Systems, methods, and apparatus for providing personalized footwear

Abstract

In some embodiments, systems, devices, and methods are provided herein that facilitate providing personalized footwear, including designing and providing personalized modular lasts and personalized footwear components. In one aspect, a design of a personalized modular last is generated from evaluation information associated with a user, the evaluation information including at least one foot measurement of the user, and optionally other foot shape information, pathological information, gait and biomechanical information or contextual information associated with the user. Generating a design of a personalized modular last includes determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based on evaluation information associated with a user. The last extension may be further determined to further personalize the design of the personalized modular last. In a further aspect, a design of at least one personalized shoe component is generated based on a design of a personalized modular last and / or evaluation information associated with a user. Methods of making personalized footwear are also provided, as well as using previously traversed footwear feedback to update personalized footwear designs.

Description

Related Applications

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 318,272, filed on March 9, 2022, which is incorporated herein by reference in its entirety. Technical Field

[0002] The present invention relates generally to personalized footwear, and more particularly, to the design and manufacture of personalized footwear based on an assessment of individual footwear needs. Background Art

[0003] Footwear fit has potential impacts on a human, industry, and global level. Poor footwear fit can cause personal pain to the wearer and have far-reaching impacts across the entire footwear industry value chain - from footwear design to manufacturing to retail. At the human level, foot health can play a central role in mobility and quality of life. Studies have shown that 63% to 72% of people wear ill-fitting footwear, with the impact extending far beyond personal pain to deformities and disease. In fact, improperly fitting footwear has been shown to be associated with a number of medically relevant conditions (e.g., reduced short-term and long-term mobility, poor quality of life, etc.). It is newly discovered that finding a better-fitting footwear is often a manual trial-and-error process that wastes consumers' time and money, and results in ill-fitting footwear or footwear that causes pain.

[0004] The footwear industry's current sizing systems oversimplify fit and fail to capture a wide range of foot morphologies, pathologies, gaits, and biomechanics. For example, individuals with medical conditions (e.g., finger deformities, arthritis, diabetes, etc.) and older adults with aging needs are difficult to address with any existing footwear sizing / selection system.

[0005] Today, the shoe industry is poorly equipped to achieve a proper fit. First, there is no industry standard for shoe sizing, at least there can be significant variations between brands, shoe types, models, and annual releases. This makes it difficult for customers to find the right fit. In addition, the industry's standard sizing method is also two-dimensional, based on foot width and length. This is emphasized by the industry's common use of lasts, which are mechanical forms, usually made of wood, metal, or plastic, shaped like a human foot, and used to set the size, contour, and shape of the shoe during the manufacturing process. Although lasts will vary based on the type of shoe (e.g., men's boots, women's oxford shoes), a given type or model of last has sizes and dimensions that vary or "grade" in a systematic manner that parallels the shoe size. This limited size of lasts is exacerbated by the fact that lasts tend to be symmetrical, with the right side being a mirror image of the left side. The human anatomy cannot adhere to such rules of sizing and symmetry. As a result, the industry fails to account for the complex shape of the foot, which has 26 bones, 33 joints, 100+ ligaments, tendons, and muscles. It also fails to take into account other factors, such as individual gait: footwear is a device that enables human movement, interacting with the 7,000+ nerve endings in each foot, sending signals to the rest of the body during movement. Unlike clothing fit (which can be approximate), an imperfect footwear fit (which does not account for the shape and complexity of the foot's movement) can lead to pain (a physiological sign of biofeedback that something is not good for the body). Given the complex interaction between footwear and the human anatomy and physiology, understanding the science of footwear fit is critical to making appropriate footwear recommendations and resolving returns due to fit.

[0006] Furthermore, part of the problem lies further upstream in the footwear value chain. Footwear design practices still rely on basic trial and error with qualitative feedback from a limited sample of foot models. Many footwear manufacturers lack the necessary data to optimize the design of mass-produced shoes to fit a wider population. Even if footwear manufacturers were to consider personalizing footwear for consumers, today’s mass manufacturing technology is too inefficient to do so, resulting in a waste of resources and labor. For this reason, custom footwear remains a handmade, expensive, and non-scalable process, which puts the high cost and, therefore, price point of custom shoes out of the reach of the average consumer.

[0007] Currently, customization or personalization of shoes can include creating custom shoe components (such as custom insoles or pads) and / or focusing on a shoe last. For example, in one method of making a custom shoe, a pre-made last can be selected for creating the shoe based on the length and width of the customer's foot. In this method, the details and materials of the shoe components can be customized to meet some of the customer's needs and manufactured using one of the above-mentioned manufacturing techniques. However, because the actual size and shape of the shoe is based on a pre-made last, which only approximates the customer's foot, this method cannot fully capture the customer's individual shape differences, such as the height of the arch or the shape of the heel.

[0008] In another approach, a shoemaker can build a custom last for each individual based on the measurements of the individual's foot, but this can be a wasteful, time-consuming process that lacks scalability and is often very expensive for the customer. Additionally, this approach also typically fails to integrate a wide range of biometric, geometric, and contextual data relevant to achieving the user's ideal fit. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Embodiments of systems, devices, and methods related to providing personalized footwear are disclosed herein. This specification includes the accompanying drawings, in which:

[0010] Figure 1 is a flow chart of a method for providing personalized footwear according to some embodiments.

[0011] Figure 2 is a flow chart of a method for designing a personalized modular last for providing personalized footwear, according to some embodiments.

[0012] Figure 3 is a schematic diagram of an exemplary system for providing personalized footwear, according to some embodiments.

[0013] Figure 4 is a flow chart of a method for providing personalized footwear according to some embodiments.

[0014] Figure 5 is a flow chart of a method of analyzing image data to determine foot shape attributes associated with a user, according to some embodiments.

[0015] Figure 6 is a flow chart of a method of acquiring and displaying foot pathology data according to some embodiments.

[0016] Figure 7 is a flow chart of a method of analyzing image data to determine attributes of a foot pathology associated with a user, according to some embodiments.

[0017] Figure 8 is a flow chart of a method of performing gait assessment according to some embodiments.

[0018] Fig. 9 is a flow chart of a method of analyzing plantar pressure data to determine biomechanical properties associated with a user, according to some embodiments.

[0019] Fig.10 is a flow chart of a method of analyzing contextual data according to some embodiments.

[0020] Fig.11 is a simplified schematic diagram of a method of designing personalized footwear to provide personalized footwear according to some embodiments.

[0021] Fig.12 is a flow chart of a method of providing footwear data according to some embodiments.

[0022] Fig.13 is a perspective view of a personalized modular last according to some embodiments.

[0023] Fig.14 Included are diagrammatic side views of different modular core lasts according to some embodiments.

[0024] Fig.15 is a top plan view of a modular core last according to some embodiments.

[0025] Fig.16 A top plan view of a region of a personalized modular last is shown, according to some embodiments.

[0026] Fig.17 Views showing additional areas of a personalized modular last according to some embodiments.

[0027] Fig.18 A simplified schematic diagram of a method of providing a personalized shoe component design is shown in accordance with some embodiments.

[0028] Fig.19 is a flow chart of a method of providing or updating a design of personalized footwear to provide personalized footwear, according to some embodiments.

[0029] Fig. 20 is a flow chart of a method of providing or updating a design of personalized footwear to provide personalized footwear, according to some embodiments.

[0030] Fig.21 is a flow chart of a method of analyzing sensor data to determine personal perception attributes associated with a user, according to some embodiments.

[0031] Fig. 22 is a flow chart of a method of creating a design or digital drawing of personalized footwear, according to some embodiments.

[0032] Fig.23 is a flow chart of a method of updating a personal foot model according to some embodiments.

[0033] Fig.24 is a flow chart of a method of creating and updating a design of a personalized product of footwear, according to some embodiments.

[0034] Fig.25 is a flow chart of a method of creating and updating a design of a personalized product of footwear, according to some embodiments.

[0035] Elements are shown in the figure for the purpose of simplification and clarity, and are not necessarily drawn to scale. For example, the size and / or relative position of some elements in the figure can be exaggerated relative to other elements to help promote the understanding of the various embodiments of the present disclosure. In addition, common but well-known elements that are useful or necessary in commercially feasible embodiments are usually not depicted to facilitate the less obstructed observation of various embodiments. Certain actions and / or steps can be described or depicted in a specific order of occurrence, and those skilled in the art will understand that such specificity about sequence is actually not required. The terms and expressions used herein have the common technical meanings of such terms and expressions given by those skilled in the art as described above, unless different specific meanings are set forth in addition herein. DETAILED DESCRIPTION

[0036] In general, according to various embodiments, systems, devices, and methods for providing personalized footwear are described herein. More specifically, the present disclosure includes providing personalized modular shoe lasts and personalized shoe parts based on information related to a user. Also described are systems and methods for providing or manufacturing personalized footwear by designing personalized modular shoe lasts and personalized shoe parts based on an assessment of individual footwear needs. In addition, the teachings herein may be used in conjunction with other personalized footwear manufacturing tools, systems, and processes, such as those described in U.S. Patent Application No. 63 / 155,171 filed on March 1, 2021, U.S. Patent Application No. 63 / 277,818 filed on November 10, 2021, and International Application No. PCT / US2022 / 017641 filed on February 24, 2022, all of which are incorporated herein by reference in their entirety.

[0037] In one aspect, Figure 1As shown, a method for providing a personalized footwear product is disclosed, the method comprising obtaining evaluation information associated with an individual or a user, the evaluation information comprising at least one foot measurement of the individual; generating a design of a personalized modular last, wherein the personalized modular last comprises a modular core last. In some methods, generating the design of the personalized modular last comprises determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based at least in part on at least one foot measurement of the individual or based on the evaluation information associated with the individual; and generating a design of at least one personalized shoe component based at least in part on the design of the personalized modular last and / or the evaluation information associated with the individual.

[0038] In some embodiments, the method further includes sending the design of the personalized modular last and the design of the at least one personalized shoe component for manufacturing. In some embodiments, they are sent to a manufacturing execution system. Manufacturing the personalized footwear product includes providing a personalized modular last, using the personalized modular last to manufacture the personalized footwear product, and incorporating the at least one personalized shoe component into the personalized footwear product, such as Figure 4 shown.

[0039] The present disclosure further contemplates a personalized modular last for providing personalized footwear to an individual, the personalized modular last comprising a modular core last and one or more last extensions. In some methods, the modular core last comprises one or more modular core last components selected from a set of different modular core last components based at least in part on foot measurements associated with the individual and / or assessment information associated with the user. In some embodiments, one or more last extensions are removably coupled to the modular core last at selected attachment points on the modular core last, and the one or more last extensions and the selected attachment points may be selected based at least in part on foot measurements associated with the user and / or other assessment information associated with the user.

[0040] Further disclosed is a method for designing or providing a personalized modular shoe last, such as Figure 2As shown, the steps include: receiving evaluation information (including foot measurements) for a user, generating a design of a personalized modular last, wherein the personalized modular last includes a modular core last. In some methods, generating the design of the personalized modular last includes determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based at least in part on the user's foot measurements and / or evaluation information associated with the user, and optionally, determining whether one or more last extensions should be included in the personalized modular last based on the evaluation information associated with the user, and / or determining one or more last extensions based on the evaluation information associated with the user. In some embodiments, the method may include sending the design of the personalized modular last for manufacturing, or sending it to a manufacturing execution system. In some embodiments, the method includes manufacturing or otherwise providing one or more last extensions (if included), and removably coupling them to the modular core last to provide a personalized modular last. In further embodiments, the personalized modular last can be used to manufacture a personalized footwear product for a user. A corresponding system for designing or providing a personalized modular last is also disclosed.

[0041] The present disclosure also contemplates a system for providing personalized footwear products, such as Figure 3 As shown, the system includes at least one processor that can communicate with at least one electronic user device, the processor being configured to receive evaluation information associated with a user, the evaluation information including at least one foot measurement of the user, and generate a design of a personalized modular shoe last, wherein the personalized modular shoe last includes a modular core shoe last. In some methods, generating the design of the personalized modular shoe last includes determining a modular core shoe last model by selecting one or more modular core shoe last model components from a library of modular core shoe last model components based at least in part on the user's foot measurement and / or the evaluation information associated with the user. The processor may be further configured to generate a design of at least one personalized shoe component based at least in part on the design of the personalized modular shoe last and / or the evaluation information associated with the user.

[0042] In some embodiments, the system further includes a manufacturing execution system, wherein the processor is configured to send the design of the personalized modular last and the design of the at least one personalized shoe component to the manufacturing execution system. In such configurations, the manufacturing execution system can be configured to provide or manufacture the at least one personalized shoe component, provide the personalized modular last, use the personalized last to manufacture the personalized footwear product, and include the at least one personalized shoe component in the personalized footwear product.

[0043] In another aspect, the systems and methods provided herein can provide the design of personalized modular lasts and / or personalized shoe components based on comprehensive personal data associated with a user. Such personal data can include data related to foot shape, foot pathology, gait and biomechanics, demographics, lifestyle, location, and / or product preferences associated with the user, as well as feedback on previously worn shoes.

[0044] Further disclosed is a system for providing a personalized footwear product, the system comprising at least one processor that can communicate with at least one electronic user device, the processor being configured to: obtain evaluation information associated with a user, wherein the evaluation information includes at least one foot measurement of the user, and generate a design for a personalized modular last, wherein the personalized modular last may include a modular core last. In some methods, generating the design for the personalized modular last includes determining a modular core last model by selecting one or more modular core last components from a library of modular core last components. In operation, the components may be selected from the library of components based at least in part on at least one foot measurement of the user. In addition, the processor may be further configured to generate a design for at least one personalized shoe component based on the design of the personalized modular last and, optionally, the evaluation information associated with the user.

[0045] In some methods, the processor is further configured to obtain feedback about a previously worn product of footwear that was manufactured based on the design of the personalized modular last and the design of the personalized shoe component. In use, the processor may be configured to generate an updated design of the personalized modular last and / or an updated design of at least one personalized shoe component based at least in part on the feedback about the previously worn product of footwear.

[0046] Also contemplated are methods and systems for designing one or more personalized shoe components, including obtaining assessment information associated with a user (wherein the assessment information may include one or more foot measurements) and generating designs for one or more personalized shoe components. In some configurations, generating the designs for one or more personalized shoe components includes, for example, selecting a preset shoe component design from a preset shoe component design library using an algorithm based on a design of a personalized modular shoe last personalized for the user, and optionally the assessment information associated with the user. The method may be used for at least one or all of the personalized shoe components. In addition, the methods and systems described herein may also use an algorithm to adjust the size of the selected preset shoe component design based on the design of the personalized modular shoe last personalized for the user and, optionally, the assessment information associated with the user. In addition, in some methods, the methods and systems allow the user or design to select materials for the personalized shoe component based on the design of the personalized modular shoe last and / or the assessment information associated with the user.

[0047] Although the personalized footwear described herein may include many personalized components generated and manufactured with a particular user in mind, such footwear typically also includes one or more standardized components, such as, for example, laces and rivets, etc. The teachings herein allow a user or designer to identify which footwear components are appropriately customized and which standardized items or components can be used. In addition, these teachings may be employed to adjust some of the standard, off-the-shelf components to create personalized shoe components, thereby manufacturing personalized footwear in an economical and efficient manner.

[0048] In some embodiments, the systems and methods may include determining personal attributes and / or footwear attributes associated with a user based on the user's received evaluation information, and designing a personalized footwear product based at least in part on the determined personal attributes and / or footwear attributes associated with the user.

[0049] It is contemplated that the systems, devices, and methods described herein may be employed by a plurality of potential users, including, but not limited to, shoppers, consumers, businesses (such as shoe retailers or shoe designers), electronic marketplaces, shoe manufacturers, shoe providers or suppliers, medical professionals, sales professionals, or any other entity involved in the supply, design, or manufacture of footwear or footwear-related products.

[0050] The following description should not be understood in a limiting manner, but is made only for the purpose of describing the general principles of the exemplary embodiments. References throughout this specification to "one embodiment", "an embodiment", "some embodiments", "an implementation", "some implementations", "some applications", or similar language mean that the description of specific features, structures, or characteristics related to the embodiment can be included in an embodiment. Therefore, the appearance of the phrases "in one embodiment", "in an embodiment", "in some embodiments", "in some implementations", and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0051] As mentioned above, Figures 1 to 4Each of the figures illustrates an exemplary system or method for providing a personalized modular last or personalized footwear according to certain embodiments of the present disclosure.As shown, initially, assessment information related to a user is received, the assessment information including at least foot measurements of the user.

[0052] The user-related assessment information described herein includes personal data that can be received by the processor from one or more electronic user devices. For example, a user interface on a mobile phone or an application on a computer can be used to provide the processor with the user's foot measurements. In some embodiments, the assessment information is received and used to design and provide a personalized modular shoe last and / or personalized shoe component. For example, the user's foot measurements can be received and used directly to select or determine the components of the personalized shoe last or personalized shoe component. In alternative embodiments, such as Fig.12 As shown, the evaluation information may be received 1205 and additionally analyzed to determine (1207, 1209) one or more personal attributes or footwear attributes associated with the user that may affect the user's footwear needs or the calculated footwear fit. The design of the personalized modular last and, optionally, the design of one or more personalized shoe components may then be based at least in part on one or more of the determined personal attributes or footwear attributes.

[0053] The assessment information may include various forms of personal data, such as image data associated with the user, video data associated with the user, responses to an electronic questionnaire, or responses to an electronic request for information from the user. The personal data may include one or more of personal foot shape data (such as foot measurements, geometry, or morphology), personal pathology data, personal gait data, personal biomechanical data, and personal contextual data. The personal data may be received or input into the system in any manner. In some methods, the personal data may be collected by instructing the user, such as by referring to Figure 6 The method described herein may further include analyzing the personal data to determine one or more personal attributes associated with the user. Such personal attributes may include one or more traits, characteristics, or preferences that may affect footwear needs and fit. In some embodiments, the personal data may be analyzed to determine one or more personal foot shape attributes associated with the user or individual. In some embodiments, the personal data may be analyzed to determine one or more personal pathological attributes associated with the user. In some embodiments, the personal data may be analyzed to determine one or more gait or biomechanical attributes associated with the user. In some embodiments, the personal data may be analyzed to determine one or more personal contextual attributes associated with the user. In some methods, references such as Figure 5 , Figure 7 , Figure 8 , Fig. 9 and Fig.10Describe one or more methods to analyze personal data.

[0054] In some embodiments, the method may further include determining one or more footwear attributes associated with the user based on the personal attributes or based on evaluation information associated with the user, such as Fig.12 In some methods, the personal data may be analyzed via one or more modules associated with a system for providing personalized footwear, such as in reference Figure 3 The system 300 is depicted.

[0055] In some embodiments, gait data may be collected and used to provide personalized modular lasts and / or personalized shoe components. In one approach, a processor may determine one or more personal gait or biomechanical attributes that may affect a user's footwear needs and / or a calculated footwear fit. Such gait and biomechanical data may include various forms of data associated with a user, such as image data or video data. For example, gait data may include a video capturing a user's gait (i.e., the user walking) under specified conditions, and biomechanical data may include a pressure map of plantar pressure during standing, walking, and / or any other activity. In some approaches, gait and biomechanical data may be collected using a reference image or a computer program product. Figure 8 The method may include tracking a limb of a user on a video and obtaining kinematic measurements by analyzing the tracked limb. The gait data, tracking points, and kinematic measurements may be analyzed to determine individual gait information and / or one or more gait attributes associated with the user. For example, such gait information or gait attributes may include step time, step length, gait speed, stance time, swing time, foot position, plantar flexion and extension, ankle plantar flexion and extension, knee flexion and extension, joint loads, foot loads, other lower limb dynamics, or other features of the user's gait.

[0056] Embodiments may also include determining 830 one or more footwear attributes associated with the user based on personal gait or biomechanical information or attributes associated with the user. In some methods, the user may be provided with a system for providing personalized footwear, such as a reference Figure 3 One or more modules associated with the system 300 described above are used to analyze gait and biomechanical data to determine personalized footwear properties.

[0057] Figure 3 An exemplary system 300 for providing personalized footwear is illustrated, according to some embodiments. Figure 3 An exemplary data structure is shown; however, it should be understood that other suitable data structures may be used by system 300 .

[0058] The components of system 300 may communicate directly or indirectly, such as through one or more distributed communication networks, such as network 380. For example, network 380 may include a LAN, WAN, Internet, cellular, Wi-Fi, Bluetooth, and other such communication networks or a combination of two or more such networks. The various components of system 300 may also be hardwired.

[0059] It is contemplated that one or more processors may be associated with any of the components described in the system 300. The term processor broadly refers to any microcontroller, computer, control circuit, or processor-based device having a processor, memory, and programmable input / output peripherals that are typically designed to manage the operation of other components and devices. It is further understood to include common accompanying accessory devices, including memory, transceivers for communicating with other components and devices, etc. These architectural options are well known and understood in the art and do not require further description here. The processor may be configured to implement one or more of the methods, steps, actions, and / or functions described herein (e.g., by using a corresponding program stored in memory, which will be well understood by those skilled in the art).

[0060] The system 300 may simply receive the assessment information from an electronic user device in various ways, or may additionally include one or more electronic user devices 350. The user device may be configured to receive recommendations, prompts, queries, surveys, notifications, alerts, instructions, user input, or other information. The electronic user device may include, for example, a smart phone, a tablet computer, a laptop computer, a personal computer, a smart watch, etc.

