DESC:BACKGROUND
Technical Field
[0001] The embodiments herein generally relate to a style suggestion system, and more particularly, to a system and method of analyzing human body, providing fashion styling suggestions and previewing.

Description of the Related Art
[0002] Generally, people buy apparel and fashion accessories based on their mental intuition. The people who are buying the apparel and the fashion accessories might not be sure how the apparel and the fashion accessories suitable to them and how stylish they are. Hence buying apparel and the fashion accessories based on their mental intuition provides complexity and consumes more time. This creates a lot of discomforts to both the apparel stores and the customers. The apparels and the fashion accessories that are bought on their mental intuition may not satisfy them after physically trying. Further it might increase the return rates of the apparel and the fashion accessories in the ecommerce and digital shopping.
[0003] Considerable efforts have been made in recent years to enhance ecommerce and digital shopping by virtually trying and previewing apparels and the fashion accessories. The existing systems include lot of human intervention like selecting apparels and the fashion accessories and entering body measurements manually. The existing systems also do not support obtaining user attributes automatically which in turn might include errors in entering body measurements. Hence each time the body measurements need to be verified and entered. The existing systems further do not to recommend or provide style suggestions including hair styles and fashion accessories to the user.
[0004] Accordingly, there remains a need for a system and method for analyzing human body, providing fashion styling suggestions and previewing the fashion styling suggestions.
SUMMARY
[0005] In view of the foregoing, an embodiment herein provides a system for enhancing digital shopping and ecommerce experience of a user. The system includes a style suggestion server that is communicated to a computing device. The style suggestion server includes a memory that stores a set of instructions and a processor that executes the set of instructions and is configured to (i) process a 3D user model and user attributes, (ii) retrieve filtered apparels and fashion accessories from an apparel and fashion accessories inventory in a database of the computing device, (iii) generate scores for each of the filtered apparels and the fashion accessories based on the user attributes, user preferences and other environmental factors using a machine learning algorithm, (iv) sort the filtered apparels and the fashion accessories from high scores to low scores and (v) generate one or more draped 3D user models by draping each of the filtered apparel and the fashion accessories on the 3D user model. The 3D user model and the user attributes are obtained from the computing device. The filtered apparels and fashion accessories are stored as the 3D apparel models and the 3D fashion accessories models respectively on a style suggestion database. The machine learning algorithm assigns the high scores to the filtered apparels and the fashion accessories when corresponding 3D apparel model and 3D fashion accessories model of the filtered apparels and the fashion accessories are stylish and suitable on the 3D user model. The machine learning algorithm assigns the low scores to the filtered apparels and the fashion accessories when the corresponding 3D apparel model and the 3D fashion accessories models of the filtered apparels and the fashion accessories are not stylish and not suitable on the 3D user model. The draped 3D user models virtually depict the user wearing each of the filtered apparels and fashion accessories.
[0006] In one embodiment, the 3D user model and the user attributes are obtained in the computing device by (i) scanning the user to obtain the user attributes using one or more depth camera sensors and (ii) analyzing the user attributes to generate the 3D user model. The user attributes include height measurement, shoulder width measurement, chest measurement, waist measurement, hip measurement, upper body height, neck length, skin tone, hair color, or hair style of the user.
[0007] In another embodiment, the user attributes are obtained by scanning and analyzing shape of the user using a user shape analysis technique.
[0008] In yet another embodiment, the filtered apparels and fashion accessories are stored in the database using the computing device. The computing device obtains the apparel attributes and the fashion accessories attributes from the apparel and fashion accessories inventory of the database and filters and selects the apparel and fashion accessories based on the user attributes and the user preferences using apparel filters and accessories filters.
[0009] In yet another embodiment, the apparel attributes include an apparel type, an apparel material, apparel cut specification, an apparel pattern, an apparel neckline, apparel color, apparel fashion level or apparel measurements. The fashion accessories attributes include fashion accessories size, fashion accessories color, fashion accessories type or fashion accessories material.
[0010] In yet another embodiment, the computing device provides one or more predefined queries to the user to determine fashion sensibility level of the user based on the user’s answers to the one or more predefined queries.
[0011] In yet another embodiment, the computing device receives the one or more draped 3D user models from the style suggestion server; and displays each of the draped 3D user models to the user in the order of the high scores to the low scores.
