Patent ID: 12235890

In the drawings, like reference numbers generally indicate identical or similar elements. Additionally, generally, the left-most digit(s) of a reference number identifies the drawing in which the reference number first appears.

DETAILED DESCRIPTION

The following aspects are described in sufficient detail to enable those skilled in the art to make and use the disclosure. It is to be understood that other aspects are evident based on the present disclosure, and that system, process, or mechanical changes may be made without departing from the scope of an aspect of the present disclosure.

In the following description, numerous specific details are given to provide a thorough understanding of aspects. However, it will be apparent that aspects may be practiced without these specific details. To avoid obscuring an aspect, some well-known circuits, system configurations, and process steps are not disclosed in detail.

The drawings showing aspects of the system are semi-diagrammatic, and not to scale. Some of the dimensions are for the clarity of presentation and are shown exaggerated in the drawing figures. Similarly, although the views in the drawings are for ease of description and generally show similar orientations, this depiction in the figures is arbitrary for the most part. Generally, the system may be operated in any orientation.

Certain aspects have other steps or elements in addition to or in place of those mentioned. The steps or elements will become apparent to those skilled in the art from a reading of the following detailed description when taken with reference to the accompanying drawings.

System Overview and Function

FIG.1is an example system100for performing searching operations according to aspects. The search operations refer to suggested results of products and services that are likely to be acceptable to a user and improve an opportunity for user engagement with the content provided in the search results. For example, whenever a user is shopping on a website or on a mobile app associated with a content provider or performing a search on a third party platform, many times users may click on an image and interact with the image (e.g., zoom in on different portions of the image) to look at more details of the image. For example, if the image includes multiple components, e.g., an image of a vehicle that includes certain modifications, external components (e.g., wheels, tires, colors, after market components and accessories, etc.), and/or interior specifications (e.g., multi media systems, navigation systems, transmission, etc.), a user may zoom into the image to find out more about those details. When a user visually interacts with an image through zoom navigation for example, system100may capture such interaction and build (or augment) a user profile based on such image interactions. It can be appreciated that a user may be any person, entity, computer code, etc., that engages a browsing activity on the website or the mobile app, including, for example, a user.

In some aspects, the user may be interested to find out more about a specific search result (such as a product) and can do so by zooming into an image of the search result. Current web analytics, however, at best may capture that a user viewed an image and not that the user navigated the image and zoomed in to view components of the image. For example, current web analytics do not provide information relating to the actual x,y plane that the user is viewing or detect components/aspects of an image that the user interacted with. This creates a significant missed opportunity in gathered data that can potentially be mined and analyzed to compile future search results (e.g., product recommendations). According to some aspects, when the user interacts with a displayed image, system100may capture such interactions with the image and components of the image zoomed into by the user, and generate/augment a user profile with such data that can enhance a search capability of system100.

In some aspects, system100provides for solutions that can improve searching abilities by aligning search results more closely with user behavior, and especially with user behavior relating to browsing activities of images, such as product images. In some aspects, system100may be implemented on a server104. Server104may be a variety of centralized or decentralized computing devices. For example, server104may be a mobile device, a laptop computer, a desktop computer, grid-computing resources, a virtualized computing resource, cloud computing resources, peer-to-peer distributed computing devices, a server farm, or a combination thereof. The cloud computing resources may be part of a cloud computing environment. The cloud computing environment may be a public or private cloud service. Examples of a public cloud include Amazon Web Services (AWS), IBM Cloud, Oracle Cloud Solutions, Microsoft Azure Cloud, and Google Cloud, as examples. A private cloud refers to a cloud environment similar to a public cloud with the exception that it is operated solely for a single organization.

Server104may be centralized in a single room, distributed across different rooms, distributed across different geographic locations, or embedded within a network106. In aspects, server104can be coupled with network106to communicate with other devices, such as a client device102and content provider device130. While server104can couple with network106to communicate with other devices (for example client device102), server104can also be a stand-alone device.

In some aspects, client device102may be any of a variety of devices, such as a user mobile device, for example a smart phone, a cellular phone, a personal digital assistant, a tablet computer, a notebook computer, a laptop computer, a handheld scanner, etc. Client device102can couple, either directly or indirectly, to network106to communicate with server104or may be a stand-alone device.