[0061] One or more user interfaces 355 may be associated with the electronic user device 350. The user interface may be used for user input and / or for output display. For example, the user interface may include any known input device, such as one or more buttons, knobs, selectors, switches, keys, touch input surfaces, audio inputs, and / or displays, etc. The user interface 355 may further include lights, visual indicators, display screens, etc., to convey information to the user, such as, but not limited to, communications of the following: footwear recommendations; instructions for capturing images or videos of the user's foot or gait; questions and information requests about pathologies or contextual information associated with the user; prompts or instructions for indicating sensations associated with the user's foot image; sensory maps illustrating sensations associated with the foot image; notifications; prompts; errors and / or other such information. The sensory map may be two-dimensional or three-dimensional. In this way, the system may receive data or information about foot shape, pathologies, gaits, biomechanics, or contextual attributes associated with the user via the user interface.

[0062] Electronic user equipment can also be equipped with one or more sensors.Sensor can include one or more of the following: image sensor (e.g., camera), temperature sensor, gyroscope, light sensor and global positioning system (GPS) sensor.In some methods, image sensor can be able to record video at a frame rate of at least about 60 frames per second (fps), about 59.94fps, about 50fps, about 30fps or about 24fps in some aspects.In some methods, image sensor can be able to record at a resolution of at least about 1440p, about 1080p or about 720p in some aspects.Users can use image sensors (such as cameras in electronic user equipment) to capture one or more still images or pictures and / or one or more videos of the foot, limbs or parts thereof of the user.

[0063] In one embodiment, a user may complete a virtual footwear evaluation using one or more electronic user devices by submitting at least one of: responses to a series of questions, one or more captured pictures of the user's foot, and one or more captured videos of the user's gait. In this manner, information may be provided to determine a personalized modular last and / or personalized shoe components to produce personalized footwear that fits the user appropriately. In one embodiment, one or more sensors and / or one or more cameras from an electronic user device may be used to acquire any necessary data (e.g., pictures, videos, etc.) from the user to design and provide personalized footwear or otherwise assess the user's footwear needs.

[0064] The system 300 may also include various modules to determine and / or analyze information for providing personalized footwear to the user. In some embodiments, the system 300 may include one or more of the following: a foot shape module 302, a pathology module 304, a gait and biomechanics module 306, and a context module 308. The system 300 may employ these modules to acquire raw data and determine, evaluate, or otherwise extract personal attributes associated with the user based on the raw data. Personal attributes (e.g., foot shape attributes, pathology attributes, gait and biomechanics attributes, context attributes) may include a collection of individual personal traits, characteristics, and product preferences, which may affect the calculated footwear fit and footwear personalization. For example, personal attributes may include: foot shape attributes (such as 3D foot shape, 3D foot size, 2D foot shape, and 2D foot size); foot pathology attributes (such as pain, disease, unobservable pathology, observable pathology, and other foot-associated medical conditions); gait attributes (such as gait patterns, measurements, and kinematics); biomechanical attributes (such as plantar pressure patterns, measurements, and dynamics); and contextual attributes, such as demographic, lifestyle, location, product preference, and style preference data.

[0065] The foot shape module 302 may be used to acquire and analyze data related to a user's foot. The foot shape module 302 may include one or more algorithms (such as machine learning algorithms) that may analyze various forms of data (including but not limited to image data) to determine one or more foot shape attributes associated with the user. The foot shape attributes may include a 3D model, 3D characteristics and dimensions, 2D characteristics and dimensions, and other measurements of a foot, limb, or portion thereof that may be useful in determining the user's footwear needs. In some embodiments, the raw data acquired by the foot shape module 302 and the foot shape attributes associated with the user (i.e., as determined by the foot shape module 302) may be stored in a personal fit database 334. For example, the foot shape attributes and raw data associated with the user may be associated with a user profile in the personal fit database 334. The foot shape module 302 may be used to determine the user's footwear needs based on the user's footwear profile. Figure 5 It is also contemplated that one or more predetermined foot shape measurements and / or foot shape attributes may be received by the foot shape module 302. For example, one or more foot shape measurements or foot shape attributes may have been predetermined (e.g., in a medical assessment or other assessment method) and stored in one or more databases that may be in communication with the foot shape module 302. In this manner, predetermined foot measurements or attributes may be received by the foot shape module 302 without evaluating video and image data.

[0066] like Figure 3 As shown, the foot shape module 302 may include one or more model reconstruction algorithms 311. The model reconstruction algorithm 311 may, for example, construct a model of the user's foot, limb, or part thereof based on image data of the user's foot, limb, or part thereof. In some methods, the model reconstruction algorithm 311 may be a convolutional neural network (CNN) model trained for two-dimensional (2D) to three-dimensional (3D) model reconstruction. The foot shape model may also include a reference foot model database 309. The reference foot model database 309 may include a collection of 3D foot models and associated photos. In some methods, the data in the reference foot model database 309 may be used to train the model reconstruction algorithm 311.

[0067] The foot shape module 302 may also include one or more foot shape classification algorithms 310. The foot shape classification algorithm 310 may be used to determine one or more foot shape attributes associated with a user, for example, based on image data of a user's foot, limb, or part thereof. The foot shape classification algorithm 310 may be a machine learning model trained to identify one or more foot shape attributes based on imaging data and / or a 3D model of a user's foot, limb, or part thereof. The foot shape module may further include a reference foot shape category database 312 that includes foot shape attributes based on a population of people having shared parameters (i.e., shared 3D model, image data parameters).

[0068] In operation, the foot shape module 302 can analyze the foot shape data to determine at least one foot shape attribute associated with the user. The foot shape model 302 can create a high-quality and accurate 3D model of the user's foot using an image of the user's foot, limb, or portion thereof. Such a 3D model can help to accurately capture the complex nature of the foot shape. The foot shape module 302 can utilize machine learning (including but not limited to neural networks) and spatial computing. It is contemplated that one or more of the machine learning algorithms (such as the model reconstruction algorithm 311 or spatial computing) can be applied to the image to assess the size and morphology of the foot.

[0069] The pathology module 304 may be used to acquire and analyze data related to foot pathologies associated with a user. The pathology module 304 may include one or more algorithms (such as machine learning algorithms) that may analyze various forms of data (including, but not limited to, image data) to determine one or more foot pathology attributes associated with the user. The foot pathology attributes that may be determined using the foot pathology module may include observable foot pathologies and / or non-observable foot pathologies. In some embodiments, the raw data acquired by the pathology module 304 and / or the pathology attributes associated with the user (i.e., as determined by the foot shape module 304) may be stored in the personal fit database 334. For example, the pathology attributes and / or raw data associated with the user may be stored in a user profile in the personal fit database 334.

[0070] like Figure 3 As shown, the foot shape module 304 may include one or more observable pathology classification algorithms 313. The observable pathology classification algorithms 313 may include one or more machine learning models trained to identify observable foot pathologies based on image recognition. The pathology module 304 may further include one or more reference observable pathology databases 315. The reference observable pathology database 315 may include reference image data of a population with observable foot pathologies. In some approaches, the data in the reference observable pathology database 315 may be used to train the observable pathology classification algorithm 313.

[0071] The pathology module 304 may also include one or more unobservable pathology classification algorithms 314. The unobservable pathology classification algorithms may include one or more machine learning models trained to identify unobservable foot pathologies based on image analysis. The pathology module 304 may further include one or more reference unobservable pathology databases 316. The reference unobservable pathology databases 316 may include reference image data of a population with observable foot pathologies. In some approaches, the data in the reference observable pathology databases 316 may be used to train the observable pathology classification algorithms 314.

[0072] In operation, the pathology module 304 can receive data related to a pathology associated with a user. In some approaches, the pathology module 304 can use an electronic user device to acquire or otherwise receive the data. In one embodiment, the pathology module 304 can use a reference Figure 6 and Figure 7 The image data may be acquired and analyzed by one or more steps of the method described in the foregoing. In some methods, the pathology module may identify pathological attributes that may affect the footwear needs of the user. It is also contemplated that one or more predetermined foot pathological attributes or foot pathological information may be received by the pathology module 304. For example, one or more foot pathological attributes may have been predetermined (e.g., in a medical evaluation or other pathological evaluation method) and stored in one or more databases that may communicate with the pathology module 304. In this manner, the predetermined foot pathological attributes or information may be received by the pathology module 304 without evaluating the video and image data.

[0073] The pathology module 304 can identify both observable and unobservable foot pathologies that may affect the user's footwear needs. In some approaches, the pathology module 304 can be configured to obtain or otherwise receive foot photographs, sensory maps, and / or questionnaires. In one example, to obtain the user's foot image data, the system 300 can prompt the user to take a photo or video of the user's foot.

[0074] In some embodiments, image analysis may be applied by one or more processors associated with the foot pathology module 304. For example, image analysis may be applied to sensory maps to collect data regarding pain and sensitivity corresponding to specific foot conditions and pathologies, which may ultimately impact an individual's footwear needs. In an exemplary embodiment, the pathology module 304 may also analyze responses to surveys or questionnaires to collect data regarding pre-existing medical and / or foot conditions (e.g., diabetes, heel spurs, shin splints, or other foot conditions), prior injuries (e.g., stress fractures, ankle sprains, or other types of foot and leg injuries), and medical procedures (e.g., plantar pedis surgery, bunionectomy, or other types of foot and leg medical / surgical procedures).

[0075] The gait and biomechanics module 306 may be used to acquire and analyze data related to the gait and biomechanics of a user. The gait and biomechanics module 306 may include one or more algorithms (such as machine learning algorithms) that may analyze various forms of data (including but not limited to image data or video data) to determine one or more gait and biomechanical attributes associated with the user. Gait attributes may include, for example, gait patterns, characteristics, measurements, and kinematics. Biomechanical attributes may include, for example, plantar pressure patterns, measurements, and kinematics. In some embodiments, raw data or information acquired by the gait and biomechanics module 306 and / or gait attributes associated with the user (i.e., as determined by the gait and biomechanics module 306) may be stored in a personal fit database 334. For example, gait attributes and raw data associated with a user may be associated with a user profile in the personal fit database 334.

[0076] like Figure 3As shown, the gait and biomechanics module 306 may include one or more gait classification algorithms 318, and may include one or more biomechanics algorithms 319. The gait classification algorithm 318 may determine the gait attributes associated with the user based on, for example, image data and / or video data. In some methods, the gait classification algorithm 318 may include a machine learning model trained to track the user's gait training based on gait video analysis and a collection of associated gait attributes. The gait module 306 may further include one or more reference gait databases 320. In some methods, the reference gait database 320 may include gait attributes based on a population with shared parameters. The biomechanics classification algorithm 319 may determine the biomechanical attributes associated with the user based on, for example, pressure sensor data. In some methods, the biomechanics classification algorithm 319 may include a machine learning model trained to track the user's gait training based on a pressure map analysis and a collection of associated biomechanical attributes. The gait and biomechanics module 306 may further include one or more reference biomechanics databases 321. In some approaches, reference biomechanical database 321 may include biomechanical properties based on a population of people having shared parameters.

[0077] In operation, the gait and biomechanics module 306 can receive data related to the user's gait (such as image data and / or video data) and the user's biomechanics (such as plantar pressure). In one example, the gait and biomechanics module 306 can generate a user's gait data according to the reference. Figure 8 The gait and biomechanics module 306 may acquire and analyze image data and / or video data according to one or more steps of the methods described herein to determine one or more gait attributes associated with the user. In some methods, the gait and biomechanics module may identify gait attributes, such as gait patterns or characteristics, that may affect the footwear requirements of the user. In another example, the gait and biomechanics module 306 may determine the gait attributes, such as gait patterns or characteristics, associated with the user according to the reference Fig. 9 The method of one or more steps described herein is used to obtain and analyze the plantar pressure data to determine one or more biomechanical attributes associated with the user. It is also contemplated that one or more predetermined gait and biomechanical attributes may be received by the gait and biomechanics module 306. For example, one or more gait and biomechanics measurements or gait and biomechanics attributes may have been predetermined (e.g., in a medical assessment or (multiple) other gait assessment or biomechanical assessment methods) and stored in one or more databases that can communicate with the gait and biomechanics module 306. In this way, the predetermined gait and biomechanics attributes may be received by the gait and biomechanics module 306 without evaluating the video and images.

[0078] Context module 308 may be used to obtain and analyze data related to context attributes associated with a user. For example, context attributes associated with a user may include data or information related to demographics, lifestyle, location, product preferences, and style preferences. In some embodiments, the raw data obtained by context module 308 and the context attributes associated with a user (i.e., determined by context module 302) may be stored in personal fit database 334. For example, the context attributes associated with a user and the raw data may be associated with a user profile in personal fit database 334.

[0079] like Figure 3 As shown, the context module 308 may include one or more of the following: a demographic database 322, a lifestyle database 324, and a location database 326. The demographic database 322 may include data on key demographic factors known to be associated with specific foot characteristics. The lifestyle database 324 may include data on lifestyle factors that may affect the user's footwear. The location database 326 may include data on location-related factors that may affect the user's footwear needs.

[0080] In operation, the context module 308 may receive data from the user, for example, through a survey or questionnaire related to demographics, product preferences, lifestyle, and location (i.e., the climate or environment in which the user will wear their footwear). The context module 308 may utilize data from one or more of the databases (i.e., the demographic database 322, the lifestyle database 324, and the location database 326) to determine one or more context attributes associated with the user. For example, the context module 308 may use the demographic database 322 to determine personal context attributes associated with the user based on demographic data. The context module 308 may use the lifestyle database 324 to determine personal context attributes associated with the user based on lifestyle data. The context module 308 may use the location database 326 to determine personal context attributes associated with the user based on location data.

[0081] Figure 3Exemplary data structures for the foot shape module 302, the pathology module 304, the gait and biomechanics module 306, and the context module 308 are illustrated. However, it is contemplated that the system 300 may utilize alternative data structures to operate the foot shape module 302, the pathology module 304, the gait and biomechanics module 306, and the context module 308. For example, any of these modules may communicate with any of the databases illustrated in the system 300. Furthermore, while illustrated as separate modules, it is contemplated that a single engine, processor, server, or computing device may be employed to perform each of the collection and analysis of foot shape, pathology, gait, biomechanics, and context data. Additionally, while Figure 3 Each of the modules and databases depicted in the is depicted as part of a single system, but it should be understood that any of the modules and / or databases may be installed in separate systems or on separate servers.

[0082] The system 300 may also include a personal attribute analysis module 328. The personal attribute analysis module 328 may analyze the assessment information or one or more personal attributes (i.e., foot shape attributes, pathological attributes, gait and biomechanical attributes, and / or situational attributes) associated with the user to determine one or more footwear attributes associated with the user. The footwear attributes may include any characteristics, features, or product specifications that may affect the calculated footwear fit and / or footwear personalization. The footwear attributes may include, for example, information related to the construction (e.g., design, seams, and size), materials, material attributes (e.g., elasticity, stretchability, flexibility, breathability, waterproofness), style, appearance, designated categories, aesthetic descriptors, etc. of the footwear product. It is also contemplated that the footwear attributes may include characteristics, features, or product specifications that may affect the effect of the footwear product on the skin or the interaction of multiple materials used in the footwear product.

[0083] The personal attribute analysis module 328 may include one or more classification algorithms. The classification algorithm may analyze personal attributes or assessment information associated with the user to determine one or more footwear attributes associated with the user based on the personal attributes or assessment information of the user. In some methods, the footwear classification algorithm may include one or more machine learning or CNN models to identify footwear attributes based on various inputs. Such inputs may include, but are not limited to, 3D models, foot shape attributes, pathological attributes, gait and biomechanical attributes, and / or situational attributes. The input may further include any form of raw assessment information input into the system 300, such as image data, video data, situational data, questionnaire responses, etc.

[0084] The system 300 may also include one or more prioritized footwear classification modules 330, which may include one or more classification algorithms. The prioritized footwear classification modules 330 may include one or more machine learning models trained to identify footwear attributes associated with a user based on the user's personal attributes (i.e., foot shape attributes, pathological attributes, gait and biomechanical attributes, and / or situational attributes). In this way, the prioritized footwear classification module 330 may identify the footwear attributes that best suit the user based on the user's personal attributes. The prioritized footwear classification module 330 may communicate with one or more attribute relationship databases 332. The attribute relationship databases 332 may include data on the relationships between various footwear attributes and personal attributes (i.e., foot shape attributes, pathological attributes, gait and biomechanical attributes, situational attributes).

[0085] The following describes the operation Figure 3 Various methods of foot shape module, pathology module, gait and biomechanics module and situational module described in the text. For example, these methods can be used to evaluate personal attributes associated with users to provide personalized footwear. Personal attributes can evaluate one or more of foot shape, pathology, gait, biomechanics and situational information (such as user's demographics, lifestyle, location and / or product preferences). Such personal attributes can affect footwear product design to produce products that best suit the user's fit, comfort and preferences. One or more of the modules can operate together to obtain a comprehensive understanding of the personal attributes associated with the user. As discussed above, one or more modules can be used to provide the user with input for the design of personalized footwear, and no module is required to perform the method described herein. In addition, as mentioned above, it is also conceivable that assessment information (i.e., foot shape, pathology, gait, biomechanics information, etc.) is input into the design of personalized footwear without further determination or analysis via any module.

[0086] Foot shape module

[0087] The following describes an exemplary method of acquiring and displaying foot shape data according to some embodiments. Figure 3 The electronic user device described herein may be used to perform one or more steps of the method. In some methods, the method may be performed by or in combination with reference to Figure 3 The foot shape module 302 described above is executed. One or more steps of the method may be used to acquire and display foot pathology data.

[0088] The method includes acquiring or otherwise receiving image data associated with a user's foot, limb, or part thereof. The image data may include, for example, photos, images, and / or videos. In some methods, the image data may include three photos of an individual's foot from a specific angle. The image data may be acquired or otherwise captured via an image sensor (such as a camera) associated with an electronic user device. In one example, a user may use a camera to take three photos of each foot to capture the following three views: top, inside, and outside. The image data may include any number of views, such as any number of views between 1 to 20, 1 to 10, and 1 to 5, and may also include other views, such as a front view, a rear view, and a look-up view. In some methods, the camera may provide augmented reality (AR) guidance to ensure the correct positioning of the camera. It may also be conceived that the method is not limited to image data, but may include acquiring data representing or indicating other forms of a user's foot, limb, or part thereof. For example, it may also be conceived that image data may be acquired from one or more databases.

[0089] In some methods, the image data can capture the user's foot relative to another object (e.g., a sheet of standard letter paper). In one embodiment, the user places his / her foot on a sheet of standard letter paper to obtain one or more views.

[0090] The method may also optionally include instructing the user regarding image data acquisition. In some methods, instructions may be transmitted to the user, for example, via a user interface associated with an electronic user device, before or during the user capturing image data. The prompt may instruct the user to take a specific number of photos, take photos from a specific view or angle, place a specific foot, limb, or part thereof within the view of the image sensor, adjust lighting, position the foot, limb, or part thereof relative to another object, or adjust the position of the electronic user device used to acquire the image data.

[0091] After acquiring the image data, the method may optionally include displaying an image of the user's foot, limb, or part thereof to the user via a user interface. In some methods, the image may be a 3D model of the user's foot generated at least in part based on the received image data.

[0092] In one embodiment, the user interface may also display one or more personalized recommendations about one or more footwear product specifications based at least in part on the foot shape data. The recommendations may suggest one or more footwear products, sizes, styles, models, materials, material properties (e.g., breathability, viscoelasticity, stretchability, tear strength, flexibility, water resistance, wear resistance, etc.), weights, tongue features, lining features, insole features, toe features, outsole features, upper features, sole features, heel height, foot support, or other footwear attributes to the user.

[0093] Figure 5 An exemplary method 500 for analyzing image data of a user's foot, limb, or part thereof to determine foot shape information or one or more foot shape attributes associated with the user is shown. In some embodiments, one or more steps of the method 500 may be performed by or in conjunction with reference to Figure 3 The foot shape module 302 described above is executed.

[0094] Method 500 includes receiving 505 image data of a user's foot, limb, or portion thereof. In some embodiments, the image data may include using a reference Figure 4 The method described herein acquires image data. For example, the image data may include image data of a user's foot, limb, or part thereof, which are from three views: top, inside, and outside. The image data may include any number of views, such as any number of views between 1 and 20, 1 and 10, and 1 and 5, and may also include other views, such as a front view, a rear view, and a bottom view. Then, the method generates 510 a 3D model of the user's foot, limb, or part thereof based on the image data. In some methods, at least one model reconstruction algorithm may be applied to the image data to generate the 3D model. In some methods, the model construction algorithm may also extract at least one size of the user's foot, limb, or part thereof from the image data and / or the 3D model. In some embodiments, the model reconstruction algorithm may be used to extract at least one foot size associated with the user's foot, limb, or part thereof from the image data. In some methods, the model reconstruction algorithm may be a reconstruction of a 2D image trained by a CNN model into a 3D model. For example, a CNN model may be trained using a reference set of 3D models and associated photos from three sets of views. Additionally, method 500 may further include determining at least one foot dimension in two dimensions using image processing, for example based on the layout of the foot on paper (e.g., as captured in the image data.) Such dimensions may also be used to construct a 3D model of the user's foot or a portion thereof.

[0095] The method 500 may further include analyzing 520 the 3D model of the user's foot, limb, or portion thereof to determine at least one foot shape attribute associated with the user. In some embodiments, the foot shape attributes may be determined by applying one or more foot shape classification algorithms to the 3D model and / or image data. In some methods, the foot shape classification algorithm may be a machine learning algorithm trained to identify one or more foot shape attributes based on the 3D model and / or image data. In other embodiments, the foot shape attributes may be determined using spatial calculations to evaluate at least one foot size or morphology associated with the user's foot, limb, or portion thereof using image data.

