Data communications network and method for facilitating the purchase of wearable items in an online environment

Abstract

The present invention relates to a data communications network and method for facilitating the identification, selection and purchase of wearable items in an online environment by consumers. In particular, the present invention provides a data communications network and method that assists a consumer to identify clothing, headgear and / or footwear that best accommodates their own physical size measurements and that also satisfies a particular wearable item requirement (eg. a wardrobe requirement such as ensuring stock levels of particular clothing items are maintained) and at least one preference (eg. a preferred fit or fashion style) of the consumer.

Description

DATA COMMUNICATIONS NETWORK AND METHOD FOR FACILITATING THE PURCHASE OF WEARABLE ITEMS IN AN ONLINE ENVIRONMENTFIELD OF THE INVENTION

[0001] The present invention relates to a data communications network and method for facilitating the identification, selection and purchase of wearable items in an online environment by consumers. In particular, the present invention provides a data communications network and method that assists a consumer to identify clothing, headgear and / or footwear that best accommodates their own physical size measurements and that also satisfies a particular wearable item requirement (eg. wardrobe requirement) and at least one preference (eg. preferred fashion style) of the consumer.BACKGROUND OF THE INVENTION

[0002] The emerging popularity of the internet over the past decade has changed how consumers purchase items, and online shopping (e-commerce) has become an important part of global retail. Online shopping is predicted to experience continued growth as consumers seek the convenience of online shopping and the competitive pricing of online goods. Other factors contributing to the continued growth of the e-commence industry include an expanding range of products that are available online, the recent Covid-19 pandemic, faster internet speeds, more accessible internet and increased online transaction security.

[0003] With respect to wearable items such as clothing, headgear and footwear, despite the convenience associated with online shopping, many consumers experience confusion and frustration when searching for, identifying and receiving such items purchased online. For example, users may struggle with selecting wearable items that are available for purchase online since they are not sufficiently interested and / or experienced to appropriately select wearable items that will correspond with their existing wardrobe (ie. match their preferred fashion style). Indeed, many individuals don’t have a good understanding of what already exists and what is missing from their own wardrobes. Fashion stylists can be employed by individuals who prefer this responsibility to beoutsourced since they do not have the time, interest, inclination and / or experience to curate their own outfits and their own wardrobe. However, for the vast majority of individuals, the option of contracting a fashion stylist on an ongoing basis is not viable due to the expense associated with same.

[0004] Accordingly, users who don’t have the benefit of their own fashion stylist often struggle to identify wearable items available for purchase that are likely to be a good fit for their body part size and that also satisfy the user’s requirements and / or preferences, such as the user’s fashion style. Furthermore, wearable items that are purchased online may require a specific sizing / dimension in order to be fit for purpose yet once received appear unsuitable despite the consumer ordering according to their correct size. In particular, items such as clothing, headgear and footwear typically have high rates of return due to a change of mind of the user when such purchased items are considered not to match with the user’s fashion style, and / or due to incorrect sizing or a poor / uncomfortable fit that primarily arises due to variations in “standard” sizing between manufacturers and / or jurisdictions. For example, a size 10 dress in the United States may not necessarily be the same size as compared with a size 10 dress in Australia. Accordingly, a consumer in Australia who would normally purchase a size 10 dress according to Australian sizing standards, may actually be a size 6 according to US sizing standards. Additionally, sizing differs across all European countries.

[0005] Although many consumers order items online that may be the correct dimension (size) in one aspect (for example, a size 8 shoe), the item may still be a poor fit due to another dimensional aspect being inappropriate, (eg. a narrow width) as a result of the particular style of footwear and / or the particular manufacturer. This necessitates the consumer exchanging the item for an item of an alternative style / size or requesting return of their money with both options requiring the consumer to send the purchased item back to the online retailer. Returning items in this way can also result in additional expense for the consumer and / or the retailer.

[0006] Although most online retailers allow the return of items according to ‘change of mind’ policies, frustration experienced with the receipt of incorrectly styled and / or sized items tends to cause consumers to avoid online shopping for such items which results in reduced online sales revenue for retailers. Furthermore, returning items results in a waste of the consumer’s time since the consumer is not only required to wait for an initial orderto be processed, packaged and delivered, but is further inconvenienced if required to return their purchased item and await receipt of any alternative replacement item. Similarly, returning items wastes time and resources on the part of the retailer and is therefore also undesirable from a retailer perspective.

[0007] Returning unsuitable items also increases vehicle traffic (associated with pickup and delivery of returned or re-ordered items) and wastes resources associated with the requirement to re-package and return such items. For example, the return of unsuitable items results in the wastage of packaging materials and fuel which has a negative impact upon the environment, particularly when considering e-commence of wearable items is typically implemented on a global scale. Returning unsuitable items also results in increased internet traffic and wastage of computing resources including processing, memory and data communication resources associated with increased online communications between consumers and online retailers, including when clarifying the nature of the return and / or effecting and confirming the return.

[0008] Furthermore, an increase in the rate of return of items purchased online increases the pressure placed upon delivery service providers as a result of longer delivery times and sub-optimal performance. Ultimately, all of these problems reduce consumer confidence with respect to shopping online and ultimately dissuades many consumers, particularly consumers seeking items such as clothing, headgear and footwear.

[0009] Accordingly, there exists a need for a network, system and / or method that enables individuals to readily identify and purchase wearable items online that increases the prospects of a “good fit” with respect to both size and that also satisfies a particular clothing requirement and / or preference of the user. This would be expected to conserve computing resources and reduce the rate of returned items and thereby avoid frustration experienced by consumers, online retailers and delivery service providers. Reducing the rate of return of items purchased online will also avoid wasting fuel associated with delivery and return of such items by road, sea and air, and also avoid wasting packaging, thereby reducing the negative impact of such wastage of resources on the environment.

[0010] The reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any suggestion, that the prior art forms part of the common general knowledge.SUMMARY OF THE INVENTION

[0011] In one aspect, the present invention provides a computer-implemented data communications network including connected data communications devices and a method of operating same to facilitate the purchase of wearable items in an online environment, the method including, receiving, by one or more processors, information relating to a wearable item requirement and at least one preference of a user, obtaining, by one or more processors using an image capture facility, multiple optical images of one or more body parts of the user along with an object of known dimensions that is attached to, or located in proximity with, the body part(s) and thereby also in view of the image capture facility and resolving, by one or more optical resolution techniques, the images to generate a three-dimensional model of the body part(s), wherein the resolving of optical images to generate the three-dimensional model includes comparison of the body part images with the object of known dimensions to further provide sizing information regarding the three-dimensional model of the body part(s), determining, by one or more processors, the physical dimensions of the body part(s) according to the sizing information provided by the three-dimensional model, searching, by one or more processors using a searching and recommendation facility, one or more data repositories associated with a plurality of retailers offering a range of wearable items for purchase, the information stored in the one or more data repositories identifying detailed dimensions of each wearable item and classifying each wearable item, wherein the searching is conducted to determine wearable items that include detailed dimensions similar, according to a similarity threshold, to those dimensions of corresponding body part(s) of the user as determined from the sizing information provided by the three-dimensional model, and which are classified such that the purchase of the item will satisfy the user’s wearable item requirement and the at least one preference, providing, by one or more processors, for display on a data communications device associated with the user, the identified one or more wearable items that include similar detailed dimensions as compared with the physical dimensions of the corresponding body part(s), and which are classified such that the purchase of the item will satisfy the wearable item requirement and the at least one preference of the user.

[0012] In an embodiment, the one or more data repositories associated with the plurality of retailers include one or more of, retailer websites, retailer social media pages,or other hardware and / or software associated with the retailers including retailer databases.

[0013] In an embodiment, the received information relating to a wearable item preference of the user includes one or more of, a brand preferred by the user, a fit preferred by the user (eg. tight, regular or loose), a fashion style preferred by the user, a fashion style or fit determined on behalf of the user, or a current fashion style trend that is determined and / or monitored using one or more artificial intelligence techniques.

[0014] In an embodiment, the preferred brand, fashion style or fit is determined on behalf of the user by using an image capture facility that obtains multiple optical images of a current wardrobe of the user which stores existing wearable items of the user, wherein the images are resolved using one or more optical resolution techniques to identify the user’s preferred brand, fashion style or fit.

[0015] In an embodiment, the range of wearable items offered by retailers include attributes that are classified according to one or more categories and sub-categories which enables identification of wearable items to provide to the user based upon automatically comparing the classified attributes with attributes associated with the wearable item in corresponding categories and sub-categories.

[0016] In an embodiment, the received information relating to a wearable item requirement of the user includes a current stock level of particular wearable items of the user. This may be based on the user requiring the item(s) according to a lack, or insufficient number, of such item(s) in the user’s wardrobe.

[0017] In an embodiment, the current stock level of particular wearable items of the user is determined using an image capture facility that obtains multiple optical images of a physical wardrobe of the user which stores existing wearable items of the user, wherein the images are resolved using one or more optical resolution techniques to determine item attributes classified according to category (eg. clothing, footwear, headgear) and sub-categories (eg. clothing type, brand, style, size, etc), and the current stock level of each item stored in the user’s current wardrobe.

[0018] In an embodiment, obtaining optical images of the body part(s) and / or physical wardrobe of the user includes utilizing an optical hardware component associated with a mobile data communications device, including passing an optical lens associated with theoptical hardware component over the body part(s) and / or physical wardrobe from a range of different angles to capture multiple optical images and enable generation of a three- dimensional model.

[0019] In an embodiment, obtaining optical images of the body part(s) and / or physical wardrobe of the user includes utilizing fixed body image capture hardware associated with the user or a retailer which includes one or more optical lenses configured to capture multiple optical images and enable generation of a three-dimensional model.

[0020] In an embodiment, the images of the body part(s) and / or physical wardrobe of the user are captured along with an object of known dimensions that is within view of the optical lens(es) and attached, or located in proximity with, the body part(s) and / or physical wardrobe such that resolution of the images to generate the three dimensional model includes comparison of body part images or items within the wardrobe with the object of known dimensions to determine attributes of the three dimensional model or individual items within the wardrobe.

[0021] In an embodiment, the method further includes providing guidance regarding adequate capture of the body part(s) and / or physical wardrobe of the user, and audible and / or visual prompts to guide the user when capturing images including when the user has attained sufficient images to enable generation of the three-dimensional model of the body part(s) or identification of items within the wardrobe with sufficient data to determine the attributes of features in the model or attributes associated with wardrobe items.

[0022] In an embodiment, the resolution of the images to determine the three- dimensional model is assisted by data input by the user, including data manually entered by the user in relation to one or more physical dimensions of the body part(s) unable to be determined from the resolution of images and generation of the model.

[0023] Similarly, guidance may be provided regarding adequate capture of the user’s wardrobe, and audible and / or visual prompts may be provided to guide the user when capturing images including when they have attained sufficient images with sufficient data to enable a stock level of each category and / or subcategory of each wearable item stored in the wardrobe. This process may also be assisted with the use of artificial intelligence techniques and through manual data input by the user.

