Product recommendation system

Abstract

A method involves receiving measurement data for at least part of a body of a user from a scanning device 80, determining at least one sizing guide associated with at least one product, and providing

Description

Field The present specification relates to a product recommendation system, particularly in relation to items of apparel. Background It is known to purchase products, such as apparel, on real or virtual shopping platforms. There remains a need for improvement in purchasing efficiency and experience. Summary In a first aspect, this specification describes a method comprising: receiving measurement data for at least part of a body of a user from a scanning device; determining at least one measurement profile of a user; determining at least one sizing guide associated with at least one product; and providing one or more sizing suggestions for the user based at least in part, on the at least one measurement profile and the at least one sizing guide. In some examples, the measurement data is generated by a plurality of sensors at the scanning device, wherein the plurality of sensors are configured to scan at least part of the body of the user. In some examples, the measurement data comprises sensor data from the plurality of sensors from a plurality of angles. Some examples include storing the measurement data and / or the measurement profile of the user in a database in an anonymous manner. Some examples include generating a profile code for the user, wherein the profile code is usable for providing sizing suggestions for the user for a plurality of entities. In some examples, the one or more sizing suggestions are determined using a machine learning model. In some examples, the machine learning model is trained with training data comprising measurement data associated with a plurality of users, and sizing data associated with a plurality of entities. In some examples, the sizing suggestions comprise at least one of: suggestion of whether a size of an unused product would be tight, accurate, or loose; suggestion of whether a size of a product after use would be tight, accurate, or loose; suggestions based on user preference; and / or suggestions based on sizing guides of a specific product from a specific entity. In some examples, at least part of the body of the user comprises at least one foot of the user, wherein the sizing guide relates to footwear. In some examples, the at least one product may comprise one or more of clothing, accessories, footwear, or headwear. In a second aspect, this specification describes an apparatus configured to perform any method as described with reference to the first aspect. In a third aspect, this specification describes computer-readable instructions which, when executed by computing apparatus, cause the computing apparatus to perform any method as described with reference to the first aspect. In a fourth aspect, this specification describes a computer program comprising instructions for causing an apparatus to perform at least the following: receiving measurement data for at least part of a body of a user from a scanning device; determining at least one measurement profile of a user; determining at least one sizing guide associated with at least one product; and providing one or more sizing suggestions for the user based at least in part, on the at least one measurement profile and the at least one sizing guide. In a fifth aspect, this specification describes a computer-readable medium (such as a non-transitory computer-readable medium) comprising program instructions stored thereon for performing at least the following: receiving measurement data for at least part of a body of a user from a scanning device; determining at least one measurement profile of a user; determining at least one sizing guide associated with at least one product; and providing one or more sizing suggestions for the user based at least in part, on the at least one measurement profile and the at least one sizing guide. In a sixth aspect, this specification describes an apparatus comprising: at least one processor; and at least one memory including computer program code which, when executed by the at least one processor, causes the apparatus to: receive measurement data for at least part of a body of a user from a scanning device; determine at least one measurement profile of a user; determine at least one sizing guide associated with at least one product; and provide one or more sizing suggestions for the user based at least in part, on the at least one measurement profile and the at least one sizing guide. In an seventh aspect, this specification describes an apparatus comprising a first module configured to receive measurement data for at least part of a body of a user from a scanning device; a second module configured to determine at least one measurement profile of a user; a third module configured to determine at least one sizing guide associated with at least one product; and a fourth module configured to provide one or more sizing suggestions for the user based at least in part, on the at least one measurement profile and the at least one sizing guide. In an eighth aspect, this specification describes an apparatus comprising: a plurality of sensors configured to scan at least part of a body of a user; a rotating element for enabling at least one of the plurality of sensors to scan at least part of the body from a plurality of angles; a storage module configured to store data from the plurality of sensors; wherein the data from the plurality of sensors is usable for determining at least one measurement profile for the user, wherein the measurement profile of the user is usable to provide one or more sizing suggestions for the user dependent upon a sizing guide for at least one product. In some examples, the plurality of sensors comprise one or more of: laser sensors or infrared sensors. In some examples, the plurality of sensors comprise a first group of sensors arranged to scan at least one foot of the user. In some examples, the at least one product may comprise one or more of clothing, accessories, footwear, or headwear. In some examples, the apparatus of the eighth aspect is configured to provide data for performing any method described with reference to the first aspect. Brief Description of Drawings Example embodiments will now be described, by way of example only, with reference to the following schematic drawings, in which: FIG. 1 is a block diagram of a system in accordance with an example embodiment; FIG. 2 is a flowchart of an