[0096] Foot shape attributes may include any foot shape characteristics that may affect footwear. Foot shape attributes may include foot dimensions, such as arch height, foot width, full foot width, mid-foot width, foot length, sole width, toe-to-toe size, sole circumference, sole angle, heel circumference, lateral metatarsal length, medial metatarsal length, arch length, instep height, and instep distance. Foot shape attributes may also include shape-related characteristics or morphology, such as hallux direction, toe direction, or foot type. By some methods, foot shape attributes may include about 10, 8, 6, 4, or 2 dimensions, which capture at least 75%, at least 85%, at least 90%, and in some cases about 92.6% of the shape changes of the user's foot. In one example, foot shape attributes include 6 dimensions, including arch height, sole width combination and toe-toe distance, full foot width, foot type, and mid-foot width. In some embodiments, foot shape attributes may be determined by applying one or more foot shape classification algorithms to 3D models and / or image data. In some approaches, the foot shape classification algorithm can be a machine learning algorithm trained to identify one or more foot shape attributes based on the 3D model and / or image data. In other embodiments, the foot shape attributes can be determined using spatial calculations to assess at least one foot dimension or morphology associated with a user's foot, limb, or portion thereof using image data.

[0097] It is also contemplated that one or more foot shape measurements or foot shape attributes may be received from a database. For example, one or more foot shape measurements or foot shape attributes may have been predetermined (e.g., in a medical assessment or other foot shape assessment method). In this manner, predetermined foot shape measurements and / or foot shape attributes may be received without evaluating video and image data. That is, steps 505-520 are not required to determine one or more foot shape measurements or foot shape attributes associated with the user.

[0098] Then, method 500 includes determining a footwear attribute associated with the user based on at least one foot shape attribute or foot shape information. The footwear attribute associated with the user may include attributes of a footwear product that best suits the user based on the user's foot shape attribute. In one example, if the user's foot shape attribute is a high arch, the footwear attribute associated with the user may be a high arch support. In another example, when the foot shape attribute associated with the user is an overlapped toe or other toe deformity, the footwear attribute associated with the user may be a material with high elasticity. In some embodiments, a classification algorithm may be used to identify at least one footwear attribute that best suits the user based on the user's foot shape attribute. In some methods, the classification algorithm may include a machine learning model trained to identify footwear attributes associated with the user based on the user's foot shape attribute.

[0099] In some embodiments, method 500 may include determining footwear attributes associated with a user using established relationships between various footwear attributes and various foot shape attributes. In an exemplary embodiment, the relationship between the footwear attributes and the foot shape attributes may be quantified by scoring the footwear attributes (e.g., a score of 0-100) based on the foot shape attributes. A high score may indicate a strong preference for a particular parameter, while a low score indicates a poor fit for a particular parameter. The relationship between the footwear attributes and the foot shape attributes may be stored, for example, in a database.

[0100] Method 500 may optionally include associating foot shape attributes and / or footwear attributes with the user 530. In some methods, foot shape attributes and / or footwear attributes may be stored in a database such as Figure 3 The exemplary personal fit database depicted in FIG. 1 is associated with a user. For example, the database may include a user profile storing one or more foot shape attributes associated with the user. In some examples, the user profile may also store one or more footwear attributes associated with the user.

[0101] By acquiring and analyzing foot shape data in the above manner, precise 3D characteristics of a user's foot, limb, or part thereof may be captured and used to provide personalized footwear tailored to the user's foot shape.

[0102] Pathology Module

[0103] Figure 6 An exemplary method 600 of acquiring and displaying foot pathology data according to some embodiments is shown. One or more electronic user devices (such as a reference Figure 3 The electronic user device described herein may be used to perform one or more steps of method 600. In some methods, method 600 may be performed by or in conjunction with reference to Figure 3One or more steps of method 600 may be used to acquire and display foot pathology data.

[0104] Method 600 includes acquiring 605 or otherwise receiving image data associated with a user's foot, limb, or part thereof. The image data may include, for example, photos, images, and / or videos. The image data may be acquired or otherwise captured via an image sensor (such as a camera) associated with an electronic user device. In some methods, the image data may include three photos of an individual foot from a specific angle. For example, a user may use a camera to take three photos of each foot to capture the following three views: top, inside, and outside. The image data may include any number of views, such as any number of views between 1 to 20, 1 to 10, and 1 to 5, and may also include other views, such as a front view, a rear view, and a bottom view. The image data may be acquired using a camera having a function of providing AR guidance for correct camera positioning. It is also conceivable that method 600 is not limited to image data, but may include data in other forms representing or indicating a user's foot, limb, or part thereof. It is also conceivable that, for example, image data is acquired from one or more databases.

[0105] The method 600 may also optionally include providing 610 to the user instructions, such as on how to acquire the image data. For example, the instructions may be provided, such as by sending a prompt to the user via a user interface associated with the electronic user device before or during the user capturing the image data. The instructions may instruct the user to take a specific number of pictures, take pictures from a specific view or angle, place a specific foot, limb, or part thereof within the view of the image sensor, adjust the lighting, or adjust the position of the electronic user device used to acquire the image data.

[0106] After acquiring the image data, the method 600 may also include displaying 615 an image of the user's foot, limb, or part thereof to the user via a user interface. In some methods, the image may be a 3D model of the user's foot generated at least in part based on the received image data. The method 600 also includes receiving 620 an indication of one or more sensations associated with the image of the user's foot, limb, or part thereof. In some embodiments, the user may draw the sensation onto one or more images by indicating the area experiencing one or more sensations. The sensation drawing may be performed using a user interface of an electronic user device. In one method, the user may indicate sensations on one or more images by, for example, drawing on the user's foot image using a stylus or finger on a touch screen of an electronic user device. In some embodiments, the user may select various colors or marks to indicate various sensations associated with the foot, limb, or part thereof captured in the image. For example, sensations may include numbness, burning, pain, pins and needles, itching, soreness, throbbing pain, etc. It is also conceivable that the user may draw or otherwise indicate one or more injuries, medical conditions, medical procedures, etc. on the image data.

[0107] Method 600 also includes receiving 625 information about one or more foot pathology attributes associated with the user. In some methods, such information may be received from a database containing one or more previously determined foot pathology attributes. In other methods, a questionnaire may be transmitted to the user to obtain information about one or more foot pathology attributes. In some methods, the questionnaire may be transmitted to an electronic user device associated with the user and displayed via a user interface of the device. The questionnaire may include one or more questions, prompts, or requests for information related to the pathology attributes associated with the user. The questionnaire may relate to any foot pathology attribute, including, but not limited to, pre-existing medical and foot conditions, injuries, medical procedures affecting foot function and gait, foot biomechanics, history of foot injuries, history of limb injuries, history of foot medical procedures, history of lower extremity medical procedures, problems or success with wearing other shoes, and any other medical information related to the foot. In response to receiving the questionnaire, the user may enter information related to the query, prompt, or information request via the user interface. The method may further optionally include associating the foot pathology attribute with the user. In some methods, the foot shape attribute may be in a database (such as Figure 3 For example, the database may include a user profile storing one or more foot shape attributes associated with the user.

[0108] The user interface may also display one or more personalized recommendations about one or more footwear product specifications based at least in part on the pathology data. The recommendations may recommend one or more footwear products, sizes, styles, models, materials, material properties (e.g., breathability, viscoelasticity, stretchability, tear strength, flexibility, waterproofness, wear resistance, etc.), weights, tongue features, lining features, insole features, toe features, outsole features, upper features, sole features, heel height, foot support, construction (i.e., stitching, mesh), or other footwear attributes to the user. The recommendations may be used to design personalized footwear.

[0109] Ultimately, the foot pathology information and / or foot pathology attributes may be used to determine 630 the design of a personalized modular last and any necessary personalized shoe components.

[0110] Figure 7 An exemplary method 700 for analyzing image data to determine attributes of a foot pathology associated with a user is shown. In some embodiments, one or more steps of method 700 may be performed by or in conjunction with reference to Figure 3 The pathology module 304 described here is executed.

[0111] Method 700 includes receiving 705 image data of a user's foot, limb, or portion thereof. In some embodiments, the image data may include using a reference Figure 5 The image data may include image data of a user's foot, limb, or part thereof from three views: top, inside, and outside. The image data may include any number of views, such as any number of views between 1 and 20, 1 and 10, and 1 and 5, and may also include other views, such as a front view, a rear view, and a bottom view.

[0112] Then, method 700 includes analyzing 710 the image data to determine at least one foot pathology attribute, such as an observable foot pathology. Such observable foot pathologies may include toe deformities (e.g., hammer toes, claw toes, mallet toes, crossed toes), hallux valgus, bunions, blisters, or other general traumas. Observable pathologies may be automatically identified, for example, based on image data of a user's foot, limb, or portion thereof. In some embodiments, an observable pathology classification algorithm is applied to the image data to identify one or more observable foot pathologies. In some methods, the observable pathology classification algorithm may be a machine learning model trained to identify observable foot pathologies based on image recognition. In some instances, the machine learning model may be trained using reference images of individuals with known observable foot pathologies.

[0113] The method 700 also includes receiving 715 an indication of a sensation corresponding to the one or more images of the user's foot. In some embodiments, such an indication may be in the form of a sensation map, such as a reference map. Figure 6 The method then includes analyzing 720 the indication of sensation to determine at least one foot pathology attribute, such as an unobservable foot pathology attribute. The unobservable foot pathology attributes may include, for example, central metatarsalgia, plantar fasciitis, heel spurs, and diabetic neuropathy, among others. It is contemplated that by receiving an indication of sensation corresponding to an area of ​​a user's foot (e.g., by mapping the sensation to image data of the user's foot), it is possible to establish a correlation between the sensation and an unobservable pathology that may affect footwear needs. In some embodiments, at least one unobservable foot pathology may be determined by applying an unobservable pathology classification algorithm to the sensation map or some other indication of sensation corresponding to the image of the user's foot. In some methods, the unobservable pathology classification algorithm may include a machine learning model trained to identify unobservable foot pathology based on image analysis. For example, a reference sensation map of a foot with known unobservable foot pathology may be used to train the machine learning model.

[0114] It is also contemplated that one or more foot pathology attributes may be received from a database. For example, one or more foot pathology attributes may have been predetermined (e.g., during a medical evaluation or other evaluation process). In this manner, predetermined foot pathology data and / or foot pathology attributes may be received without evaluating image data and / or questionnaires. That is, steps 705-720 are not required to determine one or more foot pathology attributes associated with the user.

[0115] Then, the method 700 includes determining 725 a footwear attribute associated with the user based on at least one foot pathology attribute. The footwear attribute associated with the user may include an attribute of a footwear product that best suits the user based on the foot pathology attribute of the user. For example, if the foot pathology attribute of the user is diabetic neuropathy, the footwear attribute associated with the user may be a closed-toe style. In another example, if the foot pathology attribute associated with the user is a bunion (hallux valgus), bunionitis scaly, or other toe deformity, the footwear attribute associated with the user may be a shoe upper with high elasticity and / or a shoe upper without seams. In another example, if the foot pathology attribute associated with the user is hyperhidrosis, the footwear attribute associated with the user may be a material with high breathability. In some embodiments, a classification algorithm may be used to identify at least one footwear attribute that best suits the user based on the foot pathology attribute of the user. In some methods, the classification algorithm may include a machine learning model trained to identify footwear attributes associated with the user based on the foot pathology attribute of the user. In some embodiments, the method may determine the footwear attributes associated with the user by using relationships established between various footwear attributes and various foot pathology attributes. In one exemplary embodiment, the relationship between the footwear attribute and the foot pathology attribute can be quantified by scoring the footwear attribute based on the foot pathology attribute (e.g., a score of 0-100). A high score may indicate a strong preference for a particular parameter, while a low score indicates a poor fit for a particular parameter. The relationship between the footwear attribute and the foot pathology attribute can be stored, for example, in a database.

[0116] Method 700 may optionally include associating 730 the foot pathology attributes and / or the footwear attributes with the user. In some methods, the foot pathology attributes and / or the footwear attributes may be stored in a database such as Figure 3 The user profile may be associated with a user in an exemplary personal fit database depicted in FIG. 1 . For example, the database may include a user profile storing one or more foot pathology attributes associated with the user. In some examples, the user profile may also store one or more footwear attributes associated with the user.

[0117] Through Figure 6-Figure 7 By acquiring and analyzing pathology data in the manner described herein, observable and unobservable pathologies associated with a user's foot, limb, or portion thereof may be captured and used to provide personalized footwear tailored to the user's foot pathology.

[0118] Gait module

[0119] The following describes an exemplary method of acquiring and displaying gait data according to some embodiments. Figure 3 The electronic user device described herein may be used to perform one or more steps of the method. In some methods, the method may be performed by or in combination with reference to Figure 3 The method may be performed by the gait and biomechanics module 306 described herein. One or more steps of the method may be used to acquire and display gait and biomechanics data associated with a user.

[0120] The method includes obtaining or otherwise receiving video data associated with the user's gait. The video data may include, for example, a video of the user walking. In some methods, the video data may include two videos of the user walking, wherein the video is obtained from two specific views. The two specific views may be, for example, a front view and a side view. The video data may be obtained or otherwise captured via an image sensor (such as a camera) associated with an electronic user device. In some methods, the video data may be unmarked video data. It is also contemplated that the method is not limited to video data, but may include data representing and indicating other forms of the user's foot, limbs, or parts thereof. For example, it is also contemplated that video data may be obtained from one or more databases. Such data may be, for example, pre-recorded videos or images of the user's gait or other pre-captured data.

[0121] Method can also optionally include providing instructions such as about how to obtain video data to the user. For example, instructions can be provided by sending prompts to the user via a user interface associated with an electronic user device, such as before or during the user captures video data. Instructions can indicate that the user (or the user's assistant) records a video of a specified duration, shoots a video from a specific view or angle, adjusts the video background, allows the user to walk a specified number of steps or strides, adjusts the frame rate of video capture, adjusts lighting, or adjusts the position of the electronic user device for obtaining video data. After obtaining or otherwise receiving the video data of the gait of the user, the method can optionally include a video showing the gait of the user. For example, a video can be displayed on the user interface of the electronic user device.

[0122] For example, after acquiring the video data, the method may also optionally include displaying one or more footwear products via an electronic product catalog. A user may view the footwear products on a user interface of an electronic user device. The product catalog may include various footwear products, models, and / or styles. The user has the option of selecting one or more footwear products being viewed, for example, via a user interface of an electronic device. The user may then select a particular footwear product, model, or style.

[0123] The user interface may also display one or more personalized recommendations about one or more footwear product specifications based at least in part on the gait and biomechanical data. The recommendations may suggest one or more footwear products, sizes, styles, models, materials, material properties (e.g., breathability, viscoelasticity, stretchability, tear strength, flexibility, waterproofness, wear resistance, etc.), weights, tongue features, lining features, insole features, toe features, outsole features, upper features, sole features, heel height, foot support, construction (i.e., stitching, mesh), or other footwear attributes for the user. The recommendations may be used to design personalized footwear.

[0124] Figure 8 An exemplary method 800 for performing gait assessment according to some embodiments is shown. In some embodiments, one or more steps of method 800 may be performed by or in conjunction with reference to Figure 3 The gait and biomechanics module 306 described above is executed.

[0125] Method 800 includes receiving 805 video data of a user's gait. In some embodiments, the video data may include image data obtained using the method described above. For example, the video data may include a video of a user walking. In some methods, the video data may include two videos of a user walking, wherein the videos are obtained from two specific views. It is contemplated that the video data may be an unmarked video, wherein no obvious object is placed on an area of ​​interest (e.g., a limb or foot) for tracking. In some examples, anatomical features, personal features, skin color, and deformities are excluded from intentional marking. The video data may then be parsed 810 into frames.

[0126] Method 800 further includes (815) tracking at least one limb of the user. For example, one or more points on a limb of the user may be tracked. The points that may be tracked may include one or more of the following: lateral condyle of the knee, patella (i.e., kneecap), proximal tibia, distal tibia, ankle joint, ankle joint including the ankle joint, foot, foot including the navicular bone, and toes. In some embodiments, tracking may be unmarked, or tracked in a view of the video without a user reference marker. In some methods, tracking may be performed using one or more tracking-based machine learning algorithms to identify key points on a limb of the user. In some methods, tracking involves tracking the movement of one or more points between frames of the video.

[0127] Method 800 may further include analyzing 820 the tracked limbs to determine at least one gait attribute (such as a kinematic gait measurement). Kinematic gait measurements may include, for example, step time (i.e., the duration between the ipsilateral and contralateral heel strike events), step length (i.e., the horizontal distance the heel moves between the ipsilateral and contralateral heel strikes), gait speed (i.e., step length divided by step time), stance time (i.e., the time between consecutive heel strikes and toe-off events), and stance swing (i.e., the time from toe-off to consecutive heel strike events). Gait attributes may also include foot position, plantar flexion, ankle flexion, joint load, foot load, and other lower limb or foot dynamics. Tracking points on a video frame may provide detailed information about gait. In some methods, motion analysis may be applied to tracking points on a video frame to assess gait.

[0128] Method 800 may further include analyzing 825 kinematic gait measurements to determine at least one gait attribute. Gait attributes may include any gait category, pattern, or characteristic that may provide information for the user's footwear needs. In some embodiments, one or more gait classification algorithms may be used to determine the kinematic gait measurements. In some methods, the gait classification algorithm may include one or more machine learning models trained to track points based on one or more of the following: gait videos, gait patterns, gait measurements, and gait characteristics. In some examples, the machine learning model may be trained using reference gait data from a population with known gait parameters.

[0129] It is also contemplated that one or more gait measurements or gait attributes may be received from a database. For example, one or more gait measurements or gait attributes may have been predetermined (e.g., in a gait lab or by another gait assessment method). In this manner, predetermined gait attributes and / or gait properties may be received without evaluating video and image data. That is, steps 805-825 are not required to determine one or more gait measurements or gait attributes associated with the user.

[0130] Method 800 also includes determining 830 footwear attributes associated with the user based on at least one gait attribute. The footwear attributes associated with the user may include attributes of a footwear product that best suits the user based on the user's gait attributes. For example, if the user's gait attribute is underpronation, the footwear attribute associated with the user may be high arch support. In some examples, gait attributes such as heel contact, forefoot contact, or midfoot contact may be related to cushioning and / or sole thickness (i.e., footwear attributes). Therefore, in some examples, for gait attributes such as heel contact, forefoot contact, or midfoot contact, footwear attributes associated with cushioning and / or sole contact may be associated with the user. In some embodiments, a classification algorithm may be used to identify at least one footwear attribute that best suits the user based on the user's gait attributes. In some methods, the classification algorithm may include a machine learning model trained to identify footwear attributes associated with the user based on the user's gait attributes.

[0131] In some embodiments, method 800 may use the established relationships between various footwear attributes and various gait attributes to determine footwear attributes associated with a user. In an exemplary embodiment, the relationship between the footwear attributes and the gait attributes may be quantified by scoring the footwear attributes (e.g., a score of 0-100) based on the gait attributes. A high score may indicate a strong preference for a particular parameter, while a low score indicates a poor fit for a particular parameter. The relationship between the footwear attributes and the gait attributes may be stored, for example, in a database.

[0132] Method 800 may optionally include associating gait attributes and / or footwear attributes with the user 835. In some methods, gait attributes and / or footwear may be stored in a database such as Figure 3 The user profile may be associated with a user in an exemplary personal fit database depicted in FIG. 1 . For example, the database may include a user profile storing one or more gait attributes associated with the user. In some examples, the user profile may also store one or more footwear attributes associated with the user.

[0133] By acquiring and analyzing gait data in the manner described above, gait attributes associated with a user's foot, limb, or portion thereof may be captured and used to provide personalized footwear tailored to the user's gait.

[0134] Fig. 9 An exemplary method 900 for performing a biomechanical assessment according to some embodiments is shown. In some embodiments, a reference Figure 3 One or more steps of the gait and biomechanics module 306 are described.

[0135] Method 900 includes receiving 905 plantar pressure data of a user. In some embodiments, the pressure data may include pressure color map data and / or numerical pressure data obtained using a sensor. For example, the pressure data may correspond to a user standing or walking. In some methods, the pressure data may include one or both feet of a user walking. It is contemplated that the pressure data may be digital, color map, and / or waveform, and may be collected from one or more sensors or separate sensing units simultaneously. The pressure data may then be classified 910 into different foot regions, which may include forefoot, midfoot, and rearfoot, and may be further classified into medial and lateral aspects.

[0136] The method 900 further includes tracking 915 at least one area of ​​plantar pressure in real time or after recording. For example, one or more points on the sensor on one or both feet of the user can be tracked. The points that can be tracked can be one or more locations in one or more areas of the area 910 of the foot. In some embodiments, the location of the sensing unit on the sensor will assist in tracking. In some methods, one or more tracking-based machine learning algorithms can be used to perform tracking.

[0137] The method 900 may further include analyzing 920 the tracked limb to determine at least one biomechanical attribute (such as a plantar pressure measurement). The biomechanical attributes may also include foot position, plantar flexion, ankle flexion, joint loading, foot loading, and other lower limb or foot dynamics. In some methods, motion analysis may be applied to the tracked points on the video frame to assess gait.

[0138] The method 900 may further include analyzing 925 biomechanical pressures and / or other related measurements to determine at least one biomechanical attribute. The biomechanical attributes may include any biomechanical category, pattern, or characteristic that may provide information for the user's footwear needs. In some embodiments, one or more biomechanical classification algorithms may be used to determine the biomechanical measurements. In some methods, the biomechanical classification algorithm may include one or more machine learning models trained to track points based on one or more of a pressure color map, pressure values, sensor units, foot regions, and foot postures. In some examples, the machine learning model may be trained using reference biomechanical data from a population with known biomechanical parameters.

