0189] Operation 216 can be followed by operation 218, implementing, based at least in part on the at least one user input, a change to the 3D layout. For example, in one embodiment, a garment design module, such as 1 ION, can implement, based at least in part on the at least one user input, a change to the 3D layout. In this example, a garment design module, such as 1 ION, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, implement, based at least in part on the at least one user input, a change to the 3D layout. The change to the 3D layout can, for example, include a change in a dimension, a change in amaterial type, a change in a design, a change to a garment or to a component of a garment. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can process the at least one user input, and uses at least one geometric or 3D modeling algorithm that can include open source and / or machine learning technology to implement a change to the 3D layout.

[0190] FIG. 3 depicts an example flow diagram for a method 300 in accordance with one or more example embodiments of the disclosure. The method 300 can begin with operation 302, generating, based at least in part on the 2D information, repeating artwork information. For example, in one embodiment, a garment design module, such as 11 ON, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate, based at least in part on the 2D information, repeating artwork information. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can process the 2D information associated with one side of a garment, and implement a generative Al algorithm that can include open source modeling and / or machine learning techniques to generate repeating artwork information. In one example, one or more inputs from a user, such as 108N, in response to prompts from a garment design module, such as 110N, can facilitate generating, via a text-to-image technology, repeating artwork information. In any instance, the repeating artwork information can include one or more repeating artwork options. At least one of the repeating artwork options can be generated by eliminating certain areas and / or filling in areas to create a suitable repeating artwork option and / or repeating artwork option that includes linear or diagonal artwork.

[0191] Operation 302 can be followed by operation 304, generating, based at least in part on the repeating artwork information, alternate colorways information. For example, in one embodiment, a garment design module, such as 110N, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate, based at least in part on the repeating artwork information, alternate colorways information. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can process the repeating artwork information, and implement a generative Al algorithm that can include open source modeling and / or machine learning techniques to generate alternate colorways information. In one example, one or more inputs from a user, such as 108N, in response to prompts from a garment design module, such as 11 ON, can facilitate generating, via a text-to-image technology, alternate colorways information. Inputs from a user can include, but are not limited to, color balance, artwork for color, selected color standards for random replacement, orselected areas for a new color standard, Prompts can include, but are not limited to, identifying one or more areas for placement of a color, or requesting a color for a new standard for specific placement.

[0192] Operation 304 can be followed by operation 306, generating or receiving selected base material information. For example, in one embodiment, a garment design module, such as 110N, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate or receive selected base material information. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can generate selected base material information. In another example, the associated 2D-to-3D design converter 114A and / or associated Al module 116A can receive selected base material information from one or more historical data sources, which can include data associated with materials. In any instance, a garment design module, such as 1 ION, can utilize a generative Al algorithm that can include open source modeling and / or machine learning techniques to generate or receive selected base material information, based at least in part on one or more historical data sources.

[0193] Operation 304 can be followed by operation 306, generating or receiving, based at least in part on the repeating artwork information, alternate colorways information, selected base material information, and 3D layout of the garment, construction information for the garment, wherein the construction information includes at least one of: instructions to adjoin together one or more components of the garment, a material selection for the garment, or artwork application information. For example, in one embodiment, a garment design module, such as 110N, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate or receive, based at least in part on the repeating artwork information, alternate colorways information, selected base material information, and 3D layout of the garment, construction information for the garment, wherein the construction information includes at least one of: instructions to adjoin together one or more components of the garment, a material selection for the garment, or artwork application information. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can generate, based at least in part on the repeating artwork information, alternate colorways information, selected base material information, and 3D layout of the garment, construction information for the garment. In another example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can receive, based at least in part on the repeating artwork information, alternate colorways information, selected base material information, and 3D layout of the garment, construction information for the garment.

[0194] FIG. 4 depicts an example flow diagram for a method 400 in accordance with one or more example embodiments of the disclosure. The method 400 can begin with operation 402, receiving, based at least in part on a user input, custom solution information, wherein the custom solution information includes at least one of: customer-specific information, modelspecific information, fit model-specific information, body dimension information, body composition information, anatomical information, physiological information, demographic information, ergonomic information, patient-specific information, therapeutic information, diagnostic information, healthcare information, temperature control information, compression information, wound healing information, medical standard information, safety information, military specification information, athletic information, injury prevention information, compression information, or computing information. For example, in one embodiment, a garment design module, such as 110N, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, receive, based at least in part on a user input, custom solution information. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can process a user input, and implement a generative Al algorithm that can include open source modeling and / or machine learning techniques to generate or otherwise provide custom solution information. In one example, a garment design module, such as 110N, can access one or more data storage devices, such as an external database or data library including historical data.

[0195] Operation 402 can be followed by operation 404, generating, based at least in part on the 2D information associated with one side of a garment, the 2D information associated with one or more other sides of the garment, and custom solution information, a 3D layout of a garment. For example, in one embodiment, a garment design module, such as 110N, can via the associated 2D-to-3D design converter 114A and / or associated Al module 116N, generate, based at least in part on the 2D information associated with one side of a garment, the 2D information associated with one or more other sides of the garment, and custom solution information, a 3D layout of the garment. In this example, the associated 2D-to-3D design converter 114A and / or associated Al module 116N can process 2D information associated with one side of a garment, the 2D information associated with one or more other sides of the garment, and custom solution information, and use a generative Al algorithm including open source and / or machine learning techniques to generate a 3D layout of the garment. In any instance, a garment design module, such as 110N, can discern a garment architecture based at least in part on 2D information associated with one side of a garment, the 2D informationassociated with one or more other sides of the garment, and custom solution information, and generate a 3D layout of the garment.

