Patent Publication Number: US-10762562-B2

Title: Database image matching using machine learning with output personalization

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 15/916,137, filed Mar. 8, 2018. The contents of the above are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to the field of image analysis using machine learning algorithms. More specifically, and without limitation, this disclosure relates to systems and methods for using machine learning algorithms to identify vehicles from images and generate personalized outputs. 
     BACKGROUND 
     Customers have limited methods for receiving accurate quotes and pricing when attempting to finance a vehicle purchase. For example, in a typical method, a customer visits an automobile dealership and selects a vehicle to purchase. After negotiating the vehicle price, the customer then fills out a lengthy credit application at the dealership and provides any information needed to complete the financing. After receiving dealership credit approval, the customer signs a contract reflecting the purchase and financing terms, such as the monthly payments and Annual Percentage Rate (APR). 
     Alternatively, some customers apply to prequalify for a loan before choosing a vehicle. Here, the customer fills out an application and provides information relating to their income, employment, residence, desired financing amount, and other required personal details. If the customer&#39;s application is approved, the lender provides an estimate of the financing details and a maximum amount approved for the vehicle purchase. The customer visits an automobile dealership with the approved pre-qualification and selects a vehicle to purchase. The customer then fills out another credit application and provides any further information required to complete the financing. After receiving dealership credit approval, the customer signs a contract reflecting the purchase and financing terms. 
     Such known techniques for auto financing suffer from multiple drawbacks, however. For example, customers must endure a lengthy, manual, and multi-step process to secure financing for a vehicle. Attempting to use existing pre-qualification methods can provide inaccurate quotes and financing terms. Furthermore, actual vehicle information may not be available at the time the customer seeks financing. Moreover, attempting to obtain more accurate pricing quotes by choosing a specific vehicle before seeking financing can limit the ability of a customer to change their minds. For example, should the customer decide at the dealership to purchase a different vehicle, a common event, the customer may need to reapply for financing for this new vehicle. 
     A need therefore exists for systems and methods for providing on-demand auto financing quotes for specified vehicles in real time. The disclosed systems and methods provide a technological solution to at least this existing problem. 
     SUMMARY 
     Embodiments of the present disclosure use machine learning systems and methods to provide personalized outputs for specified vehicles in real time. The resulting outputs can be displayed using augmented reality systems and methods on a device of the user. In this manner, the disclosed embodiments can provide a marked improvement over the subjective, manual, and multi-step processes known in the prior art. 
     One embodiment of the present disclosure is directed to a system for processing an image including a vehicle using machine learning. The system may include a processor in communication with a client device; and a storage medium storing instructions. The instructions, when executed, may configure the processor to perform operations including: determining a location of the client device; receiving an image of a vehicle from the client device; matching, using a machine learning algorithm, the image to one or more images of vehicles in a database, the database listing vehicles located at the determined location, the database including images of the vehicles; retrieving vehicle information from the database based on the matched one or more images in the database; obtaining user information relating to a financing request for the vehicle; determining a real-time quote for the vehicle based on the vehicle information and the user information; and transmitting the real-time quote for display on the client device. 
     Another embodiment of the present disclosure is directed to a computer-implemented method for processing an image including a vehicle using machine learning. The method comprises: determining a location of a client device; receiving an image of a vehicle from the client device; matching, using machine learning, the image to one or more images of vehicles in a database, the database listing vehicles located at the determined location, the database including images of the vehicles; retrieving vehicle information from the database, based on the matched one or more images in the database; obtaining user information relating to a financing request for the vehicle; determining a real-time quote for the vehicle based on the vehicle information and the user information; and transmitting the real-time quote for display on the client device. 
     A further embodiment of the present disclosure is directed to a non-transitory computer-readable medium. The computer-readable medium may store instructions that, when executed by a processor, cause the processor to operate a computer system for processing an image including a vehicle using machine learning, comprising: determining a location of a client device; receiving an image of a vehicle from the client device; matching, using machine learning, the image to one or more images of vehicles in a database, the database listing vehicles located at the determined location, the database including images of the vehicles; retrieving vehicle information from the database, based on the matched one or more images in the database; obtaining user information relating to a financing request for the vehicle; determining a real-time quote for the vehicle based on the vehicle information and the user information; and transmitting the real-time quote for display on the client device. 
     Additional objects and advantages of the present disclosure will be set forth in part in the following detailed description, and in part will be obvious from the description, or may be learned by practice of the present disclosure. The objects and advantages of the present disclosure will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. 
     It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not restrictive of the disclosed embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which comprise a part of this specification, illustrate several embodiments and, together with the description, serve to explain the disclosed principles. In the drawings: 
         FIG. 1  is a block diagram of an exemplary system, in accordance with disclosed embodiments. 
         FIG. 2  is a block diagram of an exemplary client device, in accordance with disclosed embodiments. 
         FIG. 3  is a block diagram of an exemplary database, in accordance with disclosed embodiments. 
         FIG. 4  is a flowchart of an exemplary method for generating a real-time quote for a vehicle, consistent with embodiments of the present disclosure. 
         FIGS. 5A-5C  are flowcharts of exemplary methods for identifying vehicle attributes based on an image, consistent with embodiments of the present disclosure. 
         FIG. 6  is a flowchart of an exemplary method for identifying vehicle attributes using two convolutional neural networks, consistent with embodiments of the present disclosure. 
         FIG. 7  is a flowchart of an exemplary method for comparing images using a convolutional neural network, consistent with embodiments of the present disclosure. 
         FIG. 8  is a flowchart of an exemplary method for generating pricing information, consistent with embodiments of the present disclosure. 
         FIG. 9  is a flowchart of an exemplary method for generating a real-time quote for a vehicle, consistent with embodiments of the present disclosure. 
         FIG. 10  is a flowchart of another exemplary method for generating a real-time quote for a vehicle, consistent with embodiments of the present disclosure. 
         FIG. 11  is a flowchart of a third exemplary method for generating a real-time quote for a vehicle, consistent with embodiments of the present disclosure. 
         FIG. 12  is a flowchart of a fourth exemplary method for generating a real-time quote for a vehicle, consistent with embodiments of the present disclosure. 
         FIG. 13  is an illustration of a client device capturing an image of a vehicle, in accordance with disclosed embodiments. 
         FIG. 14  is a group of exemplary graphical user interfaces capturing vehicle images, in accordance with disclosed embodiments. 
         FIG. 15  is an exemplary flow chart illustrating an identification process, in accordance with disclosed embodiments. 