[0139] It is also contemplated that one or more biomechanical measurements or biomechanical properties may be received from a database. For example, one or more biomechanical measurements or biomechanical properties may have been predetermined (e.g., in a biomechanical laboratory or by another biomechanical assessment method). In this manner, predetermined biomechanical properties and / or biomechanical properties may be received without evaluating pressure sensor or other sensor data. That is, steps 905-925 are not required to determine one or more biomechanical measurements or biomechanical properties associated with the user.

[0140] Method 900 may also include determining 930 a footwear attribute associated with the user based on at least one biomechanical attribute. The footwear attribute associated with the user may include an attribute of a footwear product that best suits the user based on the user's biomechanical attribute. For example, if the user's biomechanical attribute is underpronation, the footwear attribute associated with the user may be high arch support. In some examples, biomechanical attributes such as heel contact, forefoot contact, or midfoot contact may be associated with cushioning and / or sole thickness (i.e., footwear attributes). Therefore, in some examples, for biomechanical attributes such as heel contact, forefoot contact, or midfoot contact, footwear attributes associated with cushioning and / or sole contact may be associated with the user. In some embodiments, a classification algorithm may be used to identify at least one footwear attribute that best suits the user based on the user's biomechanical attributes. In some methods, the classification algorithm may include a machine learning model trained to identify footwear attributes associated with the user based on the user's biomechanical attributes.

[0141] In some embodiments, method 900 may use established relationships between various footwear attributes and various biomechanical attributes to determine footwear attributes associated with a user. In an exemplary embodiment, the relationship between footwear attributes and biomechanical attributes may be quantified by scoring the footwear attributes based on the biomechanical attributes (e.g., a score of 0-100). A high score may indicate a strong preference for a particular parameter, while a low score indicates a poor fit for a particular parameter. The relationship between footwear attributes and biomechanical attributes may be stored, for example, in a database.

[0142] Method 900 may optionally include associating the biomechanical attributes and / or footwear attributes with the user 935. In some methods, the biomechanical attributes and / or footwear attributes may be stored in a database such as Figure 3 The user profile may be associated with a user in an exemplary personal fit database depicted in FIG. 1 . For example, the database may include a user profile storing one or more biomechanical attributes associated with the user. In some examples, the user profile may also store one or more footwear attributes associated with the user.

[0143] Situational Module

[0144] The following describes an exemplary method of acquiring and displaying contextual data according to some embodiments. The contextual data may include information about at least one personal contextual attribute associated with a user. The contextual attribute may include, for example, demographic information, product preference information, lifestyle information, location information, user footwear needs, and any other information that may affect the user's footwear needs. One or more electronic user devices (such as a reference Figure 3 The electronic user device described herein may be used to perform one or more steps of the method. In some methods, the method may be performed by or in combination with reference to Figure 3 The context module 308 described herein may be used to perform one or more steps of the method to obtain and display context data associated with a user.

[0145] The method includes receiving demographic data associated with the user. The demographic data may include information related to age, gender, nationality, race, age, height, weight, body mass index (BMI) key variables, or any other demographic factors that may affect footwear needs, the body mass index (BMI) key variables are known to be associated with specific foot characteristics (e.g., foot shape, bone structure, foot posture (including internal rotation / external rotation) and foot deformity (including toe deformity)). In one example, a high BMI may be associated with increased ankle width, increased Achilles tendon width, increased heel width, and a thicker forefoot along the dorsal foot axis. In another example, age may be associated with heel width, Achilles tendon width, toe height, and hallux direction.

[0146] The method also includes receiving footwear preference data associated with the user. The footwear preference data may include information related to footwear style, color, brand, material, or any other factor that may affect the user's footwear selection, design, or recommendation. In one example, the user may provide information about the footwear category that the user is interested in or their (multiple) style preferences. Footwear product categories may include, but are not limited to: athletic shoes, casual walking shoes, boat shoes, boots / booties, clogs / slippers, fashion sneakers, flats, high heels, casual shoes, oxford shoes / derby shoes, sandals, and slippers. Style preferences may be based on the type and category of footwear. For style preferences, a collection of images of products of types / categories representing a wide range of styles may be presented to the user. The user may be prompted to select one or more images that match their personal style and fashion preferences.

[0147] The method also includes receiving lifestyle data associated with the user. The lifestyle data may include information related to activity level, frequency of various activities, occupation, social interests, hobbies, interests, sports, fitness courses, or other lifestyle factors that may affect footwear needs. In some methods, users may be prompted to provide information about their lifestyles, including physical activity levels or average daily steps, which may affect footwear construction, materials, and durability requirements. Certain lifestyle information may be useful contextual information for assessing footwear needs and providing users with appropriate recommendations and fit. For example, the frequency of physical activity is associated with Achilles tendon width and toe height. It is also conceivable to automatically receive lifestyle information from one or more biosensors associated with the user. For example, a biosensor may be present in a smart watch, a fitness tracker, or an electronic user device.

[0148] The method further includes receiving location data associated with the user. The location data may include information related to a country, city, state, county, geographic region, climate, address, zip code, or any other location-related factor that may affect the user's footwear needs. In an exemplary embodiment, the user is prompted to provide location information, such as the zip code of their residence. For example, the climate in which the user lives may affect their footwear breathability and / or waterproofing requirements. It is also contemplated that the location data may be automatically received from one or more sensors, such as a GPS sensor associated with the user.

[0149] It is contemplated that contextual data may be received by transmitting one or more questionnaires to a user, the questionnaires may relate to one or more categories of contextual data (i.e., demographics, product preferences, lifestyles, locations, etc.). The questionnaire may be transmitted to an electronic user device associated with the user and displayed via a user interface of the device. The questionnaire may include one or more questions, prompts, or information requests related to demographic information associated with the user, the category of footwear the user is looking for, footwear preferences, lifestyle information, and location information. In response to receiving the questionnaire, the user may input information related to the query, prompt, information request via the user interface. In some methods, the information may be in the form of a response or answer to the questions, prompts, or information requests contained in the questionnaire.

[0150] It is also contemplated that one or more of demographic information, product preference information, lifestyle information, location information, or user footwear needs may be received from a database. For example, one or more gait measurements or gait attributes may have been predetermined (e.g., in a previous survey or personal assessment). In this way, predetermined information may be received without a questionnaire.

[0151] For example, the method may also optionally include displaying one or more footwear products through an electronic product catalog. The user may view the footwear products on the user interface of the electronic user device. The product catalog may include various footwear products, models and / or styles. For example, the user may have the option of selecting one or more of the footwear products being viewed through the user interface of the electronic device. The user may then select a specific footwear product, model or style.

[00152] The user interface may also display one or more personalized recommendations about one or more footwear product specifications based at least in part on the contextual data. The recommendation may suggest one or more footwear products, sizes, styles, models, materials, material properties (e.g., air permeability, viscoelasticity, stretchability, tear strength, flexibility, waterproofness, wear resistance, etc.), weight, (multiple) tongue features, (multiple) lining features, (multiple) insole features, (multiple) toe features, (multiple) outsole features, (multiple) upper features, (multiple) sole features, heel height, foot support, construction (i.e., stitching, mesh) or other footwear attributes to the user. The recommendation may be used to design personalized footwear.

[0153] Fig.10 is a flow chart of a method 1000 for analyzing context data according to some embodiments. In some methods, the method 1000 may be performed by or in conjunction with reference to Figure 3 The scenario module described is executed.

[0154] Method 1000 may include receiving 1005 contextual data associated with a user, such contextual data may include demographic, lifestyle, and / or location data associated with the user. In some methods, the contextual data may be received as described above. In one example, the contextual data may be received in the form of a questionnaire response completed by the user, wherein the user is prompted to provide one or more of demographic, lifestyle, and location data.

[0155] Method 1000 may further include analyzing 1010 the demographic data to determine at least one contextual attribute associated with the user. A personal contextual attribute may include any personal attribute, characteristic, or feature that is related to or affected by contextual data, such as demographics, lifestyle, or location. In some methods, the demographic data may be analyzed by a computation engine (such as a reference engine). Figure 3The computing engine may access a database containing reference demographic data to determine one or more contextual attributes associated with the user based on the demographic data. The reference demographic data may include data of key contextual attributes known to be associated with specific demographic factors. Personal contextual attributes (such as ankle width, Achilles tendon width, heel width, forefoot thickness along the dorsal foot axis, hallux direction, and hallux varus) may be associated with demographic data (such as BMI results, age, or gender). Therefore, one or more personal contextual attributes may be determined based on demographic data associated with the user, for example, by utilizing known relationships between personal contextual attributes and demographic factors. In some methods, statistical analysis may be applied to the demographic data to extract demographic-related contextual attributes associated with the user.

[0156] In one example, high BMI may result in increased ankle width, Achilles tendon width, heel width, and thicker forefoot along the dorsal foot axis direction. Therefore, when demographic data indicates that the user has a high BMI, the user may be determined to have increased ankle width, Achilles tendon width, heel width, or a thicker forefoot along the dorsal foot axis direction. In another example, age may be related to Achilles tendon width, heel width, toe width, and hallux direction. Therefore, when demographic data provides information about the age of the user, the user may be determined to have specific Achilles tendon width, heel width, toe height, and hallux direction attributes. In another example, gender may be related to ankle width, Achilles tendon weight, and heel width. Therefore, when demographic data provides information about the gender of the user, the user may be determined to have specific ankle width, Achilles tendon weight, and heel width attributes.

[0157] Method 1000 may further include analyzing 1015 the lifestyle data to determine at least one contextual attribute associated with the user. In some approaches, the demographic data may be analyzed by a computing engine, such as a reference Figure 3 The computing engine may access a database containing reference lifestyle data to determine one or more contextual attributes associated with a user based on the lifestyle data. The reference lifestyle data may include data about key contextual attributes known to be associated with specific lifestyle factors. Personal contextual attributes (such as ankle width, Achilles tendon width, and toe width) may be associated with the frequency of physical activity levels (such as the frequency of sports-related activities). Thus, one or more personal contextual attributes may be determined based on the lifestyle data associated with the user, for example, by utilizing known relationships between personal contextual attributes and lifestyle factors. In some methods, statistical analysis may be applied to the lifestyle data to extract demographic-related contextual attributes associated with the user.

[0158] Method 1000 may further include analyzing 1020 the location data to determine at least one contextual attribute associated with the user. In some methods, the location data may be obtained by a computing engine (such as a reference Figure 3 The computing engine may access a database containing reference location data to determine one or more contextual attributes associated with the user based on the location data. The reference location data may include data on key contextual attributes known to be associated with specific location factors. Personal contextual attributes (such as preferences for footwear materials, material breathability, style, and waterproofness) may be associated with climate. Thus, one or more personal contextual attributes may be determined based on the location data associated with the user, for example, by utilizing a known relationship between personal contextual attributes and location factors. In some methods, statistical analysis may be applied to the location data to extract demographically relevant contextual attributes associated with the user.

[0159] It is also contemplated that one or more context attributes may be received from a database. For example, one or more context data segments or context attributes may have been predetermined (e.g., in a previous survey or personal assessment). In this manner, predetermined information or attributes may be received. That is, steps 1005-1020 are not required to determine one or more context attributes associated with the user.

[0160] The method may also include determining footwear attributes associated with the user based on at least one contextual attribute. The footwear attributes associated with the user may include attributes of footwear products that best suit the user based on the contextual attributes of the user. In some embodiments, a classification algorithm may be used to identify at least one footwear attribute that best suits the user based on the contextual attributes of the user. In some methods, the classification algorithm may include a machine learning model trained to identify footwear attributes associated with the user based on the contextual attributes of the user. The method may use the relationship established between various footwear attributes and various contextual attributes to determine the footwear attributes associated with the user. In an exemplary embodiment, the relationship between the footwear attributes and the contextual attributes can be quantified by scoring the footwear attributes (e.g., a score of 0-100) based on the contextual attributes. A high score may indicate a strong preference for a particular parameter, while a low score indicates that it is not suitable for a particular parameter. For example, the relationship between the footwear attributes and the contextual attributes may be stored in a database that can be accessed by a context module.

[0161] The method may optionally include associating the contextual data and / or footwear attributes with the user 1030. In some methods, the contextual attributes and / or footwear attributes may be stored in a database such as Figure 3The user profile may be associated with a user in an exemplary personal fit database depicted in FIG. 1 . For example, the database may include a user profile storing one or more contextual attributes associated with the user. In some examples, the user profile may also store one or more footwear attributes associated with the user.

[0162] Design your own footwear

[0163] It is further contemplated that, based on the evaluation information associated with the user, personalized footwear is designed and then manufactured. This may be initiated via a personalized footwear design and / or production engine 336. It should be appreciated that in some embodiments, the engine is a personalized footwear design engine; i.e., used only for the design of a product. In other embodiments, the engine is a personalized footwear production engine; i.e., used only for the production or manufacture of a product. In yet another embodiment, the engine is a personalized footwear design and production engine; i.e., used in both the design of a product and the production / manufacturing of a product. In another approach, there may be multiple engines, such as one being a personalized footwear design engine and another being a personalized footwear production engine.

[0164] like Figure 3 As illustrated, system 300 may include a modular last design program 370 accessing a digital library 372 of modular core last model components and a personalized shoe component design program 390 accessing a digital pattern library 392 to design personalized footwear. It is further contemplated that these programs may be combined into a single program or otherwise communicate with respect to one another.

[0165] Although other ways of initiating design are contemplated, the personalized footwear design and / or production engine 336 can initiate a modular shoe last design program and / or a personalized shoe component design program to design a personalized modular shoe last and / or a personalized shoe component for a user based on evaluation information 357 associated with the user, as described above, the evaluation information 357 may include foot shape information, pathological information, gait information, biomechanical information, and / or contextual information. In some embodiments, the evaluation information is associated with the user in the user profile. As mentioned above, in some embodiments, the evaluation information associated with the user received by the system stored in the user profile can be directly input into the design program to provide a personalized design based at least in part on the evaluation information. Alternatively, at least a portion of the evaluation information can be further analyzed to determine personal attributes and / or footwear attributes associated with the user, as described above, and the personalized design can be based at least in part on the personalized attributes and / or footwear attributes associated with the user. The determination of personal attributes may be performed via one or more of the foot shape module, foot pathology module, gait and biomechanics module, and context module described above, although determination by other means is also contemplated.

[0166] Whether the design program receives input of the user's assessment information directly or receives the user's personal attributes and / or footwear attributes from the user's assessment information, the input information includes at least one foot measurement associated with the user (or alternatively, a foot measurement via a shoe size (such as a standard shoe size)) so that the size of the personalized modular shoe last can be determined. For example, at least the foot length or both the foot length and the foot width can be received to determine the size of the personalized modular shoe last, and such measurements can be received for both the right foot and the left foot. For example, the foot size can specifically include one or more of arch height, foot width, full foot width, midfoot width, foot length, sole width, toe-to-toe size, sole circumference, sole angle, heel circumference, lateral metatarsal length, medial metatarsal length, arch length, instep height, and instep distance. In order to make the design fit closely to the user's foot, it is usually helpful to receive the following items for each foot: foot length, foot width, sole width, arch height, big toe height, instep height, arch length, heel width, arch height index, heel diagonal, sole circumference, arch circumference, instep circumference and foot waist circumference. In addition, at least the shoe size associated with certain measurements can be received, and the personalized shoe last can be designed based on the shoe size or the measurement associated with the shoe size. It is also further contemplated that the received user-related measurements can be the size or measurement associated with the size of a specific shoe of a specific shoe brand (or multiple shoes from different brands). For example, a personalized modular shoe last can be designed based on the size of the brand and the size of the shoe that is very suitable for the user.

[0167] In addition to foot measurements, the received evaluation information may include any type of information or attributes that may be relevant to any aspect of the design of personalized footwear (including the personalized modular last and / or any personalized shoe components), including but not limited to the above-mentioned foot shape information or attributes, the above-mentioned pathological information or attributes, the above-mentioned gait information or attributes, the above-mentioned biomechanical information or attributes, and the above-mentioned contextual information or attributes. Figure 5-Figure 10 The invention also provides a method for receiving information and optionally analyzing the information by any of the methods shown in the figures, or collecting and / or receiving information by other means.

[0168] For example, any of the above-described foot measurements or other aspects of foot shape or geometry (as described above) may be collected and / or evaluated via the foot shape module described above or any methods and / or functionality associated therewith. Foot measurements may also include manually acquired measurements, acquired using a Brannock device or may be collected or received via any other device capable of providing such measurements, such as 3D scanning equipment. Measurements or other foot shape data may be received and / or input in any context, such as in the context of a medical evaluation, a shoe store evaluation, a self-report, or any other foot shape evaluation.

[0169] In addition, as an example, any pathology information or pathology attributes as described herein may be collected and / or evaluated via the pathology module described above or any method and / or function associated therewith. The pathology information or pathology attributes may also be obtained and / or input through different evaluation methods, such as in the context of a medical evaluation, a shoe store evaluation, a self-report, or any other pathology evaluation.

[0170] As another example, any gait and biomechanical information or attributes as described herein may be collected and / or evaluated via the gait and biomechanics module described above or any methods and / or functions associated therewith. Gait and biomechanical information or pathological attributes may also be acquired and / or input via different evaluation modalities, such as in the context of a medical evaluation (e.g., a gait lab), a shoe store evaluation (observational evaluation), a self-report, or any other gait and biomechanical evaluation.

[0171] The context information described herein can be collected and / or evaluated via the context module described above or any method / function associated therewith. The context information or attributes can also be obtained and / or input through different evaluation methods, such as in the context of medical evaluation, shoe store evaluation, self-report, or any other context evaluation.

[0172] Figure 1 , Figure 2 , Figure 4 and Fig.12 The figure shows an exemplary method for designing a personalized footwear product, designing a personalized modular shoe last, and / or providing a personalized footwear product. Although the following description will focus primarily on Figure 1 With reference to method 100 in the drawings, it should be understood that the description is also applicable to methods 200, 400, and 1200 that share the same steps.

[0173] Design a custom modular last

[0174] In method 100, after an initial step 105 in which assessment information associated with a user (including at least one foot measurement) is received, the assessment information is used to design a personalized modular last for the user.

[0175] Personalized modular shoe lasts can be a key factor in providing personalized footwear. As mentioned, a shoe last is a mechanical form, usually made of wood, metal or plastic, shaped like a person's foot, and used to set the size, profile and shape of the shoe during the construction process, and then removed at the end of the shoemaking process. If the footwear product is to be personalized, focusing on the personalization of the shoe last can ensure that the basis of the footwear product is closely matched with the user. In addition, the shoe last is a modular shoe last, that is, including or can include a plurality of discrete components that can be combined or interchanged to create different shoe last shapes or designs, allowing more accurate personalization consistent with the user's footwear needs. The personalized modular shoe last disclosed herein, particularly the modular core shoe last component described below, can be made of any suitable material known in the art for shoe lasts, for example, including plastic (such as high-density plastic), wood or metal, or a combination of these materials. It can also be conceived that the material of the modular core shoe last component can vary according to the type of shoe last. For example, a standard sports shoe with a floating lacing tongue and a low vamp point can use a solid shoe last. When removing the last, a flexible shoe, typically with a bendable EVA and rubber sole, can be unlaced and easily pulled off the last without damaging the shoe. In contrast, shoes with a higher upper (such as oxford shoes or boots) may require a last material with articulation or bending capabilities so that the upper and sole are not damaged when the last is removed. In some embodiments, the modular lasts disclosed herein may include modular components that are easily disassembled and removed from the shoe after manufacturing, and / or may employ mechanisms known in the art for removal (e.g., v-shaped hinge lasts, sliding hinge lasts, telescopic lasts, etc.).

[0176] Designing a personalized modular last may include creating a digital design of the personalized modular last. A design program (such as the modular last design program 370 of the system 300) may be used to perform this function. In some methods, the digital design includes a 3D model of the personalized modular last, although a 2D digital drawing is also contemplated. The digital model or drawing may be created by a computer-aided design (CAD) program, however other known forms of digital drawings and design programs known in the art are also contemplated herein.

[0177] In the first step 110 of designing a personalized modular last, one or more modular core last model components are selected from a digital library 372 of modular core last model components based at least in part on evaluation information associated with a user including at least one or more foot measurements. The digital library 372 may include or be linked to one or more databases. In some exemplary embodiments, the optional modular core last model components have physical pre-manufactured modular core last component counterparts 369, which can be easily provided and / or assembled based on the design of the personalized modular last when manufacturing a personalized footwear product. However, in some embodiments, the optional one or more modular core last model components may not have pre-manufactured physical modular core last component counterparts. In this case, if a personalized footwear product is to be manufactured based on the design of a personalized modular last with one or more such modular core last model components, a physical modular core last component counterpart needs to be manufactured.

[0178] In general, the library 372 accesses a series of modular core shoe last model parts that can be selected to form the core of a personalized modular shoe last design. The modular core shoe last model parts included in the library can be configured to cover a variety of different foot sizes and shapes for a variety of different shoe types or contours. For example, for a particular style of running sneakers, there may be dozens or more modular core shoe last model parts, which cover a variety of different lengths, widths or length / width combinations. The number of modular core shoe last model parts in the library 372 is not particularly limited, but it can be conceived that in some embodiments, the number is advantageously balanced between precise fit and scalability. That is, it can include a series of modular core shoe lasts that are wide enough to allow close matching with many potential users, but not too wide that, for example, a series of physical core shoe lasts are pre-manufactured, distributed and stored in one or more manufacturing facilities, making the cost too high or otherwise impractical.

[0179] For example, the core last library can be based on standard shoe sizes and, for example, can include a certain number of different modular core lasts or last components for each half size. For example, there can be at least two, at least three, at least four, or at least 5 modular core lasts. For example, for size 8 corresponding to a specific length, there can be five core lasts corresponding to 5 different widths. In some embodiments, each half size can include even more core lasts or core last components. For example, in one embodiment, the library can include at least ten core lasts for each half size. In addition, the core last library does not have to be based on and / or classified according to standard shoe sizes, but other size systems can be used.