According to some aspects, network106may be a telecommunications network, such as a wired or wireless network. Network106can span and represent a variety of networks and network topologies. For example, network106can include wireless communication, wired communication, optical communication, ultrasonic communication, or a combination thereof. For example, satellite communication, cellular communication, Bluetooth, Infrared Data Association standard (IrDA), wireless fidelity (WiFi), and worldwide interoperability for microwave access (WiMAX) are examples of wireless communication that may be included in network106. Cable, Ethernet, digital subscriber line (DSL), fiber optic lines, fiber to the home (FTTH), and plain old telephone service (POTS) are examples of wired communication that may be included in network106. Further, network106can traverse a number of topologies and distances. For example, the network106can include a direct connection, personal area network (PAN), local area network (LAN), metropolitan area network (MAN), wide area network (WAN), or a combination thereof.

For illustrative purposes, inFIG.1, server104, client device102, and content provider device130are shown as endpoints of network106. This, however, is exemplary and it is understood that there may be different partitions between the server104, client device102, content provider device130, and network106. For example, server104and client device102may be implemented to function as part of network106. In another example, content provider device130may also communicate with server104through network106. In one example, content provider device130may capture information relating to an object108(that may be of interest to a user). In one example, object108may be a vehicle. It can be appreciated to those skilled in the art that object108may represent any product or service that may be of interest to a user, including, for example, goods, furniture, health care products, and/or services including financial services, cleaning services and the like. For illustrative purposes hereinafter, object108may be used as an example object of interest for user108.

In some aspects, server104can include modules to perform some or all of its functionality. In the aspect shown inFIG.1, the modules include an orchestration layer110, an item matching module112, and a search result generation module116. The modules and how they facilitate the operation of the server104will be discussed further below. It can be appreciated that each of the modules may be performed by one or more processors of server104, and/or may all be performed by a single processor of server104. In some aspects, some or all of the modules may be implemented on any of the devices of the system100. In the aspect shown inFIG.1, all of the modules are shown as being implemented on the server104. This, however, is exemplary and in other aspects, some or all of the modules may be implemented on client device102.

With respect to the process by which the system100generates the search results, in some aspects, and as shown inFIG.1, a content provider may generate an inventory of goods that the content provider sells. For example, the content provider, using content provider device130, may create an inventory of goods (e.g., a vehicle captured in vehicle image108) that the content provider wishes to offer for sale. In one example, the content provider may generate vehicle image108and provide metadata associated with the image. For example, metadata associated with the image may include information associated with components of the vehicles found within the image (e.g., spoiler, door, fender, model number, price, color, model range, manufacturer, and the like).

According to some aspects, server104may be configured to provide content provider device130an ability to input and associate the metadata with object108manually. For example, server104may provide a content provider (e.g., a merchant, whole seller, third party seller, search engine, and any service providing entity) associated with content provider device130a pop up graphical user interface (GUI) that enables the content provider to input metadata describing the components within the image of object108(e.g., lights, doors, windows, steering wheels, wheels, tires, color, make, model, model range, and other interior and exterior components and accessories). Server104may also enable the content provider to associate the metadata with corresponding locations of the object's components in the image. In some aspects, a location in an image is identified by a particular pixel or set of pixels in the image. In some aspects, a location in an image is identified by an x, y coordinate or a range of x, y coordinates within the image. For example, prior to uploading an inventory item into content provider database118, for example, server104may provide the content provider an ability to perform a tagging operation prior to uploading the inventory. In one example, the content provider may select a particular location or area within the image of, for example, object108, and input one or more descriptions (e.g., metadata) of the particular component shown at the particular location. For example, if the image is that of an interior portion of a vehicle, the content provider may select a location or area on the image, a steering wheel, for example, and provide metadata describing the steering wheel (e.g., size, year of make, model vehicles incorporating the steering wheel, air bag capabilities, electronic controls/functions, etc.) It can be appreciated that such tagging operations may also be performed using trained convolutional neural networks (CNN) that can process an image for the content provider and allow the content provider to populate metadata associated with the image, which will allow for scalability and more efficient production of tagged images.

According to some aspects, server104may use the metadata to capture the different components of an image of an object so as to more closely populate a user profile. For example, as will be further described herein below, upon tracking user browsing activities, server104may correlate captured user browsing activity with the metadata to generate a more comprehensive user profile. Such user profile may enable server104to provide better tailored search results for a user.