         FIG. 16  is an exemplary flow chart illustrating a training data set generation process, in accordance with disclosed embodiments. 
         FIG. 17  is an exemplary flow chart illustrating a process for generating an identification model, in accordance with disclosed embodiments. 
         FIG. 18  is an exemplary flow chart illustrating a process for handling a request for identification. 
     
    
    
     DETAILED DESCRIPTION 
     The disclosed embodiments relate to systems and methods for generating real-time quotes for vehicles. Reference will now be made in detail to exemplary embodiments and aspects of the present disclosure, examples of which are illustrated in the accompanying drawings. While numerous specific details are set forth in order to provide a thorough understanding of the disclosed example embodiments, it would be understood by those skilled in the art that the principles of the example embodiments may be practiced without every specific detail. Unless explicitly stated, the example methods and processes described herein are not constrained to a particular order or sequence, or constrained to a particular system configuration. Additionally, some of the described embodiments or elements thereof can occur or be performed simultaneously, at the same point in time, or concurrently. Reference will now be made in detail to the disclosed embodiments, examples of which are illustrated in the accompanying drawings. 
     As used herein, the term “image” may connote digital information stored in an appropriate format, for example, static image formats (e.g., bitmap, Graphics Interchange Format (“GIF”), Portable Network Graphics format (“PNG”), Joint Photographic Experts Group (“JPEG”)) or dynamic formats (e.g., animated GIF, MPEG-4, Flash Video (“FLV”), Windows Media Video (“WMV”)). 
       FIG. 1  is a block diagram of an exemplary system  100 . System  100  may be used to identify a vehicle and associated vehicle attributes (e.g., make, model, trim, year, color, etc.) based on an image of the vehicle, in accordance with disclosed embodiments. System  100  may include a server system  105  which may include an image recognizer  110 , a model generator  120 , an image classifier  130 , a location determination system  140 , an authentication module  150 , and a memory  160 . System  100  may additionally include online resource  106 , comparison resource  107 , database  180 , and client device  190 . In some embodiments, as shown in  FIG. 1 , each component of system  100  may be connected to a network  170 . However, in other embodiments components of system  100  may be connected directly with each other, without network  170 . 
     Online resource  106  may include one or more servers or storage services provided by one or more service providers, such as a provider of website hosting, networking, cloud, or backup services. In some embodiments, online resource  106  may be associated with hosting services or servers that store web pages for vehicle manufacturers and/or vehicle dealers. In other embodiments, online resource  106  may be associated with a cloud computing service such as Microsoft Azure™ or Amazon Web Services™. In yet other embodiments, online resource  106  may be associated with a messaging service, such as, for example, Apple Push Notification Service, Azure Mobile Services, or Google Cloud Messaging. In such embodiments, online resource  106  may handle the delivery of messages and notifications related to functions of the disclosed embodiments, such as image compression, notification of identified vehicles alerts, and/or completion messages and notifications. 
     Comparison resource  107  may include one or more servers or storage services configured to store comparison information. In some aspects, the one or more servers or storage services can host one or more resources provided by one or more service providers. Comparison information can include pricing information, vehicle-related information, and personal information. In some embodiments, comparison information can include records of vehicle financing transactions, such as vehicle financing applications submitted by other users of system  100 . Pricing information can include vehicle prices, monthly payment amounts, APRs, term lengths, warranty products typically purchased (e.g., tire protection plans, extended warranties), and/or amounts financed. Vehicle-related information can include vehicle make, model, vehicle identification number, color, trim, mileage, dealership of lease or purchase, geographic area of lease or purchase (zip code, city, state, etc.), date of lease or purchase, and/or other information potentially affecting the price of a vehicle. Personal information can include demographic (e.g., age), financial (e.g., income, credit rating), residence (e.g., home address), and/or prior behavior information (e.g. driving record). In some embodiments, comparison resource  107  can be configured to store comparison information in one or more databases. The one or more databases can be configured to allow retrieval of comparison information according to one or more of pricing information, vehicle-related information, and personal information. For example, comparison resource  107  can be configured to allow another component of system  100  (e.g., system  105 ) to retrieve at least a subset of comparison information (e.g., pricing information) concerning a particular vehicle make, model, and trim. In some embodiments, comparison resource  107  can include information concerning customers of a financial service provider. In some aspects, the financial service provider can be associated with one or more other components of system  100 , such as system  105 . 
     Client device  190  may include one or more computing devices configured to perform operations consistent with disclosed embodiments. For example, client device  190  may include at least one of a desktop computer, a laptop, a server, a mobile device (e.g., tablet, smart phone, etc.), a gaming device, a wearable computing device, or other type of computing device. Client device  190  may include one or more processors configured to execute software instructions stored in memory, such as memory included in client device  190 . Client device  190  may include software that when executed by a processor performs Internet-related communication and content display processes. For instance, client device  190  may execute browser software that generates and displays interfaces including content on a display device included in, or connected to, client device  190 . Client device  190  may execute applications that allow client device  190  to communicate with components over network  170 , and generate and display content in interfaces via a display device included in client device  190 . The display device may be configured to display images described in  FIG. 9 . The disclosed embodiments are not limited to any particular configuration of client device  190 . For instance, client device  190  can be a mobile device that stores and executes mobile applications that interact with server system  105  and/or online resource  106  to perform aspects of the disclosed embodiments, such as providing information about vehicles in database  180 . In certain embodiments, client device  190  may be configured to execute software instructions relating to location services, such as GPS locations. For example, client device  190  may be configured to determine a geographic location and provide location data and time stamp data corresponding to the location data. In yet other embodiments, client device  190  may capture video and/or images. 
     Database  180  may include one or more computing devices configured to provide vehicle data to one or more of server system  105 , model generator  120 , and image classifier  130 . In some aspects, such vehicle data can include vehicle inventory listings, vehicle images, vehicle features, and stored information about vehicle sales like cost or condition. Database  180  may include, for example, one or more Oracle™ databases, Sybase™ databases, or other relational databases or non-relational databases, such as Hadoop™ sequence files, HBase™, or Cassandra™. Database(s)  180  may include computing components (e.g., database management system, database server, etc.) configured to receive and process requests for data stored in memory devices of the database(s) and to provide data from the database(s). 
     While database  180  is shown separately, in some embodiments database  180  may be included in or otherwise related to one or more of server system  105 , image recognizer  110 , model generator  120 , image classifier  130 , and online resource  106 . 