[0180] In addition, as mentioned above, one or more modular core last mold components may be selected to form the design of a modular core last. That is, it is contemplated that the personalized modular last disclosed herein may have a core that includes only one component, or may have a core that includes multiple components. Fig.13 The figure shows an example of a personalized modular shoe last 1301 having a modular core 1302, which is unitary and includes only a single component. Fig.14 As illustrated, the modular core may be a unitary core 1411, but in alternative embodiments may be a dual core 1412 having two components 1416, a ternary core 1413 having three components 1416, or a quad core 1414 having four components 1416. Cores including more than four components 1416 are also contemplated.

[0181] A core comprising multiple components may have components in different configurations to allow for shape customization at different areas of the last. Fig.14 As shown, an exemplary two-component core last may have a core forefoot component and a core heel component. An exemplary three-component core last may have a core forefoot component, a core midfoot component, and a core heel component. An exemplary four-component core last may have a core toe component, a core sole component, a core midfoot component, and a core heel component. However, other configurations are possible. For example, Fig.14 The figures show components of a core last having portions of different lengths, but some embodiments may include components of a core last having portions of different heights or widths.

[0182] For modular core lasts having two or more core components, the core components are configured to be removably coupled together. In one embodiment, the core components can be interlocking pieces that are staggered together. Other coupling mechanisms can include mechanical fasteners, hook and loop fasteners, holes for pins, screws and snap-on extensions, Velcro, magnets, or removable adhesives. In some exemplary embodiments, the core components can be easily decoupled so that the core components can be reused in different configurations for different personalized lasts.

[0183] As mentioned above, designing a personalized modular last for a user includes selecting one or more modular core last model components from a digital library 372 of modular core last model components. The selection is based at least in part on the user's foot measurements and may include other foot shape, pathology, gait, biometric, or contextual information associated with the user. In some exemplary embodiments, the selection process identifies a core last model in the library 372 that is the closest match to the user's foot measurements, or the closest match given a combination of the user's foot measurements and other assessment information. For example, a core last model may be selected that includes a length and width that closely approximates the length and width of the user's foot. In another example, a core last model may be selected that closely matches the length and arch height of the user's foot.

[0184] A core last comprising more than one component advantageously allows the last to be more precisely personalized to the user's measurements. For example, if two components are selected to design a binary modular core last, the two components can be selected separately to match the unique contours of an individual's foot in a way that a monolithic core last may not be able to do. For example, if a user has a combination of a wider forefoot and a narrower heel, one size core forefoot component and a different size core heel component can be selected. By "mixing and matching" different core last components in this manner, the design of the core last is optimized to approximate the user's foot shape.

[0185] The design program may utilize an algorithm to select or determine one or more modular core last model components that best match or are closest to the user's foot measurements and other assessment information that may be associated with the user. One or more machine learning algorithms may be used, which may be trained to use a wide variety of data from the user (including any assessment information, personal attributes, or shoe attributes associated with the user), for example, to classify different core last model components and predict the best match for the user among the available core last model components. Machine learning algorithms that may be used include, but are not limited to: convolutional neural networks (CNN, R-CNN, YOLO), statistical shape analysis (SSA), support vector machines (SVM), principal component analysis (PCA), Bayesian networks, decision trees, nearest neighbor search, etc.

[0186] After selecting the modular core last model, the system is configured to determine whether to include one or more last extensions in the design based on the user's foot measurements and / or other assessment information associated with the user. It is conceivable that some users will have an excellent match with the determined core last model without any additional last extensions, so in such cases the system will determine that no last extensions are needed. However, some users may have foot measurements or other foot requirements that may benefit from extending the last in one or more areas. Therefore, the optional second step 115 of designing a personalized modular last includes determining one or more last extensions that may be added to the last design.

[0187] In some embodiments, the shape, size, and where the last extension should be attached to the modular core last is determined entirely by a system based on the user's information. It is also conceivable that the last extension is selected from a digital library of different last extension models. These digital last extension models may or may not have pre-manufactured physical counterparts that are easily used to assemble the last. If the last extension is selected from a database, it is additionally conceivable that the size of the selected extension can be adjusted based on the user's information during the design of the last extension. In some embodiments, it is conceivable that, although the modular core last components can be pre-manufactured, the last extension can be manufactured on demand based on the design of the personalized modular last, and thereafter added to the modular core last during the assembly process.

[0188] The last extension allows the core last to be extended in certain portions of the core last in order to account for variations in the user's foot or footwear needs that the core last itself may not provide. Such variations may simply include foot size or foot shape that is unique to the user, and may include certain abnormalities or pathologies that the user may have. It is also contemplated that gait and / or biomechanical information, as well as contextual information associated with the user, may also be involved in different last extension designs.

[0189] Last extensions are typically solid additions to the core last that effectively increase the size of the core last in certain areas to provide additional margin in certain portions of the final footwear product. Fig.13The figure shows a personalized modular last with several last extensions 1303, 1304. For example, one of the last extensions provides additional margin for the user's overlapping toes. It is conceivable that the last extension can be formed from a material having similar properties to the material used for the core last. As a last extension, like the core last, it must perform the function of forming and maintaining a specific foundation and shape of a footwear product when the footwear product components are spliced ​​together. The last extension should be made of a material with a suitable hardness for maintaining the intended shoe structure during the manufacturing process. Thus, the last extension can be formed, for example, of various plastics (such as high-density plastics), synthetic polymers, metals (including metal alloys) or wood, or a combination of these materials. For example, the last extension can be 3D printed, as will be described in further detail below.

[0190] Last extensions may be added to the core last in a variety of ways. In some illustrative embodiments, the last extensions are removably coupled to the modular core last to allow the modular core last and / or last extensions to be reused in different configurations of different personalized modular lasts and potentially for different users. This contributes to the efficiency and scalability of the modular last system. In one embodiment, as Fig.15 As illustrated, the modular core last 1501 may include holes 1517 for pins, screws, or snap-on extensions so that last extensions can be attached. Other coupling mechanisms may include, but are not limited to, other arrangements of holes for pins, screws, or snap-on extensions, interlocks, Velcro, magnets, and removable adhesives. A single last extension may be attached to the core via several discrete fasteners (e.g., several spaced snaps) in situations where one fastener is insufficient to ensure a tight fit with the core last. It is also contemplated that the fastening mechanism may be present across the entire interface between the last extension and the core last (e.g., as in the case of an adhesive). Additionally, a last extension may also be removably coupled to another last extension in the manner described above, rather than being removably coupled directly to the modular core last. This configuration may be described in detail below. Fig.13 , therein is shown a last extension 1304 coupled to another last extension 1303 .

[0191] Determining the last extension includes determining the size of the last extension and the location where the last extension is to be placed on the core last, ie, the precise attachment point or attachment area on the core last. Both determinations are based on evaluation information associated with the user.

[0192] In one approach, it is contemplated that one or more last extensions may be primary extensions.A primary extension may be understood as a last extension that is added to a specific, predetermined area of ​​a core last. Fig.16 and Fig.17The figure shows the area of ​​the core shoe last where the main extension can be placed. The main shoe last extension can be removably coupled to, for example, the inner sole area 1623, the outer sole area 1621, the medial heel area 1624, the outer heel area 1622, the toe area 1741, the sole circumference area 1742, the instep area 1743, or the sole area 1744 of the modular core shoe last. Depending on the type of shoe last, these areas can include larger or smaller areas. For example, compared with the inner sole area 1631 on different shoe lasts, the inner sole area 1623 constitutes a smaller area on a core shoe last. It is further contemplated that the main extension can be determined to extend over a portion or the entire area of ​​the area. In addition, in some embodiments, the extension can be determined to have a profile, or have an irregular thickness in its entire area. In this way, the main extension can advantageously modify the core shoe last to allow additional personalization of the shoe last, and even tighter footwear fit.

[0193] Secondary extensions are further contemplated. In one approach, secondary extensions may be understood as last extensions that may be added anywhere on a personalized modular last. For example, they may be removably attached anywhere on a modular core last, or they may be removably attached to a primary extension. For example, Fig.13 The figure shows a personalized modular last 1301 having a primary extension 1303 attached to the sole circumference area of ​​a core last 1302. The last 1301 further includes two secondary extensions 1304. One of the secondary extensions is attached to the primary extension 1303 at a determined attachment point on the primary extension. The other secondary extension is attached at the determined attachment point on the core last. Any number of secondary extensions may be included to personalize the last. In addition, in some embodiments, the secondary extensions are configured to be placed at more precise or localized points on the core last or primary extension (rather than only in certain areas of the core last).

[0194] In one approach, secondary extensions are determined to be attached to the core last (or other extensions attached to the core last) at selected attachment points to correspond to the user's foot shape abnormality or pathology. It is contemplated that the location (e.g., attachment point) and size of the secondary extensions are determined during the design of the personalized modular last based on the user's assessment information. For example, Fig.13One of the secondary extensions 1304 in the shoe last is sized to correspond to the location of a prominent bunion. Another secondary extension 1304 can correspond to the location of an overlapped toe. For both abnormalities, it is important to include additional margins at these locations of the shoe to prevent discomfort or worsening of the condition. The secondary extensions can provide customization suitable for abnormalities or pathologies such as bunions / buccaryon, toe deformities (e.g., hammer toes, crossed toes, overlapped toes), arch variations (flat / collapsed), or bone spurs. However, any number and type of foot shape abnormalities or pathologies can be compensated in the shoe last via secondary extensions. In addition, it is contemplated that in addition to foot shape and pathology, other types of information about the user (such as gait information, biomechanical information, or contextual information) can also provide information for the determination of the secondary extensions.

[0195] In some embodiments, the secondary extensions, like the primary extensions, may be contoured or have irregular thickness over the entire area of ​​the extension based on the needs of the user.

[0196] The step (215) of determining one or more last extensions based on the assessment information associated with the user can utilize one or more algorithms to make the determination. One or more machine learning algorithms can be used, which can have been trained to use a variety of data from the user (including any assessment information, personal attributes, or footwear attributes associated with the user) to determine, for example, which areas of the core last should be extended, the size of the extension, and the configuration of the extension (e.g., an extension on top of another extension). Such algorithms include, but are not limited to: neural networks (e.g., CNN, R-CNN, YOLO), statistical shape analysis (SSA), support vector machines (SVM), principal component analysis (PCA), Bayesian networks, decision trees, nearest neighbor search, etc.

[0197] It should be appreciated that the above design process can be performed separately for both the right and left feet of the user. This is particularly true if the information provided for each foot is different. Therefore, two designs of personalized modular lasts can be generated for the user, one for each foot. Since asymmetry between the right and left feet is quite common, designing different personalized modular lasts for each foot can advantageously provide a suitable fit, even in the case of asymmetry. The personalized modular last designs for the right and left feet of the user may ultimately utilize different modular core last components (e.g., if one foot is longer than the other, or if the user suffers from asymmetric edema), different primary extensions (e.g., if one foot has a narrower heel), or different secondary extensions (e.g., if one foot has a bunion).

[0198] Design personalized shoe parts

[0199] The present disclosure further includes designing personalized shoe components. Figure 1 The exemplary method shown includes providing a personalized footwear product or design thereof by designing both a personalized modular shoe last and one or more personalized shoe components. However, it is contemplated that the personalized shoe components described herein can be designed for use in a personalized footwear product without designing or using a personalized modular shoe last.

[0200] According to method 100, after completing the design of the user's personalized modular shoe last, a further step 120 includes designing one or more personalized shoe components. A design program (such as personalized shoe component design program 390 of system 300) can be used to perform this function. Such a program can be a computer-aided design (CAD) program that can generate a 2D or 3D model of the design, however, other known forms of digital drawing and design programs known in the art are also contemplated herein.

[0201] Typically, the design of the shoe components may include determining the shape, size, and material of the shoe components based on the design of a personalized modular last, evaluation information associated with the user, or both. The personalized shoe components that may be designed in this step are not particularly limited. They may include any component of a footwear product that has any relevance to satisfying the user's footwear needs and preferences. By one approach, a complete design for the entire shoe is generated, and thus all components of the shoe are selected. By other approaches, designs for some shoe components are generated, but not all components of the shoe.

[0202] The design of a personalized modular last provides a useful input for the design of a shoe component. Since the last design has already captured and accounted for the nuances of the user's foot shape, morphology, pathology, and other footwear needs to a large extent, it is appropriate for the shoe component to use the last design as a starting point. For example, the size of the personalized modular last can dictate certain dimensions of the upper or outsole. As another example, a last extension that provides additional space for a bunion can correspondingly dictate an extended insole.

[0203] Inputting the last design into the design of the shoe component may have added value because it allows the possibility of compensation or synergy between the personalized modular last design and the shoe component design. For example, if the design of the last does not precisely match the user's foot in one area, the material or thickness of the shoe component (e.g., the insole) can be adjusted to allow for a better fit.

[0204] While the last design can provide an overall guide for the design of the shoe components, other assessment information associated with the user can also be incorporated into the design to provide a further degree of personalization. Fig.18As shown, the input of the shoe component design includes, for example, a shoe last design 1861, and may also include foot pathology information or attributes 1862, gait and biomechanics information or attributes 1863, and contextual information or attributes, as described above. Foot shape information or attributes not included in the shoe last design may be further included.

[0205] Exemplary shoe components that may be determined in this step include upper components, insole components, midsole components, and outsole components, such as Fig.18 As shown. The shape and size of these components may depend largely on the determined dimensions of the personalized modular shoe last. However, the materials determined for these components can be determined by other evaluation information or attributes associated with the user. For example, if the foot pathology attribute associated with the user is bunion (hallux valgus), bunionitis minimi, or other toe deformity, a shoe upper material with high elasticity can be selected. In another example, if the foot pathology attribute associated with the user is hyperhidrosis, a material with high breathability can be selected for one or more shoe components.

[0206] Additional design elements to be included may also be determined based on the user's needs or preferences and may include, but are not limited to, laces, straps, air bubbles, lights, traction materials, additional padding, outsole thread patterns, specific textures, ruffles, eyelets / lace holes, seams / stitching, collar padding, toe reinforcement, added toe lift, upper linings, printed and / or embossed designs, or additional panels (such as mesh panels).

[0207] An algorithm may be utilized to determine the design of a shoe component based on the design of a personalized modular last and / or other assessment information associated with the user. One or more machine learning algorithms may be used that can be trained to use various data from the user (including any assessment information associated with the user, personal attributes or shoe attributes, and last design data) to determine an ideal shoe component design for the user. Such algorithms include, but are not limited to, neural networks (e.g., CNN, R-CNN, YOLO), statistical shape analysis (SSA), support vector machines (SVM), principal component analysis (PCA), Bayesian networks, decision trees, nearest neighbor search, and the like.

[0208] In one method, similar to the method for determining a modular core last described above, one or more algorithms use measurements and other evaluation information of the last design to identify the closest matching pattern for the type of shoe component from a digital library 392, which includes a series of patterns of different shapes or sizes for the type of shoe component. For example, in order to generate a design for a personalized upper component, an algorithm can be used to identify the closest matching upper pattern from an upper pattern library. Similarly, the closest matching midsole pattern can be identified from a midsole library, the best fit insole can be identified from an insole library, the most suitable outsole can be identified from an outsole library, and the most suitable auxiliary design element can be identified from an auxiliary design element library. In such methods, the digital patterns available for selection in the digital library 392 can have pre-manufactured physical counterparts that are ready to be incorporated into a footwear product (e.g., in a manufacturing plant). However, it is also conceivable that the availability of the digital pattern selected from the digital library 392 does not depend on the availability of the pre-manufactured physical counterparts of the digital pattern.

[0209] In another approach, one or more algorithms may generate an entirely new pattern for a shoe component based on a last design and / or other evaluation information associated with a user.

[0210] In an intermediate approach, an algorithm may be used to identify the closest matching patterns for a type of shoe component from the digital library 392 based on the last design and / or other evaluation information associated with the user, but then re-size or otherwise improve those upper patterns based on the last design and / or other evaluation information associated with the user. In some embodiments, this may be a preferred approach because it allows the shoe component to be customized to more precisely match the user, while also leveraging the value of "tried and true" preset patterns by using those patterns as a basis for further personalization.

[0211] Additionally, one or more shoe components may be designed in part or in whole by a human designer based on a previous design and / or other evaluation information associated with the user.

[0212] In some embodiments, an illustrative method or system for designing a personalized shoe component includes obtaining assessment information associated with a user (wherein the assessment information may include at least one foot measurement of the user) and generating designs for one or more personalized shoe components, wherein generating the designs for the one or more personalized shoe components includes selecting a pre-designed or preset shoe component design (or "pattern") from a preset shoe component design library using an algorithm based on a design of a personalized modular shoe last personalized to the user (such as based on the user's foot measurement or assessment information) and optionally the assessment information associated with the user. In some configurations, these teachings optionally use an algorithm to adjust the size of the selected (multiple) preset shoe component designs based on the design of the personalized modular shoe last. In addition, the method and system can select the material of the personalized shoe component based on the specific design of the personalized modular shoe last and / or the assessment information associated with the user.

[0213] The designed personalized shoe parts may include at least one of the upper part, the insole part, the midsole part and the outsole part, or any combination of these parts. For example, at least two (midsole and outsole) may be designed, at least three (upper, insole and outsole) may be designed, or all four may be designed.

[0214] The method or system may further include determining at least one auxiliary design element to be added to the design, wherein the at least one auxiliary design element includes one or more of laces, straps, bubbles, lights, traction material, additional padding, outsole thread patterns, specific textures, ruffles, eyelets, lace holes, seams, stitching, collar padding, toe reinforcement, added toe lift, upper lining, printed and / or embossed designs, or additional panels.

[0215] In one method, in particular, an upper component can be designed that includes obtaining assessment information associated with a user, the assessment information including at least one foot measurement of the user, and generating a design for a personalized upper component. In some methods, generating a design for a personalized upper component includes selecting a preset upper design from a library of preset upper designs using an algorithm based on a design of a personalized modular last personalized for the user (such as based on the user's foot measurement or the user's assessment information) and optionally the assessment information associated with the user. In addition, to obtain additional customization for the user, the algorithm can adjust the size of the selected preset upper design based on the design of the personalized modular last and optionally the assessment information associated with the user. In some embodiments, the user or the designer can select a material for the personalized upper component based on the design of the personalized modular last and / or the assessment information associated with the user.

[0216] It should be understood that a process similar to that described above may occur with respect to a midsole component, an insole component, or an outsole component.

[0217] In addition, in some configurations, certain input information may be particularly relevant to the design of a particular shoe component. As an example, in one case, the design of the upper may be based on the design of a personalized modular last (which at least includes the user's foot size and / or shape), and may also be based on foot pathology information. In another case, the design of the insole, outsole, or midsole may be based on the design of the last, and may also be based on foot pathology information, gait information, and / or biomechanical information. In another case, the customized design or inclusion of specific auxiliary design elements may be based on the design for the last, and may also be based on foot pathology information, gait information, biomechanical information, and / or contextual information associated with the user. For example, information about the user's foot size and shape (from a personalized modular last personalized for the user) coupled to information about the user's gait and information about the user's typical activities (e.g., if the user is a runner), may involve the design of added padding or certain shoelaces.

[0218] The materials that can be selected for the personalized shoe component may be limited by the manufacturing methods that can be used to manufacture the component. In addition, the materials that can be selected for the personalized shoe component may be related to a specific manufacturing method. Therefore, in some embodiments, the material for the personalized shoe component is determined in part based on the relationship between the material and the available manufacturing methods.

[0219] It is contemplated that a variety of different manufacturing techniques may be used to manufacture the personalized shoe components disclosed herein, including, but not limited to, additive manufacturing, subtractive manufacturing, and / or near net shape manufacturing, including, for example, 3D printing, 3D knitting, die cutting, and laser cutting. Depending on the technique used to manufacture the shoe components, as described above, different materials may be selected for different shoe components.

[0220] For example, laser cutting a shoe component design from a single piece of material may be the preferred technique for certain components. However, certain materials are more or less compatible with laser cutting. If the upper component is made of natural leather or suede with a maximum thickness of 1 / 8 inch, or of fabric (e.g., cotton, polyester, Gore-Tex, felt), it may cut just fine. However, materials containing PVC (such as artificial leather or leather) should be avoided because such materials release chlorine when cut. Plastic coatings or impregnated cloths should also be avoided.

[0221] As another example, the insole component to be laser cut can be made of polyester foam, polyethylene foam, polyurethane foam, EVA foam or cork, for example. Foams such as polyester (PES), polyethylene (PE) or polyurethane (PU) are particularly suitable for laser cutting. Non-contact processing ensures fast cutting without applying pressure to the material, while the heat of the laser beam can seal the edges. Ethylene vinyl acetate (EVA) foam can be particularly preferred for laser cutting and engraving insoles.

[0222] Suitable materials for laser cutting midsole components include, for example, open cell polyurethane, closed cell EVA or Gore-Tex, while, for example, rubber (chlorine-free) and EVA may be suitable materials for laser cutting outsoles.

[0223] Although the above functions of designing a personalized modular shoe last and designing a personalized shoe component are described using evaluation information associated with a user as a basis for design, it should be appreciated that personal attributes or footwear attributes as described above may also be used as a basis for design. For example, an exemplary system for providing or designing a personalized footwear product may include at least one processor that can communicate with at least one electronic user device, the processor being configured to receive evaluation information associated with a user from at least one electronic user device, the evaluation information including at least one foot measurement; determine one or more footwear attributes associated with the user based on the evaluation information; and generate a design of a personalized modular shoe last, wherein the personalized modular shoe last includes a modular core shoe last. In some methods, generating a design of a personalized modular shoe last includes: determining a modular core shoe last model by selecting one or more modular core shoe last model components from a library of modular core shoe last model components based on at least one of the one or more footwear attributes associated with the user. In addition, the processor may be further configured to generate a design for at least one personalized shoe component based on the design of the personalized modular shoe last and / or one or more of the one or more footwear attributes associated with the user. Fig.12 The figure illustrates an example method incorporating the above-described functionality.