algorithm in accordance with an example embodiment; FIGs. 3 to 5 are block diagrams of systems in accordance with example embodiments; FIG. 6 is a flowchart of an algorithm in accordance with an example embodiment; FIG. 7 is a sequence diagram in accordance with an example embodiment; FIGs. 8 to 10 are block diagrams of systems in accordance with example embodiments; FIG. 11 is a block diagram of components of a system in accordance with an example embodiment; and FIG. 12 shows an example of tangible media for storing computer-readable code which when run by a computer may perform methods according to example embodiments described above. Detailed Description The scope of protection sought for various embodiments of the invention is set out by the independent claims. The embodiments and features, if any, described in the specification that do not fall under the scope of the independent claims are to be interpreted as examples useful for understanding various embodiments of the invention. In the retail industry, customers may shop for products that may be required to have a particular size based on the customers' measurements and / or preferences. Customers may not have adequate time or opportunities to try on such products before purchase (e.g. products may not be in stock in relevant stores, or the customer may be purchasing the products online without trying on). As such, the customers may merely guess the size of the product for purchase based on the size they usually wear. However, different products (e.g. different types of clothing, accessories, or footwear, or clothing or footwear from different retailers, different brands, different manufacturers) may have different sizing guides. For example, a customer who normally wears a small (S) size shirt from a first brand, may not fit well in a small (S) size shirt from another brand. Similarly, a customer who normally wears a small (S) size shirt from the first brand, may not fit well in a small (S) size dress from the same first brand (due to sizing differences based on the type of clothing, or as the customer may prefer a lose fit in a certain type of clothing but a tight fit in another type of clothing). As such, it may be desirable for a customer to know which size of a particular product would be appropriate before purchasing said product. This may also beneficial for customers (e.g. as less time and money may be spent on shopping) and also retailers (as the number of returns may be reduced significantly if customers buy the right size initially). The purchasing experience may then be made more efficient and enjoyable for the consumers, and also provide significant benefits to the retailers. The example embodiments below provide a system and method for providing product recommendations to customers based on their measurements. FIG. 1 is a block diagram of a system, indicated generally with the reference numeral 10, in accordance with an example embodiment. The system 10 comprises a computation module 11 that, for example, may receive measurement information associated with a user (e.g. a customer). The computation module 11 may further receive product information associated with one or more products (e.g. one or more types of products (clothing, accessories, footwear, or headwear) from one or more brands or retailers, where the user may be interested in purchasing said one or more products). Based on the received measurement information and product information, the computation module 11 may provide, as an output, one or more sizing suggestions associated with the user and the respective one or more products. For example, the output of the computation module 11 may provide sizing suggestions to the user regarding which size of a particular product may fit the user the best. The sizing suggestions may be based on a comparison of the measurement information associated with the user, and the product information associated with the product. The sizing suggestion may further optionally be based on user preferences (e.g. whether the user prefers a loose fit, slim fit, or oversize clothing), and may also optionally be based on product information such as product measurements (e.g. product measurements of the product when unused, or when used (e.g. measurements may change when product stretches (e.g. from being used) or shrinks (e.g. from being washed)). In an example embodiment, the sizing suggestions comprise at least one of: suggestion of whether a size of an unused product would be tight, accurate, or loose; suggestion of whether a size of a product after use would be tight, accurate, or loose; suggestions based on user preference ; and / or suggestions based on sizing guides of a specific product from a specific entity. The example embodiments below provide solutions for obtaining accurate measurement information and product information such that the sizing suggestions provided to the user allows the user to make an informed decision of which products to purchase. FIG. 2 is a flowchart of an algorithm, indicated generally by the reference numeral 20, in accordance with an example embodiment. The algorithm 20 may be performed at a computation module, such as the computation module 11 described with reference to FIG. 1. The algorithm 20 may start with operation 21, where measurement data may be received, from a scanning device, for at least part of a body of a user. For example, the scanning device may be a three-dimensional scanning device, which may comprise means (e.g. sensors) for measuring one or more parts of the body of the user (e.g. measuring the parts of the body at a plurality of cross-sectional regions). In one example, the measurement data is generated by a plurality of sensors at the scanning device, where the plurality of sensors are configured to scan at least part of the body of the user. In one example, the measurement data comprises sensor data from the plurality of sensors from a plurality of angles. The physical measurements of the user may comprise a plurality of data points (e.g. a large number of data points, such as 100,000 to millions of data points). At operation 22, at least one measurement profile of the user may be determined based, at least in part, on the received measurement data. The measurement data may comprise one or more of height measurement, weight measurement, and / or measurement of width and / or length of one or more cross sections of one or more parts of the user's body. For example, the measurement profile of the user may comprise user details, linked (e.g. in a database) with the measurement data of the user. The