[0012] In one aspect, a method for enhancing digital shopping and ecommerce experience of a user is provided. The method includes (i) processing a 3D user model and user attributes, (ii) retrieving filtered apparels and fashion accessories from an apparel and fashion accessories inventory in a database of the computing device, (iii) generating scores for each of the filtered apparels and the fashion accessories based on the user attributes, user preferences and other environmental factors using a machine learning algorithm, (iv) sorting the filtered apparels and the fashion accessories from high scores to low scores and (v) generating one or more draped 3D user models by draping each of the filtered apparel and the fashion accessories on the 3D user model. The 3D user model and the user attributes are obtained from the computing device. The filtered apparels and fashion accessories are stored as the 3D apparel models and the 3D fashion accessories models respectively on a style suggestion database. The machine learning algorithm assigns the high scores to the filtered apparels and the fashion accessories when corresponding 3D apparel model and 3D fashion accessories model of the filtered apparels and the fashion accessories are stylish and suitable on the 3D user model. The machine learning algorithm assigns the low scores to the filtered apparels and the fashion accessories when the corresponding 3D apparel model and the 3D fashion accessories models of the filtered apparels and the fashion accessories are not stylish and not suitable on the 3D user model. The draped 3D user models virtually depict the user wearing each of the filtered apparels and fashion accessories.
[0013] In one embodiment, the 3D user model and the user attributes are obtained in the computing device by scanning the user to obtain the user attributes using one or more depth camera sensors and analyzing the user attributes to generate the 3D user model. The user attributes include height measurement, shoulder width measurement, chest measurement, waist measurement, hip measurement, upper body height, neck length, skin tone, hair color, or hair style of the user.
[0014] In another embodiment, the user attributes are obtained by scanning and analyzing shape of the user using a user shape analysis technique.
[0015] The style suggestion system eases to filter the apparel and the fashion accessories that exactly suitable to the user. The style suggestion system further enables the user to virtually try and preview style suggestions. The style suggestion system enhances the ecommerce and digital shopping experience of the user by analyzing the user behavior and rendering the style suggestions in the desired order.
[0016] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0018] FIG. 1 illustrates a system view of a user interacting with a style suggestion system to obtain style suggestions according to an embodiment herein;
[0019] FIG. 2 illustrates an exploded view of the style suggestion system of FIG. 1 according to an embodiment herein;
[0020] FIG. 3 illustrates an exploded view of a style suggestion server of FIG. 1 according to an embodiment herein;
[0021] FIG. 4 depicts an exemplary view of one or more predefined fashion sensibility levels according to an embodiment herein;
[0022] FIG. 5 depicts an exemplary view of one or more draped 3D user models depicting style suggestions according to an embodiment herein;
[0023] FIG. 6 depicts a predefined pose provided by the user for obtaining user attributes and generating 3D user model according to an embodiment herein;
[0024] FIG. 7 is a flow diagram that illustrates a method of obtaining the user attributes and generating the 3D user model using the style suggestion system according to an embodiment herein;
[0025] FIG. 8 is a flow diagram that illustrates a method of enhancing digital shopping and ecommerce experience of a useraccording to an embodiment herein;
[0026] FIG. 9 illustrates an exploded view of a computing device according to the embodiments herein; and
[0027] FIG. 10 is a schematic diagram of computer architecture used in accordance with the embodiment herein.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0028] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0029] As mentioned, there remains a need for a system and method for analyzing human body, providing fashion styling suggestions and previewing the fashion styling suggestions. The embodiments herein achieve this by providing a style suggestion system and a style suggestion server for providing style suggestions and previewing. Referring now to the drawings, and more particularly to FIGS. 1 through 10, where similar reference characters denote corresponding features consistently throughout the figures, preferred embodiments are shown.