[0196] In certain embodiments, a 3D layout can include instructions for a custom 3D engineered garment, such as footwear, gloves, accessories, or other products tailored to a particular industry, for instance, fashion, healthcare, military, and athletics. In this example, a garment design module, such as 11 ON, can receive a request for a desired solution for a garment. A garment design module, such as 11 ON, may process the desired solution and provide requisite performance details. As a non-limiting example, a garment design module, such as 11 ON, may determine if the requested solution and performance details may require conductive elements. A garment design module, such as 1 ION, may process inspiration artwork associated with the requested performance details. As a non-limiting example, a user can upload artwork inspiration in various formats including images and sketches. This inspiration can be analyzed by a garment design module, such as 1 ION, and craft a custom design for the garment relating to the provided artwork inspiration. In some embodiments, a garment design module, such as 11 ON, may process performance details associated with the solution. The performance details may be associated with a set of therapeutics or a diagnosis. The performance details may be associated with ergonomics, actuators, and performance of the 3D artwork associated with the solution. In some embodiments, the solution may include protection or safety features for the 3D artwork. The solution may include aesthetic modifications, computer systems, and communications systems. Accordingly, a garment design module, such as 11 ON, may utilize the inspiration artwork and the solution for the process. A garment design module, such as 1 ION, may proceed to receive data associated with the application of the artwork to a specific context. A garment design module, such as 11 ON, may receive data associated with at least one of: body dimensions, sex of the model for application, and fit of the artwork to the model application. As a non-limiting example, a garment design module, such as 1 ION, may prompt a user to enter body dimensions, age, and sex of a digital fit model (mannequin), which can aid in generating a 3D model of the body. A garment design module, such as 11 ON, may interact with an Al module, such as 116N, to determine anatomical and physiological arrangement based on the data associated with details of a digital fit model (mannequin). A garment design module, such as 11 ON, may utilize the inspiration artwork and the solution and performance details to determine the anatomical and physiological arrangement. In some embodiments, a garment design module, such as 11 ON, may receive desired therapeutics or diagnostics features. The therapeutic and diagnosticfeatures may include temperature control, compression, or wound healing. A garment design module, such as 11 ON, may be configured to receive a selection of an artwork application method. As a non-limiting example, a user may be prompted to select material based on the performance characteristics desired for a garment. A garment design module, such as 11 ON, may receive the selection and apply the application method to the generated solution. A garment design module, such as 1 ION, may utilize an Al module, such as 116N, to provide at least one of: a 3D digital fit model (mannequin) of the garment, artwork application guidelines, and technical material specifications associated with the 3D digital fit model (mannequin). A garment design module, such as 11 ON, may apply the selected artwork based on the chosen application method. As a non-limiting example, a garment design module, such as 1 ION, may integrate the artwork inspiration into the garment pattern and solution design. Once a garment design module, such as 11 ON, has generated the 3D digital fit model (mannequin), a garment design module, such as 1 ION, may output multiple design versions. The multiple outputs may be sorted by a garment design module, such as 110N. As a non -limiting example, a garment design module, such as 11 ON, may sort the generated model from highest to lowest benefits based on the requested solution. A garment design module, such as 11 ON, may enable a selection of the generated model for the garment pattern. In response to the selection of the generated model, a garment design module, such as 11 ON, may provide a detailed technical specification for material and artwork application for the selected model, along with guidance and instructions. In some embodiments, the approval process may utilize a 2D design. A garment design module, such as 11 ON, may provide multiple views of the 2D design for approval. Once the 2D design has been approved based on the plurality of 2D views, a garment design module, such as 1 ION, may utilize the plurality of 2D designs to generate the 3D artwork layout. As a non-limiting example, a garment design module, such as 11 ON, may enable the creation of custom garments that can be designed for celebrities or models, focusing on unique materials and artwork. Another non-limiting example may include utilizing the generated bodysuit for medical device designers such that the benefits can be tailored to provide therapeutic benefits such as compression or wound healing. Another non-limiting example can involve a garment design module, such as 1 ION, providing custom bodysuits for soldiers, such that the solutions can offer protection in extreme conditions or enhanced combat capabilities. Another non-limiting example may be generating garments for improving performance, injury prevention, or athletic applications.

[0197] FIGs. 5A-5F, 6A-6G, and 7A-7F illustrate example workflow diagrams for a user interface in accordance with one or more example embodiments of the present disclosure. One or more of the methods or processes shown can be implemented in or otherwise performed by, for example, the architecture 100 illustrated in FIG. 1. In certain instances, the methods or processes shown can be implemented in or otherwise performed by, for example, a system, apparatus, or machine, such as the machine 800 illustrated in FIG. 8, and / or by instructions stored in a computer-readable medium.

[0198] The series of FIGs. 5A-5F illustrate a garment image creation workflow 500 for a user interface in accordance with one or more example embodiments of the present disclosure. In this example, a library with at least one digital fit model (mannequin) can be utilized in the workflow.

[0199] In FIG. 5 A, a user interface 502 is illustrated to design and / or import a 2D image of a front or backside of a garment. In this example, a user can input text associated with a garment, or with an image or drawing to be imported.

[0200] In FIG. 5B, a user interface 504 is illustrated to design a 2D image of an alternative side of a garment. In this example, the user interface can generate one or more options that can be linked to a front design of the garment.

[0201] In FIG. 5C, a user interface 506 is illustrated to provide one or more recommendations including selecting a material type and artwork application. In some instances, the user interface can permit a user to identify an anatomical solution.

[0202] In FIG. 5D, a user interface 508 is illustrated to permit a user to create a digital fit model (mannequin) and / or a photorealistic fit model. In some instances, the user interface can permit a user to select a pre-existing fit model from a digital library or other data storage device.

[0203] In FIG. 5E, a user interface 510 is illustrated to adjoin one or more components associated with a garment to generate a pattern. In this example, the user interface can facilitate a review by a user of a front side of a garment, a backside of a garment, a material type, and an artwork application on a particular digital fit model (mannequin).

[0204] In FIG. 5F, a user interface 512 is illustrated to generate one more instructions and documents for a 3D layout to construct the garment.

[0205] In one aspect of the embodiment, a user interface can permit a user to select or otherwise input one or more parameters to create a digital fit model (mannequin) including, but not limited to,: gender, age, height, weight, manually added dimensions, average garment and / or shoe sizes, an average size, a body scan file, and selected areas of a garment for adjustment.

[0206] The series of FIGs. 6A-6G illustrate an anatomical solution garment design workflow 600 for a user interface in accordance with one or more example embodiments of the present disclosure.

[0207] In FIG. 6A, a user interface 602 is illustrated to generate an anatomical solution garment design.

[0208] In FIG. 6B, a user interface 604 is illustrated to input and / or upload 2D information such as a 2D image of a front of a garment and / or a backside of a garment.

[0209] In FIG. 6C, a user interface 606 is illustrated to generate a 2D image of an alternative or opposing side of a garment. In this example, the user interface can provide one or more options linked to design of a front of a garment that can achieve an anatomical solution.

[0210] In FIG. 6D, a user interface 608 is illustrated to generate one or more recommendations for a material and artwork application. In this example, the user interface can provide an option to select one or more alternatives and to view the alternatives with respect to the garment.

