Patent Application
20140180864
Example embodiments of the present application generally relate to data processing techniques. For example, the disclosure describes techniques for automatically creating a shopping recommendation for clothing based on a user's style preferences and body measurement information.
Portions of this disclosure contain material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever.
The Internet and the World Wide Web have given rise to a wide variety of on-line retailers that operate virtual stores from which consumers can purchase products (i.e., merchandise, or goods) as well as services. Although the popularity of these on-line retail sites is clearly evidenced by their increasing sales, for a variety of reasons, some consumers may still opt to purchase items in a more conventional manner—i.e., a brick-and-mortar store.
A shopper may, for example, opt to buy clothing only once they have physically tried it on to ensure that the particle article of clothing fits.
Nevertheless, real-life or offline clothes shopping is also associated with frustrations, such as difficulties that may be experienced in finding an appropriate size of clothing article in-store.
Various features of the disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which:
An example embodiment of the present disclosure describes data processing techniques for generating personalized clothes shopping recommendations for a user in an automated process. The clothes shopping recommendation may be based on a clothing profile that comprises personal style information defining clothing style preferences of the user and measurement information that indicate physical measurements of the user. A recommendation engine may automatically process inventory data about clothes inventories of merchants, to identify and recommend articles of clothing that accord with or satisfy both the clothing style preferences and the relevant physical measurements of the user. A particular recommendation may be responsive to user input of a particular objective for which shopping is to be done, in which case inventory data may be filtered to exclude articles of clothing irrelevant to the particular objective.
In-store direction may be provided to assist a user to find recommended and/or suitably sized articles of clothing, for example by means of an augmented reality display on which suitably sized articles of clothing may be highlighted. To facilitate ready access by store inventories and/or locations systems to user-specific data, the measurement information may be printed on a visual code for reading at the store. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various aspects of different embodiments. It will be evident, however, to one skilled in the art, that the present embodiments may be practiced without all of the specific details.
Embodiments are not limited to mobile devices but could be implemented partly on a mobile device and partly on a laptop or other stationary computing device. An example system may provide a style definition application or style preference gathering functionality to determine personal clothing style preferences of a user as it relates to clothing.
In an example, such a user interface may guide or prompt the user to select particular articles of clothing over other articles of clothing (see, for example,
Instead, or in addition, style preferences may be elucidated from the user's existing wardrobe or purchase history. In some examples, clothes shopping history information may be accessed and processed to identify preferences. Such shopping history information may be recognized within an application provided by a publication system such as eBay, Inc,® or may be imported into the eBay app if found within an external application.
In one embodiment, the user preference information, by way of style example data indicating user-selected clothing articles that embody style preferences, may be created by a shopper using an input mechanism (e.g., keyboard, camera, voice input) and then imported into the app. In one example, the user may provide photographic image data of images captured of existing wardrobe articles. The system may then process the image data to recognize particular articles of clothing and associated style attributes (e.g., color, color scheme, material, etc.) to identify style preferences. To assist in identification of the relevant category or type of clothing article of respective wardrobe articles, the user may be asked to identify the contents of each picture, for example by dragging and dropping wardrobe article photos into predefined categories (see again
The user interface to gather style preference information may also present the user with specific style preference questions or options, for example with option tick boxes to indicate particular brands, colors, formality level, etc, that are to the user's liking.
Information about particular physical measurements may be gathered via a corresponding user interface (see, e.g.,
The measurement information user interface may prompt the user to input vital statistics, e.g., via text boxes or the like, to indicate the gender, weight, length, shoe size, etc. of the user. Instead, or in addition, an interactive representation of a human figure may be provided, for example being gender-specific responsive to user indication of their gender (
By “interactive” it is meant that the figure dynamically changes in appearance in response to user provision of relevant physical measurements. Thus, for example, when the user indicates a particular shoulder width, the width of the figure may shrink or expand accordingly. A user that provides all of the relevant measurements prompted for by the figure's composition (in the example of
The interactive figure UI may use color-coded data fields to indicate on the figure itself which measurements are outstanding and which have been provided. Note that the physical measurements may comprise both clothes size information (e.g., dress size, pant size, etc.), as well as measurement of particular bodily dimensions.
In some embodiments, users may indicate whether or not recommended articles are to their liking, e.g., by one-click liking or disliking of recommended articles, which information may be fed back into the style preference information so that the recommendation engine is effectively self-learning.