According to some aspects, server104may receive the inventory including images and associated metadata. Server104may execute image analysis on the images as well. For example, in cases where a content provider did not provide metadata, server104may execute an image analysis algorithm on the received images to generate metadata that can improve generating search results. For example, server104may use a deep learning algorithm, such as a convolutional neural network (CNN) to analyze visual imagery to identify objects (or components of objects) or events in an image. This may be the case where an image provided by a content provider does not have sufficient metadata to describe components of an object (e.g., image may have metadata identifying a vehicle, but not additional components of the vehicle like door, color, and the like). While the present example describes a CNN algorithm deployed within server104, it can be appreciated that the CNN algorithm may also be deployed within content provider device130to enable a content provider to tag and categorize image content.

According to some aspects, applications of the CNN algorithm are not limited to generating metadata. For example, even in cases where server104receives inventory including associated images and metadata, server104may still apply the CNN algorithm to further process the received images in order to strategically allocate one or more HTML code snippets within the image. For example, server104may process each received image to generate a mapping of the components of the image and standardize the mapping of components with the metadata and specific pixels or coordinate tags within the image. For example, assuming the received image is an image including vehicle108, server104may perform image processing using CNN algorithm so as to generate a coordinate for each one of the detected components. According to some aspects, server104may then correlate the generated coordinates for each one of the detected components with the received metadata (and specific pixels/coordinate tags) from the content provider. In this regard, server104is able to know the specific coordinate of a component within the image and the metadata describing information about the component. According to some aspects, server104may then allocate one or more HTML code snippets at the generated coordinates.

The following is a non-limiting example of image processing techniques employed by server104according to aspects described herein. For example, server104receives vehicle image108from a content provider. Vehicle image108may include metadata describing components of vehicle image108and coordinate tags indicating locations of the components within the image. According to some aspects, server104processes the vehicle image108and determines that the image includes four components: headlight, windshield, grill, and bumper. Such determination may be made from processing the metadata and the coordinate tags, and/or by utilizing the CNN image processing techniques described herein. After processing the vehicle image108, server104now knows the number of components within vehicle image108, the locations of those components (both from the received coordinate tags and confirmed by the server's own generated mapping), and descriptions of those components. For example, at this stage, server104can determine that vehicle image108contains four components (headlight, windshield, grill, and bumper), where each component is located (or within what region), and descriptions of those components (e.g., chrome grill, produced in a specific year for a specific model, etc.). According to some aspects, server104may then place four HTML code snippets (also referred to as tracking pixels) within each of the four components. Server104may generate an image data file associated with the processed image (e.g., including metadata, coordinate tags, confirmed mapping, and location of the allocated HTML code snippets). Server104may store the generated image data file in content provider database118. Upon detecting a browsing operation within the image, e.g., when a user performs a zoom in operation on one of the components, e.g., bumper, the HTML code snippet is triggered and executed, generating a browsing activity tracking data output. According to some aspects, server104may then correlate the browsing activity tracking data output with the generated image data file to produce a user data file indicating that the user viewed the component and providing a time window associated with the viewing activity and all associated metadata. According to some aspects, server104can then store the user data file in a user profile stored on user database120. It can be appreciated that the example described herein is illustrative and any image of any object with a plurality of components may be processed. Such objects may include a wide variety of consumer goods, for example, as would be understood by those skilled in the art.

In some aspects, the CNN algorithm is trained by two phases, a forward phase and a backward phase. The forward phase may include a convolution layer, pooling layer, and fully-connected layer. The convolution layer may apply filters to an input image received from the content provider to generate a feature map. The pooling layer may generate a reduced feature map. The fully-connected layer may classify the features of the image using weights and biases—e.g., the four components described herein above. The values of the filters, weights, and biases may be parameters received by the CNN algorithm.

In some aspects, the CNN algorithm initially receives an input image (e.g., an image from the content provider) and randomly assigned values for filters, weights, and biases. The CNN algorithm can determine whether it correctly identified objects in the image using, for example, a loss function. Next, in the backward phase, the CNN algorithm can use backpropagation to determine whether the CNN algorithm was able to identify the objects in the image correctly. The CNN algorithm can update the values for the filters, weights, and biases using, for example, a gradient descent algorithm, and re-execute the forward phase on the input image. As such, the CNN algorithm can be trained to identify objects or events in the input image using feature learning and classification. In some aspects, feature learning includes the convolution layer and the pooling layer, while the classification includes the fully connected layer.