     Database  180  may be configured to collect and/or maintain vehicle data for vehicles displayed using online resource  106 . Database  180  can be configured to provide such data to the server system  105 , model generator  120 , image classifier  130 , and client device  190 . Database  180  may collect this data from a variety of sources, including, for instance, online resource  106 . Database  180  is further described below in connection with  FIG. 3 . 
     Image classifier  130  may include one or more computing systems configured to collect and process images. Image classifier  130  may be configured to use the processed images to create training data sets. Consistent with disclosed embodiments, these training data sets can be used to develop an identification model. For example, image classifier  130  may include an image collector that collects images for use in training a linear regression classifier or a convolutional neural network. In some embodiments, image classifier  130  can be configured to communicate with online resource  106  and to detect changes in online resource  106 . In some embodiments, detection of such changes can cause image classifier  130  to collect images and begin the classification process. 
     Model generator  120  may include one or more computing systems configured to generate models to identify a vehicle using a vehicle image. Model generator  120  may be configured to receive or obtain information from database  180 , online resource  106 , and/or image classifier  130 . For example, model generator  120  may receive a plurality of images from database  180  and online resource  106 . As and additional example, model generator  120  may also receive images and metadata from image classifier  130 . 
     In some embodiments, model generator  120  may receive requests from image recognizer  110 . As a response to such a request, model generator  120  may generate one or more identification models. In some aspects, identification models may include convolutional neural networks that determine attributes in an image based on features extracted from the image. In various aspects, identification models may include statistical algorithms. After training with a set of training images, these statistical algorithms can determine a similarity between images. For example, identification models may include regression models that estimate the relationships among input and output variables. In some aspects, identification models may additionally sort elements of a dataset using one or more classifiers to determine the probability of a specific outcome. Statistical identification models may be parametric, non-parametric, and/or semi-parametric models. A convolutional neural network model can be configured to process an image into a collection of features. The convolutional neural network can comprise an input layer, one or more middle layers, and one or more output layers. An image can be applied to the input layer. In some embodiments, the input layer can comprise multiple matrices (e.g., a matrix for each of the red, green, and blue pixel values in an RGB image). In some embodiments, the input layer can comprise a single matrix (e.g., a single two-dimensional matrix of pixel brightness values). In some aspects, each middle layer and the output layer can be a deterministic function of the values of the preceding layer. The convolutional neural network can include one or more convolutional layers. Each convolutional layer can be configured to convolve one or more spatial filters with the convolutional layer input to generate a convolutional layer output tensor. Each spatial filter function can be defined by a matrix of weights to be applied to each element of the preceding layer during a convolution operation, an offset, and an activation function. Training the convolutional neural network can comprise modifying the weights and offsets using a backpropagation algorithm. In some embodiments, the convolutional neural network can also include pooling layers and fully connected layers according to methods known in the art. Identification models may also include Random Forests, composed of a combination of decision tree predictors. Such decision trees may comprise a data structure mapping observations about something, in the “branch” of the tree, to conclusions about that thing&#39;s target value, in the “leaves” of the tree. Each tree may depend on the values of a random vector sampled independently and with the same distribution for all trees in the forest. Identification models may additionally or alternatively include classification and regression trees, or other types of models known to those skilled in the art. 
     Model generator  120  may be configured to submit models to identify a vehicle. To generate identification models, model generator  120  may analyze images that are classified by the image classifier  130 , applying machine learning methods. Model generator  120  may communicate with server system  105  via network  170  or other communication avenues. 
     Image recognizer  110  may be configured to identify vehicles based on images of vehicles. In some embodiments, Image recognizer  110  may receive a request to identify a vehicle in an image. Image recognizer  110  may receive the request directly from client device  190 . Alternatively, image recognizer  110  may receive the request from other components of system  100 . For example, client device  190  may send requests to online resource  106 , which then sends requests to server system  105 . The request may include an image of a vehicle and a location of client device  190 . In other embodiments, the request may include a video file or a streaming video feed. 
     As a response to identification requests, server system  105  may request identification models from model generator  120 . The request may include information about the image source, for example an identification of client device  190 . The request may additionally specify a location. In addition, image recognizer  110  may retrieve information from database  180 . 
     Image recognizer  110  may generate an identification result based on the information received from the client request and transmit the information to the client device. Image recognizer  110  may generate instructions to modify a graphical user interface to include identification information associated with the received image. 
       FIG. 1  shows image recognizer  110 , model generator  120 , and image classifier  130  as different components. However, image recognizer  110 , model generator  120 , and image classifier  130  may be implemented in the same computing system. 
     Location determination system  140  may include one or more computing systems configured to perform operations consistent with determining a location of a client device  190 . In some embodiments, location determination system  140  may transmit a request to client device  190  for location information. In response to the request, client device  190  may transmit location information such as geographic coordinates or identifiers to the location determination system  140  via network  170 . The location determination system  140  may map the geographic coordinates or identifiers to a specific geographic location. The location determination system  140  may include satellite based geolocation systems, e.g., GPS, Glonass, Galileo, etc.; long range terrestrial geolocation systems, e.g., LORAN; short-range network based geolocation systems, e.g., network based cellular e911; short range proximity location sensing, e.g., 802.11 access point identification, line-of-sight location identification, e.g., IRdA or other visible, sonic or invisible electromagnetic waves which are confined by barriers; RFID based location detectors; and the like. 
     Authentication module  150  may include one or more computing systems configured to perform one or more operations consistent with authenticating a user. In some embodiments, a user registers for an account and creates a user name and password. The user account information, including the user name and password, may be stored in memory  160 , for example. To access the user account, a registered user can provide the user name and password information to the authentication module  150  via client device  190 . Authentication module  150  can be configured to authenticate the user based on the received user name and password information. Authentication module  150  can then grant access to the user account. In some embodiments, user account information includes pre-qualified financing application information, as discussed further below. 
     Network  170  may be any type of network configured to provide communications between components of system  100 . For example, network  170  may be any type of network (including infrastructure) that provides communications, exchanges information, and/or facilitates the exchange of information, such as the Internet, a Local Area Network, near field communication (NFC), optical code scanner, or other suitable connection(s) that enables the sending and receiving of information between the components of system  100 . In some embodiments, one or more components of system  100  can communicate through network  170 . In various embodiments, one or more components of system  100  may communicate directly through one or more dedicated communication links. 
     It is to be understood that the configuration and boundaries of the functional building blocks of system  100  have been defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. 