[0024] In an embodiment, the display provided to the user includes the physical dimensions and / or the three-dimensional model of the body part(s) and / or wardrobe and items stored therein.

[0025] In an embodiment, based on identifying the one or more wearable items from the range of wearable items offered for purchase that include detailed dimensions similar to those dimensions of the corresponding body part(s) of the user, and which are classified such that the purchase of the item(s) will satisfy the user’s wearable item requirement and at least one preference, the method further includes, generating, by one or more processors, for display on the data communications device associated with the user, a notification alerting the user to the identified one or more wearable items, wherein the display is, an automatic notification that is not in response to a specific request from the user, or a notification in response to a user request to search for a particular wearable item or category and / or subcategory of wearable item(s). Additionally, the fashion style of any suggested items may accord with a fashion trend identified by the system as a result of monitoring posted images of persons of interest generally or alternatively, persons nominated by the user.

[0026] In an embodiment, the notification provides the user with the ability to purchase one or more of the identified wearable items.

[0027] In an embodiment, the display of the identified one or more wearable items is in response to a user request to receive a full outfit recommendation with a plurality of wearable items across a range of wearable item categories and / or subcategories (eg. a collection / ensemble of different items).

[0028] It will therefore be appreciated that the present invention involves a data communications network and method of operating same which provides a solution to the problems discussed herein with a user provided, via their data communications device and in substantially real-time, a means of receiving wearable item purchase suggestions and recommendations following the automatic and ongoing analysis of data repositories stored by wholesalers and / or retailers during which wearable items that include detailed dimensions compatible with those dimensions of corresponding body part(s) of the user, and classified such that the purchase of the item(s) will satisfy a wearable item requirement and the at least one preference of the user, are identified.

[0029] In an embodiment, the searching and recommendation facility is operable by a user utilizing a software application operable on their own data communications device.

[0030] In an embodiment, the body part(s) of the user include a single body part, a combination of body parts, a body region or regions, or the entire body of the user.

[0031] In an embodiment, the one or more processors are further operable to generate a prompt to the user to provide an updated series of optical images of the body part(s) and / or physical wardrobe for the purpose of ensuring that the physical dimensions of the body part(s) and stock levels of certain categories or subcategories of wearable items are accurate and up to date.

[0032] In an embodiment, the searching and recommendation facility further enables the user to filter the displayed one or more wearable items, according to one or more of, a name of the item, a category or subcategory of the item, a brand name associated with the item, a location or geographical zone, a retailer of the item, a price of the item, a visual attribute of the item, or a physical attribute of the item. Such filtering may also be performed automatically and this may be assisted with the use of artificial intelligence techniques.

[0033] In an embodiment, the display of the one or more wearable items is limited to only those items that include similar detailed dimensions as compared with the physical dimensions of the body part(s) according to the similarity threshold, hence avoiding any further requirement for the user to review and filter results to ensure that the displayed wearable items represent a substantially correct and / or preferred fit.

[0034] In an embodiment, the similarity threshold is automatically adjusted for particular users based on one or more of, a propensity for the user to return items, a propensity for a particular item to be returned, user traffic, or a user preference to receive improved quality item matches.

[0035] In an embodiment, the display of the one or more wearable items is further accompanied by one or more of, a fit score reflecting a fit prediction rating for the wearable item, or a request for feedback in relation to the fit of a wearable item.

[0036] In an embodiment, feedback from the user obtained via the request for feedback or using any additional means is processed using one or more artificialintelligence techniques to achieve one or more of, auto-adjust similarity thresholds for use when generating future outputs, or facilitate machine learning to improve outputs including one or more of the wearable item results displayed to users, fit scores, and automatic size conversions according to region.

[0037] In an embodiment, in the event there are no results in the display of the one or more wearable items on the basis that there are no items located having similar detailed dimensions as compared with the physical dimensions of the body part(s) according to the similarity threshold, or which don’t have a classification that satisfies the user’s wearable item requirement and at least one preference, the one or more processors are further operable to generate a prompt to the user suggesting a search of broader scope or automatically lowering the similarity threshold such that the listing includes items of the nearest dimensions and / or having the best prospects of satisfying the user’s wearable item requirement and at least one preference.

[0038] In an embodiment, selection of a particular wearable item in the display of the one or more wearable items of interest causes the software application to perform an action including any one or more of, generating one or more user interfaces providing additional information relating to the selected wearable item, generating one or more user interfaces displaying the three-dimensional model of the body part including graphical representations of the selected wearable item(s) worn by the three-dimensional model of the body part, generating one or more user interfaces displaying a wardrobe of the user including graphical representations of the selected wearable item stored within the wardrobe, generating one or more user interfaces enabling purchase of the selected wearable item(s), or operating a web browser to display a page associated with a retailer of the wearable item to thereby enable purchase of the wearable item from the retailer’s online store.

[0039] In an embodiment, the one or more processors are further operable to integrate with the data of a retailer’s online store using an application programming interface (API), such that when the user accesses the online store and views and / or searches wearable items of interest, only those wearable items of interest that include similar detailed dimensions as compared with the physical dimensions of the body part(s) of the user, and which are classified such that the purchase of the item will satisfy theuser’s wearable item requirement and / or preference, will be displayed and / or listed in search results generated from the online store.

[0040] In a further aspect, the present invention provides a computer-implemented method for facilitating the purchase of wearable items in an online environment, the method including, receiving, by one or more processors, information relating to a wearable item requirement and at least one preference of a user, obtaining, by one or more processors using an image capture facility, multiple optical images of one or more body parts of the user along with an object of known dimensions that is attached to, or located in proximity with, the body part(s) and thereby also in view of the image capture facility and resolving, by one or more optical resolution techniques, the images to generate a three-dimensional model of the body part(s), wherein the resolving of optical images to generate the three-dimensional model includes comparison of the body part images with the object of known dimensions to further provide sizing information regarding the three-dimensional model of the body part(s), determining, by one or more processors, the physical dimensions of the body part(s) according to the sizing information provided by the three-dimensional model, searching, by one or more processors using a searching and recommendation facility, one or more data repositories associated with a plurality of retailers offering a range of wearable items for purchase, the information stored in the one or more data repositories identifying detailed dimensions of each wearable item and classifying each wearable item, wherein the searching is conducted to determine wearable items that include detailed dimensions similar, according to a similarity threshold, to those dimensions of corresponding body part(s) of the user as determined from the sizing information provided by the three-dimensional model, and which are classified such that the purchase of the item will satisfy the user’s wearable item requirement and the at least one preference, providing, by one or more processors, for display on a data communications device associated with the user, the identified one or more wearable items that include similar detailed dimensions as compared with the physical dimensions of the corresponding body part(s), and which are classified such that the purchase of the item will satisfy the wearable item requirement and the at least one preference of the user.

[0041] In a still further aspect, the present invention provides a computer-readable medium having a plurality of computer instructions executable by one or more processors, that, when executed, cause the one or more processors to, receive information relatingto a wearable item requirement and at least one preference of a user, obtain, using an image capture facility, multiple optical images of one or more body parts of the user along with an object of known dimensions that is attached to, or located in proximity with, the body part(s) and thereby also in view of the image capture facility and resolving, by one or more optical resolution techniques, the images to generate a three-dimensional model of the body part(s), wherein the resolving of optical images to generate the three- dimensional model includes comparison of the body part images with the object of known dimensions to further provide sizing information regarding the three-dimensional model of the body part(s), determine the physical dimensions of the body part(s) according to the sizing information provided by the three-dimensional model, search, using a searching and recommendation facility, one or more data repositories associated with a plurality of retailers offering a range of wearable items for purchase, the information stored in the one or more data repositories identifying detailed dimensions of each wearable item and classifying each wearable item, wherein the searching is conducted to determine wearable items that include detailed dimensions similar, according to a similarity threshold, to those dimensions of corresponding body part(s) of the user as determined from the sizing information provided by the three-dimensional model, and which are classified such that the purchase of the item will satisfy the user’s wearable item requirement and the at least one preference, provide, for display on a data communications device associated with the user, the identified one or more wearable items that include similar detailed dimensions as compared with the physical dimensions of the corresponding body part(s) , and which are classified such that the purchase of the item will satisfy the wearable item requirement and the at least one preference of the user.BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Embodiments of the invention will now be described in further detail with reference to the accompanying Figures in which:

[0043] Figure 1 provides an overview of a data communications network according to an embodiment of the present invention showing, in particular, the interaction between various network components.

[0044] Figure 2 illustrates a diagram associated with an exemplary server component of the network illustrated in Figure 1.

[0045] Figure 3 illustrates an exemplary flow diagram of a process that enables a user to download and install a software application, and subsequently access, or register to use, the software application for interaction with the network illustrated in Figure 1 .

[0046] Figure 4 illustrates an exemplary flow diagram of a process that enables the user of Figure 3 to utilize a mobile or fixed image capture facility operable to capture a series of optical images of a body part of the user for the purpose of generating a digital three-dimensional model of the body part.

[0047] Figure 5 illustrates an exemplary flow diagram of a process that enables the user to utilize a searching and recommendation facility to identify one or more wearable items of interest that include similar detailed dimensions as compared with those captured during the process shown in Figure 4, and which are classified such that the purchase of the item(s) will satisfy a wearable item requirement and at least one preference of the user.

[0048] Figure 6 illustrates an exemplary flow diagram of a process that enables the user to utilize a mobile or fixed image capture facility operable to capture a series of optical images of the user’s wardrobe for the purpose of enabling the searching and recommendation facility to identify one or more existing wearable items of the user and thereby determine a wearable item requirement and at least one preference of the user.

[0049] Figure 7 illustrates an exemplary flow diagram of the process of searching multiple different data repositories associated with a plurality of different retailers of wearable items for the purpose of identifying wearable items available for purchase thatrepresent an appropriate fit for the user and satisfy a wearable item requirement and at least one preference of the user.

[0050] Figure 8 illustrates an exemplary flow diagram of a process that enables a retailer to access the software application for the purpose of integrating their online store with functionality provided by the software application.DETAILED DESCRIPTION OF EMBODIMENT(S) OF THE INVENTION

[0051] For illustrative purposes, the present disclosure is described by referring to embodiment(s) thereof. In the following description, numerous specific details are set forth to provide a better understanding of the present disclosure. It will be readily apparent, that the current disclosure may be practiced without limitation to the specific details described in respect of the one or more embodiments. In other instances, some features have not been described in detail to avoid obscuring the present disclosure.

[0052] The present invention relates to at least a computer-implemented data communications network and method for facilitating the identification and purchase of items (90) in an online environment. In an embodiment, the network and method provide a platform that hosts a computer executable software application (50) wherein the application (50) is accessible by users (30) who utilize the platform and its functionality to purchase or assist with purchasing wearable items (90) such as clothing, footwear and / or headgear in an online environment. In particular, the system may utilize a central server (20) in communication with a data communications device (40) associated with each user (30).