In the convolution layer, the CNN algorithm performs feature extraction on the input image. The CNN algorithm applies a small array of numbers (e.g., kernel) across the different portions of the input image. The kernel may also be referred to as a filter. As described above, values of the filters or kernels can be randomly assigned and optimized over time. Different filters can be applied to the input image for generating different features of maps. For example, the filter for identifying an object in the image may be different than the filter for edge detection. The kernel can be applied as a sliding window across different portions of the input image. The kernel can be summed with a given portion of the input image to generate an output value. The output value can be included in a feature map. The feature map can be a two-dimensional array. The final feature map can include an output value from the kernel applied to each portion of the input image. The features can be different edges or shapes of the image. In some aspects, the features may be the four components associated with vehicle image108. It can be appreciated that detecting features may still require a correlation with metadata in order to identify each feature and associated information (e.g., as noted herein, a steering wheel with all relevant information, or a bumper with all relevant information).

In the pooling layer, the CNN algorithm may reduce the dimensionality of the feature map. In particular, the CNN algorithm may extract portions of the feature map and discard the rest. Pooling the image keeps the important features while discarding the rest. This way, the size of the image is reduced. The CNN algorithm may use max or average pooling in the pooling layer to perform these operations. Max pooling keeps the higher values of portions of the feature map while discarding the remaining values. Average pooling keeps the average value of different portions of the feature map. The CNN algorithm may generate a reduced feature map in the pooling layer.

In the fully connected layer, the CNN algorithm may flatten the reduced feature map into a one-dimensional array (or vector). For example, the CNN algorithm may generate an intensity map of a grayscale vector corresponding to the image. In this regard, the CNN algorithm may identify features (or components) of an object within the image and generate an intensity map identifying each detected feature. Taking the image of a vehicle108as an example, CNN algorithm may generate an intensity map assigning different intensities to different detected features (or components) of the object in the image (e.g., different intensities for doors, spoilers, windows, tires, wheels, etc.). The fully connected layer may be, for example, a neural network. The CNN algorithm performs a linear and non-linear transformation on the one-dimensional array. The CNN algorithm can perform the linear transformation by applying weights and biases to the one-dimensional array to generate an output. Initially, the weights and biases are randomly initialized and can be optimized over time.

According to aspects, server104stores the inventory and associated images that have been processed by the CNN algorithm in content provider database118(e.g., image data file) on a continuous rolling basis as more images are sent from the content provider.

According to some aspects, server104builds a user profile for a user (e.g., a customer) associated with client device102and stores the profile in user database120. It can be appreciated that server104may build the user profile on a continuous basis so as to continuously enhance the user profile. Server104may then provide improved search results that closely align with a user's preferences based on the search query as well as the user profile.

Server104may build the user profile in one or more different ways. In one example, server104may build the user profile by tracking browsing activities of the user and associating the tracked browsing activities with one or more image data files stored in content provider database118as described herein. According to some aspects, if a user does not have a profile stored in user database120, server104may prompt user102to create an account. Additionally, or in the alternative, server104may also generate a user profile (e.g., a web browser cookie) and populate the generated user profile with tracked browsing activities thereafter. In this regard, the user profile may be maintained so long as the web browser cookie is not erased. In some aspects, where a user subsequently chooses to create a profile, server104may correlate the browser cookie with the user account and associated the existing profile based on the web browser cookie with the user.

According to some aspects, a new user may be interested in searching for an object (e.g., a painting or a vehicle, etc.). Server104may receive, via an application on client device102, a search query associated with the object. The term “application” refers to a software application or computer program. In aspects, if the client device102is a mobile device, the application can be analogous to a mobile application (also referred to as a mobile app.) installed on the client device102from which a user can interact with the system100. In aspects, if client device102is a notebook computer or desktop computer the application can be a desktop application. In other aspects, the application can be installed on a browser of client device102as an applet, widget, or plugin.