measurement profile may further comprise optional data, such as the user's preferred fit (loose, regular, tight, tailored, etc.). At operation 23, at least one sizing guide associated with at least one product may be determined. Next, at operation 24, one or more sizing suggestions may be provided (e.g. to the user) based on the measurement profile of the user and the at least one sizing guide. The one or more sizing suggestions may indicate the size of the one or more products (that the user may be interested in purchasing) that would be suitable for the user. In some examples, the at least one sizing guide associated with the at least one product may be received from an entity such as a retailer, brand, manufacturer, or the like. Alternatively, the at least one sizing guide may be available to access from a first database. For example, the first database may comprise one or more sizing guides associated with one or more entities respectively. The sizing guide may comprise information that is more detailed compared to sizing charts that are typically published by entities. A sizing guide may comprise details of measurements of one or more products in a plurality of sizes, for example, as shown in table 1 below: Code: 12345 Collar Chest Sleeves (Length, width at arm, width at elbow, width at wrist) Waist Length Shoulder Size S Size M Size L Table 1: Sizing guide for a Men's Shirt (Product code: 123451 In one example, the first database may comprise information relating to a plurality of products from one or more entities (e.g. brands, manufacturers, retailers, or the like). The information comprised in the first database may include sizing guides of the plurality of products, as shown in Table 1. For example, Table 1 shows a sizing guide for a Men's Shirt (e.g. product code: 12345), where the sizing guide includes dimensions (e.g. length and / or width) for one or more of the collar, chest, sleeves (e.g. different points at the sleeves, such as the length, width at arms, elbows, wrists, and / or any other point on the sleeve), inseam, waist, shirt length, shoulders, or the like. The sizing guide may correspond to a plurality of types of fits, such as slim fit, regular fit, stretch fit, wide fit, or the like. Such sizing guides may be saved for a plurality of products (e.g. different product codes) and / or a plurality of type of products (e.g. different product types such as men's shirts, women's shirts, women's dresses, trousers, etc.). In an example embodiment, the data from the one or more entities (e.g. brands, manufacturers, retailers, or the like) or data aggregator resources via one or more application program interfaces (APIs) or batch API processing. For example, API or batch API connections may be established with the entities' database(s) to access product data (e.g. in real time when the user is requesting sizing recommendation), the product data obtained may be used for creating and / or updating the first database. In an example embodiment, when a sizing guide of a particular product of interest may not be available, the sizing guide may be determined based on sizing information associated with a plurality of other products (e.g. products similar to the product that the user may be interested in purchasing, where the other products may be from the same or different entity). For example, a machine learning model may be used for determining a sizing guide of a particular product based on sizing guides associated with a plurality of other products. The machine learning model may receive as inputs the information corresponding to the measurement profile of the user, and sizing guides associated with a plurality of other similar products or other products (e.g. products that have been purchased by the user in the past and / or have been marked as a good fit by the user in the past), such that the machine learning model may determine a sizing guide for the particular product, and in turn provide a sizing suggestion for the user for the particular product. In an example embodiment, a sizing guide for footwear may comprise measurements such as internal shoe dimensions, external shoe dimensions, material of the shoe, arch width, height, or length, shape of the shoe, dimensions of the shoe at various cross sections of the shoe, or the like. This may allow the sizing suggestion for footwear to be determined based on closely comparing the user's foot measurements with the sizing guide of the footwear. In an example embodiment, the sizing suggestion provided at operation 24 may be determined based on a comparison of the user's measurement data (e.g. linked to the user's measurement profile) and the sizing guide of the relevant product. For example, if most of the user's measurements (e.g. chest, waist, etc.) are in close proximity (e.g. at least within a predefined range) of the measurements of a size S (small) of the relevant product as obtained from the sizing guide, then the size S (small) may be recommended in the sizing suggestion. However, if there is a user preference recorded in the measurement profile indicating that the user prefers loosely fitted clothes, the size M (medium) may be recommended in the sizing suggestion (as the size M may fit the user more loosely, but not too loose). FIG. 3 is a block diagram of a system, indicated generally by the reference numeral 30, in accordance with an example embodiment. The system 30 may be part of a scanning device, such as a three-dimensional scanning device. The system 30 may comprise one or more sensor modules 31, where the sensor modules may comprise a plurality of sensors, such as laser sensors and / or infrared sensors. The plurality of sensors may enable the scanning device to determine measurements associated with the user. For example, the sensors may emit electromagnetic waves, which may be reflected from the user's body, thus allowing the measurement of various parts of the user's body. Various techniques may be utilized for using such sensors in order to perform measurements of one or more body parts. The system 30 may further comprise one or more control modules 32 and one or more power modules 33. The control module may comprise at least one processing unit, at least one memory unit, and at least one communications unit. For example, the control unit 32 may process information received from the sensor(s) 31 to determine measurement data associated with the user's body. The plurality of sensors are configured to scan at least part of the body of the user. The measurement data may comprise sensor data from the plurality of sensors from