The clothing profile thus composed may be used by a recommendation engine to investigate one or more inventory databases to identify and/or select one or more articles of clothing in an inventory of a merchant that accords with the user's clothing profile, e.g., by matching both her relevant physical measurements and her style preferences.
The particular clothes inventories that are queried may be dependent on the particular application of the system. In some embodiments, the recommendation may be limited to a particular store, online or otherwise, in which case only the inventory of that store is queried. In other embodiments, multiple stores may be considered, in which case multiple associated inventories will be queried.
The recommendation may further be limited in some examples to inventory that is currently in stock, while it may in other embodiments not be so limited. A shopper at a particular store may thus, for example, by execution of a mobile app that embodies these functionalities, request and receive a shopping recommendation for articles of clothing that are of a user-preferred style, that fits the shopper physically, and that is currently available for purchase at that store.
While the shopping recommendation may in some instances be open-ended in that any article of clothing is considered based on the clothing profile of the user, other embodiments may comprise providing a recommendation for a user-provided objective or limited by a user-applied filter. The user may thus specify or provide one or more objectives or filters that are to be applied to the recommendation in advance of providing the recommendation. Instead, or in addition, the user may refine a provided recommendation by applying selective objectives and/or filters to the recommendation, responsive to which the recommendation may be adapted dynamically.
Color filters, or color scheme filters, may also be applied, for example by user interaction with a color wheel user interface device. Such user input determines the particular colors or color schemes that are defined as an objective for the shopping recommendation.
The shopper may further specify the purpose or style of outfit that is desired for recommendation, for example either by selecting one or more verbal definitions of an objective style (see
Another example filter that may be applied to the recommendation query may include price controls, by which the user can specify a price range for individual articles of clothing and/or for an outfit or collection, with the shopping recommendation being dynamically adaptable to remain within the specified price range.
In some examples, the provision of shopping objectives may include the provision of a wardrobe article of the shopper's upon which an outfit or collection is to be based. The user-provided wardrobe article thus forms a seed clothing article upon which a matching or complementary outfit or collection is to be based, being automatically compiled by the recommendation engine based on the seed article, the personal style preferences, and the personal measurement information, with reference to the relevant merchant inventories.
In some embodiments, the method may include generating and displaying a quantified indication of correspondence between the user's preferences and the recommended article, between the recommended article and associated articles in a recommended outfit, or both. In
A further feature of providing the shopping recommendation may be that instead of merely displaying one recommended article for each type of article in a recommended outfit, multiple alternative options may be displayed for each article type. See again, for example,
The clothes shopping recommendation may in some examples include articles that are already in the shopper's wardrobe. Wardrobe information provided during preference gathering may thus be considered in forming, for example, a collection or outfit recommendation, so that a user's own wardrobe article may in some instances be presented as a member of, or an option for inclusion in, a recommended outfit. Referring again to
Note that the shopping recommendation may in some instances comprise the provision of multiple candidate options or suggestions, from which the shopper can interactively mix and match. In such case, the shopping recommendation does not comprise delivery of a firm, set number of recommended articles, but instead serves substantially to reduce the number of different articles from which choices can be made, creating a pool of potential clothing choices that may justify further consideration.
Information gathered in composing the user profile may be used at a merchant establishment or other on-site location where the user is physically present as a shopper to assist the shopper in finding articles of interest in-store.
For example, the system may identify the in-store location of articles of clothing that would fit the shopper, based on the associated measurement information, and may provide direction assistance to the shopper to find those articles of clothing.
Provision of the direction assistance may be implemented through a mobile electronic device, such as a mobile phone with processing capacity, e.g., by displaying location indicators on a screen of the mobile device in association with identified or recommended articles. In some embodiments, such location indicators (e.g., a highlight box, colored screen area, dropped pin, or other on-screen indicator) may be provided as a substantially real-time overlay on streaming video captured by a camera of the device. An augmented reality view may thus be displayed on the device, through which the shopper can scan merchandised displayed at the store to find articles of clothing that are sized to fit the shopper.
In some embodiments, such on-site locator functionalities may be provided only to find clothes items that are of suitable size for the shopper, while the shopper may selectively apply filters to limit the clothing articles that are indicated during location finding. A user may, for example, identify a particular brand and article type, say Levi 527 boot-cut jeans, and may then pass the mobile phone's camera over an area where these articles are displayed in-store. The system may then automatically identify those items of interest (e.g., those 527 Levis) that fit the shopper's size.