[0211] In FIG. 6E, a user interface 610 is illustrated to generate a digital fit model (mannequin) or photorealistic fit model. In this example, the user interface can provide an option for a user to select a pre-existing digital fit model (mannequin) or photorealistic fit model from a digital library or other data storage device.

[0212] In FIG. 6F, a user interface 612 is illustrated to adjoin one or more components associated with a garment to generate a pattern. In this example, the user interface can facilitate a review by a user of a front image, back image, material type, artwork application, and digital fit model (mannequin).

[0213] In FIG. 6G, a user interface 614 is illustrated to generate one more instructions and documents for a 3D layout to construct the garment.

[0214] The series of FIGs. 7A-7F illustrate a textile artwork creation - text to image / image modification generation workflow 700 for a user interface in accordance with one or more example embodiments of the present disclosure. In some instances, the user interface can permit a user to select an artwork from a library of garment styles including a garment flat pattern and a digital fit model (mannequin).

[0215] In FIG. 7A, a user interface 702 is illustrated to generate a 2D image, such as flat artwork. In this example, the user interface can provide a user with one or more options to select and modify a particular area of a pattern and / or digital fit model (mannequin)

[0216] In FIG. 7B, a user interface 704 is illustrated to generate a repeating artwork. In this example, the user interface can permit a user to run multiple versions and / or facilitate modifications to the artwork, or adjust a specific area.

[0217] In FIG. 7C, a user interface 706 is illustrated to generate or otherwise create alternative colorways.

[0218] In FIG. 7D, a user interface 708 is illustrated to select an artwork application.

[0219] In FIG. 7E, a user interface 710 is illustrated to select a material base.

[0220] In FIG. 7F, a user interface 712 is illustrated to adjoin one or more components associated with a garment to generate a 3D layout. In this example, the user interface can provide a user with options to select an artwork application technique, select a material, select a garment sty le / flat pattern from a digital library or data storage device, select a digital fit model (mannequin) from a digital library or data storage device, or input text if a particular style may need an anatomical solution.

[0221] Some or all of the above example methods may be performed by at least one of the modules described herein and / or components thereof. Various hardware components may be used at the various operations disclosed with reference to each module. For example, although certain methods may be described to be performed by a single computing device, it should be understood that, in some embodiments, different operations may be performed by different networked elements in operative communication with the computing device. For example, at least one computing device, such as 800 in FIG. 8, may be employed in the performance of some or all of the operations disclosed with regard to the methods. Similarly, an apparatus may be employed in the performance of some or all of the operations of the methods. As such, the apparatus may include at least those architectural components as found in computing device 800.

[0222] Furthermore, although the operations of the example methods are disclosed in a particular order, it should be understood that the order is disclosed for illustrative purposes only. Operations may be combined, separated, reordered, performed in parallel, and various intermediary operations may exist. Accordingly, it should be understood that the various operations, in various embodiments, may be performed in orders that differ from the ones disclosed herein. Additionally, in certain example embodiments, at least a portion of the operations may be carried out in parallel. Moreover, various operations may be added or removed without altering or departing from the fundamental scope of the depicted methods and systems disclosed herein.

[0223] Consistent with embodiments of the present disclosure, one or more of the disclosed methods may be performed by at least one of the modules disclosed herein. In one example, a method may be embodied as, for example, but not limited to, computer-executable instructions which, when executed by a computer processor, perform a method, such as the methods 200, 300, and 400.

[0224] Although specific embodiments of the disclosure have been described, one of ordinary skill in the art will recognize that numerous other modifications and alternative embodiments are within the scope of the disclosure. For example, any of the functionality and / or processing capabilities described with respect to a particular device or component may be performed by any other device or component. Further, while various illustrative implementations and architectures have been described in accordance with embodiments of the disclosure, one of ordinary skill will appreciate that numerous other modifications to the illustrative implementations and architectures described herein are also within the scope of this disclosure.

[0225] Certain aspects of the disclosure are described above with reference to block and flow diagrams of systems, methods, apparatuses, and / or computer program products according to example embodiments. It will be understood that one or more blocks of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and the flow diagrams, respectively, may be implemented by execution of computer-executable program instructions. Likewise, some blocks of the block diagrams and flow diagrams may not necessarily need to be performed in the order presented, or may not necessarily need to be performed at all, according to some embodiments. Further, additional components and / or operations beyond those depicted in blocks of the block and / or flow diagrams may be present in certain embodiments.

[0226] Accordingly, blocks of the block diagrams and flow diagrams support combinations of means for performing the specified functions, combinations of operations for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and flow diagrams, may be implemented by special-purpose, hardware-based computer systems that perform the specified functions, operations, or combinations of special-purpose hardware and computer instructions.

[0227] In another embodiment, an example method for a computer-implemented method for garment design and production can be provided, in accordance with the present disclosure. In further aspects, a method may be configured for managing artwork for engineered or repeatingpatterns in garment production. At a first operation, the system or platform may receive, via a user interface, a 2D image or textual description of a desired artwork pattern. The input may be imported or linked to a 2D design. Standard body specifi cations to create artwork layout may be inputted or selected.

[0228] The method may proceed to another operation, where the system or platform may utilize a generative Al model to transform the received 2D image or description into multiple engineered or repeating artwork pattern options and alternative colorways. The generated options may be linked to material choices. The system or platform may generate application method recommendations based on the inputs.

[0229] The method may proceed to another operation, where the system or platform may display the generated pattern options to a user for selection. The selection may include selection of 3D artwork with body and fabric, and / or application method of the fabric. The system or platform may be configured to advise the user when selection, such as application method or material base cannot translate from original 2D design.

[0230] The method may proceed to another operation, where the system or platform may apply the selected artwork pattern to a virtual model of the garment and / or material.

[0231] The method may proceed to another operation, where the system or platform may simulate the appearance and integration of the artwork pattern on the virtual model. The platform may be configured to prompt the user to modify the 3D artwork.

[0232] The method may proceed to another operation, where the system or platform may finalize the artwork application based on user adjustments and approvals. In further aspects, the operation of finalizing may include creating artwork layout based on application selection (if possible), recommending vendors that can execute application method, for seamless / knitrecommending vendor, loom, yarn, and construction, providing instructions and specs for vendor to execute artwork application, analyzing human anatomy for bodymap / ergonomic solutions, and / or creating grading / sizing artwork layouts. In still further aspects, the user adjustments and approvals may include selecting vendor to knit or apply artwork to a material, for seamless / knit - selecting vendor and construction, targeting solution / placement for bodymap (if applicable), and / or inputting body dimensions to create alternative sizes.