[0053] It is to be understood that reference to a “body part” (55) herein is not intended to be limited to a single body part of a user (30), but may include a single body part, a combination of body parts, a region of the body, or the entire body of the user (30).

[0054] It will be further appreciated that general reference to a “user” (30) herein is intended to reference individuals who access the software application (50) for the purpose of obtaining assistance with respect to purchasing wearable items (90), as well as individuals associated with retail establishments who prefer to list wearable items (90) for purchase and utilize the functionality of the software application (50) integrated with their own systems.

[0055] The central server (20) maintains one or more processors and / or databases for performing functions, including receiving information relating to a wearable item requirement and preference(s) of the user (30), which may include a style preference of the user, a requirement to re-stock particular clothing items in the user’s wardrobe, etc. Multiple optical images (60) are obtained of one or more body parts (55) associated with the user (30) using a mobile (180) or fixed (190) image capture facility, and such images(60) are subsequently resolved, using an optical resolution technique, to generate a three- dimensional model (225) of the body part(s) (55) along with relative and / or absolute sizing information regarding the three-dimensional model (225) of the body part(s) (55). According to the sizing information provided by the three-dimensional model (225), the physical dimensions of the body part(s) may be determined. The central server (20) subsequently searches, using a searching and recommendation facility (240), one or more data repositories (75) associated with a plurality of wholesalers or retailers (70) (eg. websites, social media accounts and / or any other hardware / software associated with each retailer (70)) offering a range of wearable items (90) for purchase, the information stored in the one or more data repositories identifying detailed dimensions of each wearable item and classifying each wearable item.

[0056] Such searching is conducted to determine wearable items (90) that include detailed dimensions that are similar, according to a similarity threshold, to those dimensions of corresponding body part(s) (55) of the user (30) as determined from the sizing information provided by the three-dimensional model (225), and which are classified such that the purchase of the item will also satisfy the user’s wearable item requirement and at least one preference. The search facility enables one or more wearable items (90) that include similar detailed dimensions as compared with the physical dimensions of the corresponding body part(s), and which are classified such that the purchase of the item (90) will satisfy the wearable item requirement and at least one preference of the user (30), to be identified via software application (50) (eg. provided for display upon a graphical user interface thereof).

[0057] The skilled person will appreciate that the platform provides a means by which users (30) may identify and purchase items (90) in an online environment more efficiently (substantially in real-time) by analysing data repositories (75) associated with retailers (70) of wearable items (90) (automatically or in response to a user request) and providing an output of results to the user (30) that not only improves the accuracy of selecting a size for the particular item(s) (90) (eg. the size of a particular item of clothing, footwear or headwear) but also by ensuring that a particular wearable item requirement (eg. wardrobe requirement) and at least one preference (eg. preferred fashion style) of the user (30) is addressed, thereby resulting in an enhanced technical platform and more satisfying retail experience.

[0058] The platform already knows the user’s size since the physical dimensions of the user’s body part (55) has been determined from the generated three-dimensional model (225), although if any body part(s) are not modelled, the user (30) may be prompted to capture and upload additional images of the relevant body part(s) required to enable a particular wearable item search and selection.

[0059] In addition, a requirement and at least one preference of the user (30) with respect to the wearable item(s) is also known, eg. a requirement to stock a particular item in the user’s wardrobe, a preference of the user with respect to a particular clothing style, etc. By providing the user (30) with recommendations and reminders to re-stock certain wearable items in their wardrobe, and to select wearable items according to an appropriate fashion style, trend, etc, the user (30) is effectively provided with a fashion stylist assistant with recommendations generated and subsequently received by the user’s personal data communications device.

[0060] Therefore, when the user (30) has a need to purchase one or more wearable items (90), the software application (50) may be accessed by the user (30) to receive automatic notifications / recommendations / reminders to purchase particular items (90) in order to maintain the items in the user’s wardrobe at a particular stock level and / or according to a particular fashion style. Alternatively, the user (30) may enter a specific request for a wearable item recommendation (eg. a request for the platform to provide an item recommendation according to a particular clothing category or subcategory (eg a recommended jacket for purchase, etc).

[0061] The results presented to the user (30) also ensure that only products of an appropriate size and / or fit are displayed for selection. The user (30) is therefore also assisted in relation to identifying a “perfect fit”, the first time, without the need to extensively search for, or receive, items amongst options that are not a correct or preferred fit. The abovementioned benefits enhance consumer confidence and satisfaction, whilst minimizing the likelihood of returns of items to retailers (thereby reducing wastage of resources associated with returned items.

[0062] Figure 1 is divided into Segments which are further expanded in subsequent Figures 2 - 8. In particular, Segment 200 of Figure 1 shows the server component (20) with which the software application (50) operating on data communications devices (40) is configured to communicate. It will be apparent to the person skilled in the relevant fieldof technology that the software application (50) may be a mobile application or a web application, and similarly, the data communications device (40) utilized by user (30) may be a portable device such as a mobile phone or laptop, or alternatively a fixed location device such as a personal computer (not shown). The server component (20) is additionally detailed in Figure 2.

[0063] The skilled person will appreciate that the steps described herein may be executed by the device (40), wherein such operations are facilitated by the software application (50) operating on each device (40). According to another implementation of the present invention, the server (20) is programmed to provide most or all of the functions described herein particularly where they cannot be provided locally on the user device (40) or where it may be otherwise commercially or technically impractical. In other words, the steps described herein as performed by the device (40), or components thereof, may be associated with hardware that is located externally of the device, such as the remote central server (20) for example (ie. in a distributed architecture). Different arrangements are possible in this regard, and alternate variations will be apparent to the person skilled in the relevant field of technology.

[0064] Segment 300 of Figure 1 shows the user (30) downloading and installing the application (50) and subsequently accessing interface (160) of the application (50) in order to establish an account, as further detailed in Figure 3. Segment 400 of Figure 1 illustrates how the user (30) may establish a user profile as shown in interface (170) including capturing their detailed physical dimensions utilizing a variety of mechanisms and entering additional details which may include a wearable item requirement and at least one preference of the user (30), as further detailed in Figure 4. Segment 500 of Figure 1 illustrates an example of a request received from the user (30), wherein the request seeks a recommendation for a wearable item (90) that represents a fit for the user (30), and satisfies a wearable item requirement and at least one preference of the user (30), to enable viewing and purchasing of the item via interfaces (240, 250, 260), as detailed in Figure 5.

[0065] Segment 600 of Figure 1 illustrates an example of how the user’s wearable item requirements and / or preferences may be determined, namely, by capturing multiple optical images (65) of a physical wardrobe (270) associated with the user (30) wherein such images are subsequently resolved to identify the number and classification ofdifferent stored wearable items (90) (eg. where the classification relates to a category and / or sub-category of the item, a fashion style of the item, etc), as further detailed in Figure 6. Segment 700 of Figure 1 illustrates an example of how the platform conducts a search for wearable items (90) from a plurality of different wholesaler and / or retailer data repositories (75) in order to generate the relevant output interfaces (240, 250, 260) for display, as further detailed in Figure 7. Finally, Segment 800 of Figure 1 illustrates how the network and method of the present disclosure may integrate with an online retail store (80), as detailed in Figure 8.

[0066] As mentioned above, Figure 2 shows in greater detail Segment 200 of Figure 1 and, in particular, the server component (20) which includes infrastructure (10) upon which the platform of the present invention operates. The infrastructure (10) may be local or cloud-based. The central server (20) may operate one or more computer processors and maintain one or more databases to enable the following functionality and / or storage:• User account register (100) storing details associated with users (30) registered to use the software application (50), such as name, age, address, contact details, and any additional data which may be relevant for the purpose of identifying each user (30);• User profile register (105) storing details pertaining to created user profiles, including any captured optical images (60) of the body part(s) (55), any captured images (65) of the user’s wardrobe (270), descriptions pertaining to each wearable item captured in the wardrobe (270) including number and classification, physical dimensions of the body part(s) (55) associated with the registered user (30), data relating to any models (225) which have been created based upon the received data (eg. a two-dimensional or three- dimensional model of the user’s body part(s) (55), as described in greater detail below), and any wearable item requirement and / or preference entered by, or determined on behalf of, the user (30);• Data processing functionality (110) for processing user input commands and any additional data received for the purpose of generating relevant outputs and displays in the software application (40). For example, data processing functionality (110) may be responsible for implementing any artificial intelligence (Al) techniques as described herein, retrieving / processinginformation relating to a wearable item requirement and / or preference of the user (30), resolving the multiple optical images (60, 65) as described above, generating the three-dimensional model, processing the results of any searched conducted by the searching and recommendation facility, and generating the relevant outputs including interfaces (240, 250, 260), etc.;• Item data register (115) storing (including temporarily and permanently as required), information associated with retailer wearable items (90) located as a result of searching data repositories (75) including the detailed dimensions of the items, as well as any additional attributes pertinent to each item including item name, stock, classification including fashion style, clothing category / sub- category including type, colour, brand, style, material(s), price, etc, wherein such data may also be utilized to filter search results; and• Searching and recommendation facility I engine (120) including a search engine capable of automatically analysing multiple data repositories (75) associated with a plurality of different retailers (70) and identifying one or more items in the plurality of stored items that include similar detailed dimensions as compared with the physical dimensions of body part(s) of particular users (30) as determined from the three-dimensional models (225), and which are classified such that the purchase of the items will satisfy the user’s wearable item requirement(s) and / or preference(s). The facility (120) may also facilitate the filtering of search results displayed to the user (30) based upon item attributes.

[0067] Figure 2 also depicts that server (20) is configured to enable communications (130) with the user devices (40) and, in particular, the software application (50) operating on each user device (40), as well as communications (140) with data repositories (75) associated with item retailers (70). Such communications may occur via the internet or similar network.

[0068] Figure 3 shows in greater detail Segment 300 of Figure 1 and, in particular, the steps associated with the user (30) installing the application (50), which may be achieved by downloading the application (50) from an application store. A similar process may apply to any additional user associated with, for example, item retailer (70) seeking to install / use the application (50). The user (30) may create an account using theapplication (50) and the account information may be stored in the user account register (100), as described above. The user account register (100) may capture information sufficient to enable each user (30) to be correctly identified, and such details may also be validated against data stored in one or more public and / or private databases.

[0069] The process of installing the application (50) is indicated by arrow (150). The interface (160) shown in Figure 3 allows each user (30) to download and install the application (50) in order to access the functionality thereof, including to create and maintain a user profile as shown in interface (170) in Figure 4. In other words, once the application (50) has been accessed by a user (30), the user (30) may be presented with an interface, identical or similar to interface (170), to allow the user (30) to create and maintain a user profile including providing the user (30) with the ability to add any requirement(s) and preference(s) of the user (30) insofar as their wearable items (90) are concerned (eg. a requirement to re-stock the user’s wardrobe with a particular item, a fashion style preferred by or determined on behalf of the user (30), a fashion trend, a preferred clothing fit, etc), and to add / edit details and access additional functionality of the application including to request wearable item purchase recommendations. Upon uploading sufficient information, the user (30) will be successfully registered such that the user (30) becomes a registered user who may then utilize the functionality of the application (50), which may be based on a subscription level of the user (30).