In such embodiments, the method may comprise generating an information carrier on which the shopper's measurement information is stored, and reading the information carrier on-site at the store, to facilitate the performance of size-matching queries through an inventory of the merchant establishment in question. One example of such an information carrier is a visual code, e.g., a barcode or a Quick Response (QR) code, that may be printed by the shopper at her home computer system and taken by the shopper to the store. At the store, the barcode or QR code may be read by a store computer system to identify matching articles in its inventory.
In other embodiments, on-site location functionality may be combined with provision of automated clothes recommendation, so that a user may, e.g., enter one or more shopping objectives via the mobile phone (e.g., by means of an interface such as that shown in
Finding the location of respective articles of clothing in order to provide user direction thereto may be accomplished by one or more of a variety of methods. In one example, each article of clothing may be provided with a unique visual identifier or tag, with the direction system being configured to process image data captured by the user's mobile phone in order to recognize respective visual tags. Such visual tags may comprise visual codes, e.g., barcodes or QR codes, or may be visual tags that are typically larger in size and are sized and dimensioned for optical recognition at greater distances than is the case with, e.g., barcodes.
Instead, or in addition, each article of clothing may be provided with a wireless tag, such as an RFID tag, which may be read by a receiver in the user's phone, and/or by a store-wide reader system. In the former instance, the user's phone may read and recognize the respective signals. In the latter instance, map information may be managed by, e.g., a store computer system to map the locations of respective articles of information to a store layout. Such map information may be accessed on the user's phone, e.g., by wireless communication with the store computer system.
The system may comprise tracking the mobile phone's location in the merchant establishment, in order to establish the user's position relative to articles of interest. The tracking system may comprise a global positioning system (GPS), and/or may comprise an indoor positioning system (IPS) that operates through wireless, infrared, or sonic location finding signals.
As mentioned earlier, user measurement information may be entered by specifying specific body dimensions and/or by providing clothing sizes. However, clothes sizes, e.g., dress numbers or classifications of the size of clothes as Large, Medium, etc. (see, e.g.,
The method may thus include matching inventory data to user profile information based on true size information of the clothes in the inventory in preference to matching inventory data to user profile information based on brand/merchant provided size classifications. Such true size information for clothes inventory data may be obtained from the respective merchants/brands, and may be incorporated in the inventory data for recommendation purposes.
In one embodiment, the method may include implementing a size provision system in which clothes suppliers provide true size information about respective articles of clothing by generating respective information carriers on which the true size information is stored. For example, the method may include the attachment of a visual code (e.g., bar code or QR code) to respective articles of clothing. Such code may then be scanned by the merchants, to read the true size information for incorporation thereof in the relevant inventory database.
Relationships between respective brand/merchant sizing systems may be identified and applied in refining user measurement information, for example by receiving information about size classifications for articles of clothing of desired size from particular suppliers. For example, the user may specify not merely that his shirt size is Large, but that his shirt size for a particular brand name is Large. Such information may then be adjusted before incorporation in the user's clothing profile to account for a typical sizing classification for the particular brand name.
In some embodiments the user profile and shopping objective filters may be created by the shopper at home, or at some other location, using a computer. In some embodiments, the shopping recommendation is created on, or imported to, the mobile device, either manually by the shopper or automatically, and returned to the mobile device in optimized format. Alternatively, the shopping recommendation may be returned to both the home computer and the mobile device for use as the shopper desires.
One benefit of the described example systems and methods are that it allows for more satisfying on-line shopping. Provision of user measurement information, which may be combined with gathering and collating supplier clothes measurements in increased detail, increases the ability to perform on-line shopping that accounts for the user's particular body profile. Finding items in a store that fit can further be facilitated by on-site direction assistance, which may avoid invariably frustrating and sometimes fruitless in-store searches for articles of clothing that fit.
Provision of recommended alternatives further narrows down the number of options to be considered by the user, based on her own preferences, making on-line shopping a more manageable proposition compared to conventional searches in which the available options returned by a search can be overwhelming.
In an example, the shopper 110 can connect to the network-based publication system 120 via a client device 115 (e.g., desktop, laptop, smart phone, PDA, or similar electronic device capable of some form of data connectivity). The network-based publication system 120 will receive and process a query from the shopper's 110 client device 115. In examples where direction assistance is to be provided, location information specifying the physical or geographical location of the shopper 110 may be received with the query. For example, if the client device 115 is a mobile device, a GPS unit may inform the client device 115 of its location, such that the location information of the device can be shared with the network-based publication system 120. Other known techniques for deriving location information may be used with both mobile and non-mobile client computing devices, for example, such as desktop computers, etc. For instance, with some embodiments, the location information indicating the location of the shopper 110 may be explicitly specified by the shopper 110, for example, by the shopper 110 interacting with a map.