a plurality of angles. The control unit 32 may further send the measurement data (e.g. to a cloud server) using the communications unit(s) and / or store said measurement data (e.g. in a local storage unit). The control unit 32 may optionally send instructions to a user interface (e.g. visual screen or audio speaker) to provide instructions to a user to stand in a position for obtaining accurate measurement data. The one or more power modules 33 may be responsible for providing power to the various modules of the system 30. The system 30 may be powered via mains electricity and / or may be battery-powered. The system 30 may further comprise one or more mechanical modules 34, such as a rotating arm or a rotating base, for example, in order to allow the sensors 31 to obtain measurements at a plurality of angles. This may allow the system 30 to obtain a 360-degree three-dimensional scan of the user. Alternatively, or in addition, the system 30 may comprise sensors arranged in such a way that a three-dimensional scan of the user's body may be obtained without any rotating elements. In one example, the system 30 may further comprise a storage module configured to store data from the plurality of sensors. The storage module may be implemented using a hardware storage unit and / or using software storage, such as a cloud server. The data from the plurality of sensors is usable for determining at least one measurement profile for the user. The measurement profile of the user is usable to provide one or more sizing suggestions for the user dependent upon a sizing guide for at least one product. FIG. 4 is an example illustration of a scanning device, indicated generally by the reference numeral 40, in accordance with an example embodiment. The scanning device 40 may be used for obtaining measurement data associated with a user. The scanning device 40 may comprise an enclosure 41 in which the user may be able to enter in order to allow the scanning device 40 to use one or more sensors (e.g. sensor module 31) to obtain measurement data associated with the user. The scanning device 40 may further comprise a plurality of sensors arranged along the enclosure 41, and / or arranged along an arm 43. The arm 43 may be part of a mechanical module (e.g. mechanical module 34) and may comprise elements enabling the arm 43 to rotate, fold, twist, or otherwise mechanically alter or move positions in order to obtain measurement data associated with the user from a plurality of angles. The scanning device 40 may further comprise a foot scanning platform 42, which may comprise additional sensors for obtaining measurements associated with a foot and / or feet of the user. The feet scanning platform 42 is described in further detail with reference to FIG. 5. FIG. 5 is an illustration, indicated generally by the reference numeral 50, of a foot scanning platform 42, according to an example embodiment. The feet scanning platform 42 may comprise a first foot scanning portion 52, which is shown in a zoomed in view 53. The first foot scanning portion 52 may comprise one or more sensors 54 and 55. One or more of the sensors 54 and 54 may be movable (e.g. vertically movable, horizontally movable, and / or rotatable) such that measurements associated with the user's foot / feet may be obtained from a plurality of angles. The first foot scanning portion 52 may further have a visual indicator 56 and / or audio indicator for guiding the user to stand at an accurate position at which the scanning device may obtain accurate measurements for one or more parts of the user's body. In an example embodiment, a three-dimensional scan of one or both of the user's feet may be obtained from the sensors on the foot scanning platform 42. FIG. 6 is a flowchart of an algorithm, indicated generally by the reference numeral 60, in accordance with an example embodiment. The operations of algorithm 60 may be performed at a computation module, such as the computation module 11 as described with reference to FIG. 1. The algorithm 60 may start with operation 61, where measurement data associated with a user may be received. As described earlier, the measurement data may be obtained at and received from a scanning device, such as the scanning device 30 or 40. At operation 62, other information associated with the user may be received. For example, the other information may comprise sizing preferences (e.g. user preferring a loose fit or tight fit), weight and height measurements, or the like. At operation 63, the received measurement data (e.g. raw measurement data and / or processed measurement data) and / or other information may be stored, for example in a database, in a secure manner. In one example, in addition to storing the measurement data, one or more transformations may be applied in order to standardize the measurements for compatibility with different sizing guides. For example, a machine learning model may be used for arranging the plurality of data points corresponding to the measurement data into categories of sizes (e.g. to classify which data points may be amalgamated to determine a shoulder size, which data points may be amalgamated to determine a collar size, or the like). At operation 64, a profile code (e.g. a QR. code) may be generated for the user, such that the stored data associated with the user may be accessible via the profile code. For example, while browsing and / or making a purchase in-store or online, a user may present the profile code (e.g. a unique code) from a user interface (e.g. UI of a mobile application or software application that may display the profile code), or may access a retailer's website and / or application with a unique profile (e.g. login ID or above profile code). After presenting the profile code or logging in with the unique profile, sizing suggestion(s) may be provided for one or more items of choice. For example, a selected product in a size as suggested by the sizing suggestion may be identified by a product identifier (e.g. SKU (stock keeping unit), EAN (European Article Number), and / or other product identifiers). In an example embodiment, the one or more sizing suggestions are determined using a machine learning model. For example, the machine learning model is trained with training data comprising measurement data of a plurality of users, and sizing data associated with a plurality of entities (e.g. retailers, manufacturers, and / or brands). In an example embodiment, the stored data us encrypted and / or may be stored such that the data cannot be linked directly to the user's identity details in case