In aspects, the user can utilize the application to provide information to the system100. In aspects, the information can include information about the item the user wants to purchase. In aspects, this may be provided manually by selecting the item from a list of items displayed on the screen of the application and having the application transmit pre-populated information about the item to the other components of the system100. In other aspects, the information may be provided in a search string provided by the user. It can be appreciated that the user may search or browse for items stored in content provider database118. Accordingly, if a user of the client device102is browsing for an item via the application, server104may begin to track user interaction with the images as further described herein.

For the purposes of this disclosure and with respect toFIG.1, the system100is described with respect to the scenario where a user may be browsing for and/or attempting to purchase a vehicle displayed in vehicle image108(and/or associated components). This, however, is not meant to limit the system100solely to aspects for purchasing a vehicle. The system100can function similarly with other items users desire to purchase or search for. For example, a user may be interested in purchasing a painting, and may view images of specific paintings with specific scenic motifs (e.g., nature) and/or genre (e.g., abstract) and/or color (e.g., light tones). Such information may be discerned by server104according to examples described herein in order to generate a more robust user profile that can narrow search results. For example, if it is determined from detection of the browsing history that a user is browsing a painting by a famous painter, then server104may populate such metadata information in the user profile. Accordingly, when a user attempts to search for a painting in the future, even if the user inputs search strings relating to general art, color pallets, etc., server104may prioritize search results that of the famous painter that meet the search criteria provided by the user.

In some aspects, the search request is received by server104at the orchestration layer110. The orchestration layer110may be a service of the system100that can serve as an entry point for the search request. In aspects, the orchestration layer110can coordinate information retrieval and perform task allocations for the server104. For example, the orchestration layer110can comprise one or more software applications, programs, or lines of code that when executed by a processor can implement instructions that facilitate information transmission and retrieval to and/or between the modules of the server104, and can coordinate execution of the modules.

In some aspects, once the search request is received at the orchestration layer110, the orchestration layer110passes the search request to the item matching module112. The item matching module112identifies the item the user wants to purchase based on the search request and based on the user profile. In cases where the user has yet to establish a profile or where the user has a very limited profile, matching module112may provide recommendations relying on other similar user profiles in user database120or third party database122. The third-party database122can contain product information that may be used to generate a list of alternative inventory items. In some aspects, third-party database122may be managed by a third-party vendor, which is external and independent of the company or institution implementing the system100.

By way of example, where the user wants to purchase the vehicle in vehicle image108, the list of alternative inventory items can comprise cars that are similar to or the same as car108in terms of make, model, body style, vehicle category, features, etc. that were the subject of past searches of other users. It can be appreciated that where a user has an established profile stored in user database120, orchestration layer110may rely on the user profile to generate search results and/or alternative inventory items.

In some aspects, item matching module112processes the received search request from orchestration layer110. In doing so, item matching module112may access user database120to access a profile associated with the user (and including one or more user data files) and content provider database118to search for objects/products that match the search request. In some aspects, item matching module112may refine the search based on the user profile (e.g., the one or more user data files). For example, if a user has previously searched for a car, and the one or more user data files indicate that the user prefers white cars, then item matching module112may refine the search to focus on white cars.

In some aspects, once candidate objects/products are identified by item matching module112, they are then passed to the search result generation module116. The search result generation module116can generate a search result and transmission to client device102.

It can be appreciated that in some aspects, server104may assign different weights to search results refined by the user profile. For example, where server104knows that the user has previously browsed through images of white cars, search results including white cars may receive higher priority weight than search results including cars of different colors. In this regard, the user may receive a prioritized search result that prioritizes search results that more closely match the user's prior browsing behavior while also presenting the user with other search results to provide a tailored and complete searching experience.

In aspects, system100can also learn from the search results it generates, and can have feedback input back into system100relating to whether the search results were satisfactory to the user or not, whether subsequent searches were performed, and whether a sale occurred or not, so as to continually learn and to constantly improve its ability to generate search results offers. For example, this may be done by having the search request for which system100generated search results fed back into the databases (e.g., user database). This feedback may be used by system100as data from which it can generate future search results. In this way, system100can continuously build on its outputs and further refine its ability to provide customized and tailored search results as the data points increase. In aspects, machine learning processes and/or algorithms may be used to facilitate this learning process and train system100to generate improved search results. For example, machine learning processes such as neural networks may be implemented as a part of system100to learn patterns of search requests/results, so that system100can use those learnings and/or patterns to generate future search results. In this way, system100may become more intelligent as the data pool of user interaction increases.