In an example, the merchant 130 can operate computer systems, such as an inventory system 132 and/or an in-store positioning system 190. The network-based publication system 120 can interact with any of the systems used by merchant 130 for operation of the merchant's 130 retail or service business. In an example, the network-based publication system 120 can work with the inventory system 132 (as well as, in some instances, a point of sale system) to obtain access to inventory available at individual retail locations run by the merchant 130. This inventory information can be used in both generating shopping recommendations, item listings, and selecting and ordering search results served by the network-based publication system 120.
Those merchants 130 that have brick-and-mortar outlets may further include code readers 180 to read personal profile codes 170 that may be carried by the shopper 110 to the merchant 130, the personal profile code containing personal measurement information encoded in, for example, a QR code. In some embodiments, the personal profile code 170 may additionally carry personal style information for automatic consideration by the merchant computer system and/or network-based publication system 120 in formulating a shopping recommendation or suggestion mix.
The database 290 can optionally include merchant databases 292, user profile database 294, and/or inventory data 273. The user profile database 294 may comprise, for each registered user, a clothing profile 272 that includes personal measurement information 278 and personal clothing preference information 275.
The mobile device 115 represents one example device that can be utilized by a shopper to provide input and/or instructions to the clothing profiler 263 and/or the recommendation engine 260, and to receive shopping recommendation information and/or direction assistance from the system 200. The mobile device 115 may be any of a variety of types of devices (for example, a cellular telephone, a PDA, a Personal Navigation Device (PND), a handheld computer, a tablet computer, a notebook computer, or other type of movable device). The mobile device 115 may interface via a connection 210 with a communication network 220. Depending on the form of the mobile device 115, any of a variety of types of connections 210 and communication networks 220 may be used.
For example, the connection 210 may be Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other type of cellular connection. Such connection 210 may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, or other data transfer technology (e.g., fourth generation wireless, 4G networks). When such technology is employed, the communication network 220 may include a cellular network that has a plurality of cell sites of overlapping geographic coverage, interconnected by cellular telephone exchanges. These cellular telephone exchanges may be coupled to a network backbone (for example, the public switched telephone networks (PSTN), a packet-switched data network, or other types of networks).
In another example, the connection 210 may be Wireless Fidelity (Wi-Fi, IEEE 802.11x type) connection, a Worldwide Interoperability for Microwave Access (WiMAX) connection, or another type of wireless data connection. In such an embodiment, the communication network 220 may include one or more wireless access points coupled to a local area network (LAN), a wide area network (WAN), the Internet, or other packet-switched data network.
In yet another example, the connection 210 may be a wired connection, for example an Ethernet link, and the communication network may be a LAN, a WAN, the Internet, or other packet-switched data network. Accordingly, a variety of different configurations are expressly contemplated.
A plurality of servers 230 may be coupled via interfaces to the communication network 220, for example, via wired or wireless interfaces. These servers 230 may be configured to provide various types of services to the mobile device 115. For example, one or more servers 230 may execute location based service (LBS) applications 240, which interoperate with software executing on the mobile device 115, to provide LBSs to a shopper. LBSs can use knowledge of the device's location, and/or the location of other devices and/or retail stores, etc., to provide location-specific information, recommendations, notifications, interactive capabilities, and/or other functionality to a shopper. With some embodiments, the LBS operates in conjunction with the publication application 255 and search engine 261, in particular, to provide direction assistance to the shopper via the mobile device 115, for example to direct the shopper to items of interest in her vicinity via augmented reality-type display. Also, an LBS application 240 can provide location data to a network-based publication system 120, which can then be used to arrange a set of recommendation articles, based on distance and/or travel time between two locations. Knowledge of the mobile device's location, and/or the location of clothing articles of interest, may be obtained through interoperation of the mobile device 115 with a location determination application 250 executing on one or more of the servers 230. Location information may also be provided by the mobile device 115, without use of a location determination application, such as application 250. In certain examples, the mobile device 115 may have some limited location determination capabilities that are augmented by the location determination application 250.
Additional detail regarding providing and receiving location-based services can be found in U.S. Pat. No. 7,848,765, titled “Location-Based Services,” granted to Phillips et al. and assigned to Where, Inc. of Boston, Mass., which is hereby incorporated by reference.