[0030] FIG. 1 illustrates a system view of a user 102 interacting with a style suggestion system 106 to obtain style suggestions according to an embodiment herein. The system includes a computing device 104, the style suggestion system 106 and a style suggestion server 110. The user 102 interacts with the style suggestion system 106 to obtain the style suggestions. The style suggestion system 106 questions or provides one or more predefined queries to the user 102 to determine fashion sensibility level of the user 102. The style suggestion system 106 determines the fashion sensibility level of the user 102 based on the user’s 102 answers to the one or more predefined queries. The fashion sensibility level of the user 102 may be one among one or more predefined fashion sensibility level levels. In an embodiment, the style suggestion system 106 allows the user 102 to directly preview and select the desired predefined fashion sensibility level. In one embodiment, the user 102 may provide pose like a predefined pose. In one embodiment, the predefined pose eases to obtain the user attributes from the user 102. The style suggestion system 106 scans the user 102 and obtains the user attributes using one or more depth camera sensors. The user attributes are obtained from the user 102 initially only once using a user attributes obtaining module. The user attributes are stored for future references in a user profile on a style suggestion database. The user 102 may initially sign up the style suggestion system 106 using a user id and an authentication password to create the user profile.
[0031] The user 102 may be any age, any height, any width, any color, any fashion sensibility level and any gender. The user attributes include but it is not limited to height measurement, shoulder width measurement, chest measurement, waist measurement, hip measurement, upper body height, neck length, skin tone, hair color, hair style etc. The style suggestion system 106 analyzes the user attributes and generates 3D user model. The style suggestion system 106 obtains apparel attributes and fashion accessories attributes from the apparel and fashion accessories inventory of a client database. The apparel attributes includes but it is not limited to an apparel type, an apparel material, apparel cut specification, an apparel pattern, an apparel neckline, apparel color, apparel fashion level, apparel measurements, etc. The fashion accessories attributes include fashion accessories size, fashion accessories color, fashion accessories type, fashion accessories material etc.
[0032] The style suggestion system 106 processes an apparel and fashion accessories filtering module to filter and select the apparel and the fashion accessories based on user attributes (including user measurements) and user preferences using apparel filters and accessories filters. The filtering of apparel and the fashion accessories is performed by using the apparel filters and the accessories filters through the style suggestion system 106 associated with the computing device 104. The computing device 104, without limitation, may be selected from a mobile phone, a Personal Digital Assistant, a tablet, a desktop computer, a laptop. The apparel filters include but not limited to a size filter, a type filter, a body shape filter, a color filter, a seasonal filter, a fashion level filter and a geographical filter etc. The fashion level filter is used to filter and select at least one of but not limited to party apparel, formal apparel, a traditional apparel etc. The size filter is used to select apparel based on size. The type filter is used to filter and select at least one of but not limited to cotton, nylon, polyester, etc. The body shape filter is used to filter and select apparel based on body shapes (for example slim fit, regular fit, etc.) of the user 102. The color filter is used to filter and select apparel based on desired color of the user 102. The seasonal filter is used to filter and select apparel based on seasonal requirements (for example cotton is preferable in summer). The geographical filter is used to filter and select apparel based on geographical requirements (for example cotton is preferable in South India). The apparel may be at least one of but not limited to (i) pants, (ii) shirts, (iii) T-shirts, (iv) chudidhars, (v) skirts, (vi) sarees, (vii) kurtas etc. The fashion accessories include but not limited to watches, shoes, jewels, belts, wallets, slippers, etc. The fashion accessories filters include but not limited to a size filter, a type filter, a body shape filter, a color filter, a seasonal filter, a fashion level filter and a geographical filter etc. The fashion accessories filters are similar to apparel filter used to filter and select desired fashion accessories based on requirements of the user 102.
[0033] The style suggestion server 110 processes the 3D user model and the user attributes that are obtained from the computing device 104. The style suggestion server 110 retrieves the filtered apparel and fashion accessories from the client database and stores as 3D apparel models and the 3D fashion accessories models respectively on the style suggestion database. The style suggestion system 106 communicates the user attributes and the 3D user model to the style suggestion server 110 through a network 108. In one embodiment, the network 108 is a wired network. In one embodiment, the network 108 is a wireless network. The wireless network may be at least one of but not limited to (a) a WIFI network, (b) a Bluetooth network, (c) a ZIGBEE network, etc.