[0070] As mentioned above, Figure 4 shows in greater details segment 400 of Figure 1 , and in particular, the use of the application (50) by user (30) to establish a profile as shown in interface (170). Obtaining multiple optical images of one or more body parts (55) of the user (30) may include utilizing an image capture component associated with the mobile user device (40), such as camera (180), or any other available hardware such as a fixed image capture component, eg. commercial image capturing hardware (190) located at a bricks and mortar premises and capable of obtaining the multiple optical images (60). The image capture facility associated with software application (50) may integrate with such hardware in order to enable the software application to capture and store the optical images (60).

[0071] As shown in Figure 4, where an image capture device such as camera (180) is used, multiple optical images (60) may be captured of the body part(s) (55) of the user (30) from a range of different angles by capturing a series of images as the camera lensis passed over the body part(s) (55) (eg. by taking multiple “selfies” from different angles). Alternatively, the user (30) may request assistance from another user (not shown) to capture the series of images using device (50).

[0072] The determination of dimensions (sizing information) associated with features of the three-dimensional model (225) may be absolute or relative. For example, an absolute measurement may use absolute dimension values that are not based on the size of something else. In the example of relative dimensions, the images (60) may capture the body part (55) along with another object (not shown) of known dimensions that is also within view of the camera (180). For example, the object may be attached, or located in proximity with, the body part (55) such that resolution of the images (60) to generate a three-dimensional model (225) of the body part (55) may include a comparison of body part images (60) with the object of known dimensions (not shown).

[0073] In one example, the user (30) may indicate their interest in a hat and may therefore be prompted to obtain various images of their head, which will enable relevant dimensions such as the user’s head circumference to be determined. In another example, the user may be interested in shoes and capturing various images of their feet will determine relevant dimensions such as the user’s foot length, width and arch height to be determined. In another example, the user (30) may be prompted to capture images of their entire body so that the process of capturing dimensions again in relation to individual body parts does not need to be repeated (unless it is recommended to do so due to a change, or a predicted change, in the size and / or shape of the user’s body). For example, where the user is a child, changes in the size and / or shape of the user’s body will occur much more rapidly as compared with an adult, hence younger users may be requested to update their body part measurements more frequently.

[0074] The use of an optical image capture device to obtain a series of optical images and generate a three-dimensional model of an object therefrom is known in respect of health services generally including dental services in particular wherein optical images are utilised to generate a three dimensional model of teeth that require a crown or other dental augmentation. Such procedures require an accurately dimensioned model of the existing dental environment.

[0075] The software application (50) executing on the user’s device (40) may provide guidance regarding adequate capture of body part images and may provide prompts, bothaudibly and / or visually, to guide the user (30) or another user assisting the user (30), when passing the device camera (180) over body parts (55). For example, the guidance may relate to when sufficient images (60) have been attained to enable the system to generate a three-dimensional model (225) of the body part (55) with sufficient data to determine the dimensions of features in the model (225).

[0076] As shown in interface (210), in addition to generating the three-dimensional model (225) and thereby determining the physical dimensions of the body part(s) (55) based upon the received multiple optical images (60), one or more dimensions may also be determined based upon data that is input manually by the user (30), eg. data that is input into application (50) by the user (30) based upon physically measuring (220) their own body dimensions using a tape measure or similar measuring device. Such manual entry of physical dimensions may not necessarily be required, and it is envisaged that same will only be required when one or more physical dimensions are unable to be resolved from the three-dimensional model (225).

[0077] In order to satisfy privacy regulations that may be applicable, it may be necessary for the application (50) to delete any received optical image(s) (60) of the user (30). In this regard, the application (50) may be programmed to automatically delete any received optical images (60) once relevant physical dimensions have been determined from the three-dimensional model (225). The software application (50) may also be operable to prompt the user (30) to provide updated image data (eg. updated optical images) of the user’s body part(s) (55) at frequent intervals for the purpose of ensuring that the stored physical dimensions of the body part(s) are recently captured and hence substantially representative of the current physical dimensions of the body part(s) (55). The skilled addressee will appreciate that the weight of the user (30) may fluctuate and it may therefore be preferable to ensure that data relating to the physical dimensions of the user (30) is captured frequently.

[0078] As previously mentioned, the software application (50) may be operable to create and display a three-dimensional model (225) of the body part(s) (55) of the user (30) based upon resolution of the images (60), as shown in interface (230) of Figure 4. Whilst not shown, the physical dimensions of the body part(s) of the user (30) as determined from the three-dimensional model (225) may also be displayed in interface (230) (eg. in a dimensions table that may be subsequently updated / edited). The interface(230) may allow a user (30) to manipulate the image to better view certain body parts or regions, eg. rotate, zoom in, zoom out, etc. The skilled addressee will appreciate that the three-dimensional model (225) will enable the user (30) to visualize the relative shape and size of their body part(s) (55). As described in greater detail below, such a model (225) will also enable the user (30) to visualize what a particular wearable item (90) or items (eg. a collection or ensemble of items) will look like when worn, used or carried by the model depicted in interface (230).

[0079] The user (30) may be provided with wearable item purchase recommendations as described below in response to the user (30) entering a specific request for such recommendations, or alternatively, the user (30) may be provided with recommendations automatically wherein the user (30) is notified regarding the automatic identification of particular wearable item(s) available for purchase which may be of interest to the user or may address the requirements of the user (30) as described herein.

[0080] In one embodiment, the notification to a user (30) regarding a wearable item occurs when the platform detects a new item of clothing that represents a good fit for the user (30) and wherein, the fashion style accords with a fashion trend also detected by the platform. In this regard, the platform may monitor fashion trends by processing posted images of famous people (eg. celebrities, politicians, musicians, actors etc) and establishing the current fashion style or trend in respect of the clothing worn by these famous people. The fashion trend may also be established by monitoring the clothing in images of “persons of interest” selected by the user (30). In any event, once a wearable item representing a good fit for the user (30) that accords with a current fashion trend becomes available, a notification may be generated and transmitted to the user’s (30) data communications device including additional data such as available purchase points, costs at each purchase point, images of the wearable item (including a representation of the item as worn by the user according to a three dimensional model of the user and as worn by the famous person, or person of interest) and the availability of the item at each purchase point.

[0081] The extent to which an item is considered to be a good fit may be represented by a “fit score” which is essentially a fit prediction rating. If a user (30) enters “US Nike M” in a search query, or if this is a search query that is automatically generated based on resolving images (60) of the user’s body part(s) to determine an M sizing and / or basedon a user preference for Nike clothing in a particular size, the platform may return a result for “Ell Adidas 48” based on the automated size conversion functionality. The returned information may also display a fit score of say 0.92 or 92%. If this represents the best fit, then the output may include a description to the effect that “Your best fit: Adidas Ell 48 - proceed to checkout”. Accordingly, different functionality may be combined to form multiround user flows and in this example the functionality that is combined is size conversion (seamless cross-region sizing), fit prediction (fit score), and checkout. An example of this software code iteration is as follows:@app .post ( " / shopping- j ourney" ) async def shopping_j ourney (user_data : diet = Body (...) ) :# Step 1: Convert size async with httpx . AsyncClient ( ) as client: size_resp = await client. get ("https : / / alp-api . com / size-converter" , params= {"size" : user_data [ "input_size" ] , "from" : user_data [ " f rom_brand" ] , "to": user_data [ " to_brand" ] , "region" : user_data [ "region" ] } converted_size = size_resp . son ( ) . get ( "convertedSize" )# Step 2: Run fit check fit_req = { "user_prof ile" : user_data [ "profile" ] , " target_size" : converted_size, "threshold": 0.85 } fit_resp = await client . post ( "https : / / geniescan . com / fit- check" , j son=f it_req) fit_score = f it_resp . j son ( ) . get ( " f it_score" )# Step 3: Return journey summary return {" converted_si ze" : converted_size ," fit_score" : fit_score ," recommendation" : " Proceed to checkout" i f fit_score> 0 . 85 else"Adj ust si ze"}

[0082] Figure 5 shows in greater detail segment 500 of Figure 1 and, in particular, an example of a process in which the user (30) enters a request to receive wearable item purchase recommendations in relation to a particular category or sub-category of wearable item.

[0083] For example, the application (50) may enable the user (30) to select a particular wearable item of interest through a keyword search. For example, the user (30) may be a female interested in purchasing a new dress. The user (30) may enter into the search engine a keyword relating to the category or a subcategory of the item, (eg. category “dresses”), and based upon such input, relevant data repositories (75) will be searched and a list of applicable items (ie. a list of available dresses which correspond with the physical dimensions of the user (30) and which satisfy the user’s wearable item requirement and preferences), will be generated). Any search results may be subsequently filtered by one or more additional attributes of the item including, for example, a sub-category associated with the dress, a location or geographical area in which the dress is available for purchase (eg. within a particular geographical vicinity of the user (30)), a retailer name, a price of the dress, a visual attribute of the dress (eg. colour) and a physical attribute of the dress (eg. the dress material).

[0084] In an alternative embodiment, and as shown in interface (240) of Figure 5, the search facility may display a range of item categories. When viewing such an interface, the user (30) may observe and select a particular category of interest (eg. trousers (245)). Interface (250) in Figure 5 provides an example of a search listing after the user has selected the graphical illustration of “trousers” (245) from the item selection interface (240). As shown in interface (250), there is a list of available trousers offered for purchase by one or more retailers (70), and it will be appreciated that all trousers in this listing will be of an appropriate size and fit for the user (30) and will satisfy the predetermined requirement and at least one preference of the user (30). For example, if the wearable item preference of the user is to maintain a vintage fashion style or theme in theirwardrobe, then only those trousers which have been classified as such by retailers will be provided as potential purchase options. The provided options will also have detailed dimensions that correspond with the physical dimensions according to a similarity threshold determined in respect of the relevant body part(s) (55) submitted by the user (30) (eg. in this example, the waist and leg dimensions of the user (30)).

[0085] In other words, the listing of trousers shown in interface (250) of Figure 5 is limited to only those trousers that include similar detailed dimensions as compared with the relevant physical dimensions of the user (30) as determined from the three- dimensional model (225), and which satisfy the user’s preference for vintage style clothing, hence there is no requirement for the user (30) to review and filter results in an effort to ensure that items listed are appropriate for the user (30). As a result, the user (30) avoids scrolling through numerous irrelevant results.