[0233] In even further aspects, the generative Al model may be configured to automatically adjust the scale, orientation, and color of the artwork patterns to optimize aesthetic appeal and manufacturability. The system or platform may include an operation of integrating the finalized artwork patterns into production files that are compatible with digital printing or embroiderymachinery. The user interface may provide tools for manual tweaking of the artwork patterns, including adjustments to color gradients, pattern repeats, and edge definitions. The system or platform may be capable of storing and cataloging finalized artwork patterns in a digital library, accessible for future modifications or reuse in different projects. The simulation may include dynamic testing of the artwork pattern under various virtual lighting conditions and on different fabric simulations to assess visual effects and fabric behavior. Augmented reality (AR) may be used to project the artwork patterns onto physical samples or live models for real -world visualization and assessment. The generative Al model may use historical design data and current fashion trends to suggest artwork patterns that align with market preferences. The system or platform may also be configured to generate a technical pack for each finalized artwork pattern that includes specifications for production, such as material requirements, stitching details, and color codes, and / or to automatically notify production teams and suppliers upon approval of the artwork patterns to expedite the manufacturing process.

[0234] In further aspects, various embodiments of the platform may include: a 3D design module configured to convert 2D garment designs into 3D models, allowing users to visualize and interact with the designs in a three-dimensional space; a user interface configured to enable users to manipulate and modify the 3D models, including resizing, reshaping, and retexturing; and / or a simulation module configured to apply the 3D models to virtual mannequins for fit and movement analysis. The 3D design module may utilize advanced computational geometry algorithms to ensure accurate and realistic conversion of 2D designs into 3D models. The system or platform may further include a material properties integration tool within the 3D design module, configured to simulate the behavior of various fabrics and materials on the 3D models based on their physical properties such as elasticity, weight, and texture.

[0235] The user interface may also include a real-time rendering feature that displays changes to the 3D models as they are being made, providing immediate visual feedback to the user. The system or platform may also include a virtual fitting room feature within the simulation module, configured to allow users to conduct virtual fit tests of the 3D models on different body types and sizes, and to record the fit data for further analysis. The 3D design module may be capable of generating multiple variations of a 3D model from a single 2D design, providing users with a range of design options to choose from or further customize.

[0236] The system or platform may also include an automated adjustment tool configured to automatically suggest modifications to the 3D models to improve fit, aesthetics, or manufacturability based on predefined criteria. The 3D design module may include a dynamicsimulation feature that simulates the movement of the garment in various scenarios such as walking, sitting, or stretching, to assess the design's functionality and comfort. The 3D design module may also include an integration with a pattern generation tool, configured to automatically generate sewing patterns from the 3D models that are optimized for fabric utilization and manufacturing efficiency. The system or platform may also include a feedback mechanism that collects user inputs and preferences during the 3D modeling and virtual fitting processes and uses this feedback to refine the 3D models and improve the design process.

[0237] In further embodiments, a method can be adapted for a reverse method for artwork creation for engineered or repeating patterns. For example, the user may provide imports or create 2D artwork inspiration, select design pattern layout from historical data or imports 3D model, run 2D versions of artwork for the selected fit model or mannequin, link material choice if applicable (specs can change artwork layout), approves backside version to create 3D / flat artwork layout, and / or select artwork application method. The system or platform may be configured to combine 2D artwork with historical or imported 3D model, convert 2D design into 3D versions of the artwork inspiration, apply prompts to modify 3D artwork versions, provide flat artwork layout, and recommend application methods of artwork and guidance. For example, the system or platform may utilize garment construction from history or import existing 3D model / garment pattern. The Al generative model may then apply artwork inspiration to a garment pattern. Once a 2D version is approved by a user, the system or platform may run and approve alternative side versions such that the system or platform can create a 3D layout. The system or platform or user may select artwork application method and base material if applicable. The system or platform may then provide development, tech specs of material / artwork application, guidance and instructions. The system or platform can create alternative colorways of the approved artwork layout.

[0238] In other embodiments, a method can be adapted for 2D material artwork creation in accordance with the present disclosure. Similarly, the user may input one or more of: verbal descriptors or inspiration image to run artwork versions, revise artwork as needed until final selection is made, run repeat and alternative colorways, and / or link a material choice if applicable. Similarly, here, the system or platform may be configured to create 2D artwork versions from entered descriptors or inspirational image, create repeat and alternative colorways from selected artwork, recommend artwork application methods and guidance to manufacturers, and / or show artwork applied to a material type, fiber content and / or garment on a 3D fit model. The Al generative model may be configured to create the textile artworkdesign and use keyword descriptors to build multiple design versions. Revisions by the user may also provide multiple modified versions. The system or platform may be configured to create repeat artwork and alternative colorways. The system or platform may then apply artwork to a material, style, and / or 3D digital fit model (mannequin) to see a garment design. The system or platform may generate guidance to the manufacturer for application technique and the like.

[0239] In further aspects, an example method for artwork application can be provided in accordance with certain embodiments of the present disclosure. The system or platform may initially determine an artwork that is in the material construction and / or applied to the material surface. As described above, the system or platform may analyze the user selections and inputs along with the garment specifications and parameters to determine compatibility of the application method and make recommendations to the user. The system or platform may proceed to select first and second subtypes of the selected application method. The selections may be linked to a material library from all material (solid and pattern). The second subtypes may be further broken down by additional subtypes. The system or platform may be configured to provide guidance and development instructions based at least in part on specific user selections.

[0240] Another example method for garment design and production can be provided in accordance with certain embodiments of the present disclosure. In further aspects, a method may be configured for creating anatomical and physiological artwork solutions in garment production. The method may be used for targeted solution / placement for bodymap, and in various industries such as: fashion, medical, military, fire, police, athletics, computing, technology, electronics, interiors, automotive, aerospace, energy, and the like.

[0241] At one operation, the system or platform may receive, via a user interface, input regarding specific anatomical and physiological requirements for a garment or material. The input may include artwork inspiration or select creation from library, and / or include specific medical or ergonomic needs that the artwork must accommodate, such as pressure points, body composition, muscular / skeletal structure, tissue density, flexibility zones, or thermal regulation areas. The input may also include solution and / or performance details (determine if conductive elements are required), for example, therapeutic, diagnostic; ergonomic advantages / actuators / performance; protection / safety; aesthetic modifications; and / or communication / computing / smart textiles. The user interface also allows for the input of bodydimensions, age, sex of fit model (if applicable); and / or data from medical scans or body scans to create highly personalized and precise artwork solutions.