[0224] Determining footwear attributes may be advantageous in designing personalized footwear because the footwear attributes provide a relationship between the footwear design and personal information or attributes related to the user's foot shape, pathology, gait and biomechanics, or other contextual information. For example, the footwear attributes may have the following relationship with foot shape, pathology, gait and biomechanics, and contextual information:

[0225] Table 1

[0226] Thus, determining the user's footwear attributes may optimize the design of a personalized modular last and / or a personalized shoe component.

[0227] Creating personalized footwear

[0228] It is further contemplated that a personalized footwear product is manufactured based on the design of a personalized modular last and / or a personalized shoe component. In an illustrative embodiment, a personalized footwear product is manufactured based on both the design of a personalized modular last and the design of a personalized shoe component.

[0229] Figure 4 The figure illustrates a method 400 for providing a personalized footwear product, including the manufacture of the product. After the design of the personalized modular last and / or one or more personalized shoe components has been completed, an optional further step 425 includes sending the design to a manufacturing execution system 360. The manufacturing execution system 360 as used herein can control one or more operating parameters for a footwear manufacturing process and can include one or more manufacturing devices configured to perform functions when manufacturing a footwear product. For example, the manufacturing execution system may include a combination of a core last and extensions, an algorithm for instructing a machine to perform additive manufacturing, an algorithm for instructing a machine to perform subtractive manufacturing on a material using specific spatial coordinates, and / or an instruction set for assembling a product. Figure 3 As indicated, the manufacturing execution system 360 may also include a store of pre-manufactured modular last components 369 or other pre-manufactured components (such as last extensions or shoe components) that can be readily ("on demand") used to manufacture the personalized footwear products disclosed herein. These components may be pre-manufactured on site or from a supplier.

[0230] The footwear product generated by manufacturing can be initiated via the personalized footwear design and / or production engine 336 or by some other means. For example, the personalized footwear design and / or production engine 336 can send a signal to one or more manufacturing devices (e.g., 361, 363, 365, 367) in the manufacturing execution system 360 to control the operation of the device. The signal can be a program composed of code to run the machine or instruct the machine to perform a specific manufacturing task.

[0231] Usually, if Figure 4As indicated, manufacturing a personalized footwear product as disclosed herein includes the following steps: (step 430) providing a personalized modular shoe last based on the design of the personalized modular shoe last, (step 435) manufacturing one or more personalized shoe components based on the design of one or more personalized shoe components, and (step 440) manufacturing the personalized footwear product using the personalized modular shoe last, which includes incorporating the one or more personalized shoe components into the product. In addition, in some embodiments, the personalized footwear product may utilize the personalized modular shoe last in manufacturing, but may not include any personalized shoe components. In addition, in some embodiments, one or more personalized shoe components that constitute the final personalized footwear product may be manufactured. For example, a user may only need one or more personalized shoe components to use in a shoe that is already owned, and therefore, the personalized shoe components are not incorporated into a newly manufactured shoe. In addition, it is contemplated that in some cases, during the manufacture of the footwear product, the personalized shoe components may be incorporated into the footwear product without using the personalized modular shoe last.

[0232] The manner in which the personalized modular last is manufactured and / or assembled depends to some extent on the complexity of the design of the personalized modular last. It is contemplated that in simpler designs, such as when the design indicates a binary core last with two designated modular core last components, the two modular core last components are already pre-manufactured 369 and exist, and only need to be identified and assembled (i.e., removably coupled together in any of the manners described above).

[0233] In addition, a core last and / or extension catalog and numbering system may be developed to assist in the manufacture and assembly of the last and shoe. For example, the catalog may be alphabetic and / or numerical. For example, a core last component may be designated 8D, while two last extensions may be designated A108 and B107. Any number of cataloging systems may be used so that components may be cataloged and identified. Assembly of the core last components and extensions may be performed by machine or by hand.

[0234] If the design of a personalized modular last dictates that one or more last extensions are required, pre-manufactured last extensions (e.g., already manufactured for a previous last) may be used and removably coupled to the modular core last at attachment points dictated by the design. With such an approach, the pre-manufactured core extensions may not exactly match the last extensions determined by the design. Thus, it may be particularly advantageous to provide the required last extensions as needed, and then removably couple the manufactured last extensions to the modular core last to provide a personalized last. The last extensions may be made via any suitable manufacturing equipment, including additive manufacturing equipment (such as a 3D printer), subtractive manufacturing equipment (such as a laser cutter and / or CNC machine, etc.), and / or near-net shape manufacturing equipment (such as a stamping, etc.).

[0235] As mentioned above, one or more personalized shoe components may be pre-manufactured, mass-produced, or custom-manufactured, or may need to be manufactured on demand, depending on the design method. As explained, if the design of the personalized shoe component determines the shoe component by selecting the closest matching preset pattern for the user from the digital library 392, such pattern may be pre-manufactured and can be used to manufacture and / or assemble the footwear product. However, if the design process determines the shoe component by resizing the preset pattern to provide a better fit for the user or by generating a pattern from scratch, the shoe component may need to be manufactured on demand.

[0236] As discussed above, a variety of manufacturing techniques may be used to manufacture shoe components, which may depend on the type of shoe component and / or the type of material used. In some methods, for example, a 3D printer may be instructed to produce a portion of a shoe, such as, for example, a sole, an insole, and / or a footbed, and a 3D knitting machine may be instructed to complete other portions of the shoe, such as, for example, an upper and a tongue. Additionally, the 3D printer may then be instructed to produce additional portions of the shoe, such as, for example, a toe cap. As described above, laser cutting may be a preferred manufacturing technique for many shoe components.

[0237] When providing or manufacturing personalized modular lasts and shoe parts, the personalized modular lasts are used to manufacture footwear products, and the shoe parts are incorporated into the footwear products using lasts and shoemaking techniques known in the art. For example, when the shoe is in production, the sewn pattern can be stretched over the last to create the shape of the shoe (called a "last"). There are several different lasting techniques that can be used to pull the pattern into shape. These include lasting, stretching, sewing, lasting, lasting, hand-lasting, and machine-lasting. Once the upper pattern fits tightly to the last, it can be attached to the outsole. The last holds the soft upper in place, whether the sole is sewn or glued.

[0238] In this way, a personalized footwear product is provided that is highly compatible with the user's personal footwear needs.

[0239] It is further conceivable that one or more modular core last components and one or more last extensions are separable after being used to manufacture personalized footwear, and are stored and reused to assemble one or more different personalized modular lasts, thereby providing different personalized footwear products for the same user or one or more different users.

[0240] In some embodiments, system 300 may also communicate with a transportation system, for example, via a personalized footwear design and / or production engine 336. In one embodiment, engine 336 may send a signal to a transportation system to automatically place an order for manufacturing and / or transmitting one or more manufactured personalized footwear products to a specific user and / or individual. In one example, the footwear product may be directly transported to the user. In another example, the footwear product may be transported to a store or a nearby location for pickup. In another method, engine 336 may first identify a personalized footwear design recommended for a specific user and / or individual. In one method, a user may select one or more of the recommended personalized footwear designs, place an order, and manufacture and transport the product. In some embodiments, engine 336 may control the manufacture of the footwear product for a specific user, and automatically transport the product to the user. In this way, engine 336 may customize orders for specific users. It is also conceivable that after transporting or otherwise providing one or more customized footwear products to a user, system 300 may also receive feedback to (multiple) footwear products. For example, system 300 may transmit a questionnaire or survey to a user to receive information, such as regarding the comfort, fit, aesthetics, or other experience with a footwear product, and, for example, whether the user plans to keep the footwear product or wishes to make some adjustments to the product.

[0241] For example, in one embodiment, a medical practitioner may place an order for a patient after entering assessment information about the patient using an electronic user device. In this case, the footwear product may be designed and manufactured and may be shipped directly to the patient or the medical practitioner's office. After the patient has worn the footwear product for a period of time, the medical practitioner may follow up with the patient and ask for feedback about the footwear product. If necessary or desired, the medical practitioner may use the feedback to adjust a subsequent order for the footwear product for the patient.

[0242] Feedback and analysis after wearing

[0243] In some embodiments, system 300 may be operable to obtain feedback on footwear products from users directly and / or indirectly, so as to adjust the design of footwear products in the future, thereby providing users with a new footwear product cycle that is constantly improving. Feedback from previously worn footwear can change, for example, the design, construction and / or material of new footwear. Feedback may be provided directly by the user, or may be provided by other parties. In the former case, the user may provide feedback by providing information about the impact of previously worn footwear products on the user's foot shape, pathology, gait, preference, etc., via any means and methods described herein. In the latter case, feedback on previously worn footwear products may be determined from scientists or researchers, medical practitioners, designers, manufacturers, etc., and may include, for example, extracting and analyzing sensor data, performing post-wear analysis of footwear structures and materials, and considering other aspects of the interaction between the user's foot and footwear products. These measures for obtaining feedback on previously worn footwear products are described below.

[0244] By one approach, the methods herein can be utilized to produce personalized footwear for a specific individual, the personalized footwear being designed based on the specific individual's foot pathology, gait, biomechanics, and other use-related aspects. In addition, personalized footwear may evolve over the life cycle of the individual, and analysis of footwear worn by the specific individual, as well as potentially analyzing updated scans and other sensed information, can be utilized to adjust footwear recommendations or manufactured footwear to account for changes. In addition, receiving feedback on previously designed or manufactured footwear creates an iterative process, allowing specific individuals to receive updated designs of footwear and footwear products over time. This method is contrasted with standard mass production of footwear, which provides limited size options and is based on a limited understanding of consumer needs, resulting in poor fit and, thereby, poor health (based on the record of the impact of wearing inappropriately sized footwear on health).

[0245] It is further contemplated that footwear usage data and / or post-wear data of footwear previously worn by a user (including previously worn personalized footwear products made according to the systems and methods disclosed herein) may be included in evaluation information associated with the user provided for the purpose of designing and / or manufacturing personalized footwear products.

[0246] For example, an illustrative system may include at least one processor that can communicate with at least one electronic user device, the processor being configured to: obtain evaluation information associated with a user, the evaluation information including feedback about a previously worn footwear product, generate an updated design for a personalized footwear product, the updated design for the personalized footwear product including a design for a personalized modular last and a design for at least one personalized shoe component, wherein the design for the personalized modular last includes a modular core last model determined by selecting one or more modular core last model components from a library of modular core last model components based at least in part on the feedback about the previously worn footwear product, wherein the design of the at least one personalized footwear component is based at least in part on the design of the personalized modular last and / or the feedback about the previously worn footwear product.

[0247] Another illustrative system may include at least one processor that can communicate with at least one electronic user device, the processor being configured to: obtain evaluation information associated with a user, the evaluation information including at least one foot measurement of the user, generate a design of a personalized modular last, wherein the personalized modular last includes a modular core last, wherein generating the design of the personalized modular last includes determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based at least in part on the at least one foot measurement of the user, the processor being further configured to generate a design of at least one personalized shoe component based on the design of the personalized modular last and, optionally, the evaluation information associated with the user; the processor being further configured to obtain feedback about a previously worn footwear product, the previously worn footwear product being manufactured based on the design of the personalized modular last and the design of the personalized shoe component; the processor being further configured to generate an updated design of the personalized modular last and / or an updated design of at least one personalized shoe component based at least in part on the feedback about the previously worn footwear product.

[0248] Feedback regarding previously worn footwear may include updated assessment information, such as foot shape information, gait information, biomechanical information, pathological information, and / or contextual information.

[0249] Furthermore, in use, the systems and methods herein can update a user profile associated with a user based on feedback regarding previously worn footwear. The user profile can store information associated with the user, such as feedback regarding previously worn footwear, and one or more previous designs of previously personalized footwear, and can communicate with a design software program to update the design based on the feedback.

[0250] Receiving feedback about a previously worn footwear product may allow the design of the personalized footwear product to be updated in different ways. For example, the feedback may result in an updated design, compared to the previously worn footwear product (or a previous footwear design upon which the previously worn footwear product was based), that includes at least one construction change, at least one material change, or a combination of at least one construction change and at least one material change of the personalized footwear product. The design of the personalized last may be updated (e.g., one or more last extensions may be added to the last), the design of one or several different shoe components may be updated, or both the last and shoe component designs may be updated. In some cases, the system may determine that no design updates are needed based on the post-wear feedback.

[0251] In some embodiments, feedback includes analysis of previously worn footwear to determine the extent of wear and tear patterns and other aspects. The shoe can be analyzed in its entirety or as a single component to determine the qualitative and quantitative degradation of the structure and material of the shoe after wear. For example, the structural change can be determined by comparing the previously worn shoe or its component with the original shoe shape, foot shape, gait, biomechanics and / or contextual information associated with the user. For example, the structure of the upper component can be deformed compared to the original shape. For example, material changes are determined by evaluating according to the original shoe shape, foot shape, gait, biomechanics and / or contextual information associated with the user. For example, the material of the insole or midsole of the shoe can be compressed, fatigued, cracked, unevenly shaped or torn. The changes in the structure and materials of the footwear product can be quantified by using structural mechanics tests, including but not limited to torsional flexibility, tensile strength, bending, extension, pull-out, creep and relaxation tests.

[0252] In use, these teachings allow a user to wear an initial personalized footwear formed on a modular last (such as footwear manufactured via the processes described herein), and return the initial personalized footwear for post-wear processing or analysis to provide an updated personalized footwear. For example, if a portion of a shoe has been stretched or deformed, the systems described herein can update a portion of the updated personalized footwear (such as a shoe component) to have a specific elastic or flexible material at that location and / or can form the updated personalized footwear on a modular last having an added last extension (e.g., a primary or secondary last extension) in the stretched and deformed area.

[0253] In one illustrative embodiment discussed further below, the feedback can include footwear condition data. Systems and methods can employ one or more sensors in the footwear to obtain footwear condition data regarding, for example, temperature, humidity, acceleration, pressure, alignment, posture / tilt, and / or other footwear attributes of the footwear during use of the footwear to provide an updated design based on how the footwear is worn by a particular user. By one approach, the measured data can be compared to other measured attributes from a database for comparison purposes.

[0254] In use, the sensed data can be combined with other data about the user provided and / or collected. For example, the method herein can utilize two-dimensional measurement, three-dimensional measurement and dynamic measurement, such as, for example, by a camera or sensor outside the footwear and / or a footwear sensor configured to obtain data about the microclimate in the footwear as described below, to obtain foot measurement, foot scanning, static images and / or video data. By utilizing some or all of these data, the system and method herein help to reduce the user's main shoe pain points, including heel sliding, arch support and toe fit. For example, although the sole width or circumference, heel diagonal and arch height can be measured statically, the dynamic analysis of the footwear used allows the measurement of the sole bending point, the plantar or dorsiflexion point and the level of arch flexibility or rigidity, and adapting to these factors greatly improves the comfort of the wearer.

[0255] As described below, methods and systems are configured to measure humidity and temperature (to determine the microclimate within a worn shoe) as well as acceleration and pressure (to determine the user's alignment and gait), which are then taken into account in personalized footwear design and / or manufacturing.

[0256] Fig.19 FIG. 19 shows an exemplary method 1900 for providing or updating a personalized footwear design based at least in part on analyzing footwear condition data to determine sensed personal attributes associated with a user. In some embodiments, one or more steps of method 1900 may be combined with reference to Figure 3 In some approaches, both physical and / or contextual information may be associated with a user in a database, such as the database described in further detail below or the database described above with respect to Figure 3 Describe those databases.

[0257] Method 1900 includes identifying 1905 a user profile having previously worn footwear associated with the user, and receiving and analyzing 1910 footwear condition data associated with the user from one or more sensors. For example, the sensors may include pressure sensors, accelerometers, gyroscopes, humidity sensors, and / or temperature sensors, among others.

[0258] The method 1900 also includes determining 1915 a sensed personal attribute associated with the user profile based on the footwear condition data from the one or more sensors.

[0259] In step 1920, method 1900 includes receiving personal attribute data or assessment information, which may include both physical data and contextual data, to be associated with a user profile. The method also includes providing or updating 1925 a design of a personalized footwear product based at least in part on the footwear condition data, the sensed personal attributes, and / or the physical (e.g., foot shape, pathology, gait, biomechanics) or contextual personal attributes or assessment information, the personalized footwear product may include a personalized modular last and any personalized shoe components. Updates to the design of the personalized footwear product may include, for example, updates to the selected personalized modular last (its selected modular core components, its selected last extensions and the locations of the extensions), and updates to the personalized shoe components (e.g., the type or number of components included, the size of the components, the materials of the components).

[0260] The method 1900 may repeat identifying 1905 the user profile having previously worn footwear associated therewith after providing or updating 1925 the personalized footwear design. In this manner, the method may be iterative and continually useful to the user.

[0261] Fig. 20 FIG. 2000 illustrates an exemplary method 2000 for providing or updating a personalized footwear design according to some embodiments. In some embodiments, one or more steps of method 2000 may be combined with reference to Figure 3 In some approaches, both physical information and / or contextual information may be associated with a user in a database, such as the database described in further detail below.

[0262] Method 2000 includes identifying 2005 a user profile having previously worn footwear associated with the user. In some embodiments, the method includes receiving 2010 personal post-wear attributes associated with the user profile, wherein the personal post-wear analysis is used to determine the personal post-wear attributes, such as described below with reference to Fig.23 The analysis is described in further detail.

[0263] The method 2000 also generally includes receiving and analyzing 2015 footwear condition data associated with the user profile from one or more sensors. For example, the sensor may include a pressure sensor, an accelerometer, a gyroscope, a humidity sensor, a temperature sensor, and / or any other available sensor. The method 2000 also includes determining 2020 a sensed personal attribute associated with the user profile based on the footwear condition data.

[0264] Method 2000 also includes receiving 2025 microclimate attributes associated with a user profile. The microclimate attributes are determined by microclimate analysis, which analyzes data from at least one of a humidity sensor or a temperature sensor at least in part. The analysis uses sensors placed in specific parts of the entire footwear to determine various aspects of temperature and / or humidity within the footwear to determine local microclimates in certain parts of the footwear. The microclimate data can be used to update the footwear design or user profile accordingly by using a processor or an electronic user device. In addition to the microclimate data sensed in the shoe, in some embodiments, the methods and systems described herein can solicit user preferences or evaluation data about how the user feels or responds to various sensed microclimate levels in the shoe. The microclimate can be determined at least in part by data from humidity and / or temperature sensors, and can be used to determine a comfortable microclimate preferred by the user, but there can also be a predefined database to determine footwear recommendations based on other physical or situational personal attributes discussed above, including but not limited to geographic location, such as altitude, ambient humidity of the surrounding area, or time of year.

[0265] In some configurations, the method 2000 also includes receiving 2030 personal attribute data or personal assessment information (both physical and / or contextual) to be associated with the user profile. The method also includes providing or updating 2035 a design of a personalized footwear product based at least in part on the footwear condition data, the sensed personal attributes, and / or the physical (e.g., foot shape, pathology, gait, biomechanics) or contextual personal attributes or assessment information, the personalized footwear product may include a personalized modular last and any personalized shoe components. Updates to the design of the personalized footwear product may include, for example, updates to the selected personalized modular last (its selected modular core components, its selected last extensions and the locations of the extensions), and updates to the personalized shoe components (e.g., the type or number of components included, the size of the components, the materials of the components).

[0266] Method 2000 can repeat after providing or updating 2035 the personalized footwear design to return to identifying 2005 the user profile having previously worn footwear associated therewith. In fact, while the initial step 2035 can provide a personalized footwear design, subsequent analysis can update the previous design.

[0267] Fig.21An exemplary method 2100 of determining sensed personal attributes according to some embodiments is illustrated. The method 2100 includes receiving 2105 footwear condition data for footwear previously worn by a user. The footwear has been worn for a period of time. In some configurations, the period of time is several months. In other configurations, the period of time is about 6 months, but shorter and longer durations are contemplated herein. The method 2100 further includes analyzing 2110 data from one or more sensors.

[0268] Analyzing 2110 data from one or more sensors optionally includes analyzing 2115 data from an accelerometer. By one approach, the accelerometer helps determine foot alignment of the footwear. Additionally, the accelerometer can have other uses, such as identifying and monitoring foot strike and foot alignment patterns throughout an individual's gait cycle while walking, running, and performing other sports and physical activities, and monitoring the mobility of patients managing medical conditions (such as patients recovering from a stroke). The present disclosure contemplates both a single accelerometer and multiple accelerometers strategically placed within the footwear to determine acceleration or orientation in several areas of the footwear.

[0269] Analyzing 2110 data from one or more sensors optionally includes analyzing 2120 data from a temperature sensor. The temperature sensor can determine the internal temperature inside the footwear or around the foot, but the temperature sensor can have other uses, such as measuring local temperature changes in different areas of the entire foot, which can be an indication of tissue damage. The present disclosure contemplates both a single temperature sensor and several temperature sensors, with humidity sensors strategically placed within the footwear to determine the temperature in several areas of the footwear and assist in determining the above Fig. 20 Microclimate properties described in .