of any data breaches. This may ensure that the data is stored securely and / or in an anonymous manner, and the privacy of the users is protected. As such, for example, raw measurement data may not be accessible and / or stored in order to provide a system where the user's measurement data may be encoded and / or stored in an encrypted manner. In this way, retailers and / or manufacturers may not be able to use private data of the user for profit or in any way the user may not have knowledge about. Furthermore, in an example embodiment, the user or the retailer may not be able to access the detailed measurement data. For example, the user may be able to receive sizing suggestions based on the generated profile code, as receiving details measurement data may not be useful for the user to understand the most suitable size for any specific product they may be interested in. FIG. 7 is a sequence diagram, indicated generally by the reference numeral 70, in accordance with an example embodiment. The sequence diagram 70 shows example operations performed at one or more elements of a system according to example embodiments. For example, the sequence diagram 70 shows example operations performed by a customer 710 (e.g. a user), a retailer or manufacturer 720 (e.g. a retailer selling products from one or more brands or manufacturers and / or manufacturer producing products from one or more brands or entities), a scanning device 730, a processing unit 740, and a product database 750. In an example embodiment, on the customer side 710, a user may access, at operation 711, a first software application (e.g. mobile software application, desktop software application, kiosk software application, and / or web-based application). The first software application may comprise functionality for providing sizing suggestions to the user for one or more products from one or more retailers, manufacturers and / or brands. The first software application may be used as a standalone application, and / or may be used in conjunction with (e.g. linked as a plugin, as an add-on, or the like) applications, websites, or in-store kiosks for one or more retailers, manufacturers, or brands. For example, accessing the first software application may comprise one or more of downloading, installing and / or otherwise accessing the first software application. The user may then create and / or edit their profile on the first software application at operation 712. The profile may comprise their identity details (e.g. name, contact information, or the like) and may optionally comprise one or more user preferences relating to products (e.g. preferences regarding type of fit, material, style, etc.). The user may then obtain, at operation 713, a body scan (e.g. obtained from a scanning device), such that the body scan comprises measurement data associated with the user and may be linked to the user's profile on the first software application. The body scan may be obtained from a scanning device (e.g. scanning device 730). The user may be provided with sizing suggestions for one or more products (described in further detail below), such that the user may purchase one or more products online (operation 714) and / or purchase one or more products in store (operation 715). In an example embodiment, on the retailer and / or manufacturer side 720, there may be an in-store kiosk 721. The in-store kiosk may provide access to the first software application to any customer (e.g. the user) for creating and / or accessing their profile on the first software application. The retailer and / or manufacturer 720 (e.g. which may comprise a retail store associated with one or more brands) may also comprise a scanning device 722 (e.g. similar to the scanning device 40 described with reference to FIG. 4) in their store. The user may obtain a body scan (713) by using the scanning device 722 located in store. The retailer and / or manufacturer may enable creating a basket 723 (on the store website and / or in-store) comprising products that the user may be interested in purchasing, and the products on the basket may be linked to the profile of the user (e.g. identified by a user ID) on the first software application. For example, linking the basket to the user profile may allow the accurate size (based on the sizing suggestions determined based on the measurement data of the user and the product information) of the products to be added to the basket. As such, the user may purchase (purchase online (714), or purchase in store (715)) one or more products, in the suggested size, as included in the basket 723. In an example embodiment, the scanning device 730, which may be installed at one or more stores associated with one or more retailers, manufacturers, and / or brands (720), may enable obtaining three dimensional measurements associated with users at operation 731. For example, the measurement data may be obtained when the user is positioned inside an enclosure (41) of the scanning device 730 (e.g. similar to the scanning device 40). The scanning device may further measure weight and height of the user at operation 732, and further measure feet (e.g. length, width, and / or various cross-sections of the feet) of the user at operation 733. Based on one or more of the three-dimensional measurements, weight, height, and measurements of the feet, a body scan (e.g. full 3D body scan, such as an avatar of the user) associated with the user may be created at operation 734. In an example embodiment, the processing unit 740 (e.g. similar to the computational module 11) may perform various operations in order to provide sizing suggestions to the user. At operation 741, a profile of the user (e.g. the profile created at operation 712 for the first software application) may be stored (e.g. at a storage unit (local or remote server) and a user ID may be assigned to the user. The measurement data associated with the user (e.g. data obtained at operations 731, 732, 733, and / or 734) may also be stored at operation 742. The measurement data may then be optimized at operation 743 (e.g. one or more transformations may be applied in order to standardize the measurements for compatibility with different sizing guides, as described with reference to FIG. 6). The optimization may include receiving product information 750 from a product database 751, where the product information may comprise sizing guide, material information, or the like of one or more products. The product information may be received and / or obtained from the retailer, manufacturer and / or brand. The measurement data may