[0242] At another operation, the system or platform may generate, using a generative Al model, artwork designs that incorporate anatomical and physiological features tailored to the input requirements. The generative Al model may be configured to determine average anatomical / physiological arrangement based on fit model details and create fit mannequin / fit model; and / or utilize a database of human anatomical data to ensure that the artwork accurately aligns with body contours and movement patterns.

[0243] At another operation, the system or platform may apply the generated artwork designs to a virtual model of the garment or material. Material construction and / or artwork application method may be selected if applicable.

[0244] At another operation, the system or platform may simulate the interaction of the artwork with the virtual model to assess fit, comfort, and functionality. The system or platform may apply artwork inspiration to a garment pattern, integrating the solution in the design versions, and provide versions in order of highest to lowest benefits for the identified solution.

[0245] At another operation, the system or platform may adjust the artwork based on simulation results and user feedback.

[0246] At another operation, the system or platform may finalize the artwork for production, including detailed specifications for application methods and material choices. For example, once a 2D version is approved by the user, the system or platform may run and approve alternative side versions and create a 3D layout. The system or platform may also be configured to provide development details, specs, application, guidance, instructions and / or execution of artwork.

[0247] The system or platform may be configured to integrate the finalized artwork with smart textiles that can monitor and respond to physiological changes in the wearer and / or to prototype the garment or material with the applied artwork using 3D printing or other rapid prototyping technologies to test physical prototypes before mass production.

[0248] The system or platform may be configured to use augmented reality (AR) to project the artwork onto the wearer’s body in real-time for immediate assessment and adjustment. The simulation may include stress testing the artwork under various physical activities to evaluate durability and functionality.

[0249] The system or platform may also generate a compliance report that verifies the artwork meets all relevant health and safety standards for medical or athletic use and / or providerecommendations for environmentally sustainable materials and application methods that can be used to create the artwork while minimizing environmental impact.

[0250] In further aspects, the method can be configured to create personalized and anatomically optimized artwork using medical or body scan data, including: receiving, via a user interface, medical or body scan data of an individual; processing the received data to generate a detailed 3D model of the individual's body; designing, using a generative Al model, artwork that is anatomically optimized based on the 3D body model; applying the designed artwork to the 3D body model to assess fit and coverage; simulating the performance of the artwork under various conditions to ensure comfort and functionality; collecting user feedback on the simulated artwork; and finalizing the artwork design for production, including specifications for materials and application methods tailored to the individual's needs.

[0251] The medical or body scan data may include detailed information about body dimensions, posture, and skin elasticity, which are used to tailor the artwork precisely.

[0252] The system or platform may be configured to adjust the artwork based on specific medical requirements or therapeutic needs of the individual, such as areas requiring compression or enhanced breathability. The generative Al model may incorporate historical data on similar body types and medical conditions to enhance the accuracy and effectiveness of the artwork design. The system or platform may include the capability to integrate the finalized artwork with wearable technology devices, such as sensors for monitoring physiological responses. The user interface may allow healthcare professionals to input additional medical annotations or modifications to the artwork directly on the 3D body model. The system or platform may be configured to use of virtual reality (VR) to allow the individual to visualize and interact with the artwork applied to their avatar in a virtual environment before final approval, and / or the simulation may include dynamic movement analysis to test the artwork's adaptability to the individual's range of motion and activity levels.

[0253] The system or platform may be configured to generate a digital twin of the individual that can be used for ongoing customization and updates to the artwork as the individual's medical or physical condition changes. The system or platform can also provide an automated recommendation for the type of fabric and printing techniques that are most suitable for the specific characteristics of the artwork and the individual's skin sensitivity.

[0254] In further aspects, the generative Al module may be further configured to design therapeutic garments tailored to specific medical conditions, such as compression garments for circulatory issues or post-surgical recovery wear, using input from medical professionals. Theuser interface may include features for medical professionals to input patient-specific anatomical data, such as measurements from medical imaging or body scans, to customize the garment designs for individual therapeutic needs. The system or platform may also include a database comprising a library of anatomy-specific artwork patterns designed to align with various body parts, and the generative Al model may be configured to apply these patterns to garments based on the specific therapeutic requirements of the user. The system or platform may also include production monitoring that includes specialized protocols for the manufacturing of medical-grade garments, ensuring that the garments meet stringent health and safety standards required for medical use, and logistics monitoring configured to prioritize and expedite shipments of materials and finished garments to medical facilities and patients, ensuring timely delivery for therapeutic use.

[0255] The data analytics of the system or platform may be configured to analyze the effectiveness of therapeutic garments by collecting and processing user feedback and clinical data, facilitating continuous improvement of garment designs based on medical outcomes, and the generative Al model may be configured to incorporate biomechanical models to simulate the physical stresses and strains on therapeutic garments during typical patient activities, enhancing the predictive accuracy of garment performance under actual use conditions.

[0256] FIG. 8 is a block diagram of an example of a system or computing device, such as machine 800, and computer-readable medium in accordance with one or more example embodiments of the disclosure. In other embodiments, a machine may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, such as, for example, the environment 100 shown in FIG. 1, the machine 800 may operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine 800 may act as a peer machine in peer-to-peer (P2P) (or other distributed) network environments. The machine 800 may be a server (e.g., a realtime server), a computer, an automation controller, a network router, a switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), or other computer cluster configurations.

[0257] Examples, as described herein, may include or may operate on logic or a number of components, modules, or mechanisms. Modules are tangible entities (e.g., hardware) capable of performing specified operations when operating. A module can include hardware. In an example, the hardware may be specifically configured to carry out a specific operation (e.g., hardwired). In another example, the hardware may include configurable execution units (e.g., transistors, circuits, etc.) and a computer-readable medium containing instructions where the instructions configure the execution units to carry out a specific operation when in operation. The configuration may occur under the direction of the execution units or a loading mechanism. Accordingly, the execution units are communicatively coupled to the computer-readable medium when the device is operating. In this example, the execution units may be a member of more than one module. For example, under operation, the execution units may be configured by a first set of instructions to implement a first module at one point in time and reconfigured by a second set of instructions to implement a second module at a second point in time.