       FIG. 2  is a block diagram of client device  190 , in accordance with disclosed embodiments. In one embodiment, client device  190  may include processor  202 , location sensor  204 , input/output (I/O) system  206 , and memory  210 . In some embodiments, client device  190  may take the form of a mobile computing device (e.g., a wearable device, smartphone, tablets, laptop, or similar device), a desktop (or workstation or similar device), or a server. Alternatively, client device  190  may be configured as a particular apparatus, embedded system, dedicated circuit, and the like based on the storage, execution, and/or implementation of the software instructions that perform one or more operations consistent with the disclosed embodiments. According to some embodiments, client device  190  may be configured to provide a web browser or similar computing application capable of accessing web sites, consistent with disclosed embodiments. 
     Processor  202  may include one or more known processing devices, such as mobile device microprocessors manufactured by Intel™, NVIDIA™, or various processors from other manufacturers. As would be appreciated by one of skill in the art, the disclosed embodiments are not limited to a particular processor type. 
     Memory  210  may include one or more storage devices configured to store instructions for performing operations related to disclosed embodiments. For example, memory  210  may be configured with one or more software instructions, such as program  212 , that when executed by processor  202 , can cause client device  190  to perform operations according to the disclosed embodiments. The disclosed embodiments are not limited to separate programs or computers configured to perform dedicated tasks. For example, client device  190  can be configured to perform the disclosed functions of client device  190  by one or more programs stored in memory  210  (e.g., program  212 ). In some aspects, memory  210  can be configured to store data  216  used by one or more programs  212 . 
     In certain embodiments, memory  210  may store a vehicle identification application  214  that may be executed by processor(s)  202  to perform one or more identification processes consistent with disclosed embodiments. In certain aspects, vehicle identification application  214 , or another software component, may be configured to request identification from server system  105  or determine the location of client device  190 . 
     Location sensor  204  may include sensor(s) and/or system(s) capable of determining a location of client device  190 , such as a Global Positioning System (GPS) receiver, Bluetooth transceiver, or WiFi transceiver. When location sensor  204  includes multiple sensor(s) and/or system(s), client device  190  can be configured to determine geographic coordinates or identifiers, such as latitude or longitude based on data provided from multiple sensor(s) and/or system(s). Client device  190  can be configured to send the geographic coordinates or identifiers determined by location sensor  204  to other components of system  100  via, for example, network  170 . 
     I/O system  206  may include one or more devices configured to allow data to be received and/or transmitted by client device  190  and to allow client device  190  to communicate with other machines and devices, such as other components of system  100 . For example, I/O system  206  may include a screen for providing information to the user. I/O system  206  may also include components for NFC communication. I/O system  206  may also include one or more digital and/or analog devices that allow a user to interact with client device  190  such as a touch-sensitive area, buttons, or microphones. I/O system  206  may also include one or more accelerometers to detect the orientation and inertia of client device  190 . I/O system  206  may also include other components known in the art for interacting with server system  105 . 
     In some embodiments, client device  190  may include image sensor  220  (e.g., a digital camera). Image sensor  220  can be configured to take images or video and send it to other component of system  100  via, for example, network  170 . 
     The components of client device  190  may be implemented in hardware, software, or a combination of both hardware and software, as will be apparent to those skilled in the art. 
       FIG. 3  is a block diagram of an exemplary database  180 , in accordance with disclosed embodiments. Database  180  may include a communication device  302 , one or more database processors  304 , and database memory  310  including one or more database programs  312  and data  314 . In some embodiments, database  180  may be hosted on one or more computing devices (e.g., desktops, workstations, servers, and computing clusters). 
     Communication device  302  may be configured to communicate with one or more components of system  100 , such as online resource  106 , comparison resource  107 , server system  105 , model generator  120 , image classifier  130 , and/or client device  190 . In particular, communication device  302  may be configured to provide to model generator  120  and image classifier  130  images of vehicles that may be used to generate a convolutional neural network or an identification model. 
     The components of database  180  may be implemented in hardware, software, or a combination of both hardware and software, as will be apparent to those skilled in the art. For example, although one or more components of database  180  may be implemented as computer processing instruction modules, all or a portion of the functionality of database  180  may be implemented instead in dedicated electronics hardware. 
     Data  314  may be data associated with websites, such as online resource  106 . Data  314  may include, for example, information from websites of vehicle dealers or vehicle manufacturers. Data  314  may include images of vehicles and information of vehicles, such as cost, condition, and dealers offering the vehicle. Data  314  may include vehicle inventory information for a particular vehicle dealership. Data  314  may include a map of a dealership lot and include specific location information for each vehicle in the dealership based on the map. 
       FIG. 4  is a flowchart of an exemplary method  400  for generating a real-time quote for a vehicle, consistent with embodiments of the present disclosure. Method  400  can include the steps of identifying vehicle attributes based on an image, obtaining pricing data, and generating a quote. Method  400  can be performed using system  100 . In some embodiments, one or more steps of method  400  can be performed by client device  190 . In various embodiments, one or more steps of method  400  can be performed by system  105 . In certain embodiments, method  400  can be performed entirely by system  105  (or by client device  190 ). 
     After starting in step  401 , system  100  can identify vehicle attributes based on an image in step  403 . In some embodiments, identification of vehicle attributes can be performed using system  105 . For example, system  105  can be configured to use image recognizer  110  to identify vehicle attributes. In some aspects, image recognizer  110  can be configured to use a model generated by model generator  120 . In some aspects, system  105  can be configured to receive the image from client device  190 . In various embodiments, identification of vehicle attributes can be performed using client device  190 . For example, client device  190  can be configured to receive a model generated by model generator  120 . In various aspects, client device  190  can be configured to obtain an image (e.g., using image sensor  220 ). Client device  190  can be configured to apply this image to the model to identify vehicle attributes. 
     After step  403 , system  100  can obtain pricing data in step  405 . In some embodiments, the pricing data can be obtained using system  105 . For example, system  105  can be configured to obtain pricing data from one or more of database  180 , online resource  106 , or comparison resource  107 . As an additional example, system  105  can be configured to obtain data  314  from database  180 . As a further example, system  105  can be configured to obtain from comparison resource  107  at least one of pricing information, vehicle-related information, and personal information. As an additional example, system  105  can be configured to obtain from online resource  106  at least one of information retrieved from the website of a vehicle manufacturers and/or vehicle dealer. In some aspects, system  105  can be configured to receive information concerning a user from client device  190 . In various embodiments, the pricing data can be obtained using client device  190 . For example, client device  190  can be configured to interact with at least one of a user, database  180 , online resource  106 , or comparison resource  107  to obtain the pricing data. 