then be processed at operation 744, for example, by comparison with the received product information. Based on said comparison, sizing suggestion(s) may be provided to the user at operation 745. The sizing suggestions may be provided by applying said suggestions to the basket 723 and / or may be provided by otherwise indicating the optimal size of the products to the user. FIG. 8 is an example illustration of a scanning device, indicated generally by the reference numeral 80, in accordance with an example embodiment. The scanning device 80 comprises the elements of the scanning device 40, as described with reference to FIG. 4. The scanning device 80 (e.g. similar to scanning devices 722 or 730) may be located in a store associated with one or more retailers, manufacturers, or brands, or may be located in any location (e.g. public locations such as airports, shopping malls, restaurants, or the like) where a user may be able to access the scanning device for obtaining measurement data associated with them. A user, illustrated by the user 83, may be positioned inside the enclosure 41 of the scanning device 80, such that one or more sensors of the scanning device 80 may obtain measurement data associated with the user 83, and may optionally measure weight, height, and / or feet measurements associated with the user. The scanning device 80 may then store the measurement data (such that a full body scan may be generated at the scanning device and / or remotely at a computational module). The scanning device 80 may optionally comprise a user interface 82. The user interface 82 may comprise a visual and / or audio user interface that can be used by the user 83 for understanding the correct position or posture (e.g. star pose orT pose with the hands raised and legs positioned apart from each other) for standing inside the enclosure 41. The user interface 82 may further be used for displaying information relating to the profile of the user, relating to products (e.g. products in the user's basket), and / or relating to sizing suggestions for said products. In an example embodiment, the user interface 82 may be used as a kiosk 721 as described with reference to FIG. 7. In an example embodiment, a user may update their profile on the first software application. For example, over time, the user's preferences and / or measurements may change. The user may update their preferences on the user interface, and their measurements may be updated any time the user uses a scanning device for creating and / or updating their measurement data. FIG. 9 is an illustration, indicated generally by the reference numeral 90, of a three-dimensional body scan, according to an example embodiment. The illustration 90 shows a body scan of a user, such as the user 83, that may be obtained when the user 83, is positioned within the scanning device 80 and a plurality of sensors of the scanning device 80 are used for obtaining measurement data associated with the user 83. As shown in the illustration 90, measurement data may be obtained at a plurality of cross-sections of one or more parts of the body of the user. The cross-sections are illustrated by example line 91 at the user's chest, and by example lines 92 at the user's arm. The three-dimensional body scan may be stored at a database (e.g. at operation 63 and / or operation 742). In an example embodiment, the three-dimensional body scan may not be provided to the user, as it may be complicated to understand the plurality of measurements, and it may be complicated and may not be useful for the user to navigate through a large amount of data points. The user is instead provided with sizing suggestions that the first software application may determine based on the measurement data of the user. FIG. 10 is a block diagram of a system, indicated generally by the reference numeral 100, in accordance with an example embodiment. The system 100 provides an illustration of a user interface on a mobile device, where a user may be able to use the user interface for determining sizing suggestions for one or more products. For example, a user interface view 101 shows a shirt (e.g. a product that is in the shopping basket for the user on a third party website, such as the website of the retailer, manufacturer, and / or brand). The user may be provided an option 103 for linking the basket with a user profile. For example, the user profile may be accessed either by using a QR. code, or by entering user credentials (e.g. user ID and / or password). Once the basket is linked to the user profile, a sizing suggestion may be provided. For example, the user interface view 102 shows that the sizing suggestion for the relevant shirt is size M (medium). The user may then purchase the size as suggested in the sizing suggestion, or may prefer to purchase another product or size if they desire. In an example embodiment, the basket may be linked to a different user's profile (e.g. the user may want to purchase products for a friend), as long as the user has permission to access and / or link to the different user's profile. It would be appreciated that even if a first user has access to a second user's profile, they may only be able to know what the sizing suggestion is, and may not have access to detailed measurement data of the second user. This may be beneficial in protecting the privacy of the second user. The first user may be granted access to the second user's profile by the second user. In an example embodiment, a user may add a certain size of a product in their basket, and the sizing suggestion may be provided in order to indicate whether the selected size would be suitable, too tight, or too loose. For example, the user selects a shirt in size S. Based on the user's profile, the sizing suggestion may indicate that size S would be too tight for the user, and may optionally indicate that the material of the shirt is shrinkable, and therefore a size S is not recommended. In another example, the user selects the shirt in size M (which may be the accurate size for the user when the shirt is unused). Based on the user's profile, the sizing suggestion may indicate that size M would be suitable for the user when the shirt is unused, but the shirt's material may cause the shirt to shrink after a wash (e.g. thus recommending that size M may be more suitable). In another example, in a scenario where the first software application is unable to find a suitable sizing suggestion for a certain product for the user based on one or more sizing guides as stored internally, an entity (e.g. retailer / manufacturer) may be able