[0086] In the event that there are no results in the listing shown in interface (250), (eg. on the basis that there are no items located which have similar detailed dimensions as compared with the physical dimensions determined in respect of the user’s body part (55) and satisfying the user’s particular requirement(s) and preference(s)), the user (30) may be prompted in interface (250) to expand the scope of their search so that the listing includes items with the nearest physical dimensions (eg. the slim or full fit version of the same item) and / or have the best prospects of satisfying the user’s wearable item requirement(s) and / or preference(s) (eg. alternative styles with a similar fit as compared with the user’s preferred fit). The expanded scope of the search may include other countries beyond the country in which the user is located I domiciled.

[0087] The use of artificial intelligence techniques may also enhance feedback to users (30) including automatically adjusting the similarity threshold when required and / or explaining potential trade-offs associated with selecting an item according to a recommendation.

[0088] When a particular item from interface (250) is selected by the user (30), one or more additional interfaces may be automatically generated, such as interface (260) of Figure 5 that provides additional information relating to the selected item (90). The selection of a particular item (90) may further cause the software application (50) to perform one or more additional actions, including generating the three-dimensional modelrepresentation of the selected item (90) fitted over the relevant body part(s) (55) (ie. a display showing a graphical overlay of clothing on the relevant body part(s) for user visualisation).

[0089] As shown in Figure 5, the interface (260) may further include a prompt (265) that, when selected, enables the user (30) to purchase the selected item (90) without navigating away from the software application (50). Alternatively, as shown in Figure 8, the selection of the prompt (265) may automatically cause (300) a web browser to display a web page associated with a retailer (70) (eg. a home page or the product purchase page), thereby also enabling purchase of the item using the retailer’s online store (80).

[0090] As previously described, the searching and recommendation facility ensures that only those items of interest that include similar detailed dimensions (according to a similarity threshold) as compared with the physical dimensions of the body part of the user (30), and which are classified such that purchase of the item will satisfy a particular requirement and preference of the user (30), are identified and provided in a search result listing (or similar display) presented to the user (30). This ensures that only those items which are likely to represent a fit for the user (30), and which satisfy a particular requirement (eg. a requirement to include or restock a particular article of clothing in the user’s wardrobe) and preference (eg. a preference for a particular fashion style), are displayed.

[0091] The similarity threshold may be predefined, and the threshold may be different for different body parts and / or items available for purchase. For example, an important measurement for men’s shirts is the neck region to ensure that the shirt does not fit too tightly around the user’s neck. The similarity threshold may be defined such that a higher level of similarity is required in relation to the dimensions of the user’s neck region relative to the collar region of the shirt, as compared with another region of body part / shirt such as the chest region for example where a close fit may not be considered as important. The software application (50) may provide default similarity thresholds, but may also provide a facility that enables users (30) to revise the thresholds according to their requirements and preference(s).

[0092] The similarity threshold may also be automatically adjusted. For example, a user (30) may frequently return items and the similarity threshold may be automatically increased as a result such that only higher confidence “best fit” recommendations aredisplayed or recommended in response to future requests of the user (30) (ie. stock filtering becomes more strict). An example software code iteration is as follows:@app . post ( " / fit-threshold" ) async def f it_threshold (user_f eedback : diet = Body ( . . . ) ) :# user_f eedback = { " returns" : 2 , " satis f ied_orders" : 15 } ratio = user_f eedback [ " satis f ied_orders" ] / max ( l , us er_f eedback [ " returns" ] + 1 ) threshold = 0 . 75 i f ratio > 5 else 0 . 90 # adj ust dynamically return { " adj usted_similarity_threshold" : threshold }

[0093] The similarity threshold, which influences the number of results presented to users (30), may also be automatically adjusted based on other factors such as periods of high traffic (eg. during a Black Friday sale). During such periods, the threshold may be raised to ensure fewer API calls sent and to stabilize the data communications network.

[0094] The abovementioned functionality may be facilitated by leveraging one or more artificial intelligence (Al) techniques including machine learning, (eg. the use of machine learning algorithms) to identify the best fit for particular users (eg. predict that a particular user with a broader shoulder width might need a larger size in certain brands, even in the event their chest measurement suggests a smaller size), as well as analyse gathered data, identify patterns, predict future preferences of the user (30), predict style shifts, and predict future body size changes in the user (30). Such predictions may also facilitate more specific searching through data repositories (75) and hence more accurate search results and outputs by better determining requirements and preferences of users (30), (eg. by determining that a user (30) prefers athletic fit clothing for casual wear but regular fit for formal or business attire).

[0095] In an example, predictive analytics may be utilised to take into account future body changes and style shifts. The platform may predict that a user is likely to gain three kilograms in Winter and that looser fits will trend. A “next size up” or “relaxed fit” filter may be automatically suggested to the user (30), or the user may be provided with a recommendation to move from Medium to Large size to avoid returns (eg. “We recommend ordering size L to account for upcoming changes”). Once again, a fit score may accompany this output to provide a level of confidence for the purchase, (eg. “Fitcheck confirms confidence >0.9”). Example software code that may be implemented to achieve same includes:@app .post ( " / predictive- j ourney" ) async def predictive_j ourney (user_data : diet = Body (...) ) :# Step 1 : Predict future body changes features = [ user_data [ "age" ] , user_data [ "weight" ] , user_data [ "height" ] , user_data [ "activity" ] , user_data [ " style_t rends " ] prediction = body_predictor . predict ( [ features ] ) [0]# Step 2: Adjust size suggestion future_size = "L" if prediction [ "weight " ] > user_data [ "weight " ] + 3 else "M"# Step 3: Re-check fit async with httpx . AsyncClient ( ) as client: fit_resp = await client . post ( "https : / / genie scan . com / fit- check" , j son= {"user_prof ile" : user_data," target_size" : future_size,"threshold": 0.85}) return {"predicted_weight " : prediction [ "weight" ] ," future_size_recommendation" : future_size, " f it_check_result " : f it_resp . son ( ) }

[0096] Furthermore, artificial intelligence techniques may facilitate the identification of variations in sizing standards across different brands and regions, and accurately convert sizes when recommending a best fit taking into account the differences in sizingstandards. In this regard, a size normalization layer may convert brand / regional sizes to numeric body-space vectors. Feedback from users post-purchase in relation to the fit of wearable items may also facilitate learning, and outputs (including automatic adjustments to the similarity threshold(s)) which may be improved over time as a result. Future growth patterns in e-commerce may also be taken into account, including increased demand for sustainable fashion or a shift toward certain styles. For example, Al techniques can assist predicting a rise in demand for eco-friendly products and guide retailers (70) to stock more sustainable items, aligning with consumer preferences.

[0097] Returning to the example of where a user (30) enters a request or where a request is automatically generated on behalf of a user for “US Nike Men’s M”, an Al- assisted automatic size conversion may be implemented to generate the result “EU Adidas 48” based on seamless cross-region sizing. An example software code iteration in this regard is as follows: from fastapi import FastAPI , Query import httpx app = FastAPI ( )# Central GenieScan API endpoint@app . get ( " / convert- si ze" ) async def convert_size (user_si ze : str, from_brand : str, to_brand : str, region : str ) : async with httpx .AsyncClient ( ) as client :# Call external Apparel Label Provider (ALP ) API resp = await client . get ( f "https : / / alp- api . com / si ze-converter" , params= { " si ze" : user_size , " from" : from_brand, " to" : to_brand, " region" : region } return { " converted_size" : resp . j son ( ) . get ( " convertedSi ze" ) }

[0098] The skilled addressee will appreciate that by analysing the data repositories (75) of associated retailers (70) and thereby identifying and displaying only a subset of all of the available items available from such retailers based upon the determined physical dimensions of the body part(s) (55) and satisfaction of the user’s particular requirement and preference(s), as compared with all of the available wearable items (which may total hundreds or thousands of items), for selection by the user (30) will significantly conserve processor and memory resources of the device (40) as well as the server (20) and data network communication resources. Of course, this also has the effect of substantially reducing the consumption of electrical energy and other resources that are required to operate large data communications networks and data storage facilities.

[0099] In another example, instead of showing 50 jackets in response to a user request, only 12 guaranteed-fit jackets (ie. those with a fit score higher than a predetermined fit score threshold) will be displayed. An example code iteration in this regard is as follows:@app . get ( " / consumer- stock" ) async def consumer_stock (user_id : str ) :# Fetch user profile profile = get_user_prof ile (user_id)# Query central GenieScan API async with httpx . AsyncClient ( ) as client : resp = await client . post ( "https : / / geniescan . com / filterstock" , j son= {"user_prof ile" : profile ," retailer_stock" : get_retailer_stock ( )} ) return resp . j son ( )

[0100] Figure 6 shows segment 600 of Figure 1 in greater detail, and in particular, an embodiment in which the user’s wearable item requirement(s) and / or preference(s) is determined on behalf of the user (30). In particular, multiple optical images (65) of the user’s wardrobe (270) which stores existing wearable items of the user (30) are obtainedthrough the use of an image capture component similar to that used to capture images (60) of the user’s body part(s). The image capture component may be associated with the mobile user device (40), such as camera (180), or any other available hardware such as a fixed image capture component. Multiple optical images (60) may be captured of the user’s wardrobe (270) from a range of different angles by capturing a series of images as the camera lens is passed over the wardrobe (270).

[0101] The captured images may include still images and / or short video sweeps and the wardrobe content information captured may also be cross-validated by documents uploaded by users including, but not limited to, retailer receipts and e-receipts. Raw frames may be converted to feature embeddings, and once the embeddings and detections are committed, the raw frames may be subsequently deleted. In an example, a user may upload three images from their wardrobe and a retailer e-receipt. Feature embeddings may be generated across visual and document inputs, and upon completion the platform will be able to confirm that “you own two pairs of black jeans, three shirts from a particular retailer, and one leather jacket”.

[0102] An example software code iteration which may be incorporated to enable acceptance of the received images, video sweeps, e-receipts, etc, is:@app . post ( " / wardrobe -ingest" ) async def wardrobe_ingest ( data : diet = Body ( . . . ) ) :# Data can include stills , short video sweeps , and receipts embeddings = [ ] i f " images" in data : for img in data [ " images " ] : embeddings . append ( vision_model . encode ( img) ) i f "video" in data : frames = extract_f rames ( data [ "video" ] ) for frame in frames : embeddings . append ( vision_model . encode ( frame ) ) i f " receipts" in data :text_items = parse_receipts ( data [ " receipts" ] ) embeddings . extend ( receipt_encoder ( tex t_i terns ) ) return { "wardrobe_embeddings " : embeddings }

[0103] Using a similar technique as that described earlier when capturing images (60) of body parts, the capturing of images (65) of a user’s wardrobe (270) may be with reference to an object of known dimensions (eg. an A4 sheet placed on one of the shelves) that is within view of the optical lens such that resolution of the images (65) includes comparison of wearable items with the object of known dimensions to determine approximate dimensions of the wearable items in the wardrobe (270).