In some aspects, system100can also be implemented as a real-time system such that system100generates the search results in real-time from when the search request is received. Real-time refers to system100generating the search results within milliseconds or seconds from when the search request is received so that the search results are able to be transmitted to client device102almost instantly from when the client device102provides the request.

It can be appreciated that the system100described above improves the state of the art from conventional systems because it provides a novel way to generate highly targeted search results that can expedite result matching for users, and increase engagement opportunities with content providers. The system does this by capturing user interaction with images and creating or augmenting a user profile that is relied on when generating search results. The ability of system100to provide these targeted search results significantly reduces search time by leveraging technology and data in an intelligent manner to derive search results likely to be acceptable to a user and more closely identifies relevant content.

System100also improves conventional systems by implementing machine learning features that can enable the system100to learn from the search results it generates, and shifting user browsing behavior. Moreover, system100also improves conventional systems by implementing machine learning features that can perform image processing and correlate portions of images with metadata that can then be matched with user search requests. The machine learning capabilities can ensure that system100learns from its past search results and user interactions and learns patterns based on past search results, browsing data and purchases. In this way, system100can continuously improve its ability to generate search results, leading to a more intelligent computing system that can ultimately provide search results that do not need to be revised by the user.

Methods of Operation

FIG.2is an example method200tracking user activity, according to aspects. Method200may be performed as a series of steps. While method200is described with reference to system100as an example, a person of skill in the art would recognize that other content delivery systems may also be used to implement method200.

At step202, system100may receive an image from an external source, e.g., a content provider.

At step204, system100may generate an intensity map corresponding to the image. As described herein, the intensity map may be used to extract features associated with one or more objects within the image. At step206, system100may place an HTML code snippet at a coordinate location within the image, the code snippet being configured to output a browsing activity tracking data. According to some aspects, the coordinate location may correspond to a detected feature. In one example, where an intensity map is generated, the coordinate location may correspond to a detected change in the grayscale vector of the intensity map. This may enable feature identification/extraction of a plurality of features (e.g., where an object has a plurality of components).

At step208, system100may render an interactive page on a user interface. According to some aspects, the interactive page may be rendered within an application, a web browser, or the like. According to some aspects, the interactive page may include an image and metadata associated with the image. For example, a search may have already been requested by a user or a user is simply browsing one or more merchant catalogs provided by system100. As described herein, the image may be an image of an object (e.g., an item of interest for the user like vehicle108) and the metadata associated with the image may be descriptive information of the object. At step210, system100may detect a browsing activity associated with the image. For example, as described herein, system100may detect a user browsing/interaction with an image. At step212, system100may activate the HTML code snippet in response to detecting the browsing activity at the coordinate location. At step214, system100may correlate the code snippet output with the metadata to generate image browsing track data. At step216, system100may generate or augment an existing user profile to include the image browsing track data. Such data, as described herein, may be used to refine and improve search results provided to the user for future searches, as described further with reference toFIG.3.

FIG.3is an example method300of operating the system100according to aspects. While method300is described with reference to system100as an example, a person of skill in the art would recognize that other content delivery systems may also be used to implement method300.

At step302, system100may receive a search query for an item of interest from the user. At step304, system100may perform a search operation for the item of interest. As described herein, system100may perform the search operation based on the search query and the user profile generated by system100. At step306, system100may provide one or more search results based on the user's profile. At step308, system100may store the search results in the user browsing profile for continued adaptation/growth of system100.

It can be appreciated that system100may perform additional steps (not illustrated herein) when providing improved search capabilities for a user. For example, system100may monitor a time window associated with browsing activity, and in response to the time window exceeding a predetermined time threshold, capture the browsing activity tracking data output by the HTML code snippet. For example, a user may browse an image for a few seconds, which may indicate that the user is not necessarily focused on a specific component. Accordingly, system100may elect to ignore this browsing activity until it exceeds a predetermined time threshold (e.g., five seconds), which may indicate to system100that the user is interested. According to some aspects, system100may detect a zoom-in activity associated with the browsing activity, and measure a time duration of the zoom-in activity. In response to the time duration being above a predetermined threshold value, system100may identify at least one coordinate associated with a zoom-in region (e.g., a detected feature) and identify metadata associated with the at least one coordinate (e.g., descriptions of the extracted feature). System100may then correlate the identified at least one coordinate with the identified metadata to generate updated image browsing track data, and augment the user browsing profile with the updated image browsing track data.