     After step  405 , system  100  can be configured to generate a quote in step  407 . The quote can depend on the pricing data gathered in step  405 . For example, the quote can include at least one of a vehicle price, monthly payment amount, APR, term length, or amount financed based on the pricing data. In some embodiments, the quote can be generated by system  105 . In various embodiments, the quote can be generated by client device  190 . After step  407 , method  400  can end at step  409 . 
       FIGS. 5A-5C  are flowcharts of exemplary methods for identifying vehicle attributes based on an image, consistent with embodiments of the present disclosure. In some embodiments, system  100  can use the exemplary methods depicted in  FIGS. 5A-5C  to identify vehicle attributes based on an original image in step  403  of method  400 . 
     According to method  510 , after starting in step  511 , system  100  can be configured to identify vehicle attributes from an original image in step  513 . In some aspects, system  105  can be configured to identify the vehicle attributes. In various aspects, client device  190  can be configured to identify the vehicle attributes. The vehicle attributes can be identified from an image obtained by client device  190 . The vehicle attributes can be identified using a model generated by model generator  120 . After step  513 , method  510  can end at step  519 . 
     According to method  520 , after starting in step  521 , system  100  can be configured to identify potential vehicle attributes from an original image in step  523 . The identification of potential vehicle attributes in step  523  may proceed similarly to the identification of vehicle attributes in step  513 . After step  523 , system  100  can retrieve vehicle images in step  525 . In some aspects, the vehicle images can be retrieved from a database associated with one or more dealerships and/or vehicle manufacturers (e.g., database  180 ). In some aspects, this database can include records associating images with vehicle attributes. For example, a record can include one or more images of a vehicle and one or more vehicle attributes for that vehicle. As an additional example, a record could include images of a Chevy Bolt and the vehicle attributes “Chevy” and “Bolt”. In some embodiments, system  100  can be configured to create the records by extracting the vehicle attributes from the one or more images. In various aspects, the vehicle images can be retrieved using one or more of the vehicle attributes identified in step  523 . For example, images of vehicles with one or more attributes similar to those vehicle attributes identified in step  523  can be retrieved. As an additional example, when the vehicle attributes “Chevy” and “Bolt” are extracted from the original image in step  523 , the record describe above could be identified, and the associated images of the Chevy Bolt retrieved. In various embodiments, images can be retrieved in this manner by client device  190  or system  105 . After step  525 , system  100  can be configured to confirm the vehicle identification in step  527 . In some embodiments, system  100  can be configured to compare the original image to the retrieved image. In certain aspects, the comparison can be performed by client device  190  or system  105 . The comparison can be performed using machine learning features extracted from the original image and the images retrieved from the database. System  100  can be configured to determine that the original image and the one or more retrieved images are similar when the comparison satisfies a matching criterion. In some aspects, system  100  can identify vehicle attributes of the original image based on the potential vehicle attributes identified in step  523  and the vehicle attributes associated with the one or more retrieved images. For example, system  100  can be configured to select the potential vehicle attributes as the actual vehicle attributes when they match the vehicle attributes associated with the one or more retrieved images. As an additional example, system  100  can be configured to select the vehicle attributes associated with the one or more retrieved images as the actual vehicle attributes. For example, the vehicle image can be retrieved by make and model, and the value for trim and year can be selected from the vehicle attributes associated with the one or more retrieved images. In this manner, the potential vehicle attributes can be used to retrieve a limited number of images from the database, reducing the number of required image comparisons to determine the actual vehicle attributes. After step  527 , method  520  can end at step  529 . 
     According to method  530 , after starting in step  531 , system  100  can be configured to confirm a vehicle identification in step  537 . In some embodiments, system  100  can be configured to compare an original image to the images in a database (e.g., database  180 ). In certain aspects, the comparison can be performed by client device  190  or system  105 . The comparison can be performed using machine learning features extracted from the original image and the images in the database. System  100  can be configured to determine that the original image and the one or more retrieved images are similar when the comparison satisfies a matching criterion. After identifying similar images in step  537 , system  100  can be configured to retrieve vehicle attributes associated with the matched images in step  538 . For example, the database can be configured to store images of vehicles associated with attributes of the depicted vehicles. System  100  can match the original image to images in the database. The vehicle attributes for the original image can then be retrieved from the database based on the matching image(s). In some aspects, comparing all images in the database to the original image may be practicable. After step  538 , method  530  can end at step  539 . 
       FIG. 6  is a flowchart of an exemplary method  600  for identifying vehicle attributes (as described above with regards to  FIGS. 5A and 5B ) using two convolutional neural networks, consistent with embodiments of the present disclosure. After starting in step  601 , a component of system  100  (e.g., client device  190  or system  105 ) can receive an image in step  603 . In some embodiments, the image can be obtained using an image sensor  220 . In various embodiments the image can be retrieved from a database (e.g. database  180 ). In step  605 , a component of system  100  (e.g., client device  190  or system  105 ) can extract machine learning features from the retrieved image. In some embodiments, these features can be extracted by inputting the image, or a version of the image, into a first convolutional neural network. The first convolutional neural network can be configured to output the machine learning features. In step  607 , a component of system  100  (e.g., client device  190  or system  105 ) can extract vehicle attributes from the machine learning features. In some embodiments, these features can be extracted by inputting the machine learning features into a second convolutional neural network. In some embodiments, the second convolutional neural network can be distinct from the first convolutional neural network. The second convolutional neural network can be configured to output indications of the vehicle attributes. After step  607 , method  600  can end at step  609 . 
       FIG. 7  is a flowchart of an exemplary method  700  for comparing images using a convolutional neural network (as described above with regards to  FIGS. 5B and 5C ), consistent with embodiments of the present disclosure. As shown in  FIG. 7 , system  100  can extract features from a database image and a mobile device image, compare these features, and determine whether the images are similar. 