An example geo-location concept discussed within U.S. Pat. No. 7,848,765 is a geofence. A geofence can be defined as a perimeter or boundary around a physical location or mobile object (e.g., a shopper). A geofence can be as simple as a radius around a physical location defining a circular region around the location. However, a geofence can be any geometric shape or an arbitrary boundary drawn on a map. A geofence can be used to determine a geographical area of interest for the calculation of demographics, advertising, presenting search results, or similar purposes. Geofences can be used in conjunction with identifying and presenting search results, as described herein. For example, a geofence can be used to assist in determining whether a shopper (or mobile device associated with the shopper) is within a geographic area of a particular merchant. If the shopper is within a geofence established by the merchant or the publication system, the systems discussed herein can use that information to identify and present recommendation results (e.g., via a mobile device associated with the shopper).
An Application Programming Interface (API) server 414 and a web server 416 are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers 418. The application servers 418 host one or more publication modules 420 (in certain examples, these can also include search engine modules, commerce modules, advertising modules, and marketplace modules, to name a few); payment modules 422; clothing information gathering modules 470 to gather user measurement information and personal style preferences, e.g. via UIs such as that shown in
The publication modules 420 may provide a number of publication and search functions and services to shoppers that access the networked system 402. The payment modules 422 may likewise provide a number of payment services and functions to shoppers. The payment modules 422 may allow shoppers to accumulate value (e.g., in a commercial currency, such as the U.S. dollar, or a proprietary currency, such as “points”) in accounts, and then later to redeem the accumulated value for products (e.g., goods or services) that are advertised or made available via the various publication modules 420, within retail locations, or within external online retail venues.
While the various modules described above are shown in
Further, while the system 400 shown in
The web client 406 accesses the various modules on application server 418 via the web interface supported by the web server 416. Similarly, the programmatic client 408 accesses the various services and functions provided by the modules via the programmatic interface provided by the API server 414. The programmatic client 408 may, for example, be a smartphone application.
A measurement UI may then be presented, at operation 506, an example of which is shown in
In some embodiments, the method 500 may thereafter comprise producing a visual code that carries the user measurement information, e.g., by printing a QR code or the like, at operation 560. When the user thereafter visits a merchant establishment or store to shop for clothes, the printed code may be read at the store, at operation 563, enabling the merchant computer system or other applicable processor to query an inventory of the merchant in question, at operation 569, to identify in-stock articles that fit the user (based on the scanned measurement information) at operation 575. The user may, instead, provide search parameters that limit the type of articles returned as identified in-stock articles, at operation 572, so that in-store direction assistance and item location is based not only on user measurements, but also on user style preferences.
Thereafter, the user may be provided direction assistance, at operation 578, e.g., via merchant system communication with her mobile device 115, to the identified, suitably sized, in-stock clothing articles. As described previously at greater length, such direction assistance may include the display of on-screen location indicators in an augmented reality-type display.
Returning now to the login operation 503, the method 500 may in other embodiments include gathering personal style information, at operation 515, instead of or in addition to gathering the measurement information. This may include presenting an on-screen preference gathering UI (see e.g.,
Gathering style preference information may further comprise conducting a guided online style question and answer session, at operation 535, and may also comprise automated auditing or investigation of the user's shopping history from available sources.
At operation 534, the style example data is processed to identify or distill the user's particular personal clothing style preferences. This information is combined with the corresponding measurement information to create a clothing profile for the user, at operation 510.
When user input is received that initiates a recommendation query, at operation 542, e.g., by providing one or more recommendation objectives (e.g., pricing, formality level, brand names, clothing purpose, activity for which an outfit is desired, a seed wardrobe article for which matching articles are to be found, color wheel input, etc.) relevant inventory data from multiple online merchants (or from one or more query-limited merchants) is read, at operation 539, and is filtered, at operation 545, by the recommendation engine based on both the personal style information and the measurement information, as well as on the query parameters, to select, at operation 548, and display to the user one or more articles of clothing that accord with the style, measurements, and query parameters of the user.
The presentation of these selections may comprise provision of an online shopping recommendation, at operation 551, which may be iterative and interactive responsive to user provision of additional criteria or rejection and/or acceptance of one or more recommended clothing articles. As shown in example the recommendation of
The method 500 may include providing direction assistance to the user, at operation 578, should the shopper decide to visit a physical merchant establishment.
In other embodiments, the visual code (e.g., QR code/barcode) produced at operation 560 may include both measurement info and style preferences, in which case the method 500 may include identifying in-stock articles, at operation 572, that not only fit the user physically, but that correspondence to the user's style preferences.