[0258] In certain embodiments, systems and methods of the present disclosure can be embodied as, for example, but not limited to, a website, a web application, a desktop application, a backend application, or a mobile application compatible with a computing device 802 shown in FIG. 8. The computing device 802 may include, but not be limited to, a laptop, a tablet computing device, a desktop computer, a server computing device, a smartphone, a drone, a wearable computing device, an embedded computing device, a handheld computing device, an industrial computing device, a remotely operable recording device, a mobile computing device, a mobile communications device, a processor-based computing device, or a processor-based communications device.

[0259] In some instances, certain embodiments of the present disclosure can be hosted on a centralized server or a cloud computing service. Although the associated methods and processes of FIGs. 2-7 have been described to be performed by a computing device 802, one of ordinary skill will understand that, in some embodiments, different operations may be performed by one or more computing devices, similar to 802, in operative communication on at least one network.

[0260] Embodiments of the present disclosure may include a system 800 with a computing device 802 having a central processing unit (CPU) 804, a communication bus 806, a memory device 808, a power supply unit (PSU) 810, and one or more input / output (I / O) devices 812. The CPU 804 can be coupled to the memory device 808 and any number of VO devices 812 via the communication bus 806, some or all of which can be powered by the PSU 810. One ofordinary skill can understand that, in some embodiments, each component may actually be multiple components for redundancy, high availability, and / or performance purposes. The combination of the presently disclosed components is configured to perform the operations of the methods disclosed herein.

[0261] The I / O devices 812 may include, but not limited to, a non-volatile storage sub-module 814, a communication network sub-module 816, a sensors sub-module 818, and a peripherals sub-module 820.

[0262] In a system consistent with an embodiment of the disclosure, the CPU 804 can include at least one CPU core, and in some embodiments, may include one or more identical CPU cores, such as, but not limited to, homogeneous multi-core systems. In any instance, the CPU 804 can read and execute program instructions which may be used across many application domains, for example, but not limited to, general purpose computing, embedded computing, network computing, digital signal processing (DSP), and graphics processing (GPU). The CPU 804 may simultaneously run multiple instructions on separate CPU cores.

[0263] Consistent with the embodiments of the present disclosure, the computing device 802 may employ the communication bus 806 that permits transfers data between components inside the computing device 802, and / or one or more computing devices, similar to 802. The bus 806 may embody internal and / or external hardware and software components, for example, but not limited to a wire, an optical fiber, various communication protocols, and / or any physical arrangement that provides the same logical function as a parallel electrical bus. The bus 806 may include at least one of a parallel bus, wherein the parallel bus carries data words in parallel on multiple wires; and a serial bus, wherein the serial bus carries data in bit-wise serial form. The bus 806 may embody a variety of topologies, for example, but not limited to, a multidrop / electrical parallel topology, a daisy chain topology, and connected by switched hubs, such as a USB bus.

[0264] Further, the computing device 802 may employ hardware integrated circuits that store information for immediate use in the computing device 802, also known as primary storage or memory 806. The memory 806 can operate at relatively high speed, distinguishing it from the non-volatile storage sub-module 814, which may be referred to as secondary or tertiary storage, which provides relatively slower access to information but offers relatively higher storage capacity. The data contained in memory 806 may be transferred to secondary storage via techniques such as, but not limited to, virtual memory and swap. The memory 806 may be associated with addressable semiconductor memory, such as integrated circuits consisting ofsilicon-based transistors, which may be used as primary storage or for other purposes in the computing device 802. The memory 806 may include other embodiments, such as, but not limited to volatile memory, non-volatile memory, and semi-volatile memory.

[0265] Further, computing device 802 may employ a communication device, such as an I / O device 812 between an information processing system, such as the computing device 802, and the outside world, for example, but not limited to, human, environment, and another computing device, similar to 802. The I / O module 812 can regulate one or more inputs and outputs with regard to the computing device 802, wherein the inputs can be a variety of signals and data received by the computing device 802, and the outputs can be a variety of signals and data sent from the computing device 802. The I / O module 812 can interface with a variety of hardware, such as, but not limited to, non-volatile storage 814, network communication devices 816, sensors 818, and peripherals 820. The variety of hardware can be used by at least one of, but not limited to, humans, the environment, and another computing device similar to 802 to communicate with the present computing device 1002. The I / O module 812 may include a variety of forms, for example, but not limited to channel I / O, port mapped I / O, asynchronous I / O, and direct memory access (DMA).

[0266] Further, the computing device 802 may employ a non-volatile storage sub-module 814, which may be referred to by a person having ordinary skill in the art as one of secondary storage, external memory, tertiary storage, off-line storage, and auxiliary storage. The nonvolatile storage sub-module 814 may not be accessed directly by the CPU 804 without using an intermediate area in the memory 808. The non-volatile storage sub-module 814 may not lose data when power is removed. The memory 808 and / or non-volatile storage sub-module 814 may include a machine readable medium on which is stored one or more sets of data structures or instructions (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions may also reside, completely or at least partially, within a main memory, within the static memory, or within the hardware processor during execution thereof by the machine 800. In an example, one or any combination of the hardware processor, the main memory, the static memory, or the storage device may constitute machine-readable media.

[0267] While the computer-readable medium or machine-readable medium is illustrated as a single medium, the terms “computer-readable medium” or "machine-readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) configured to store the one or more instructions.

[0268] Various embodiments may be implemented fully or partially in software and / or firmware. This software and / or firmware may take the form of instructions contained in or on a non-transitory computer-readable storage medium. Those instructions may then be read and executed by one or more processors to enable performance of the operations described herein. The instructions may be in any suitable form, such as but not limited to source code, compiled code, interpreted code, executable code, static code, dynamic code, and the like. Such a computer-readable medium may include any tangible non-transitory medium for storing information in a form readable by one or more computers, such as but not limited to read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; a flash memory, etc.

[0269] The term “machine-readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 800 and that cause the machine 800 to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding, or carrying data structures used by or associated with such instructions. Non-limiting machine-readable medium examples may include solid-state memories and optical and magnetic media. In an example, a massed machine-readable medium includes a machine-readable medium with a plurality of particles having resting mass. Specific examples of massed machine-readable media may include non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), or electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD- ROM disks.