     After starting in step  701 , a component of system  100  (e.g., client device  190  or system  105 ) can obtain an image from a database (e.g., database  180 ) in step  703   a . For example, client device  190  or system  105  can be configured to communicate with database  180  to retrieve an image. After step  703   a , a component of system  100  (e.g., client device  190  or system  105 ) can extract machine learning features from the image using a convolutional neural network in step  705   a . Similarly, after starting in step  701 , a component of system  100  (e.g., client device  190  or system  105 ) can obtain an image from a mobile device (e.g., using image sensor  220  of client device  190 ) in step  703   b . For example, system  105  can be configured to communicate with client device  190  to retrieve an image captures by image sensor  220 . After step  703   a , a component of system  100  (e.g., client device  190  or system  105 ) can extract machine learning features from the image using a convolutional neural network in step  705   a . Likewise, after step  703   b , a component of system  100  (e.g., client device  190  or system  105 ) can extract machine learning features from the mobile image using a convolutional neural network in step  705   b . In some aspects, system  100  can be configured to extract machine learning features from the database image and the mobile image using different components of system  100 . For example, system  105  can extract the machine learning features of the database image, and client device  190  can extract the machine learning features of the mobile device image. After steps  705   a  and  705   b , component of system  100  (e.g., client device  190  or system  105 ) can calculate a comparison metric in step  707 . In some embodiments, this calculation can involve inputting the machine learning features extracted from the database image and the machine learning features extracted from the mobile device image into a classifier, such as another neural network. In additional embodiments, a component of system  100  can be configured to calculate a metric based on the extracted machine learning features (e.g., an inner product of the machine learning features extracted from the database image and the machine learning features extracted from the mobile device image). A value of the metric can indicate the degree of similarity between the images. In some instances values of the metric can increase with increasing similarity. In various instances values of the metric can decrease with increasing similarity. After step  707 , a component of system  100  (e.g., client device  190  or system  105 ) can determine whether the calculated comparison metric satisfies a matching criterion. For example, when the metric increases with increasing similarity, the matching criterion can be satisfied when a value of the metric exceeds a threshold value. After step  709 , a component of system  100  (e.g., client device  190  or system  105 ) can identify the image as either a similar image (step  713 ) or a dissimilar image ( 711 ). System  100  can be configured to rely upon this determination when confirming identification of a vehicle (e.g., in step  527  of method  520  or in step  537  of method  530 ). After step  711  or step  713 , method  700  can end at step  715 . 
       FIG. 8  is a flowchart depicting a method  800  for generating pricing data, consistent with embodiments of the present disclosure. In some embodiments, system  100  can use method  800  in step  405  of method  400 . After starting in step  801 , a component of system  100  (e.g., client device  190  or system  105 ) can retrieve information from at least one of the following categories: general vehicle information, general customer information, specific vehicle information, and specific customer information. General vehicle information  803  can include general pricing information (e.g., MSRP, invoice price, rebates). For some vehicles, general vehicle information  803  may be the only information available. In some embodiments, general vehicle information  803  can be retrieved from at least one of database  180 , online resource  106 , or comparison resource  107 . General customer information  805  can include the financing terms received by other users for the identified vehicle (within a certain period of time, in a certain geographic area, for users having certain characteristics, etc.). In some embodiments, general customer information  805  can be retrieved from comparison resource  107 . Specific vehicle information  807  can include the price and available rebate information for a particular car (e.g. when the image concerns a particular car at a dealership). In some embodiments, specific vehicle information  807  can be retrieved from at least one of database  180  or online resource  106 . Specific customer information  809  can include prequalification information for a particular user (credit rating, income, etc.). In some embodiments, specific customer information  809  can be retrieved from at least one of system  105  or client device  190 . For example, a customer can interact with system  105  (or client device  190 ) to prequalify for vehicle financing, and specific customer information can be received and stored by system  105  (or client device  190 ) during that prequalification. As an additional example, a customer can interact with client device  190  after providing an image of a vehicle. In this example, the customer can provide specific customer information to enable system  100  to generate a personalized quote for the vehicle in the image. After retrieving data in one or more of steps  803 ,  805 ,  807 , and  809 , system  100  can generate pricing data in step  811 . Generating the pricing data can include storing the retrieved data on at least one of system  105  and client device  190 . In some aspects, generating the pricing data can include organizing the retrieved data according to a common schema (e.g., a common record layout with common datatypes and formats, regardless of the origin of the retrieved data). After step  811 , method  800  can finish at step  813 . In this manner, a component of system  100  can be configured to generate pricing data using one or more of database  180 , system  105 , comparison resource  107  and online resource  106 . In some embodiments, system  100  can be configured to indicate that a quote is a personalized quote when the quote is generating using specific vehicle information and specific customer information. System  100  can be configured to indicate otherwise that the quote is an estimate. 
       FIG. 9  is a flowchart of an exemplary method  900  for generating a real-time quote for a vehicle, according to disclosed embodiments. In some embodiments, method  900  may be carried out by one or more components of system  100  (e.g., system  105  and/or client device  190 ). In some aspects, method  900  can proceed similarly to the methods described above with regards to  FIGS. 4, 5B and 5C . 
     In step  902 , a component of system  100  (e.g., system  105  or client device  190 ) can determine a location of a user&#39;s client device. For example, client device  190  can obtain a location from location sensor  204 . As an additional example, location determination system  140  may initiate a request for a location of client device  190 . Client device  190  may transmit location information, such as longitude, latitude, or other geographic identifiers, to the location determination system  140  in response to the request. The location information may be obtained using location sensor  204  in client device  190 . In some embodiments, system  105  can determine that location services on client device  190  is not enabled. The system may then transmit information for display on client device  190  requesting location services be enabled. Once the location services transmits location information, location determination system  140  may determine a specific geographic location of client device  190  based on the transmitted location information. 
     In step  904 , a component of system  100  (e.g., system  105  or client device  190 ) can receive a first image of a vehicle from an image sensor of client device  190 . For example, system  105  may send instructions for taking a picture of a vehicle to a user interface displayed on client device  190 . In some embodiments, the user may be instructed to take a picture of a vehicle using an image sensor  220 , such as a camera, of the client device  190 . The user interface may include instructions guiding the user to take the image of the vehicle, as described in further detail below in relation to  FIG. 14 . The image may then be transmitted to image recognizer  110  through network  170 . 
     In step  906 , a component of system  100  (e.g., system  105  or client device  190 ) can match the first image to one or more images of vehicles in a database to identify the vehicle. In some aspects, the database (e.g., database  180 ) can include records for vehicles located at the determined location. For example, the database can contain vehicle inventory information for a particular vehicle dealership located at the determined location. In some embodiments, the vehicle inventory information can include a listing of every vehicle available at the dealership and related information. For example, related information may include a vehicle make, model, vehicle identification number, color, trim, mileage, and/or other identifying information of a vehicle. Related information may also include images of the exterior and/or interior of the vehicle. In some embodiments, related information can include the specific location of the vehicle in the dealership based on a map of the dealership lot. In some embodiments, system  105  can be configured to retrieve related information in step  404 . In various embodiments, such related information can be retrieved in step  408 . 