[0034] The style suggestion server 110 generates scores for each of the filtered apparels and fashion accessories based on the user attributes, user preferences and other environmental factors using a machine learning algorithm. The machine learning algorithm assigns high scores to the filtered apparels and the fashion accessories when corresponding 3D apparel model and 3D fashion accessories model of the filtered apparels and the fashion accessories are stylish and suitable on the 3D user model. The machine learning algorithm assigns low scores to the filtered apparels and the fashion accessories when the corresponding 3D apparel model and the 3D fashion accessories models of the filtered apparels and the fashion accessories are not stylish and not suitable on the 3D user model. The style suggestion server 110 sorts the filtered apparel and fashion accessories from the high scores to the low scores. The style suggestion server 110 generates one or more draped 3D user models by draping each of the filtered apparel and the fashion accessories on the 3D user model. The one or more draped 3D user models virtually depict the user 102 wearing each of the filtered apparels and fashion accessories. The style suggestion server 110 communicates the one or more draped 3D user models to the style suggestion system 106. The style suggestion system 106 further displays each of the draped 3D user models to the user 102 in the order of the high scores to the low scores. In an embodiment, the user 102 can preview each of the draped 3D user models in his /her desired order. In another embodiment, the style suggestion system 106 learns user behavior of previewing the one or more draped 3D user models and reorders the one or more draped 3D user models in subsequent iterations.
[0035] FIG. 2 illustrates an exploded view of the style suggestion system 106 of FIG. 1 according to an embodiment herein. The style suggestion system 106 includes a database 202, a fashion sensibility interviewing module 204, a user attributes obtaining module 206, an apparel and fashion accessories attributes obtaining module 208, an apparel and fashion accessories filtering module 210, 3D user model and attributes communication module 212, a draped 3D user model receiving module 214 and a draped 3D user models preview module 216. The user attributes obtaining module 206 scans and analyzes the shape of the user 102 using the user shape analysis technique and obtains the user attributes. The user attributes obtaining module 204 further generates the 3D user model using the user attributes.
[0036] The apparel and fashion accessories attributes obtaining module 208 obtains the apparels attributes and the fashion accessories attributes for each of the apparels and fashion accessories from the apparels and fashion accessories inventory stored in the client database. The apparel attributes and the fashion accessories attributes are obtained using the apparels attributes obtaining technique and the fashion accessories attributes obtaining technique respectively. The apparel and fashion accessories filtering module 210 filters and selects the apparels and the fashion accessories from the apparel and fashion accessories inventory based on the user attributes (including user measurements) and the user preferences (i.e. through filters) through the apparel filters and the fashion accessories filters. The 3D user models and attributes communication module 212 communicates the 3D user model and the user attributes to the style suggestion server 110 through the network 108. The draped 3D user models receiving module 214 receives the one or more draped 3D user models from the style suggestion server 110. The draped 3D user models preview module 216 displays the one or more draped 3D user models in the order of high scores to the low scores onto the display of the computing device 104. The draped 3D models preview module 216 enables the user 102 to preview the one or more draped 3D user models (i.e. style suggestions) in his/her desired order. The draped 3D model preview module 216 further enables the user 102 to preview the one or more draped 3D user models with any combination of the filtered apparels and fashion accessories. The database 302 stores the user profile, the user attributes and the 3D user model.
[0037] FIG. 3 illustrates an exploded view of the style suggestion server 110 of FIG. 1 according to an embodiment herein. The style suggestion server 110 includes a style suggestion database 302, 3D user model and attributes receiving module 304, a filtered apparel and fashion accessories retrieving module 306, a scores generation module 308, a filtered apparel and fashion accessories sorting module 310, a draped 3D user models generation module 312 and a draped 3D user models communication module 314. The 3D user model and attributes receiving module 304 receives the 3D user model and the user attributes from the style suggestion system 106 through the network 108. The filtered apparel and fashion accessories retrieving module 306 retrieves the filtered apparels and the fashion accessories based on the filtering performed by the user 102 from the apparel and fashion accessories inventory in the client database. The scores generation module 308 generates scores for each of the filtered apparels and the fashion accessories based on the user attributes, user preferences and other environmental factors (includes seasonal and geographical factors) using the machine learning algorithm. The machine learning algorithm assigns the high scores to the filtered apparels and the fashion accessories when corresponding 3D apparel model and 3D fashion accessories model of the filtered apparels and the fashion accessories are stylish and suitable on the 3D user model. The machine learning algorithm assigns the low scores to the filtered apparels and the fashion accessories when the corresponding 3D apparel model and the 3D fashion accessories models of the filtered apparels and the fashion accessories are not stylish and not suitable on the 3D user model. The filtered apparels and the fashion accessories sorting module 312 sorts the filtered apparels and the fashion accessories on the order of the high scores to the low scores. The filtered apparels and the fashion accessories sorting module 312 further sorts the filtered apparels and the fashion accessories in another order based on user behavior of previewing the one or more draped 3D user models by the user 102. The draped 3D user models generation module 306 generates the one or more draped 3D user models by draping each of the filtered 3D apparel models and the 3D fashion accessories models on the 3D user model. In an embodiment, the one or more draped 3D user models depicts the user 102 wearing each of the 3D apparel models and the 3D fashion accessories models. The draped 3D user models communication module 314 communicates the one or more draped 3D user models to the style suggestion system 106.