GUI Interfaces of the System

FIG.4is an example GUI400displaying the search results to a user according to aspects. In some aspects, GUI400may be displayed on the client device102, via the application. In some aspects, what may be displayed on the GUI400as part of the search result is information related to the search result. For example, when the search result is a vehicle (e.g., vehicle108), GUI400may include related information such as vehicle type and model number402and any other attributes associated with the vehicle, e.g., colors404. In aspects, other information may be displayed. For example, in the aspect where the user wants to purchase the car108, a graphic of the car108and/or a description of the features of the car108can also be displayed in a window or pane of the GUI400along with price and financing information.

Components of the System

FIG.5is an example architecture500of one or more components of system100according to aspects. The components may be the components of server104on which system100is implemented, or may be components of client device102. In aspects, the components may include a control unit502, a storage unit506, a communication unit516, and a user interface512. The control unit502may include a control interface504. The control unit502may execute a software510to provide some or all of the intelligence of system100. The control unit502may be implemented in a number of different ways. For example, the control unit502may be a processor, an application specific integrated circuit (ASIC), an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), a field programmable gate array (FPGA), a graphics processing unit (GPU) or a combination thereof.

The control interface504may be used for communication between the control unit502and other functional units or devices of system100. The control interface504may also be used for communication that is external to the functional units or devices of system100. The control interface504may receive information from the functional units or devices of system100, or from remote devices520, for example the third-party database122, or may transmit information to the functional units or devices of system100, or to remote devices520. The remote devices520refer to units or devices external to system100.

The control interface504may be implemented in different ways and may include different implementations depending on which functional units or devices of system100or remote devices520are being interfaced with the control unit502. For example, the control interface504may be implemented with a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), optical circuitry, waveguides, wireless circuitry, wireline circuitry to attach to a bus, an application programming interface, or a combination thereof. The control interface504may be connected to a communication infrastructure522, such as a bus, to interface with the functional units or devices of system100or remote devices520.

The storage unit506may store the software510. For illustrative purposes, the storage unit506is shown as a single element, although it is understood that the storage unit506may be a distribution of storage elements. Also for illustrative purposes, the storage unit506is shown as a single hierarchy storage system, although it is understood that the storage unit506may be in a different configuration. For example, the storage unit506may be formed with different storage technologies forming a memory hierarchical system including different levels of caching, main memory, rotating media, or off-line storage. The storage unit506may be a volatile memory, a nonvolatile memory, an internal memory, an external memory, or a combination thereof. For example, the storage unit506may be a nonvolatile storage such as nonvolatile random access memory (NVRAM), Flash memory, disk storage, or a volatile storage such as static random access memory (SRAM) or dynamic random access memory (DRAM).

The storage unit506may include a storage interface508. The storage interface508may be used for communication between the storage unit506and other functional units or devices of system100. The storage interface508may also be used for communication that is external to system100. The storage interface508may receive information from the other functional units or devices of system100or from remote devices520, or may transmit information to the other functional units or devices of system100or to remote devices520. The storage interface508may include different implementations depending on which functional units or devices of system100or remote devices520are being interfaced with the storage unit506. The storage interface508may be implemented with technologies and techniques similar to the implementation of the control interface504.

The communication unit516may enable communication to devices, components, modules, or units of system100or to remote devices520. For example, the communication unit516may permit the system100to communicate between the server104on which the system100can be implemented and the client device102. The communication unit516may further permit the devices of system100to communicate with remote devices520such as an attachment, a peripheral device, or a combination thereof through the network106.

As previously indicated, the network106may span and represent a variety of networks and network topologies. For example, the network106may be a part of a network and include wireless communication, wired communication, optical communication, ultrasonic communication, or a combination thereof. For example, satellite communication, cellular communication, Bluetooth, Infrared Data Association standard (IrDA), wireless fidelity (WiFi), and worldwide interoperability for microwave access (WiMAX) are examples of wireless communication that may be included in the network106. Cable, Ethernet, digital subscriber line (DSL), fiber optic lines, fiber to the home (FTTH), and plain old telephone service (POTS) are examples of wired communication that may be included in the network106. Further, the network106may traverse a number of network topologies and distances. For example, the network106may include direct connection, personal area network (PAN), local area network (LAN), metropolitan area network (MAN), wide area network (WAN), or a combination thereof.