     In some embodiments, as described above with regards to  FIG. 5B , system  100  can, in step  906 , extract attributes from the first image and match the first image to a subset of images in the database based on the extracted attributes. In various embodiments, as described above with regards to  FIG. 5C , system  100  can, in step  906 , match the first image to the images in the database. 
     In some embodiments, system  100  can provide instructions to display on client device  190  a request for confirmation that the user is at the identified location, such as the particular vehicle dealership. After receiving confirmation from the user via the client device, the component of system  100  (e.g., system  105  or client device  190 ) can access database  180  to retrieve one or more images of vehicles in the database. 
     In some embodiments, image recognizer  110  (or client device  190 ) can use a machine learning algorithm to match the first image to the one or more images in the database (e.g., database  180 ). In some aspects, the machine learning algorithm may be a convolutional neural network, as disclosed above. In some embodiments, as described above with regards to  FIGS. 5B and 5C , system  105  may retrieve vehicle information from the database in step  908  once the vehicle is identified. For example, the first image may be matched to an image of a vehicle listed in the vehicle inventory of the dealership (e.g., as described above with regard to  FIG. 7 ). Once the system determines a match, the related vehicle information can be retrieved from the database. As used herein, a “match” may include a similarity between the images that is greater than a predefined threshold. As described above, in some embodiments, the vehicle information for the dealership can be retrieved at step  906 . In such embodiments, the relevant vehicle information can be selected at step  908  from the previously retrieved vehicle information. 
     In some embodiments, image recognizer  110  (or client device  190 ) can verify that the match relates to the correct vehicle. For example, image recognizer  110  can determine a match between the first image and one or more images relating to one or more vehicles listed in the database (e.g., database  180 ). In some aspects, system  105  may then transmit the one or more matched images to the user interface of client device  190 . System  105  may transmit instructions for display on the client device  190 , requesting confirmation from the user to verify the identified vehicle. For example, the user interface may be prompted to select an image of the correct vehicle. In some embodiments, image recognizer  110  may verify that the match relates to the correct vehicle by determining the expected location of the vehicle. For example, image recognizer  110  may obtain a map of the dealership from database  180 , including the expected location of each vehicle listed in the dealership inventory on the map. Image recognizer  110  may further use geographical identification information embedded in the first image to determine a location of the imaged vehicle. For example, client device  190  may embed the first image with metadata providing geographic location information when the first image is captured. In some embodiments, image recognizer  110  may compare the expected location of the identified vehicle with the determined location of the imaged vehicle to verify that the identified vehicle is correct. In various aspects, in place of system  105 , client device  190  can determine the match, provide the one or more matched images for display independent of system  105  for user confirmation, and/or verify the that the match relates to the expected vehicle location. 
     In step  910 , system  105  can obtain user information relating to a vehicle financing request. In some embodiments, the user can create a user account and can submit an application to pre-qualify for vehicle financing. For example, the user may visit a website relating to the lender to register for a user account. The user account registration may require creating authentication information such as a username and password. Once the user account is registered, the authentication information can be used to access the account. For example, the user may be prompted to enter authentication information via a user interface on the client device  190  to access the account. In some embodiments, the application to pre-qualify for vehicle financing includes a request for user information. The user information may include the name, email address, home address, phone number, mailing address, date of birth, and/or social security number of the user. The user information may further include employment information including the name of the employer, job title, and/or annual income of the user. In some embodiments, after submitting the application to pre-qualify, the user can log into their account to provide system  105  with the required user information relating to the vehicle financing request. 
     In some embodiments, system  105  can transmit a vehicle financing application form for display on client device  190  to obtain the user information in step  910 . The application form may include a request for user information similar to the application to pre-qualify discussed above. In some embodiments, the application form can include a shortened questionnaire to provide a prompt real-time quote. For example, the questionnaire may request information relating to the vehicle condition, the user&#39;s credit, the amount desired to finance, and/or the preferred term length. In some embodiments, the shortened questionnaire information is sufficient to provide the required user information. 
     In step  912 , a component of system  100  (e.g., system  105  or client device  190 ) can determine a real-time quote for the vehicle based on the vehicle information obtained at step  908  and the user information obtained at step  910 . In some embodiments, the real-time quote includes pricing information relating to the identified vehicle. For example, the pricing information may include a vehicle price, monthly payment amount, APR, term length, and/or amount financed. This real-time quote can be personalized based on the information obtained from the user in step  910  (e.g., through pre-qualification and/or a questionnaire). As a non-limiting example, the real-time quote can reflect the user&#39;s credit, the amount desired to finance, and/or the preferred term length. 
     In step  914 , client device  190  can display the real-time quote. In some embodiments, system  105  can transmit the quote for display on client device  190 . For example, the real-time quote may be transmitted via network  170  and displayed on a user interface on client device  190 . In some embodiments, the user may manipulate different factors to determine how the quote is affected. For example, the user may add, remove, or make changes to a service contract, gap insurance, or down payment to determine the effect on payment options, APR, and/or monthly payments relating to the quote. In various embodiments, client  190  can generate, display, and update the real-time quote in response to user input. 
       FIG. 10  is a flowchart of an exemplary method  1000  for generating a real-time quote for a vehicle, according to disclosed embodiments. In some embodiments, method  1000  may be carried out by one or more components of system  100  (e.g., system  105  and/or client device  190 ). In some aspects, method  1000  can proceed similarly to the methods described above with regards to  FIGS. 4, 5B, 5C, and 8 . 
     In some embodiments, steps  1002 - 1010  may proceed as described above with regards to  FIG. 9 . For example, in step  1002 , a component of system  100  (e.g., system  105  or client device  190 ) can determine a location of a user&#39;s client device. In step  1004 , a component of system  100  (e.g., system  105  or client device  190 ) can receive a first image of a vehicle from an image sensor of the client device  190 . In some embodiments, this image may then be transmitted to image recognizer  110  through network  170 . 
     In step  1006 , a component of system  100  (e.g., system  105  or client device  190 ) can match the first image to one or more images of vehicles in a database to identify the vehicle. The database can include records for vehicles located at the determined location. In some aspects, the database can include vehicle inventory information, such as a listing of every vehicle available at the dealership and related information. For example, related information may include a vehicle make, model, vehicle identification number, color, trim, mileage, and/or other identifying information of a vehicle. Related information may also include images of the exterior and/or interior of the vehicle. In some embodiments, related information can include the specific location of the vehicle in the dealership based on a map of the dealership lot. 