[0104] Additional details which may be interpreted from the resolved image(s) (65) include, but are not limited to, identification of items which are garments (eg. wearable items) and non-garments (eg. hangers), instance detection and pose (eg. hanged, folded or boxed), and attributes such as category / sub-category as defined herein. Still further details that could be determined in respect of wearable items include the amount of wear (eg. detection of pilling) which may give rise to recommendations to replace older looking items. In a particular example, following a scan of the wardrobe (270), the platform may determine that in the wardrobe there is, placed on hangers, a navy blazer with a notch lapel, whilst folded is a grey crewneck t-shirt in cotton. The platform may tag conditions such as “visible pilling on two sweaters - flagged for replacement”. An example software code iteration which may be incorporated to enable scene understanding and instance attribution is:@app . post ( " / scene-detect" ) async def scene_detect ( image : diet = Body ( . . . ) ) :# Stage 1 : Segment garments mask = garment_segmenter . predict ( image [ " frame" ] )# Stage 2 : Instance detection (hanger vs folded vs boxed) instances = instance_detector . detect ( image [ " frame" ] , mask)# Stage 3 : Attribute classi fication ( category subcategory microstyle )results = [ ] for inst in instances : attr = attribute_classi f ier . classi fy ( inst ) results . append ( { " instance" : inst . id, " attributes" : attr } ) return { "detections" : results }

[0105] In this regard, threshold rules and learned predictors may trigger events such as “under-stock” or “style refresh” in circumstances where wearable item counts drop, their condition degrades, or when style drift is detected. In a particular example, a user may have seven shirts, one blazer and zero rain jackets recorded in their wardrobe (270), and the condition status may indicate that three of the shirts are heavily worn. The platform may generate an automatic output to the effect that there is an under-stock of rain jackets and that shirts need to be replaced. An example software code iteration which may be implemented to enable same is:@app . get ( " / stock-model / { user_id } " ) async def stock_model (user_id : str ) : items = db . f etch_user_items (user_id) stock_summary = { } for item in items : node = item . taxonomy_node stock_summary [node ] = stock_summary . get (node ,0 ) + 1 return { " stock_summary" : stock_summary }

[0106] Accordingly, the images (65) may be resolved to automatically identify the user’s requirement(s) and / or preference(s). For example, the platform may identify that the user’s clothes relate to a particular category (eg. jackets) and sub-category (eg. bomber style) and conclude that the identified fashion style (eg. vintage) is a preference of the user. In another example, the platform may identify that a particular category or subcategory of wearable items does not exist in the wardrobe or is low in stock, and may thereby conclude that a requirement of the user (30) is to stock another item in the same category and / or subcategory. In a yet further example, the platform may identify that some existing wearable items in the user’s wardrobe are too worn or will no longer fit the user(30) and on that basis may conclude that a requirement of the user (30) is to replace an item that represents a better fit.

[0107] It will be appreciated that the range of wearable items (90) offered by retailers (70) may be classified according to their fashion style which enables identification of wearable items to provide to the user (30) by automatically comparing the fashion style of the wearable item (90) with one or more of the fashion styles preferred by the user, the fashion style determined on behalf of the user, or a current fashion style trend.

[0108] The range of wearable items (90) offered by retailers may also be classified according to item category and / or sub-category. For example, each item category may include one or more sub-categories (eg. under the clothing category, subcategories may include jackets, shirts, underwear, socks, etc, and under the subcategory jackets, additional subcategories including brand of jacket, etc). As mentioned above, the images (65) may be resolved to determine the one or more item categories and the one or more item sub-categories, as well as the current stock level of each item category and subcategory stored in the user’s physical wardrobe (270). Since the range of wearable items offered by retailers (70) and / or stored in one or more accessible repositories (75) are classified according to the same or similar categories and / or subcategories, particular wearable items (90) may be provided (recommended) to the user (30) based on the user (30) requiring the item(s) according to a lack or an insufficient number of such item(s) in the user’s current wardrobe.

[0109] The platform may be configured to ensure that the mapping between wardrobe (270) and retailer databases (75) is consistent by ensuring that the same or similar categories and sub-categories by which items are defined and classified in the repositories are used to classify the items (90) that are detected in the user’s wardrobe (270). In an example, the resolved images (65) may indicate that the wardrobe contains a blazer, and in circumstances where the attributes of blazers stored in repositories (75) are predominantly of one size (eg. 48ELI), material (eg. wool) and silhouette (eg. slim), these same attributes may be determined based on resolving images (65) in a manner that identifies the same attributes and / or by requesting information pertaining to these attributes from the user. An example software code iteration for achieving same is:@app . post ( " / taxonomy- classi fy" ) async def taxonomy_classi f y ( item : diet = Body ( . . . ) ) :node = taxonomy_model . predict ( item [ " embedding" ] ) retailer_attrs = attr_head . predict ( item [ " embedding" ] ) return { " taxonomy_node" : node , " retailer_attributes" : retailer_attrs }

[0110] The use of a size normalization layer as previously described may apply equally to items captured during a wardrobe scan. In particular, brand and regional sizes may be converted to numeric body-space vectors, and there may be continuous learning based on post-purchase fit feedback. In an example, a user may store a medium -sized (M) Nike shirt in their wardrobe (270). The converted size may be Chest: 102cm and Sleeve: 63cm, but for another brand such as Zara, the same M sized label may result in a converted size of Chest: 98cm and Sleeve: 61cm. The platform allows both to be aligned to body-space vectors to ensure a correct match with the sizes stored in retailer repositories (75). An example software code iteration for this normalization step is: def normalize_size (brand_si ze , brand, category) : base_vector = si ze_lookup [ category] [brand_si ze ] of fset = brand_variance_model . predict ( { "brand" : brand, " category" : category } ) return [base + of f for base , of f in zip (base_vector, of fset ) ]

[0111] Users may also be prompted for feedback in relation to the classification of categories and subcategories pertaining to captured wearable items, as well as in respect of the categories and subcategories of items stored in retailer repositories (75) that are matched. In this regard, a classification explanation may be provided to a user to the effect that there is a 92% chance that an item in the wardrobe (270) is a blazer, and that other options considered were suit jacket (81 %) and coat (65%). The user may be prompted for instant feedback to confirm the correct selection, and over time, the model accuracy increases for the user’s wardrobe (270). An example software code iteration for this validation step is:@app . post ( " / class! f y-wi th- conf idence" ) async def classi f y_with_conf idence ( item : diet = Body ( . . . ) ) :preds , scores = classi fier . predict_prob ( item [ " embedding" ] ) topk = [ { " class" : c, " score" : s } for c, s in zip (preds [ : 3 ] , scores [ : 3 ] ) ] return { " candidates" : topk, " explanation" : "Lapels & hem match blazer, notch lapel" }

[0112] Various additional requirements and / or preferences of the user (30) may be automatically determined and used when considering which data repositories (75) to search and which wearable items (90) to display to the user (30), and in this regard, the present invention is not limited to any one particular embodiment described herein. For example, a preference of the user may be to ensure that their wardrobe is always stocked according to current fashion trends. It will be appreciated that maintaining a regular scan of the user’s wardrobe may afford regular determinations regarding whether or not the user’s existing wearable items are sufficiently stocked and / or correspond with current fashion trends, and if not, appropriate purchase recommendations may be generated and displayed to the user (30).

[0113] The software application (50) executing on the user’s device (50) may also provide guidance regarding adequate capture of the wardrobe (270) and items stored therein, and may provide prompts, both audibly and / or visually, to guide the user (30) when passing the device camera (180) over different wearable items stored in the wardrobe (270) (including hanging and internally stored items). For example, the guidance may relate to when sufficient images (65) have been attained to enable the platform to predict a fashion style of the user (30), or to estimate the number of particular items (90) stored in the wardrobe according to particular categories and subcategories.

[0114] In addition to generating the abovementioned outputs based upon the received multiple optical images (65), one or more requirements and / or preferences of the user (30) may also be determined based upon data that is input manually by the user (30), (eg. data that is input into application (50) by the user (30)).

[0115] Figure 7 shows in greater detail Segment 700 of Figure 1 and, in particular, the analysing (searching) of multiple different data repositories (75) associated with multiple different wholesalers and / or retailers (70) as required to enable the provision of wearable item purchase recommendations to users (30). It will be appreciated that wherethe user (30) has requested or requires multiple items to form a complete outfit (eg. ensemble), recommendations relating to multiple different wearable items may be provided simultaneously to the user (30), and in this regard the user (30) may also be provided with the ability to mix and match different items in order to view different available options on a full body model displayed in the interface (260).

[0116] The same software application interfaces shown in Figure 5, including interface (250) which displays the output of search results, and interface (260) which displays selected item(s) (90) placed over the modelled body part(s), are used again in Figures 6 and 7 to demonstrate similar functionality.

[0117] Figure 8 shows in greater detail Segment 800 of Figure 1 and, in particular, Figure 8 illustrates the ability of item wholesalers and / or retailers (70) to also access the software application (50) for the purpose of integrating their data repositories including online stores (80) and the like. Accordingly, the application (50) that is made available to retailers includes alternate functionality as compared with the application that is made available to consumers (30). The data that is stored in the data repositories (75) may include items (90), prices, lead times, stock levels, shipping windows, prices including detailed dimensions of the items (90) in each of the retailer’s available sizes, and may include additional details such as a category and sub-category classification of each item (90) according to their fashion style, an image of the item, item stock, relevant links, etc.

[0118] Such stored information may be presented to retailers accessing the application (50) by use of a retailer supply graph (not shown). When matching repository items with user or platform-initiated requests for wearable items (90), a constraint solver may be used to ensure that the matching takes into account supply and may further rank the output items by taking into account additional constraints such as budget and geography. For example, if a user also specifies a budget of less than $200, the output may include multiple return matches ranked such that the top ranked matches relate to items under $200. An example software code iteration for this validation step is:@app . post ( " / supply-match" ) async def supply_match ( gap : diet = Body ( . . . ) ) : candidates = query_retailers ( gap [ " taxonomy_node" ] , gap [ " size_vector" ] )ranked = rank_by_constraints (candidates, gap [ "constraints" ] ) return { "ranked_candidates" : ranked}

[0119] The integration of data repositories (75) may be achieved using an application programming interface (API) (eg. one or more of Amazon Web Services, Microsoft Azure and Oracle Cloud). In this way, the platform is able to quickly and efficiently search for and identify relevant items across a plurality of different data repositories (75), with particular focus upon those items that include similar detailed dimensions as compared with the physical dimensions of the relevant body part of the user (30) and which are classified such that purchase of the item(s) will satisfy the user’s wearable item requirement(s) and preference(s).