to send product dimensions and / or sizing charts or other product specifications through an API associated with the first software application, such that a size recommendation may be determined based on the received information and a sizing suggestion may be provided to the user accordingly. For completeness, FIG. 11 is a schematic diagram of components of one or more of the example embodiments described previously, which hereafter are referred to generically as processing systems 300. A processing system 300 may have a processor 302, a memory 304 closely coupled to the processor and comprised of a RAM 314 and ROM 312, and, optionally, user input 310 and a display 318. The processing system 300 may comprise one or more network / apparatus interfaces 308 for connection to a network / apparatus, e.g. a modem which may be wired or wireless. Interface 308 may also operate as a connection to other apparatus such as device / apparatus which is not network side apparatus. Thus, direct connection between devices / apparatus without network participation is possible. The processor 302 is connected to each of the other components in order to control operation thereof. The memory 304 may comprise a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD). The ROM 312 of the memory 304 stores, amongst other things, an operating system 315 and may store software applications 316. The RAM 314 of the memory 304 is used by the processor 302 for the temporary storage of data. The operating system 315 may contain computer program code which, when executed by the processor implements aspects of the algorithms 20 and 60 described above. Note that in the case of small device / apparatus the memory can be most suitable for small size usage i.e. not always hard disk drive (HDD) or solid-state drive (SSD) is used. The processor 302 may take any suitable form. For instance, it may be a microcontroller, a plurality of microcontrollers, a processor, or a plurality of processors. The processing system 300 may be a standalone computer, a server, a console, or a network thereof. The processing system 300 and needed structural parts may be all inside device / apparatus such as loT device / apparatus i.e. embedded to very small size In some example embodiments, the processing system 300 may also be associated with external software applications. These may be applications stored on a remote server device / apparatus and may run partly or exclusively on the remote server device / apparatus. These applications may be termed cloud-hosted applications. The processing system 300 may be in communication with the remote server device / apparatus in order to utilize the software application stored there. FIG. 12 shows tangible media, specifically a removable memory unit 365, storing computer-readable code which when run by a computer may perform methods according to example embodiments described above. The removable memory unit 365 may be a memory stick, e.g. a USB memory stick, having internal memory 366 for storing the computer-readable code. The internal memory 366 may be accessed by a computer system via a connector 367. Other forms of tangible storage media may be used. Tangible media can be any device / apparatus capable of storing data / information which data / information can be exchanged between devices / apparatus / network. Embodiments of the present invention may be implemented in software, hardware, application logic or a combination of software, hardware and application logic. The software, application logic and / or hardware may reside on memory, or any computer media. In an example embodiment, the application logic, software or an instruction set is maintained on any one of various conventional computer-readable media. In the context of this document, a "memory" or "computer-readable medium" may be any non-transitory media or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer. Reference to, where relevant, "computer-readable storage medium", "computer program product", "tangibly embodied computer program" etc., or a "processor" or "processing circuitry" etc. should be understood to encompass not only computers having differing architectures such as single / multi-processor architectures and sequencers / parallel architectures, but also specialised circuits such as field programmable gate arrays FPGA, application specify circuits ASIC, signal processing devices / apparatus and other devices / apparatus. References to computer program, instructions, code etc. should be understood to express software for a programmable processor firmware such as the programmable content of a hardware device / apparatus as instructions for a processor or configured or configuration settings for a fixed function device / apparatus, gate array, programmable logic device / apparatus, etc. As used in this application, the term "circuitry" refers to all of the following: (a) hardware-only circuit implementations (such as implementations in only analogue and / or digital circuitry) and (b) to combinations of circuits and software (and / or firmware), such as (as applicable): (i) to a combination of processor(s) or (ii) to portions of processor(s) / software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a server, to perform various functions) and (c) to circuits, such as a microprocessor(s) or a portion of a microprocessor(s), that require software or firmware for operation, even if the software or firmware is not physically present. If desired, the different functions discussed herein may be performed in a different order and / or concurrently with each other. Furthermore, if desired, one or more of the above-described functions may be optional or may be combined. Similarly, it will also be appreciated that the flow charts of Figures 2 and 6, and sequence diagram of FIG. 7 are examples only and that various operations depicted therein may be omitted, reordered and / or combined. It will be appreciated that the above described example embodiments are purely illustrative and are not limiting on the scope of the invention. Other variations and modifications will be apparent to persons skilled in the art upon reading the present specification. Moreover, the disclosure of the present application should be understood to include any novel features or any novel combination of features either explicitly or implicitly disclosed herein or any generalization thereof and during the prosecution of the present application or of any application derived therefrom, new claims may be 5 formulated to cover any such features and / or combination of such features.