[0038] FIG. 4 depicts an exemplary view of one or more predefined fashion sensibility levels according to an embodiment herein. The exemplary view depicts the one or more predefined fashion sensibility levels for the user 102 to preview and select the desired predefined fashion sensibility levels based on his/her mental intuition.
[0039] FIG. 5 depicts an exemplary view of the one or more draped 3D user models depicting the one or more style suggestions according to an embodiment herein. The exemplary view depicts the one or more draped 3D user models (depicting the one or more style suggestions) wearing each of the filtered apparels and the fashion accessories (including hair style) from the apparels and fashion accessories inventory. The one or more draped 3D user models depict how the filtered apparel and the fashion accessories stylish and suitable on the 3D user model.
[0040] FIG. 6 depicts the predefined pose provided by the user 102 for obtaining user attributes and generating 3D user model according to an embodiment herein. The user 102 poses like the predefined pose for obtaining the user attributes and the 3D user model using the style suggestion system 106. The predefined pose eases to obtain the user attributes and to generate the 3D user model. In an embodiment, the predefined pose can be varied.
[0041] FIG. 7 is a flow diagram that illustrates a method of obtaining user attributes and apparel attributes using the style suggestion system 106 of FIG. 1 according to an embodiment herein. At step 702, the one or more predefined queries are provided to the user 102 to determine the fashion sensibility level of the user 102. At step 704, the user 102 is scanned and the user attributes are obtained to generate the 3D user model. At step 706, the apparel attributes and the fashion accessories attributes are obtained for each of the apparel and the fashion accessories from the apparel and fashion accessories inventory. At step 708, the apparel and the fashion accessories are filtered and selected based on the user attributes, user preferences and environmental factors. At step 710, the 3D user model and the user attributes are communicated to the style suggestion server 110. At step 712, the one or more draped 3D user models are received from the style suggestion server 110. At step 714, the one or more draped 3D user models draping each of the filtered apparels and the fashion accessories (i.e. depicting the style suggestions) are displayed onto the display of the computing device 104. In an embodiment, the user 102 can preview the one or more draped 3D user models in the order of high scores to low scores. In another embodiment, the user 102 can preview the one or more draped 3D user models in the desired order based on his/her mental intuition.
[0042] FIG. 8 is a flow diagram that illustrates a method of enhancing digital shopping and ecommerce experience of a user according to an embodiment herein. At step 802, the 3D user model and the user attributes are processed. At step 804, the apparels and the fashion accessories are retrieved from the apparels and the fashion accessories from the apparel and fashion accessories inventory in the database of the computing device 104.. At step 806, the scores are generated for each of the filtered apparels and fashion accessories based on the user attributes, user preferences and other environmental factors using the machine learning algorithm. At step 808, the filtered apparels and fashion accessories are sorted in the order of the high scores to the low scores. At step 810, the one or more draped 3D user models are generated by draping each of the filtered 3D apparel models and 3D fashion accessories models on the 3D user model. At step 812, the one or more draped 3D user models in the order of high scores to the low scores are communicated to the style suggestion system 106.
[0043] FIG. 9 illustrates an exploded view of the computing device 104 having a memory 902 having a set of computer instructions, a bus 904, a display 906, a speaker 908, and a processor 910 capable of processing a set of instructions to perform any one or more of the methodologies herein, according to an embodiment herein. The processor 910 may also enable digital content to be consumed in the form of video for output via one or more displays 906 or audio for output via speaker and/or earphones 908. The processor 910 may also carry out the methods described herein and in accordance with the embodiments herein.