[0120] Retailers may also be provided with useful analytics such as the average fit score determined in respect of each of their products and hence which items in their inventory are most likely to sell with fewer returns. An example software code iteration in this regard is as follows:@app .post ( " / retailer-dashboard" ) async def retailer_dashboard ( stock : diet = Body (...) ) :# stock = {"items": [...] , "profiles": [...] } item_stats = { } async with httpx . AsyncClient ( ) as client: for item in stock [" items "] : fit_scores = [] for profile in stock [ "prof lies "] : resp = await client . post ( "https : / / genie scan . com / fit- check" , j son= {"user_prof ile" : profile," target_size" : item [ "size" ] , "threshold": 0.85}) fit_s cores . append ( resp . j son ( ) . get ( " f it_score" , 0 ) )item_stats [ item [ " id" ] ] = sum ( f it_scores ) / len ( f it_s cores ) return { " item_f it_likelihood" : item_stats }

[0121] An artificial intelligence-based conversational agent may be utilised to enhance the experience of users. For example, such an agent may be configured to conduct voice interactions with users over a telephony network to elicit constraints and obtain purchase authorization, adjust similarity thresholds during the interaction responsive to user feedback and / or returns, and complete purchases using retailer APIs. In this regard, interaction transcripts may be used to update model parameters as described herein. In one example, the conversational agent may contact a user to ask “I found three rain jackets. None are slim fit, but regular cut matches your size. Okay to broaden search?”. If the user responds “yes”, the agent may automatically adjust the similarity threshold from say 0.9 to 0.85 and thereby retrieve additional items (90). An example code iteration for this process is shown below: class VoiceAgent : def handle_dialog ( sel f , user_input , gap ) : i f "budget" in user_input : gap [ " constraints" ] [ "budget" ] = user_input [ "budget" ] i f not perf ect_match ( gap ) : adj ust_threshold ( gap, delta=+ 0 . 05 ) return supply_match ( gap )

[0122] Furthermore, a user may be prompted to approve a purchase using an agent call. According to an example where a user approved the purchase of a rain jacket, the checkout may auto-execute on the retailer API, and the platform may immediately update the wardrobe (270) “Rain Jacket added, expected delivery three days”. An example code iteration to achieve same is shown below:@app . post ( " / purchase" ) async def purchase ( item : diet = Body ( . . . ) ) : resp = retailer_api . checkout ( item [ " id" ] , token=item [ "user_token" ] ) i f resp [ " status " ] == " success" :db . add_to_wardrobe ( item [ "user_id" ] , item) return { " status" : "purchased" , "wardrobe_updated" : True }

[0123] A person skilled in the relevant field of technology will appreciate that the integration of artificial intelligence techniques as described herein improves the operation of the data communications network when providing a solution to the problem(s) described above. The techniques described herein increase efficiencies and effectiveness in each process, improving the platform to not only meet its intended purpose but also continuously improve through learning and adaptation. Examples of artificial intelligence platforms that may be suitable to enable the software application (40) to perform the artificial intelligence techniques described herein may include, but are not limited to, ChatGPT and Google DeepMind.

[0124] In any event, a data communications network, method and / or computer readable medium according to the present invention provides individual users with guidance and suggestions regarding wearable items that are generally only available to people with sufficient wealth to afford to engage a personal fashion stylist. However, the present invention may be used to assist every user to create and maintain a fashion style with wardrobe items that represent a good fit according to their body size and individual body part dimensions.

[0125] As will be readily appreciated, conducting regular online searches in an attempt to maintain an up to date understanding regarding fashion trends can consume a significant amount of time and computing resources whereas an automated system that monitors and detects emerging fashion trends avoids significant computer processing and computer network resource usage that would otherwise occur. Despite the significant reduction in computer resource usage arising from a single user awaiting notifications, as and when appropriate, rather than conducting individual searches, the benefits arising when hundreds, thousands or possibly millions of users engage a platform according to the present invention is substantial and substantially ameliorates the significant consumption of natural resources that otherwise occurs when there is significant usage of computer processing and computer network resources.

[0126] Reductions in computer processing and computer network usage may also be quantified and presented to relevant users in report format. For example, a report mayindicate that during a particular search, 178 irrelevant items were avoided from a total of 200 possible results, giving rise to 89% network load reduction. Reports provided to retailers may also include (eg. on a monthly basis) additional information including, but not limited to, number of returns avoided, amount of packaging saved, transport fuel savings, and the number of irrelevant API calls avoided. Compliance data may also be logged for retailers.

[0127] The methods and systems described herein may be deployed in part or in whole with a machine that executes computer software, program codes, and / or instructions on a processor. The processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable of executing program instruction, code, binary instructions and the like. The processor may be or include a signal processor, digital processor, embedded processor, microprocessor or any variant such as a co-processor (eg. math co-processor, graphic co-processor, communication co-processor) that may directly or indirectly facilitate execution of program instruction code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and instruction code. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, computer program code and / or program instruction code described herein may be implemented in one or more threads. The thread may spawn other threads that may have assigned priorities associated with them, the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor may include memory that stores methods, codes, instructions and programs as described herein and elsewhere. The processor may access a storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM or cache.

[0128] A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In some embodiments, the processor may be a dualcore processor, quad core processor or other chip-level multiprocessor that combines two or more independent cores (ie. often referred to as a die).

[0129] The methods and systems described herein may be deployed in part or in whole by a machine that executes computer software on a server, cloud server, client, firewall, gateway, hub, router, or other such computer and / or networking hardware. The software program may be associated with a server that may include a file server, print server, domain server, internet server, intranet server or other variants such as secondary server, host server or a distributed server. The server may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium. The methods, programs or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.

[0130] The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers and / or distributed servers. Additionally, this coupling and / or connection may facilitate remote execution of programs across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the server through an interface may include at least one storage medium capable of storing methods, programs, code and / or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

[0131] The software program may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client or distributed client. The client may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium. The methods, programs or codes as described herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.

[0132] The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers or distributed servers. Additionally, this coupling and / or connection may facilitate remote execution of programs across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the client through an interface may include at least one storage medium capable of storing methods, programs, applications, code and / or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

[0133] The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules and / or components as known in the art. The computing and / or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM and the like. The processes, methods, program codes, instructions described herein and elsewhere may be executed by one or more of the network infrastructural elements.

[0134] The methods, program codes, and instructions described herein and elsewhere may be implemented in different devices which may operate in wired or wireless networks. Examples of wireless networks include 4th Generation (4G) networks (e.g., Long-Term Evolution (LTE)) or 5th Generation (5G) networks, as well as non- cellular networks such as Wireless Local Area Networks (WLANs). However, the principles described therein may equally apply to other types of networks.

[0135] The operations, methods, programs codes, and instructions described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music playersand the like. These devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute program codes, methods, and instructions stored thereon. Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute program codes. The mobile devices may communicate on a peer-to-peer network, mesh network, or other communications network. The program code may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store program codes and instructions executed by the computing devices associated with the base station.

[0136] The computer software, program codes, and / or instructions may be stored and / or accessed on machine readable media that may include computer components, devices, and recording media that retain digital data used for computing for some interval of time, semiconductor storage known as random access memory (RAM), mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory, optical storage such as CD, DVD, removable media such as flash memory (eg. USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, off-line, and the like, other computer memory such as dynamic memory, static memory, read / write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes or magnetic ink.

[0137] The methods and systems described herein may transform physical and / or or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and / or intangible items from one state to another, such as from usage data to a normalized usage dataset.

[0138] The elements described and depicted herein, including in flow charts and block diagrams throughout the figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depictedelements and the functions thereof may be implemented on machines through computer executable media having a processor capable of executing program instructions stored thereon as a monolithic software structure, as standalone software modules, or as modules that employ external routines, code, services, and so forth, or any combination of these, and all such implementations may be within the scope of the present disclosure. Examples of such machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers and the like. Furthermore, the elements depicted in the flow chart and block diagrams or any other logical component may be implemented on a machine capable of executing program instructions. Thus, while the foregoing drawings and descriptions set forth functional aspects of the disclosed systems, no particular arrangement of software for implementing these functional aspects should be inferred from these descriptions unless explicitly stated or otherwise clear from the context. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein. All such variations and modifications are intended to fall within the scope of this disclosure. As such, the depiction and / or description of an order for various steps should not be understood to require a particular order of execution for those steps, unless required by a particular application, or explicitly stated or otherwise clear from the context.

[0139] The methods and / or processes described above, and steps thereof, may be realized in hardware, software or any combination of hardware and software suitable for a particular application. The hardware may include a general-purpose computer and / or dedicated computing device or specific computing device or particular aspect or component of a specific computing device. The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices, along with internal and / or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. Itwill further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine-readable medium.

[0140] The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions.

[0141] It will be appreciated by persons skilled in the relevant field of technology that numerous variations and / or modifications may be made to the invention as detailed in the embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all aspects as illustrative and not restrictive.

[0142] Throughout this specification and claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated feature or step, or group of features or steps, but not the exclusion of any other feature or step or group of features or steps.

Claims

The claims defining the invention are as follows:

1. A computer-implemented data communications network including connected data communications devices and a method of operating same to facilitate the purchase of wearable items in an online environment, the method including: receiving, by one or more processors, information relating to a wearable item requirement and at least one preference of a user; obtaining, by one or more processors using an image capture facility, multiple optical images of one or more body parts of the user along with an object of known dimensions that is attached to, or located in proximity with, the body part(s) and thereby also in view of the image capture facility and resolving, by one or more optical resolution techniques, the images to generate a three-dimensional model of the body part(s), wherein the resolving of optical images to generate the three-dimensional model includes comparison of the body part images with the object of known dimensions to further provide sizing information regarding the three-dimensional model of the body part(s); determining, by one or more processors, the physical dimensions of the body part(s) according to the sizing information provided by the three-dimensional model; searching, by one or more processors using a searching and recommendation facility, one or more data repositories associated with a plurality of retailers offering a range of wearable items for purchase, the information stored in the one or more data repositories identifying detailed dimensions of each wearable item and classifying each wearable item, wherein the searching is conducted to determine wearable items that include detailed dimensions similar, according to a similarity threshold, to those dimensions of corresponding body part(s) of the user as determined from the sizing information provided by the three-dimensional model, and which are classified such that the purchase of the item will satisfy the user’s wearable item requirement and the at least one preference; providing, by one or more processors, for display on a data communications device associated with the user, the identified one or more wearable items that include similar detailed dimensions as compared with the physical dimensions of the corresponding body part(s), and which are classified such that the purchase of the item will satisfy the wearable item requirement and the at least one preference of the user.

2. A data communications network according to claim 1 , wherein the one or more data repositories associated with the plurality of retailers include one or more of: retailer websites, retailer social media pages, or other hardware and / or software associated with the retailers including retailer databases.

3. A data communications network according to either claim 1 or claim 2, wherein the received information relating to a wearable item preference of the user includes one or more of: a brand preferred by the user, a fit preferred by the user, a fashion style preferred by the user, a fashion style or fit determined on behalf of the user, or a current fashion style trend that is determined and / or monitored using one or more artificial intelligence techniques.

4. A data communications network according to claim 3, wherein the preferred brand, fashion style or fit is determined on behalf of the user by using an image capture facility that obtains one or more optical images of a current wardrobe of the user which includes existing wearable items of the user, wherein the images are resolved using one or more optical resolution techniques to identify the user’s preferred brand, fashion style or fit.