The communication unit516may also function as a communication hub allowing system100to function as part of the network106and not be limited to be an end point or terminal unit to the network106. The communication unit516may include active and passive components, such as microelectronics or an antenna, for interaction with the network106.

The communication unit516may include a communication interface518. The communication interface518may be used for communication between the communication unit516and other functional units or devices of system100or to remote devices520. The communication interface518may receive information from the other functional units or devices of system100, or from remote devices520, or may transmit information to the other functional units or devices of the system100or to remote devices520. The communication interface518may include different implementations depending on which functional units or devices are being interfaced with the communication unit516. The communication interface518may be implemented with technologies and techniques similar to the implementation of the control interface504.

The user interface512may present information generated by system100. In aspects, the user interface512allows users to interface with the system100. In aspects, the user interface512can present the GUI400which any users can interact with to receive offers. The user interface512may include an input device and an output device. Examples of the input device of the user interface512may include a keypad, buttons, switches, touchpads, soft-keys, a keyboard, a mouse, or any combination thereof to provide data and communication inputs. Examples of the output device may include a display interface514. The control unit502may operate the user interface512to present information generated by system100. The control unit502may also execute the software510to present information generated by system100, or to control other functional units of system100. The display interface514may be any GUI such as a display, a projector, a video screen, or any combination thereof.

In some embodiments, a tangible, non-transitory apparatus or article of manufacture comprising a tangible, non-transitory computer useable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system500and associated components described herein.

The terms “module” or “unit” referred to in this disclosure can include software, hardware, or a combination thereof in an aspect of the present disclosure in accordance with the context in which the term is used. For example, the software may be machine code, firmware, embedded code, or application software. Also for example, the hardware may be circuitry, a processor, a special purpose computer, an integrated circuit, integrated circuit cores, or a combination thereof. Further, if a module or unit is written in the system or apparatus claims section below, the module or unit is deemed to include hardware circuitry for the purposes and the scope of the system or apparatus claims.

The terms “service” or “services” referred to herein can include a collection of modules or units. A collection of modules or units may be arranged, for example, in software or hardware libraries or development kits in an aspect of the present disclosure in accordance with the context in which the term is used. For example, the software or hardware libraries and development kits may be a suite of data and programming code, for example pre-written code, classes, routines, procedures, scripts, configuration data, or a combination thereof, that may be called directly or through an application programming interface (API) to facilitate the execution of functions of the system100.

The modules, units, or services in the following description of the aspects may be coupled to one another as described or as shown. The coupling may be direct or indirect, without or with intervening items between coupled modules, units, or services. The coupling may be by physical contact or by communication between modules, units, or services.

The above detailed description and aspects of the disclosed system100are not intended to be exhaustive or to limit the disclosed system100to the precise form disclosed above. While specific examples for system100are described above for illustrative purposes, various equivalent modifications are possible within the scope of the disclosed system100, as those skilled in the relevant art will recognize. For example, while processes and methods are presented in a given order, alternative implementations may perform routines having steps, or employ systems having processes or methods, in a different order, and some processes or methods may be deleted, moved, added, subdivided, combined, or modified to provide alternative or sub-combinations. Each of these processes or methods may be implemented in a variety of different ways. Also, while processes or methods are at times shown as being performed in series, these processes or blocks may instead be performed or implemented in parallel, or may be performed at different times.

The resulting methods200and300and system100are cost-effective, highly versatile, and accurate, and may be implemented by adapting components for ready, efficient, and economical manufacturing, application, and utilization. Another important aspect of aspects of the present disclosure is that it valuably supports and services the historical trend of reducing costs, simplifying systems, and/or increasing performance.

These and other valuable aspects of the present disclosure consequently further the state of the technology to at least the next level. While the disclosed aspects have been described as the best mode of implementing system100, it is to be understood that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the descriptions herein. Accordingly, it is intended to embrace all such alternatives, modifications, and variations that fall within the scope of the included claims. All matters set forth herein or shown in the accompanying drawings are to be interpreted in an illustrative and non-limiting sense.