Claims

1. A method comprising:receiving measurement data for at least part of a body of a user from a scanning device;determining at least one measurement profile of a user;determining at least one sizing guide associated with at least one product; and providing one or more sizing suggestions for the user based at least in part, on the at least one measurement profile and the at least one sizing guide.

2. A method as claimed in claim 1, wherein the measurement data is generated by a plurality of sensors at the scanning device, wherein the plurality of sensors are configured to scan at least part of the body of the user.

3. A method as claimed in any one of the preceding claims, wherein the measurement data comprises sensor data from the plurality of sensors from a plurality of angles.

4. A method as claimed in any one of the preceding claims, further comprising storing the measurement data and / or the measurement profile of the user in a database in an anonymous manner.

5. A method as claimed in any one of the preceding claims, further comprising generating a profile code for the user, wherein the profile code is usable for providing sizing suggestions for the user for a plurality of entities.

6. A method as claimed in any one of the preceding claims, wherein the one or more sizing suggestions are determined using a machine learning model.7 A method as claimed in claim 6, wherein the machine learning model is trained with training data comprising measurement data associated with a plurality of users, and sizing data associated with a plurality of entities.

8. A method as claimed in any one of the preceding claims, wherein the sizing suggestions comprise at least one of: suggestion of whether a size of an unused product would be tight, accurate, or loose; suggestion of whether a size of a product after use would be tight, accurate, or loose; suggestions based on user preference; and / or suggestions based on sizing guides of a specific product from a specific entity.

9. A method as claimed in any one of the preceding claims, wherein at least part of the body of the user comprises at least one foot of the user, wherein the sizing guide relates to footwear.

10. A method as claimed in any one of the preceding claims, wherein the at least one product may comprise one or more of clothing, accessories, footwear, or headwear.

11. An apparatus comprising means for performing a method as claimed in any one of the preceding claims.

12. An apparatus comprising:a plurality of sensors configured to scan at least part of a body of a user;a rotating element for enabling at least one of the plurality of sensors to scan at least part of the body from a plurality of angles;a storage module configured to store data from the plurality of sensors; wherein the data from the plurality of sensors is usable for determining at least one measurement profile for the user, wherein the measurement profile of the user is usable to provide one or more sizing suggestions for the user dependent upon a sizing guide for at least one product.

13. An apparatus as claimed in claim 13, wherein the plurality of sensors comprise one or more of: laser sensors or infrared sensors.

14. An apparatus as claimed in any one of claims 12 or 13, wherein the plurality of sensors comprise a first group of sensors arranged to scan at least one foot of the user.

15. An apparatus as claimed in any one of claims 12 to 14, wherein the at least one product may comprise one or more of clothing, accessories, footwear, or headwear.

16. A computer program comprising instructions, which, when executed by an apparatus, cause the apparatus to:receive measurement data for at least part of a body of a user from a scanning device;determine at least one measurement profile of a user;determine at least one sizing guide associated with at least one product; and provide one or more sizing suggestions for the user based at least in part, on the at least one measurement profile and the at least one sizing guide.

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