Height information and weight information can be entered via text box graphical user interface (GUI) elements comprising a height input element 614 and a weight input element 621 respectively.
Clothing size classifications can be entered by selecting one of a number of selection boxes for each clothing article type. Thus, the user can, in area 628 of the interface 600, indicate her shirt size, cup size, and pant size.
More specific dimensional measurements can be inputted via a dynamically responsive doll or human
Each of these measurements may be indicated by a colored, high-contrast line on the figure, the line placement corresponding to the respective measurement dimension. Responsive to input of any measurement via the
In this example, a measurement status may be indicated by color-coding of the measurement lines 642 on the
Photos (e.g., user-provided pictures or stock photos) of existing wardrobe articles may be displayed in top row 732. The user may assist classification of the subject garment category by dragging and dropping the photos of respective wardrobe articles in respective category boxes 716. The category boxes 716 are interactive to allow the user to cycle through all available categories. A particular photo may be categorized by the user in more than one predefined category.
Additionally, style preference choices are provided for selection by associated check boxes in areas 740 and 750, respectively. In this example, choice area 740 provides various brand names for selection, and area 750 for selection of preferred fabrics/materials. Note that these are, of course, only example subject areas that may be polled by providing predefined or randomized options, and that interface 700 may be configured to cover, over time or use, a substantially greater number of style topics.
A shopping recommendation in this example comprises multiple alternative suggestions or recommended alternatives to each of a necklace 805, a dress 810, and a pair of shoes 815. The recommendation in this query was thus for an outfit comprising these three types of clothing article, with a particular necklace 822 from the user's wardrobe having been used as a seed article.
The user may cycle through the respective recommended alternatives to obtain a preview 825 in which the currently selected three alternatives are highlighted in combination and given visual predominance on the interface 800. A match score 828 may be provided for each currently selected article, in this example being represented by a certain number of dots. Responsive to cursor movement over a particular article, an information bubble 820 may fly out, to display information about the article.
The recommendation may be dynamically responsive to a number of query tools, examples of which are shown on the left of the example interface 800.
Thus, a garment selection tool 830 allows quick switching between garment types, while recommendations get updated dynamically. The garment selection tool 830 comprises a human figure (which may be based on the measurement information entered via interface 700) that is conceptually segmented into zones, in order to change garment types for inclusion in the recommended combination for the desired outfit.
A color selector 835 in the example form of a color wheel allows narrowing recommendation to a particular range of colors, or, in some embodiments to particular color schemes.
UI zones 840 and 845 allow broadly defined categories of clothing article type that may be selected to allow the user to tailor the recommendations towards one or other end of a casual-formal sliding scale, and/or a particular purpose for the recommendation query.
One-click swapping of recommended articles with a wardrobe article may be possible, while quick rating of the recommended articles may gather user-feedback for refinement of the user's clothing profile for future recommendations.
The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules or objects that operate to perform one or more operations or functions. The modules and objects referred to herein may, in some example embodiments, comprise processor-implemented modules and/or objects.
Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. The performance of certain operations may be distributed among the one or more processors, not only residing within a single machine or computer, but deployed across a number of machines or computers. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or at a server farm), while in other embodiments the processors may be distributed across a number of locations.
The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or within the context of “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs)).
The example computer system 900 includes a processor 902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 904 and a static memory 906, which communicate with each other via a bus 908. The computer system 900 may further include a display unit 910, an alphanumeric input device 912 (e.g., a keyboard), and a shopper interface (UI) navigation (or cursor control) device 914 (e.g., a mouse). In one embodiment, the display 910, input device 912 and cursor control device 914 are a touch screen display. The computer system 900 may additionally include a machine-readable storage device (e.g., drive unit) 916, a signal generation device 918 (e.g., a speaker), a network interface device 920, and one or more sensors, such as a global positioning system sensor, compass, accelerometer, or other sensor.
The drive unit 916 includes a machine-readable medium 922 on which is stored one or more sets of instructions 924 and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 924 may also reside, completely or at least partially, within the main memory 904 and/or within the processor 902 during execution thereof by the computer system 900, the main memory 904 and the processor 902 also constituting machine-readable media.
While the machine-readable medium 922 is illustrated in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions 924. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present embodiment, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
The instructions 924 may further be transmitted or received over a communications network 926 using a transmission medium via the network interface device 920 utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Wi-Fi® and WiMax® networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
Although specific example embodiments have been described herein, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the embodiments of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.
The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b), requiring an abstract that will allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, the disclosed subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.