[0270] Further, the computing device 802 may employ a communication network sub-module 816 as a subset of the VO module 812, which may be referred to as at least one of, but not limited to, a computer network, a data network, or a network. Examples of computing devices that may include a communication network sub-module 816 include, but are not limited to, personal computers, phones, servers, drones, and networking devices such as, but not limited to, hubs, switches, routers, modems, and firewalls.

[0271] Two nodes can be considered networked together when one computing device 802 can exchange information with another computing device similar to 802, regardless of any direct connection between the two computing devices. The communication sub-module 816 can support a variety of applications and services, such as, but not limited to World Wide Web (www), digital video and audio, shared use of application and storage computing devices,printers / scanners / fax machines, email / online chat / instant messaging, remote control, distributed computing, etc. The network may include one or more transmission mediums, such as, but not limited to conductive wire, fiber optics, and wireless signals. The network may include one or more communications protocols to organize network traffic, wherein application-specific communications protocols may be layered, and may be known to a person having ordinary skill in the art as being improved for carrying a specific type of payload, when compared with other more general communications protocols. Suitable communications protocols may include, but are not limited to, IEEE 802, ethemet, wireless LAN (WLAN / WiFi), Internet Protocol (IP) suite (e.g., TCP / IP, UDP, Internet Protocol version 4 [IPv4], and Internet Protocol version 6 [IPv6]). The communication sub-module 816 may have be embodied such as, but not limited to, wired communications, such as coaxial cable, phone lines, twisted pair cables (ethemet), and InfiniBand; wireless communications, such as communications satellites, cellular systems, radio frequency / spread spectrum technologies, IEEE 802.11 Wi-Fi, Bluetooth, NFC, optical communications, infrared (IR) communications, cellular systems; parallel communications; serial communications, or power line communications.

[0272] The network described above may include a variety of layouts, such as, but not limited to, bus networks such as Ethernet, star networks such as Wi-Fi, ring networks, mesh networks, fully connected networks, and tree networks.

[0273] Consistent with the embodiments of the present disclosure, the aforementioned computing device 802 may employ a sensor sub-module 818 as a subset of the I / O 808. The sensor sub-module 818 can include at least one of the device, module, or subsystem whose purpose is to detect events or changes in its environment and send the information to the computing device 802. The sensor sub-module 818 may include a variety of digital devices and analog devices, wherein if an analog device is used, an analog to digital (A-to-D) converter must be employed to interface the device with the computing device 802.

[0274] Consistent with the embodiments of the present disclosure, the aforementioned computing device 802 may employ a peripheral sub-module 820 as a subset of the I / O 808. The peripheral sub-module 820 can include one or more ancillary devices used to put information into and get information out of the computing device 802. There are at least 3 categories of devices that can be incorporated in the peripheral sub-module 820, which exist based on their relationship with the computing device 802, input devices, output devices, and input / output devices. Input devices send at least one of data and instructions to the computingdevice 802. Input devices can be categorized based on, but not limited to, modality of input, such as, but not limited to, mechanical motion, audio, visual, and tactile, whether the input is discrete, such as but not limited to, pressing a key, or continuous such as, but not limited to the position of a mouse, the number of degrees of freedom involved, such as, but not limited to, two-dimensional mice and three-dimensional mice used for computer-aided design (CAD) applications. Output devices can provide output from the computing device 802. Output devices convert electronically generated information into a form that can be presented to humans. Input / output devices that perform both input and output functions. Embodiments of the peripheral sub-module 820 can include input devices, such as human interface devices (HID), such as, but not limited to, pointing device (e.g., mouse, touchpad, joystick, touchscreen, game controller / gamepad, remote, light pen, keyboard, graphics tablet, digital pen, gesture recognition devices, and virtual reality systems.

[0275] The operations and processes described and shown above may be carried out or performed in any suitable order as desired in various implementations. Additionally, in certain implementations, at least a portion of the operations may be carried out in parallel. Furthermore, in certain implementations, less than or more than the operations described may be performed.

[0276] The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0277] The terms “computing device,” “client device,” “communication device,” “mobile device,” as used herein refer to a wireless communication device such as a cellular telephone, a smartphone, a tablet, a netbook, a wireless terminal, a laptop computer, an access point, a printer, a point of sale device, an access terminal, or other personal communication system (PCS) device. The device may be either mobile or stationary.

[0278] As used herein, unless otherwise specified, the use of the ordinal adjectives “first,” “second,” “third,” etc., to describe a common object, merely indicates that different instances of like objects are being referred to and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

[0279] Although specific embodiments of the disclosure have been described, numerous other modifications and embodiments are within the scope of the disclosure. For example, any of the functionality described with respect to a particular device or component may be performed by another device or component. Further, while specific device characteristics have beendescribed, embodiments of the disclosure may relate to numerous other device characteristics. Further, although embodiments have been described in language specific to structural features and / or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments. Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments could include, while other embodiments may not include, certain features, elements, and / or operations. Thus, such conditional language is not generally intended to imply that features, elements, and / or operations are in any way required for one or more embodiments.

[0280] Software components associated with a particular solution or system may reside and be executed on a single platform or may be distributed across multiple platforms. The multiple platforms may be associated with more than one hardware vendor, underlying chip technology, or operating system. Furthermore, software components associated with a particular solution or system may be initially written in one or more programming languages, but may invoke software components written in another programming language.

[0281] Computer-executable program instructions may be loaded onto a special-purpose computer or other particular machine, a processor, or other programmable data processing apparatus to produce a particular machine, such that execution of the instructions on the computer, processor, or other programmable data processing apparatus causes one or more functions or operations specified in the flow diagrams to be performed. These computer program instructions may also be stored in a computer-readable storage medium (CRSM) that upon execution may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement one or more functions or operations specified in the flow diagrams. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements or operations to be performed on the computer or other programmable apparatus to produce a computer-implemented process.

[0282] Additional types of CRSM that may be present in any of the devices described herein may include, but are not limited to, programmable random access memory (PRAM), SRAM, DRAM, RAM, ROM, electrically erasable programmable read-only memory (EEPROM),flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the information and which can be accessed. Combinations of any of the above are also included within the scope of CRSM. Alternatively, computer-readable communication media (CRCM) may include computer-readable instructions, program modules, or other data transmitted within a data signal, such as a carrier wave, or other transmission. However, as used herein, CRSM does not include CRCM.

[0283] All rights, including copyrights in the code included herein, are vested in and the property of the applicant. The applicant retains and reserves all rights in the code included herein, and grants permission to reproduce the material only in connection with the reproduction of the granted patent and for no other purpose.