[0270] Analyzing 2110 data from one or more sensors optionally includes analyzing 2125 data from a humidity sensor, although the humidity sensor may have other uses, such as providing a humidity value that may indicate an environment for the growth of microorganisms that may affect the spread of potential bacterial or fungal infections. The humidity sensor may determine the humidity level inside the footwear. The present disclosure contemplates both a single humidity sensor and multiple humidity sensors that are strategically placed within the footwear to determine humidity in multiple areas of the footwear and assist in determining the above Fig. 20 It is further contemplated that the humidity sensor may track the humidity of the external environment in conjunction with and / or separately from the interior of the footwear.

[0271] Analyzing 2110 data from one or more sensors optionally includes analyzing 2130 data from pressure sensors. Pressure sensors can determine high pressure areas on the sole (and / or other areas) of the footwear, but pressure sensors can have other uses, such as identifying foot strike patterns and foot alignment during an individual's gait cycle. The present disclosure contemplates both strategically placing a single pressure sensor within the footwear and strategically placing multiple pressure sensors within the footwear. The present disclosure also contemplates one pressure sensor that substantially covers the entirety of the sole of the footwear.

[0272] The present disclosure further contemplates other sensors that track other aspects of footwear, such as gyroscopes. By one approach, a gyroscope can assist in determining foot alignment in footwear. Additionally, a gyroscope can have other uses, such as identifying and monitoring foot strike and foot alignment patterns throughout an individual's gait cycle while walking, running, and performing other sports and physical activities, and monitoring the mobility of patients managing medical conditions, such as patients recovering from a stroke. The present disclosure contemplates both a single gyroscope and multiple gyroscopes strategically placed within a footwear to determine acceleration or orientation in several areas of the footwear.

[0273] The method 2100 further includes determining 2140 a sensed personal attribute associated with the user, and the user profile may be updated using the processor and / or the electronic user device. The method 2100 may be associated with the method 1900, specifically determining 1915 the sensed personal attribute. The method 2100 may be associated with the method 2000, specifically determining 2020 the sensed personal attribute.

[0274] Fig. 22 The figure illustrates an exemplary method 2200 of creating a digital drawing (such as, for example, a computer-aided design (CAD) drawing) of a personalized footwear design according to some embodiments. The method 2200 generally includes identifying 2205 a user. The user may be a user with previously designed footwear as disclosed herein, a new user, a user with a user profile, or any other user. In one illustrative method, the method 2200 includes receiving and analyzing 2210 a foot scan to determine a personal foot model. The foot scan may come from an electronic device such as a phone, a computer, a scanning module, or any other imaging device including an image sensor (e.g., a camera). The foot scan may also come from a commercially available foot scanner, and other imaging techniques may be used, such as lasers, non-ionizing radiation, and / or low-ionizing radiation.

[0275] The method 2200 also includes performing 2215 a physical post-wear analysis of the previously worn footwear. The previously worn footwear may be previously designed personalized footwear as disclosed herein, or any other footwear worn for a period of time. Fig.23Physical post-wear analysis is described in further detail.

[0276] The method 2200 also includes determining 2220 an updated foot model based on the personal foot model from the foot scan of step 2210 and the physical post-wear analysis of step 2215, which updated foot model can update the user profile accordingly by using a processor or electronic user device.

[0277] The method 2200 also includes creating 2225 a digital drawing of the shoe design. The digital drawing of the shoe design may be accomplished by creating a computer-aided design (CAD) drawing, however other known forms of digital drawings are contemplated herein. The shoe design may be a shoe, a portion of a shoe, or a shoe last that conforms to the user's foot based on the updated foot model of step 1720, or the user profile may be updated accordingly using a processor or electronic user device.

[0278] Fig.23 An exemplary method 2300 of performing a physical post-wear analysis is illustrated. The method includes receiving 2305 previously worn footwear. By some methods, the previously worn footwear has been worn for more than about 6 months, however, shorter and longer durations are contemplated herein. The method 2300 also includes starting 2310 a physical post-wear analysis, which includes analyzing 2310 the previously worn footwear to determine at least one post-wear attribute. The method 2300 also optionally includes deconstructing 2320 at least a portion of the previously worn footwear to determine an updated post-wear attribute, and a user profile may be updated accordingly by using an electronic user device.

[0279] The method 2300 may also include updating 2325 the personal foot model based on at least one of the post-wear attributes and / or the updated post-wear attributes, which may update the user profile accordingly. The method 2300 may optionally include recycling 2330 the material of the previously worn footwear in whole or in part (if deconstructed). For example, the shoe may be partially (or fully) deconstructed to analyze a portion of the data to break the shoe down into component parts that make it more suitable for recycling. The ability to recycle their shoes and improve their next pair of shoes is attractive to many consumers.

[0280] In some configurations, physical post-wear analysis of previously worn footwear helps determine certain aspects of the fit and / or use of the previously worn footwear. The post-wear analysis looks at, for example, the wear or wear pattern of the sole of the previously worn footwear, such as wear in the heel area of ​​the previously worn footwear, material degradation, and other indications of the sole, insole, midsole, or other parts of the footwear to determine the pressure points where high pressure and wear have been applied to the previously worn footwear. The physical post-wear analysis can include deconstructing at least a portion of the previously worn footwear. In addition, the methods described herein can further include reclaiming all or part of the previously worn footwear.

[0281] Fig.24 An exemplary method 2400 for making, iterating, and updating a footwear design is illustrated. The method 2400 generally includes identifying 2405 a user. The method 2400 also includes receiving and analyzing 2410 a foot scan of the user's foot to determine a personal foot model. The foot scan as described above may be provided in part from a smart phone, computer, tablet, scanning module, or any other scanning or imaging device. The method 2400 generally also includes performing a physical post-wear analysis of the previously worn footwear, as described above with reference to Fig.23 In some configurations, method 2400 includes: Fig.23 The described post-wear analysis and personal foot model of step 2410 determine an updated foot model.

[0282] The method 2400 also includes generating or updating 2425 a design of a personalized footwear product (including a design of a personalized modular last and / or a personalized shoe component as disclosed herein) based at least in part on the personal foot model and / or the updated foot model. Updates to the design of the personalized footwear product may include, for example, updates to the selected personalized modular last (its selected modular core components, its selected last extensions and locations of the extensions), and updates to the personalized shoe component (e.g., the type or number of components included, the dimensions of the components, the materials of the components). The method 2400 also includes outputting 2430 the design to a manufacturer or manufacturing facility.

[0283] Method 2400 may also include manufacturing 2435 the product of footwear. Manufacturing the product of footwear may be accomplished using additive manufacturing, subtractive manufacturing, and near-net shape manufacturing, and may include a 3D printer, a laser cutter, a 3D knitting device, a third party manufacturer, or any other manufacturing means, and as described above.

[0284] Once the manufactured footwear has been worn for a period of time, the user can return the footwear for analysis. Therefore, in step 2440, the method includes receiving 2440 previously worn footwear. A period of time can include, for example, about 6 months, but a longer or shorter duration is contemplated herein. In practice, once the user has worn shoes for a period of time, the user can return the worn footwear, so that the method includes receiving 2440 previously worn footwear. In addition, the method 2400 can continue to the above-mentioned steps (a) 2410 (about receiving and / or analyzing foot scans) and / or step (b) 2415 after receiving 2440 footwear, so that the method performs additional wear-after analysis to the previously worn personalized footwear. In such a configuration, the user profile can be updated accordingly by using a processor or an electronic user device.

[0285] It is further contemplated that an automated user footwear last may be created from an updated personalized footwear last or a customized user footwear last. The updated personalized footwear last or a customized user footwear last may be further updated using an algorithm (which may be a machine learning algorithm) to create an automated user footwear last.

[0286] In some embodiments, the system may include a footwear receiving and processing center. It is further contemplated that these centers may be separate facilities (such as a footwear receiving center and a footwear processing center). Through the center (or centers), previously worn footwear products worn by users may be received and a physical post-wear analysis may be performed on the previously worn footwear products. Post-wear analysis feedback may then be obtained through the systems and methods herein to update the design of the footwear products. The processing center may include a previously worn footwear analysis module, a footwear deconstruction module, and a footwear recycling module, and may use a method similar to that described above with reference to Fig.23 The post-wear analysis may be performed physically by a human operator, or automatically, for example, by using a scanning device.

[0287] Fig.25 The figure illustrates an exemplary method 2500 for updating a footwear design according to some embodiments. The method 2500 generally includes identifying 2505 a user. The method 2500 also includes: the processor receives 2510 footwear condition data from at least one sensor. The method 2500 also includes: the processor determines 2515 sensed personal attributes based on the footwear condition data. The method 2500 also includes: the processor transmits 2520 the sensed personal attributes to an electronic user device. The method 2500 also includes updating 2525 the personalized footwear design. The processor of the method 2500 can be one or more processors, such as a processor in the footwear and / or a second processor in the electronic user device. One or more processors or electronic user devices can directly or indirectly communicate and transmit data throughout the method or process.

[0288] In one illustrative embodiment of providing personalized footwear recommendations, at least one electronic device including an image sensor (e.g., a camera), at least one sensor, and at least one processor that can communicate with another processor can be used to provide personalized footwear recommendations. The at least one processor can receive image data from the electronic user device and / or the at least one sensor. The image data can include at least a portion of at least one foot of a user.

[0289] The at least one processor determines at least one foot shape attribute associated with the user based at least in part on the image data. The user profile may be created and updated accordingly by at least one of the at least one processor or the at least one electronic user device. Determining at least one foot shape attribute is similar to the above reference Figure 5 A detailed description of the analysis.

[0290] The at least one processor determines at least one pathological attribute associated with the user based at least in part on the image data. The user profile may be further updated accordingly by at least one of the at least one processor or the at least one electronic user device. Determining at least one pathological attribute is similar to the above reference Figure 7 A detailed description of the analysis.

[0291] The at least one processor may also receive video data captured by the image sensor. The video data may include a video of a user's gait (such as a user walking). The at least one processor may also determine at least one gait attribute associated with the user based at least in part on the video data. The user profile may be further updated accordingly by at least one of the at least one processor or the at least one electronic user device. Determining at least one gait attribute is similar to the above reference to Figure 8 A detailed description of the analysis.

[0292] The at least one processor may also receive information about a physical or situational personal attribute. The at least one processor may receive the information via a user interface associated with the electronic user device. The physical or situational personal attribute is the information described above with reference to Figure 3 The user profile may be further updated accordingly by at least one of the at least one processor or at least one electronic user device. Determining at least one physical or situational personal attribute is similar to the above reference Fig.10 A detailed description of the analysis.

[0293] The at least one processor may also receive information regarding the post-wear analysis. The at least one processor may receive the information via a user interface associated with the electronic user device. The at least one processor may also determine a post-wear attribute associated with the user. The user profile may be further updated accordingly by at least one of the at least one processor or the at least one electronic user device. Determining the post-wear attribute is similar to the above reference Fig.23 A detailed description of the analysis.

[0294] The at least one processor may also receive information about the footwear condition data. The at least one processor may receive the information via the at least one sensor. The at least one processor may also determine a sensed personal attribute associated with the user. The user profile may be further updated accordingly by at least one of the at least one processor or the at least one electronic user device. Determining at least one sensed personal attribute is similar to the above reference to Fig.21 A detailed description of the analysis.

[0295] The at least one processor may also receive information regarding the microclimate analysis. The at least one processor may receive the information via a user interface associated with the electronic user device. The at least one processor may also determine a personal microclimate attribute associated with the user. The user profile may be further updated accordingly by at least one of the at least one processor or the at least one electronic user device. Determining at least one personal microclimate attribute is similar to the above reference to Fig. 20 A detailed description of the analysis.

[0296] The at least one processor may determine at least one footwear recommendation based on the attributes to be provided to the user via a user interface associated with the at least one electronic user device.

[0297] In some aspects, the technology described herein relates to a system for providing a personalized footwear product, the system comprising at least one processor in communication with at least one electronic user device, the processor being configured to obtain assessment information associated with a user, the assessment information comprising at least one foot measurement of the user, and to generate a design for a personalized modular last, the personalized modular last comprising a modular core last. In some embodiments, generating the design for the personalized modular last comprises: determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based at least in part on the at least one foot measurement of the user.

[0298] In some embodiments, the processor is further configured to generate a design for at least one personalized shoe component based on the design of the personalized modular last and / or evaluation information associated with the user.

[0299] By some methods, the processor is configured to send the design of the personalized modular last and the design of at least one personalized shoe component to be manufactured. For example, the system may include a manufacturing execution system, and the processor is configured to send the design of the personalized modular last and / or the design of at least one personalized shoe component to the manufacturing execution system. In some embodiments, the manufacturing execution system is configured to manufacture at least one personalized shoe component, provide a personalized modular last, use the personalized last to manufacture a personalized footwear product, and include the at least one personalized shoe component in the personalized footwear product.

[0300] In some aspects, generating a design for a personalized modular last further includes determining whether to include one or more last extensions in the personalized modular last based on evaluation information associated with the user. Generating a design for a personalized modular last may further include determining one or more last extensions to be included in the personalized modular last based on evaluation information associated with the user.

[0301] In some embodiments, the personalized modular last includes a unitary modular core last and one or more last extensions removably coupled thereto, the unitary modular core last including a single modular core last component, and the processor is configured to determine the modular core last model by selecting a modular core last model component from a library of modular core last model components. In other methods, the modular core last is a binary core last including two different modular core last components, and the modular core last components are configured to be removably coupled together, for example, via at least one of an interlock, a mechanical fastener, a hook and loop fastener, a magnet, and a removable adhesive. The processor may be configured to determine the modular core component model by selecting two different modular core last model components from a library of modular core last model components to allow further customization of the modular core last.

[0302] In other methods, the modular core last includes three or more different modular core last components that are configured to be removably coupled together, for example via at least one of interlocking pieces, mechanical fasteners, hook and loop fasteners, magnets, and removable adhesives, and the processor is configured to determine a modular core last model by selecting three or more different modular core last model components from a library of modular core last model components to allow further customization of the modular core last.

[0303] In some aspects, the assessment information associated with the user further includes foot shape information, pathology information, gait information, biomechanical information, and / or contextual information. Generating a design of a personalized modular last can be based at least in part on the foot shape information, pathology information, gait information, biomechanical information, and / or contextual information.

[0304] In some embodiments, the processor may be further configured to obtain feedback about a previously worn footwear product that was manufactured based on the design of the personalized modular last and the design of the personalized footwear component. Additionally, the processor may be configured to update the design of the personalized modular last and / or update the design of at least one personalized footwear component based at least in part on the feedback about the previously worn footwear product.

[0305] The system may also include evaluation information associated with the user, the evaluation information including feedback about a previously worn product of footwear, and generating a design of the personalized modular last and / or a design of at least one shoe component is based at least in part on the feedback about the previously worn product of footwear. By one approach, the feedback about the previously worn product of footwear includes footwear condition data including at least one of accelerometer sensor data, temperature sensor data, humidity sensor data, and pressure sensor data. In an embodiment, the feedback is obtained from a physical post-wear analysis of the previously worn product of footwear.

[0306] In some aspects, the processor is configured to determine at least one sensed personal attribute based on the footwear condition data, and the design of the personalized modular last and / or the design for at least one shoe component is based at least in part on the sensed personal attribute.

[0307] In a further approach, the system may include a footwear treatment center, wherein the footwear treatment center is operable to perform a physical post-wear analysis of a previously worn article of footwear, and wherein the feedback obtained by the processor is based on the physical post-wear analysis.

[0308] In some embodiments, the processor of the system is configured to determine one or more personal attributes associated with the user from the evaluation information, wherein the one or more personal attributes include one or more of personal foot shape attributes, personal pathology attributes, personal gait attributes, personal biomechanical attributes, and personal situational attributes. In the method, generating a design of the personalized modular last can be based at least in part on the one or more personal attributes.

[0309] In some embodiments, the evaluation information includes image data, the image data including an image of at least a portion of at least one foot of a user, and the processor is configured to determine at least one personal foot shape attribute associated with the user by analyzing the image data to determine the at least one personal foot shape attribute. In the method, generating a design for the personalized modular last can be based at least in part on the one or more foot shape attributes.

[0310] In some methods, the processor may be configured to determine at least one personal pathology attribute associated with the user by analyzing the image data, and generate a design of the personalized modular last based at least in part on the at least one personal pathology attribute.

[0311] In some aspects, the evaluation information further includes video data, such as a video of the user's gait, and the processor is configured to determine at least one personal gait attribute associated with the user by analyzing the video data. Generating a design of the personalized modular last can be based at least in part on the at least one personal gait attribute.

[0312] In some embodiments, the evaluation information further includes sensor data, such as biomechanical data of the user, and the processor is configured to determine at least one personal biomechanical attribute associated with the user by analyzing the sensor data. Generating a design of the personalized modular last may be based at least in part on the at least one personal biomechanical attribute.

[0313] In some embodiments, the processor may be configured to determine at least one personal pathology attribute using at least one machine learning algorithm trained to identify at least one observable foot pathology based on image recognition. The processor may be further configured to determine at least one gait attribute using at least one machine learning algorithm trained to track points on a user based on a gait video of the user and to associate a gait pattern or feature with the user based on the tracked points.

[0314] In some methods, the processor can be configured to use at least one machine learning algorithm to determine a modular core last model and / or use at least one machine learning algorithm to determine one or more last extensions. The processor can also be configured to use at least one machine learning algorithm to generate a design for at least one personalized shoe component.

[0315] In some aspects, generating a design for a personalized modular last further includes determining one or more last extensions to be attached to a modular core last based on evaluation information associated with a user. By one approach, a manufacturing execution system is configured to manufacture the one or more last extensions and provide a personalized modular last by assembling the personalized modular last, wherein assembling the personalized modular last includes removably coupling the one or more last extensions to the modular core last based on the design of the personalized modular last. In some approaches, the removable coupling includes an interlocking member, a mechanical fastener, a hook and loop fastener, a magnet, and / or a removable adhesive.

[0316] By one approach, the modular core last and one or more last extensions are separable after use in manufacturing personalized footwear and can be reused to assemble one or more different personalized modular lasts to provide different personalized footwear products to a user or one or more different individuals.

[0317] In some embodiments, the last extensions include primary last extensions and / or secondary last extensions. The last extensions may include one or more primary last extensions, wherein the one or more primary last extensions are removably coupled to at least one of the medial ball area, lateral ball area, medial heel area, lateral heel area, toe area, ball girth area, instep area, or plantar area of ​​the modular core last. In some methods, the one or more last extensions include one or more secondary last extensions, which are removably coupled to the modular core last at selected attachment points to correspond to the user's foot shape abnormality or pathology.

[0318] In one approach, the last extension includes one or more secondary last extensions, wherein at least one of the one or more secondary last extensions is removably coupled to at least one of the one or more primary last extensions.

[0319] In some embodiments, the design of the at least one personalized shoe component includes an upper shape, an insole shape, a midsole shape, an outsole shape, and / or ancillary design elements. The processor may be configured to generate the design of the at least one personalized shoe component using an algorithm to adjust the size of the preset shoe component based on the design of the personalized modular shoe last and / or the evaluation information associated with the user. Generating the design of the at least one personalized shoe component may also include determining one or more materials for the at least one personalized shoe component based on the evaluation information associated with the user.

[0320] In some approaches, a manufacturing execution system may be configured to fabricate at least one personalized shoe component via laser cutting of a sheet of material, 3D printing, and / or 3D knitting.

[0321] In some embodiments, the material sheet for the upper is natural leather or fabric. In addition, the material sheet for the insole can be polyester foam, polyethylene foam, polyurethane foam, EVA foam or cork. The material sheet for the midsole can be open-cell polyurethane, closed-cell EVA or Gore-Tex. In one method, the material sheet for the outsole is EVA or chlorine-free rubber.

[0322] In some embodiments, the assessment information is obtained from an electronic user device.

[0323] In some aspects, the technology described herein relates to a modular last for personalized footwear, comprising: a modular core last and one or more last extensions, wherein the modular core last comprises one or more modular core last components selected from a group of different modular core last components based at least in part on evaluation information associated with a user, the evaluation information comprising at least one foot measurement of the user. In some methods, the one or more last extensions are removably coupled to the modular core last at selected attachment points on the modular core last, and the one or more last extensions and the selected attachment points are selected based at least in part on the evaluation information associated with the user.

[0324] In some methods, the modular core last is a unitary core last including a single modular core last component. In other methods, the modular core last includes two or more different modular core last components configured to be removably coupled together, the two or more different modular core last components being selected from different sets of modular core last components to allow the modular core last to be further customized according to the user's evaluation information. The two or more different modular core last components are interlocking components.

[0325] In one embodiment, the modular core last is a binary core last, which includes two different modular core last components, such as a core forefoot component and a core heel component. In another embodiment, the modular core last is a ternary core last, which includes three different modular core last components, such as a core forefoot component, a core midfoot component, and a core heel component. In a further embodiment, the modular core last is a quadruple core last, which includes four different modular core last components, such as a core toe component, a core sole component, a core midfoot component, and a core heel component.

[0326] In some aspects, the techniques described herein relate to modular lasts, wherein one or more modular core last components are selected via a machine learning algorithm that determines a close match to evaluation information associated with a user.

[0327] In some methods, a modular last includes at least a first last extension and a second last extension, wherein the first last extension is removably coupled to a modular core and the second last extension is removably coupled to the first last extension. By one approach, the second last extension is selected to correspond to a foot shape abnormality or pathology of a user. In some embodiments, at least one of the one or more last extensions is removably coupled to a region of the modular core last via at least one of an interlock, a mechanical fastener, a hook and loop fastener, a magnet, and a removable adhesive, wherein the region is an inner sole region, an outer sole region, an inner heel region, an outer heel region, a sole girth region, a toe region, an instep region, or a plantar region.