[0044] Digital content may also be stored in the memory 902 for future processing or consumption. The memory 902 may also store program specific information and/or service information (PSI/SI), including information about digital content (e.g., the detected information bits) available in the future or stored from the past. A user of the computing device 104 may view this stored information on display 906 and select an item of for viewing, listening, or other uses via input, which may take the form of keypad, scroll, or other input device(s) or combinations thereof. When digital content is selected, the processor 910 may pass information. The content and PSI/SI may be passed among functions within the computing device 104 using the bus 904.
[0045] The techniques provided by the embodiments herein may be implemented on an integrated circuit chip (not shown). The chip design is created in a graphical computer programming language, and stored in a computer storage medium (such as a disk, tape, physical hard drive, or virtual hard drive such as in a storage access network). If the designer does not fabricate chips or the photolithographic masks used to fabricate chips, the designer transmits the resulting design by physical means (e.g., by providing a copy of the storage medium storing the design) or electronically (e.g., through the Internet) to such entities, directly or indirectly.
[0046] The stored design is then converted into the appropriate format (e.g., GDSII) for the fabrication of photolithographic masks, which typically include multiple copies of the chip design in question that are to be formed on a wafer. The photolithographic masks are utilized to define areas of the wafer (and/or the layers thereon) to be etched or otherwise processed.
[0047] The resulting integrated circuit chips can be distributed by the fabricator in raw wafer form (that is, as a single wafer that has multiple unpackaged chips), as a bare die, or in a packaged form. In the latter case the chip is mounted in a single chip package (such as a plastic carrier, with leads that are affixed to a motherboard or other higher level carrier) or in a multichip package (such as a ceramic carrier that has either or both surface interconnections or buried interconnections). In any case the chip is then integrated with other chips, discrete circuit elements, and/or other signal processing devices as part of either (a) an intermediate product, such as a motherboard, or (b) an end product. The end product can be any product that includes integrated circuit chips, ranging from toys and other low-end applications to advanced computer products having a display, a keyboard or other input device, and a central processor.
[0048] The embodiments herein can take the form of, an entirely hardware embodiment, an entirely software embodiment or an embodiment including both hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. Furthermore, the embodiments herein can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[0049] The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk - read only memory (CD-ROM), compact disk - read/write (CD-R/W) and DVD.
[0050] A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.
[0051] Input/output (I/O) devices (including but not limited to keyboards, displays, pointing devices, remote controls, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.
[0052] A representative hardware environment for practicing the embodiments herein is depicted in FIG. 10. This schematic drawing illustrates a hardware configuration of an information handling/computer system in accordance with the embodiments herein. The system comprises at least one processor or central processing unit (CPU) 10. The CPUs 10 are interconnected via system bus 12 to various devices such as a random access memory (RAM) 14, read-only memory (ROM) 16, and an input/output (I/O) adapter 18. The I/O adapter 18 can connect to peripheral devices, such as disk units 11 and tape drives 13, or other program storage devices that are readable by the system. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein.
[0053] The system further includes a user interface adapter 19 that connects a keyboard 15, mouse 17, speaker 24, microphone 22, and/or other user interface devices such as a touch screen device (not shown) or a remote control to the bus 12 to gather user input. Additionally, a communication adapter 20 connects the bus 12 to a data processing network 25, and a display adapter 21 connects the bus 12 to a display device 23 which may be embodied as an output device such as a monitor, printer, or transmitter, for example.