5. A data communications network according to any one of the preceding claims, wherein the range of wearable items offered by retailers include attributes that are classified according to one or more categories and sub-categories which enables identification of wearable items to provide to the user based upon automatically comparing the classified attributes with attributes associated with the wearable item in corresponding categories and sub-categories.

6. A data communications network according to claim 5, wherein the received information relating to a wearable item requirement of the user includes a current stock level of particular wearable items of the user.

7. A data communications network according to claim 6, wherein the current stock level of particular wearable items of the user is determined using an image capture facility that obtains one or more optical images of a physical wardrobe of the user which stores existing wearable items of the user, wherein the images are resolved using one or more optical resolution techniques to determine item attributes classified according to category and sub-categories, and the current stock level of each item stored in the user’s current wardrobe.

8. A data communications network according to claim 7, wherein obtaining optical images of the body part(s) and / or physical wardrobe of the user includes utilizing an optical image capture device associated with a mobile data communications device, including passing an optical lens associated with the optical capture device over the body part(s) and / or physical wardrobe from a range of different angles to capture multiple optical images and generate a three-dimensional model of body part(s) for which images have been captured.

9. A data communications network according to claim 7, wherein obtaining optical images of the body part(s) and / or physical wardrobe of the user includes utilizing fixed body image capture hardware associated with the user or a retailer which includes one or more optical lenses configured to capture multiple optical images and generation of a three-dimensional model for any body part(s) for which images have been captured.

10. A data communications network according to either claim 8 or claim 9, wherein the images of the body part(s) and / or physical wardrobe of the user are captured along with an object of known dimensions that is within view of the optical lens(es) and attached, or located in proximity with, the body part(s) and / or physical wardrobe such that resolution of the images to generate the three dimensional model includes comparison of body part images or items within the wardrobe with the object of known dimensions to determine attributes of the three dimensional model or individual items within the wardrobe.

11. A data communications network according to any one of claims 8 to 10, wherein the method further includes providing guidance regarding adequate capture of the bodypart(s) and / or physical wardrobe of the user, and audible and / or visual prompts to guide the user when capturing images including when the user has attained sufficient images to enable generation of the three-dimensional model of the body part(s) or identification of items within the wardrobe with sufficient data to determine the attributes of features in the model or attributes associated with wardrobe items.

12. A data communications network according to any one of the preceding claims, wherein based on identifying the one or more wearable items from the range of wearable items offered for purchase that include detailed dimensions similar to those dimensions of the corresponding body part(s) of the user, and which are classified such that the purchase of the item(s) will satisfy the user’s wearable item requirement and at least one preference, the method further including, generating, by one or more processors, for display on the data communications device associated with the user, a notification alerting the user to the identified one or more wearable items, wherein the display is: an automatic notification that is provided without a specific request from the user, or a notification in response to a user request to search for a particular wearable item or category and / or subcategory of wearable item(s).

13. A data communications network according to claim 12, wherein the notification provides the user with the ability to purchase one or more of the identified wearable items.

14. A data communications network according to either claim 12 or claim 13, wherein the display of the identified one or more wearable items is in response to a user request to receive an outfit recommendation with a plurality of wearable items across a range of wearable item categories and / or subcategories.

15. A data communications network according to any one of the preceding claims, wherein the body part(s) of the user include a single body part, a combination of body parts, a body region or regions, or the entire body of the user.

16. A data communications network according to any one of the preceding claims, wherein the searching and recommendation facility further enables the user to filter the displayed one or more wearable items, according to one or more of:a name of the item, a category or subcategory of the item, a brand name associated with the item, a location or geographical zone, a retailer of the item, a price of the item, a visual attribute of the item, or a physical attribute of the item.

17. A data communications network according to any one of the preceding claims, wherein the display of the one or more wearable items is limited to only those items that include similar detailed dimensions as compared with the physical dimensions of the body part(s) according to the similarity threshold, hence avoiding any further requirement for the user to review and filter results to ensure that the displayed wearable items represent a substantially correct and / or preferred fit.

18. A data communications network according to claim 17, wherein the similarity threshold is automatically adjusted for particular users based on one or more of: a propensity for the user to return items; a propensity for a particular item to be returned; user traffic; or a user preference to receive improved quality item matches.

19. A data communications network according to either claim 17 or claim 18, wherein the display of the one or more wearable items is further accompanied by one or more of: a fit score reflecting a fit prediction rating for the wearable item; a request for feedback in relation to the fit of a wearable item.

20. A data communications network according to claim 19, wherein feedback from the user obtained from the request for feedback or using any additional means is processed using one or more artificial intelligence techniques to achieve one or more of: auto-adjust similarity thresholds for use when generating future outputs; facilitate machine learning to improve outputs including one or more of the wearable item results displayed to users, fit scores, and automatic size conversions according to region.

21. A data communications network according to any one of the preceding claims, wherein in the event there are no results in the display of the one or more wearable items on the basis that there are no items located having similar detailed dimensions as compared with the physical dimensions of the body part(s) according to the similarity threshold, or which don’t have a classification that satisfies the user’s wearable item requirement and at least one preference, the one or more processors are further operable to generate a prompt to the user suggesting a search of broader scope or automatically lowering the similarity threshold such that the listing includes items of the nearest dimensions and / or having the best prospects of satisfying the user’s wearable item requirement and at least one preference.

22. A data communications network according to any one of the preceding claims, wherein selection of a particular wearable item in the display of the one or more wearable items of interest causes the software application to perform an action including any one or more of: generating one or more user interfaces providing additional information relating to the selected wearable item, generating one or more user interfaces displaying the three-dimensional model of the body part including graphical representations of the selected wearable item(s) worn by the three-dimensional model of the body part, generating one or more user interfaces displaying a wardrobe of the user including graphical representations of the selected wearable item stored within the wardrobe,generating one or more user interfaces enabling purchase of the selected wearable item(s), or operating a web browser to display a page associated with a retailer of the wearable item to thereby enable purchase of the wearable item from the retailer’s online store.

23. A data communications network according to claim 22, wherein the one or more processors are further operable to integrate with the data of a retailer’s online store using an application programming interface (API), such that when the user accesses the online store and views and / or searches wearable items of interest, only those wearable items of interest that include similar detailed dimensions as compared with the physical dimensions of the body part(s) of the user, and which are classified such that the purchase of the item will satisfy the user’s wearable item requirement and / or preference, will be displayed and / or listed in search results generated from the online store.

24. A computer-implemented method for facilitating the purchase of wearable items in an online environment, the method including: receiving, by one or more processors, information relating to a wearable item requirement and at least one preference of a user; obtaining, by one or more processors using an image capture facility, multiple optical images of one or more body parts of the user along with an object of known dimensions that is attached to, or located in proximity with, the body part(s) and thereby also in view of the image capture facility and resolving, by one or more optical resolution techniques, the images to generate a three-dimensional model of the body part(s), wherein the resolving of optical images to generate the three-dimensional model includes comparison of the body part images with the object of known dimensions to further provide sizing information regarding the three-dimensional model of the body part(s); determining, by one or more processors, the physical dimensions of the body part(s) according to the sizing information provided by the three-dimensional model; searching, by one or more processors using a searching and recommendation facility, one or more data repositories associated with a plurality of retailers offering a range of wearable items for purchase, the information stored in the one or more data repositories identifying detailed dimensions of each wearable item and classifying each wearable item, wherein the searching is conducted to determine wearable items thatinclude detailed dimensions similar, according to a similarity threshold, to those dimensions of corresponding body part(s) of the user as determined from the sizing information provided by the three-dimensional model, and which are classified such that the purchase of the item will satisfy the user’s wearable item requirement and the at least one preference; providing, by one or more processors, for display on a data communications device associated with the user, the identified one or more wearable items that include similar detailed dimensions as compared with the physical dimensions of the corresponding body part(s), and which are classified such that the purchase of the item will satisfy the wearable item requirement and the at least one preference of the user.

25. A computer-readable medium including a plurality of computer instructions executable by one or more processors, that, when executed, cause the one or more processors to: receive information relating to a wearable item requirement and at least one preference of a user; obtain, using an image capture facility, multiple optical images of one or more body parts of the user along with an object of known dimensions that is attached to, or located in proximity with, the body part(s) and thereby also in view of the image capture facility and resolving, by one or more optical resolution techniques, the images to generate a three-dimensional model of the body part(s), wherein the resolving of optical images to generate the three-dimensional model includes comparison of the body part images with the object of known dimensions to further provide sizing information regarding the three- dimensional model of the body part(s); determine the physical dimensions of the body part(s) according to the sizing information provided by the three-dimensional model; search, using a searching and recommendation facility, one or more data repositories associated with a plurality of retailers offering a range of wearable items for purchase, the information stored in the one or more data repositories identifying detailed dimensions of each wearable item and classifying each wearable item, wherein the searching is conducted to determine wearable items that include detailed dimensions similar, according to a similarity threshold, to those dimensions of corresponding body part(s) of the user as determined from the sizing information provided by the three-dimensional model, and which are classified such that the purchase of the item will satisfy the user’s wearable item requirement and the at least one preference; provide, for display on a data communications device associated with the user, the identified one or more wearable items that include similar detailed dimensions as compared with the physical dimensions of the corresponding body part(s), and which are classified such that the purchase of the item will satisfy the wearable item requirement and the at least one preference of the user.

26. A data communications device that is configured to operably connect with a data communications network to facilitate the purchase of wearable items in an online environment, the data communications device and network operably configured to perform a method including: receiving, by one or more processors, information relating to a wearable item requirement and at least one preference of a user; obtaining, by one or more processors using an image capture facility, multiple optical images of one or more body parts of the user along with an object of known dimensions that is attached to, or located in proximity with, the body part(s) and thereby also in view of the image capture facility and resolving, by one or more optical resolution techniques, the images to generate a three-dimensional model of the body part(s), wherein the resolving of optical images to generate the three-dimensional model includes comparison of the body part images with the object of known dimensions to further provide sizing information regarding the three-dimensional model of the body part(s); determining, by one or more processors, the physical dimensions of the body part(s) according to the sizing information provided by the three-dimensional model; searching, by one or more processors using a searching and recommendation facility, one or more data repositories associated with a plurality of retailers offering a range of wearable items for purchase, the information stored in the one or more data repositories identifying detailed dimensions of each wearable item and classifying each wearable item, wherein the searching is conducted to determine wearable items that include detailed dimensions similar, according to a similarity threshold, to those dimensions of corresponding body part(s) of the user as determined from the sizing information provided by the three-dimensional model, and which are classified such that the purchase of the item will satisfy the user’s wearable item requirement and the at least one preference;providing, by one or more processors, for display on the data communications device, the identified one or more wearable items that include similar detailed dimensions as compared with the physical dimensions of the corresponding body part(s), and which are classified such that the purchase of the item will satisfy the wearable item requirement and the at least one preference of the user.

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