[0328] In some aspects, the technology described herein relates to a system for providing a personalized footwear product. The system may include at least one processor in communication with at least one electronic user device, the processor being configured to obtain evaluation information associated with a user, the evaluation information including at least one foot measurement of the user; determine one or more footwear attributes associated with the user based on the evaluation information; and generate a design for a personalized modular last, the personalized modular last including a modular core last. In some methods, generating the design for the personalized modular last includes: determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based at least in part on at least one of the one or more footwear attributes associated with the user. The processor may be further configured to generate a design for at least one personalized shoe component based at least in part on the design of the personalized modular last and optionally one or more of the one or more footwear attributes associated with the user; and wherein the processor is configured to send the design of the personalized modular last and the design of at least one personalized shoe component to be manufactured.

[0329] In some embodiments, the system further comprises a manufacturing execution system, wherein the processor is configured to send the design of the personalized modular last and the design of the at least one personalized shoe component to the manufacturing execution system. The manufacturing execution system may be configured to manufacture the at least one personalized shoe component, provide the personalized modular last, use the personalized modular last to manufacture the personalized footwear product, and include the at least one personalized shoe component in the personalized footwear product.

[0330] In some aspects, the technology described herein relates to a system for providing a personalized modular last. The system may include at least one processor that can communicate with at least one electronic user device, the processor being configured to obtain evaluation information associated with a user, the evaluation information including at least one foot measurement of the user, and generate a design for a personalized modular last including a modular core last. In some embodiments, generating the design for the personalized modular last includes: determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based at least in part on the evaluation information.

[0331] In some embodiments, the processor is configured to send the design of the personalized modular last to be manufactured. In one method, the system includes a manufacturing execution system, and the processor is configured to send the design of the personalized modular last to the manufacturing execution system, and the manufacturing execution system is configured to provide the personalized modular last.

[0332] In one method, generating a design for a personalized modular last further includes determining whether to include one or more last extensions in the personalized modular last based on evaluation information associated with the user. Additionally, generating a design for a personalized modular last may include determining one or more last extensions to be included in the personalized modular last based on evaluation information associated with the user.

[0333] In one method, a personalized modular last includes a unitary modular core last and one or more last extensions removably coupled thereto, wherein the unitary modular core last includes a single modular core last component, and a processor is configured to determine a modular core last model by selecting a modular core last model component from a library of modular core last model components. In other methods, the modular core last may include two or more different modular core last components that are configured to be removably coupled together via at least one of an interlock, a mechanical fastener, a hook and loop fastener, a magnet, and a removable adhesive, and the processor is configured to determine the modular core last model by selecting two or more different modular core last model components from the library of modular core last model components to allow further customization of the modular core last and provide a close match to the user's foot measurements.

[0334] In some aspects, the technology described herein relates to a method that includes obtaining assessment information associated with a user, the assessment information including at least one foot measurement of the user; and generating a design for a personalized modular last, wherein the personalized modular last includes a modular core last, wherein generating the design for the personalized modular last includes determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based at least in part on the at least one foot measurement of the user. The method may also include generating a design for at least one personalized shoe component based at least in part on the design of the personalized modular last and optionally based on the assessment information associated with the user.

[0335] In some aspects, the method may include manufacturing a personalized footwear product based on a design of a personalized modular last and / or a design of at least one personalized shoe component. Manufacturing the personalized footwear product may include providing a personalized modular last, using the personalized modular last to manufacture the personalized footwear product, and incorporating at least one personalized shoe component into the personalized footwear product.

[0336] In some methods, the method may include obtaining feedback about a previously worn footwear product, the previously worn footwear product being manufactured based on a design of a personalized modular last and a design of personalized shoe components, and updating the design of the personalized modular last and / or updating the design of at least one personalized shoe component based at least in part on the feedback about the previously worn footwear product.

[0337] In some embodiments, the evaluation information associated with the user includes feedback about a previously worn product of footwear, and generating a design of the personalized modular last and / or a design of at least one shoe component is based at least in part on the feedback about the previously worn product of footwear. In some methods, the feedback about the previously worn product of footwear includes footwear condition data including at least one of accelerometer sensor data, temperature sensor data, humidity sensor data, and pressure sensor data. By one method, feedback is obtained from a physical post-wear analysis of the previously worn product of footwear.

[0338] The method may further include receiving a previously worn article of footwear worn by a user and performing a physical post-wear analysis on the previously worn article of footwear, and wherein the feedback regarding the previously worn article of footwear is based on the physical post-wear analysis.

[0339] In some methods, the assessment information associated with the user further includes foot shape information, pathology information, gait information, biomechanical information, and / or contextual information. In these methods, generating a design of a personalized modular last can be based at least in part on the foot shape information, pathology information, gait information, biomechanical information, and / or contextual information.

[0340] The method may further provide that generating the design of the personalized modular last includes determining one or more last extensions to be included in the personalized modular last based on the evaluation information associated with the user.

[0341] In some methods, the method may provide: a personalized modular last includes a unitary modular core last and one or more last extensions removably coupled thereto, wherein the unitary modular core last includes a single modular core last component, and a modular core last model is determined by selecting a modular core last model component from a library of modular core last model components.

[0342] In other methods, the method may provide that the modular core last includes two or more different modular core last components, which are configured to be removably coupled together via at least one of interlocking parts, mechanical fasteners, hook and loop fasteners, magnets, and removable adhesives, and the modular core last model is determined by selecting two or more different modular core last model components from a library of modular core last model components to allow further customization of the modular core last based on evaluation information associated with the user.

[0343] In some aspects, the technology described herein relates to a system for providing a personalized footwear product, the system including at least one processor that can communicate with at least one electronic user device, and the processor is configured to obtain evaluation information associated with a user, the evaluation information including feedback about a previously worn footwear product, and generate an updated design of the personalized footwear product. In some embodiments, the updated design of the personalized footwear product includes a design of a personalized modular last and a design of at least one personalized shoe component, wherein the design of the personalized modular last includes a modular core last model, the modular core last model is determined by selecting one or more modular core last model components from a library of modular core last model components based at least in part on the feedback about the previously worn footwear product. The design of the at least one personalized shoe component can be based at least in part on the design of the personalized modular last and / or the feedback about the previously worn footwear product.

[0344] In some embodiments, the feedback about the previously worn footwear product includes at least one of the following: foot shape information, gait information, biomechanical information, pathological information, and contextual information. In one method, the feedback about the previously worn footwear product includes footwear condition data, including at least one of accelerometer sensor data, temperature sensor data, humidity sensor data, and pressure sensor data.

[0345] In some aspects, the technology described herein relates to a system in which a processor is configured to determine at least one sensed personal attribute based on footwear condition data, and an updated design of a personalized footwear product is based at least in part on the sensed personal attribute. By one method, the footwear condition data includes accelerometer sensor data, and the processor is configured to analyze the accelerometer sensor data to determine foot alignment. In another method, the footwear condition data includes temperature sensor data, and the processor is configured to analyze the temperature sensor data to determine the internal temperature of one or more areas of the footwear product. In some embodiments, the footwear condition data includes humidity sensor data, and the processor is configured to analyze the humidity sensor data to determine the humidity level of the footwear product. In addition, the footwear condition data may include pressure sensor data, and the processor may be configured to analyze the pressure data to determine the pressure distribution on the sole of the footwear product. The footwear condition data may include at least one of a humidity sensor and a temperature sensor, and the processor may be configured to determine the microclimate of the footwear product.

[0346] In one approach, feedback is obtained from a physical post-wear analysis of previously worn footwear.

[0347] In some embodiments, the system includes a footwear treatment center, wherein the footwear treatment center is operable to perform a physical post-wear analysis of a previously worn footwear product, and wherein the feedback obtained by the processor is based on the physical post-wear analysis. In some methods, the feedback about the previously worn footwear product is provided by a user.

[0348] In an embodiment, the processor is configured to determine a design of the personalized modular last by using the feedback to generate an updated design of the personalized footwear product. The updated design of the personalized footwear product may include at least one construction change, at least one material change, or a combination of at least one construction change and at least one material change of the personalized footwear product as compared to the previously worn footwear product.

[0349] In some configurations, the processor is configured to generate an updated design of the personalized footwear product by using the feedback to determine one or more last extensions to add to the design of the personalized modular last. The system may further include a user profile, and the processor is further configured to update the user profile based on feedback about the previously worn footwear product.

[0350] In some aspects, the technology described herein relates to a system for providing a personalized footwear product, the system including at least one processor in communication with at least one electronic user device, and the processor being configured to obtain assessment information associated with a user, the assessment information including at least one foot measurement of the user, and to generate a design for a personalized modular last, the personalized modular last including a modular core last. In some methods, generating the design for the personalized modular last includes determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based at least in part on the at least one foot measurement of the user. The processor may be further configured to generate a design for at least one personalized shoe component based on the design of the personalized modular shoe last and optionally evaluation information associated with the user, and in addition, the processor may be further configured to obtain feedback about a previously worn footwear product that was manufactured based on the design of the personalized modular shoe last and the design of the personalized shoe component, and the processor may generate an updated design of the personalized modular shoe last and / or an updated design of at least one personalized shoe component based at least in part on the feedback about the previously worn footwear product. For example, the feedback may be feedback obtained from a physical post-wear analysis of the previously worn footwear product. In one method, the system includes a footwear processing center that is operable to perform a physical post-wear analysis of the previously worn footwear product, and wherein the feedback obtained by the processor is based on the physical post-wear analysis.

[0351] In some methods, the feedback regarding a previously worn product of footwear includes footwear condition data including at least one of accelerometer sensor data, temperature sensor data, humidity sensor data, and pressure sensor data.

[0352] Additionally, the processor may be configured to determine at least one sensed personal attribute based on the footwear condition data, and the updated design of the personalized footwear product is based at least in part on the sensed personal attribute.

[0353] In some embodiments, the technology described herein relates to a method for designing one or more personalized shoe components, including obtaining assessment information associated with a user, the assessment information including at least one foot measurement of the user; and generating designs for the one or more personalized shoe components. In some embodiments, generating the designs for the one or more personalized shoe components includes: for each of the one or more personalized shoe components, using an algorithm to select a preset shoe component design from a preset shoe component design library based on a design of a personalized modular shoe last and optionally the assessment information associated with the user; optionally, using an algorithm to adjust the size of the selected preset shoe component based on the design of the personalized modular shoe last and optionally the assessment information associated with the user; and selecting a material for the personalized shoe component based on the design of the personalized modular shoe last and / or the assessment information associated with the user.

[0354] In one method, one or more personalized shoe components include at least one of an upper component, an insole component, a midsole component, and an outsole component. In one method, one or more personalized shoe components include at least one of an upper component, an insole component, a midsole component, and an outsole component. In a further method, one or more personalized shoe components include at least three of an upper component, an insole component, a midsole component, and an outsole component. In another method, one or more personalized shoe components include an upper component, an insole component, a midsole component, and an outsole component.

[0355] The method may also provide that generating the design of one or more personalized shoe components further includes determining at least one auxiliary design element to be added to the design. In an embodiment, the at least one auxiliary design element includes one or more of laces, straps, air bubbles, lights, traction material, additional padding, outsole thread patterns, specific textures, hems, eyelets / lace holes, seams / stitching, cuff padding, toe reinforcement, added toe lift, upper lining, printed and / or embossed designs, or additional panels (such as mesh panels).

[0356] In some embodiments, the technology described herein relates to a method for designing a personalized shoe component, including obtaining assessment information associated with a user, the assessment information including at least one foot measurement of the user; and generating a design for a personalized upper component. In an embodiment, generating the design for the personalized upper component includes using an algorithm to select a preset upper design from a preset upper design library based on a design of a personalized modular last personalized for the user and optionally the assessment information associated with the user; optionally, using an algorithm to adjust the size of the selected preset upper design based on the design of the personalized modular last personalized for the user and optionally the assessment information associated with the user; and selecting a material for the personalized upper component based on the design of the personalized modular last personalized for the user and / or the assessment information associated with the user.

[0357] In some aspects, the technology described herein relates to a method for designing a personalized shoe component, including obtaining assessment information associated with a user, the assessment information including at least one foot measurement of the user; and generating a design for a personalized insole component. Generating the design for the personalized insole component includes using an algorithm to select a preset insole design from a preset insole design library based on a design of a personalized modular last personalized for the user and optionally the assessment information associated with the user; optionally, using an algorithm to adjust the size of the selected preset insole design based on the design of the personalized modular last personalized for the user and optionally the assessment information associated with the user; and selecting a material for the personalized insole component based on the design of the personalized modular last personalized for the user and / or the assessment information associated with the user.

[0358] In some aspects, the technology described herein relates to a method for designing a personalized shoe component, including obtaining assessment information associated with a user, the assessment information including at least one foot measurement of the user; and generating a design for a personalized shoe midsole component. In an embodiment, generating the design for the personalized shoe midsole component includes: using an algorithm to select a preset shoe midsole design from a preset shoe midsole design library based on a design of a personalized modular shoe last personalized for the user and optionally the assessment information associated with the user; optionally, using an algorithm to adjust the size of the selected preset shoe midsole design based on the design of the personalized modular shoe last personalized for the user and optionally the assessment information associated with the user; and selecting a material for the personalized shoe midsole component based on the design of the personalized modular shoe last personalized for the user and / or the assessment information associated with the user.

[0359] In some aspects, the technology described herein relates to a method for designing a personalized shoe component, including obtaining assessment information associated with a user, the assessment information including at least one foot measurement of the user; and generating a design for a personalized outsole component. In an embodiment, generating the design for the personalized outsole component includes: using an algorithm to select a preset outsole design from a preset outsole design library based on a design of a personalized modular last personalized for the user and optionally the assessment information associated with the user; optionally, using an algorithm to adjust the size of the selected preset outsole design based on the design of the personalized modular last personalized for the user and optionally the assessment information associated with the user; and selecting a material for the personalized outsole component based on the design of the personalized modular last personalized for the user and / or the assessment information associated with the user.

[0360] In some aspects, the technology described herein relates to a method for designing one or more personalized shoe components, including obtaining assessment information associated with a user, the assessment information including at least one foot measurement of the user, and generating a design for a personalized outsole component. In an embodiment, generating the design for the personalized outsole component includes: using an algorithm to select a preset outsole design from a preset outsole design library based on a design of a personalized modular last personalized for the user and optionally the assessment information associated with the user; optionally, using an algorithm to adjust the size of the selected preset outsole design based on the design of the personalized modular last personalized for the user and optionally the assessment information associated with the user; and selecting a material for the personalized outsole component based on the design of the personalized modular last personalized for the user and / or the assessment information associated with the user.

[0361] Those skilled in the art will appreciate that various modifications, changes and combinations may be made with respect to the embodiments described above without departing from the scope of the present disclosure, and such modifications, changes and combinations will be deemed to be within the scope of the inventive concept.

Claims

1. A system for personalizing footwear, comprising: at least one processor in communication with at least one electronic user device, the processor being configured to: obtaining assessment information associated with a user, the assessment information comprising at least one foot measurement of the user, and generating a design of a personalized modular last, wherein the personalized modular last comprises a modular core last, Wherein generating the design of the personalized modular last includes determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based at least in part on the at least one foot measurement of the user; the processor is further configured to generate the design of at least one personalized shoe component based on the design of the personalized modular last and / or the evaluation information associated with the user.

2. The system of claim 1, further comprising a manufacturing execution system, wherein the processor is configured to send the design of the personalized modular shoe last and the design of the at least one personalized shoe component to the manufacturing execution system for manufacturing, and the manufacturing execution system is configured to manufacture the at least one personalized shoe component, provide the personalized modular shoe last, manufacture a personalized footwear product using the personalized modular shoe last, and include the at least one personalized shoe component in the personalized footwear product.

3. The system according to claim 1, characterized in that Generating the design of the personalized modular last further includes determining whether to include one or more last extensions in the personalized modular last based on the evaluation information associated with the user.

4. The system according to claim 1, characterized in that Generating a design for the personalized modular last further includes determining one or more last extensions to be coupled with the personalized modular last based on the evaluation information associated with the user.

5. The system according to claim 4, characterized in that The personalized modular last includes a unitary modular core last and one or more last extensions coupled thereto, wherein the unitary modular core last includes a single modular core last component, and the processor is configured to determine the modular core last model by selecting a modular last model component from a library of modular core last model components.

6. The system according to claim 1, wherein: The modular core last includes two or more different modular core last components configured to be removably coupled together, and the processor is configured to determine the modular core last model by selecting two or more different modular core last model components from a library of modular core last model components to allow further customization of the modular core last and provide a close match to the user's foot measurements.

7. The system according to claim 1, characterized in that The assessment information associated with the user further includes foot shape information, pathology information, gait information, biomechanical information and / or contextual information; and Wherein, generating the design of the personalized modular last is at least partially based on the foot shape information, pathological information, gait information, biomechanical information and / or situational information.

8. The system of claim 1, wherein: The assessment information includes image data including an image of at least a portion of at least one foot of the user, and the processor is configured to determine at least one personal pathology attribute by analyzing the image data; And wherein generating the design of the personalized modular last is based at least in part on the at least one personal pathological attribute.

9. The system according to claim 8, characterized in that The processor is configured to determine the at least one personal pathology attribute using at least one machine learning algorithm trained to identify at least one observable foot pathology based on image recognition.

10. The system according to claim 1, wherein: The processor is further configured to obtain feedback regarding previously worn footwear that was manufactured based on the design of the personalized modular last and the design of the personalized shoe component; as well as The processor is further configured to generate an updated design of the personalized modular last and / or an updated design of the at least one personalized shoe component based at least in part on the feedback regarding the previously worn product of footwear.

11. The system according to claim 10, characterized in that The feedback regarding a previously worn article of footwear includes footwear condition data including at least one of accelerometer sensor data, temperature sensor data, humidity sensor data, and pressure sensor data.

12. The system of claim 1, wherein: The personalized shoe components include a shoe upper shape, a shoe insole shape, a shoe midsole shape, a shoe outsole shape and / or auxiliary design elements.

13. The system of claim 4, further comprising a manufacturing execution system, wherein the processor is configured to send the design of the personalized modular last to the manufacturing execution system, wherein: The manufacturing execution system is configured to manufacture the one or more last extensions and assemble the personalized modular last, and wherein assembling the personalized modular last includes removably coupling the one or more last extensions to the modular core last based on a design of the personalized modular last.

14. The system of claim 13, wherein: The modular core last and the one or more last extensions are separable after being used to manufacture the personalized footwear and can be reused to assemble one or more different personalized modular lasts to provide different personalized footwear products for the user or for one or more different individuals.

15. A modular last for personalized footwear, comprising: A modular core last and one or more last extensions, wherein the modular core last includes selecting one or more modular core last components from a different set of modular core last components based at least in part on evaluation information associated with a user, the evaluation information including at least one foot measurement of the user; Wherein, the one or more last extensions are removably coupled to the modular core last at selected attachment points on the modular core last, and the one or more last extensions and the selected attachment points are selected at least in part based on the evaluation information associated with the user.

16. The modular shoe last according to claim 15, characterized in that: The modular last includes two or more different modular core last components, and the two or more different modular core last components are configured to be removably coupled together. The two or more different modular core last components are selected from the different modular core last component groups to allow the modular core last to be further customized according to the evaluation information of the user.

17. The modular shoe last according to claim 16, characterized in that: The modular core last is a binary core last, a ternary core last or a quadruple core last, the binary core last comprising a core forefoot component and a core heel component, the ternary core last comprising a core forefoot component, a core midfoot component and a core heel component, and the quadruple core last comprising a core toe component, a core sole component, a core midfoot component and a core heel component.

18. The modular shoe last according to claim 15, characterized in that: The one or more modular core last components are selected via a machine learning algorithm that determines a close match to the evaluation information associated with the user.

19. The modular last of claim 15, comprising at least a first last extension and a second last extension, wherein the first last extension is removably coupled to the modular core last and the second last extension is removably coupled to the first last extension.

20. The modular shoe last according to claim 15, characterized in that: At least one of the one or more last extensions is removably coupled to an area of ​​the modular core last via at least one of an interlocking piece, a mechanical fastener, a hook and loop fastener, a magnet, and a removable adhesive, wherein the area is an inner sole area, an outer sole area, an inner heel area, an outer heel area, a sole circumference area, a toe area, an instep area, or a plantar area.

21. A method comprising: obtaining assessment information associated with a user, the assessment information comprising at least one foot measurement of the user; generating a design for a personalized modular last, wherein the personalized modular last includes a modular core last, wherein generating the design for the personalized modular last includes: determining a modular core last model by selecting one or more modular core last model components from a library of modular core last model components based at least in part on the at least one foot measurement of the user; as well as A design for at least one personalized shoe component is generated based at least in part on the design of the personalized modular last and, optionally, the evaluation information associated with the user.

22. The method of claim 21, further comprising: A personalized footwear product is manufactured based on the design of the personalized modular shoe last and the design of the at least one shoe component, wherein the steps of manufacturing the personalized footwear product include: providing the personalized modular shoe last, using the personalized modular shoe last to manufacture the personalized footwear product, and incorporating the at least one personalized shoe component into the personalized footwear product.

23. The method of claim 21, further comprising obtaining feedback regarding previously worn footwear manufactured based on the design of the personalized modular last and the design of the personalized shoe component; and The design of the personalized modular last and / or the design of the at least one personalized shoe component are updated based at least in part on the feedback regarding the previously worn product of footwear.

24. The method of claim 21, wherein: Generating a design of one or more personalized shoe components includes, for each of the one or more personalized shoe components: selecting a preset shoe component design from a library of preset shoe component designs using an algorithm based on the design of the personalization module last and optionally the evaluation information associated with the user; as well as Optionally, using an algorithm to adjust the size of the selected preset shoe component design based on the design of the personalized modular last and optionally the evaluation information associated with the user; as well as A material for the personalized shoe component is selected based on the design of the personalized modular last and / or the evaluation information associated with the user.

Images