[0054] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the appended claims. ,CLAIMS:I/We Claim:

1. A system for enhancing digital shopping and ecommerce experience of a user, said system comprising:
a style suggestion server that is communicated to a computing device, wherein said style suggestion server comprises
a memory that stores a set of instructions; and
a processor that executes said set of instructions and is configured to
process a 3D user model and user attributes, wherein said 3D user model and said user attributes are obtained from said computing device;
retrieve filtered apparels and fashion accessories from an apparel and fashion accessories inventory in a database of said computing device, wherein said filtered apparels and fashion accessories are stored as 3D apparel models and 3D fashion accessories models respectively on a style suggestion database;
generate scores for each of said filtered apparels and said fashion accessories based on said user attributes, user preferences and other environmental factors using a machine learning algorithm, wherein said machine learning algorithm assigns high scores to said filtered apparels and said fashion accessories when corresponding 3D apparel model and 3D fashion accessories model of said filtered apparels and said fashion accessories are stylish and suitable on said 3D user model, wherein said machine learning algorithm assigns low scores to said filtered apparels and said fashion accessories when said corresponding 3D apparel model and said 3D fashion accessories models of said filtered apparels and said fashion accessories are not stylish and not suitable on said 3D user model;
sort said filtered apparels and said fashion accessories from said high scores to said low scores; and
generate a plurality of draped 3D user models by draping each of said filtered apparel and said fashion accessories on said 3D user model, wherein said plurality of draped 3D user models virtually depict said user wearing each of said filtered apparels and fashion accessories.

2. The system as claimed in claim 1, wherein said 3D user model and said user attributes are obtained in said computing device by
scanning said user to obtain said user attributes using a plurality of depth camera sensors, wherein said user attributes comprise height measurement, shoulder width measurement, chest measurement, waist measurement, hip measurement, upper body height, neck length, skin tone, hair color, or hair style of said user; and
analyzing said user attributes to generate said 3D user model.

3. The system as claimed in claim 2, wherein said user attributes are obtained by scanning and analyzing shape of said user using a user shape analysis technique.

4. The system as claimed in claim 1, wherein said filtered apparels and fashion accessories are stored in said database using said computing device, wherein said computing device
obtains said apparel attributes and said fashion accessories attributes from said apparel and fashion accessories inventory of said database; and
filters and selects said apparel and fashion accessories based on said user attributes and said user preferences using apparel filters and accessories filters.

5. The system as claimed in claim 4, wherein said apparel attributes comprise an apparel type, an apparel material, apparel cut specification, an apparel pattern, an apparel neckline, apparel color, apparel fashion level or apparel measurements, wherein said fashion accessories attributes comprise fashion accessories size, fashion accessories color, fashion accessories type or fashion accessories material.

6. The system as claimed in claim 2, wherein said computing device provides a plurality of predefined queries to said user to determine fashion sensibility level of said user based on said user’s answers to said plurality of predefined queries.

7. The system as claimed in claim 2, wherein said computing device
receives said plurality of draped 3D user models from said style suggestion server; and
displays each of said draped 3D user models to said user in said order of said high scores to said low scores.

8. A method for enhancing digital shopping and ecommerce experience of a user, said method comprising:
processing a 3D user model and user attributes, wherein said 3D user model and said user attributes are obtained from a computing device;
retrieving filtered apparels and fashion accessories from an apparel and fashion accessories inventory in a database of said computing device, wherein said filtered apparels and fashion accessories are stored as 3D apparel models and 3D fashion accessories models respectively on a style suggestion database;
generating scores for each of said filtered apparels and said fashion accessories based on said user attributes, user preferences and other environmental factors using a machine learning algorithm, wherein said machine learning algorithm assigns high scores to said filtered apparels and said fashion accessories when corresponding 3D apparel model and 3D fashion accessories model of said filtered apparels and said fashion accessories are stylish and suitable on said 3D user model, wherein said machine learning algorithm assigns low scores to said filtered apparels and said fashion accessories when said corresponding 3D apparel model and said 3D fashion accessories models of said filtered apparels and said fashion accessories are not stylish and not suitable on said 3D user model;
sorting said filtered apparels and said fashion accessories from said high scores to said low scores; and
generating a plurality of draped 3D user models by draping each of said filtered apparel and said fashion accessories on said 3D user model, wherein said plurality of draped 3D user models virtually depict said user wearing each of said filtered apparels and fashion accessories.

9. The method as claimed in claim 8, wherein said 3D user model and said user attributes are obtained in said computing device by
scanning said user to obtain said user attributes using a plurality of depth camera sensors, wherein said user attributes comprise height measurement, shoulder width measurement, chest measurement, waist measurement, hip measurement, upper body height, neck length, skin tone, hair color, or hair style of said user; and
analyzing said user attributes to generate said 3D user model.

10. The method as claimed in claim 9, wherein said user attributes are obtained by scanning and analyzing shape of said user using a user shape analysis technique.