Claims

CLAIMSThat which is claimed is:

1. A system for garment design, comprising: a garment design module comprising: a 2D design input interface operable to: receive 2D information associated with at least one side of a garment; and a 2D-to-3D design converter operable to: generate, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment; and an artificial intelligence (Al) module operable to: generate, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout of the garment; and output, via at one user interface, the 3D layout for review by a user.

2. The system of claim 1, wherein the artificial intelligence (Al) module is further operable to: generate or receive, based at least in part on the 3D layout of the garment, construction information for the garment, wherein the construction information comprises at least one of: a blueprint for garment construction, instructions to adjoin together one or more components of the garment, a selection of a material or textile for the garment, or artwork application information.

3. The system of claim 1, wherein the 3D layout of the garment comprises a 3D artwork design; and the artificial intelligence (Al) module is further operable to: generate, based at least in part on the 3D artwork design, 3D artwork application information to apply the 3D artwork design to the garment.

4. The system of claim 1, wherein the artificial intelligence (Al) module is further operable to: generate or receive at least one 3D digital fit model (mannequin), wherein the generating a 3D layout of the garment is further based at least in part on the at least one 3D digital fit model (mannequin).

5. The system of claim 1, wherein the generating or receiving at least one 3D digital fit model (mannequin) by the artificial intelligence (Al) module is further operable to: generate at least one 3D digital fit model (mannequin) is based at least in part on at least one of: a body scan, a user input, selection from a data source, selection of a size by an Al module, or selection of a size based on a brand.

6. The system of claim 1, wherein the artificial intelligence (Al) module is further operable to: receive, at least one user input associated with the 3D layout; and implement, based at least in part on the at least one user input, a change to the 3D layout.

7. The system of claim 1, wherein the at least one user input is based at least in part on at least one of: a fashion trend, user feedback on the 3D layout, garment dimensional information, color, artwork application construction, material type, different size, or artwork on 3D model.

8. The system of claim 1, wherein the artificial intelligence (Al) module is further operable to: generate, based at least in part on the 2D information, repeating artwork information; generate, based at least in part on the repeating artwork information, alternate colorways information; generate or receive selected base material information; and generate or receive, based at least in part on the repeating artwork information, alternate colorways information, selected base material information, and 3D layout of the garment, construction information for the garment, wherein the construction information comprises at least one of: a blueprint for garment construction, instructions to adjoin together one or more components of the garment, a material selection for the garment, or artwork application information.

9. The system of claim 1, wherein the artificial intelligence (Al) module is further operable to: receive, based at least in part on a user input, custom solution information, wherein the custom solution information comprises at least one of: customer-specific information, modelspecific information, fit model-specific information, body dimension information, body composition information, anatomical information, physiological information, demographic information, ergonomic information, patient-specific information, therapeutic information;diagnostic information, healthcare information, temperature control information, compression information, wound healing information, medical standard information, prosthetics information, safety information, military specification information, athletic information, injury prevention information, compression information, or computing information. wherein the generating, based at least in part on the 2D information associated with the at least one side of a garment, the 2D information associated with one or more other sides of the garment, a 3D layout of the garment by the artificial intelligence module is further based at least in part on the custom solution information.

10. A method for designing a garment, the method comprising: receiving 2D information associated with at least one side of a garment; generating, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment; generating, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout of the garment; and outputting, via at one user interface, the 3D layout for review by a user.

11. The method of claim 10, further comprising: generating or receiving, based at least in part on the 3D layout of the garment, construction information for the garment, wherein the construction information comprises at least one of: a blueprint for garment construction, instructions to adjoin together one or more components of the garment, a selection of a material or textile for the garment, or artwork application information.

12. The method of claim 10, wherein the 3D layout of the garment comprises a 3D artwork design; and the method further comprises: generating, based at least in part on the 3D artwork design, 3D artwork application information to apply the 3D artwork design to the garment.

13. The method of claim 10, further comprising: generating or receiving at least one 3D digital fit model (mannequin), wherein the generating a 3D layout of the garment is further based at least in part on the at least one 3D digital fit model (mannequin).

14. The method of claim 13, wherein the generating at least one 3D digital fit model (mannequin) is based at least in part on at least one of: a body scan, a user input, selection from a data source, selection of a size by an Al module, or selection of a size based on a brand.

15. The method of claim 10, further comprising: receiving, at least one user input associated with the 3D layout; and implementing, based at least in part on the at least one user input, a change to the 3D layout.

16. The method of claim 15, wherein the at least one user input is based at least in part on at least one of: a fashion trend, user feedback on the 3D layout, garment dimensional information, color, artwork application construction, material type, different size, or artwork on 3D model.

17. The method of claim 10, wherein the 3D layout comprises at least one of: garment fabrication instructions, garment pattern flat layout, artwork layout, or garment construction instructions.

18. The method of claim 10, further comprising: generating, based at least in part on the 2D information, repeating artwork information; generating, based at least in part on the repeating artwork information, alternate colorways information; generating or receiving selected base material information; generating or receiving, based at least in part on the repeating artwork information, alternate colorways information, selected base material information, and 3D layout of the garment, construction information for the garment, wherein the construction information comprises at least one of: a blueprint for garment construction, instructions to adjoin together one or more components of the garment, a material selection for the garment, or artwork application information.

19. The method of claim 10, further comprising: receiving, based at least in part on a user input, custom solution information, wherein the custom solution information comprises at least one of: customer-specific information, model-specific information, fit model-specific information, body dimension information, body composition information, anatomical information, physiological information, demographic information, ergonomic information, patient-specific information, therapeutic information; diagnostic information, healthcare information, temperature control information, compression information, wound healing information, medical standard information, prosthetics information, safety information, military specification information, athletic information, injury prevention information, compression information, or computing information.wherein the generating, based at least in part on the 2D information associated with the at least one side of a garment, the 2D information associated with one or more other sides of the garment, a 3D layout of the garment is further based at least in part on the custom solution information.

20. A computer-readable medium with computer-executable instructions stored in a memory device, the instructions operable to: receive 2D information associated with at least one side of a garment; generate, based at least in part on the 2D information, 2D information associated with one or more other sides of the garment; generate, based at least in part on the 2D information associated with the at least one side of a garment and the 2D information associated with one or more other sides of the garment, a 3D layout of the garment; and output, via at one user interface, the 3D layout for review by a user.

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