     In some embodiments, image recognizer  110  (or client device  190 ) can use match the first image to the one or more images in the database (e.g., as described above with regard to  FIG. 7 ). The training of the machine learning algorithms and the performance of the matching processes may occur as described below with regards to  FIGS. 16-18 . For example, the first image may be matched to an image of a vehicle listed in the vehicle inventory of the dealership. As disclosed above, images that match may differ by less than a predefined threshold according to some measure of similarity. When more than one vehicle matches the first image, one of the images may be selected according to predetermined criteria (e.g., the first matching image, the most-similar image, the image matching the vehicle with the lowest or highest price). Once system  105  determines a match, the vehicle information for the matching vehicle can be retrieved from the database in step  1008 . 
     In some embodiments, the component of system  100  performing the matching can be configured to request the user confirm the identified vehicle. For example, client device  190  can be configured to display image(s) of the matching vehicle(s) and a request that the user confirm the matches. As an additional example, system  105  can be configured to transmit image(s) of matching vehicle(s) to a client device of the user. System  105  can then receive a response indicating the correct vehicle (or indicating that none of the vehicles is the correct vehicle). In some embodiments, image recognizer  110  (or client device  190 ) may verify that the match relates to the correct vehicle by determining the expected location of the vehicle. For example, image recognizer  110  (or client device  190 ) may obtain a map of the dealership from database  180 , including the expected location of each vehicle listed in the dealership inventory on the map. Image recognizer  110  (or client device  190 ) may further use geographical identification information embedded in the first image to determine a location of the imaged vehicle. For example, client device  190  may embed the first image with metadata providing geographic location information when the first image is captured. In some embodiments, image recognizer  110  (or client device  190 ) may compare the expected location of the identified vehicle with the determined location of the imaged vehicle to verify that the identified vehicle is correct. 
     In step  1010 , a component of system  100  (e.g., system  105  or client device  190 ) can obtain comparison information based at least in part on the retrieved vehicle information. In some embodiments, the component can be configured to obtain comparison information from comparison resource  107 . In some aspects, the component can be configured to retrieve pricing information from comparison resource  107  based on at least one of vehicle-related information and personal information. For example, the component can retrieve pricing information for vehicles similar to the identified vehicle or matching the identified vehicle. As a further example, the component can be configured to retrieve pricing information for individuals who recently leased or purchased vehicles similar to the identified vehicle or matching the identified vehicle in a similar geographic area. As an additional example, in some embodiments system  105  can be configured to receive at least some user information from the user. In some embodiments, the component can be configured to access a user account, such as the user accounts described above with regards to  FIG. 1  and  FIG. 9 , to retrieve at least some user information. In various embodiments, client device  190  can obtain user information from the user. In some embodiments, system  105  can obtain user information from the user through the client device. In some aspects, system  105  can be configured to provide instructions to display a request for items of user information (e.g., credit score, income) or items affecting vehicle financing (desired finance amount, preferred term of financing or lease) to the client device. System  105  (or client device  190 ) can then retrieve pricing information for vehicles similar to the identified vehicle or matching the identified vehicle that were purchased or leased by individuals similar to the user. The similarity of the individuals to the user can be determined based on the received user information and the personal information stored by comparison resource  107 . In this manner, comparison information can be obtained for similar or matching vehicles purchased or leased, or a subset of such vehicles. The obtained comparison information can be defined by recency, geography, or personal characteristics of the users leasing or purchasing the similar or matching vehicles. 
     In step  1012 , a component of system  100  (e.g., system  105  or client device  190 ) can estimate a quote for the vehicle based on the vehicle information obtained in step  1008  and/or the comparison information obtained at step  1010  (e.g., as described above with regards to  FIG. 8 ). In some aspects, the component can be configured to estimate one or more quotes based on the retrieved pricing information. In various aspects, the estimated quotes can be specific to particular financing options. For example, estimated quotes can be provided for auto purchase loans of different durations and/or auto lease arrangements of different durations. In this manner, estimated quotes can be determined based on the actual pricing terms provided to other individuals. In some embodiments, the estimated quotes can be statistics of the retrieved pricing information. For example, the estimated down payment and monthly payment for a 60 month auto loan for the identified vehicle can be the mean (or median) of the down payment and monthly payment for individuals financing matching vehicles (or similar individuals financing matching vehicles) with 60 month auto loans. 
     In some embodiments, the component can be configured to estimate one or more quotes in response to input received from client device  190 . For example, the user can specify financing parameters and system  105  can estimate quotes in accordance with the specified parameters. For example, the user can select or modify a choice of leasing or loaning the vehicle; the term of the loan or lease; a down payment for the vehicle; and/or a service contract or gap insurance for the vehicle system  105  can estimate a quote for the vehicle based on this input. 
     In step  1014 , system  100  can display the real-time quote on the client device  190 . In some aspects, system  105  can transmit the one or more real-time quotes to client device  190  via network  170 . In various aspects, the one or more quotes can be displayed on a user interface of client device  190 . In some embodiments, the user interface may provide an augmented reality (AR) display. For example, client device  190  can be configured to display the quote superimposed on the image of the vehicle. For example, when client device  190  detects that the image sensor of client device  190  is positioned correctly, client device  190  can be configured to display the quote superimposed on or around the vehicle. 
       FIG. 11  is a flowchart of another exemplary method for generating a real-time quote for a vehicle, according to disclosed embodiments. In some embodiments, method  1100  may be carried out by one or more components of system  100  (e.g., system  105  and/or client device  190 ). In some aspects, method  1100  can proceed similarly to the methods described above with regards to  FIGS. 4 and 5A . 
     In some embodiments, step  1102  may proceed as described above with regards to step  904  of  FIG. 9 . For example, in step  1102 , a component of system  100  (e.g., client device  190  or system  105 ) can receive an image of a vehicle from image sensor  220 . In some embodiments, this image may then be transmitted from client device  190  to image recognizer  110  through network  170 . 
     In step  1104 , a component of system  100  (e.g., client device  190  or system  105 ) can extract one or more features from the image (e.g., as described above with regards to  FIGS. 5A and 6 ). In some embodiments, image recognizer  110  may use a machine learning algorithm, such as a convolutional neural network, to extract the one or more machine learning features. In some aspects, these features may indicate characteristics of the vehicle depicted in the image, such as a logo, a shape of headlights of the vehicle, a shape of taillights of the vehicle, a shape of a door of the vehicle, a shape of a window of the vehicle, a shape of a door handle of the vehicle, a shape of a tire of the vehicle, an overall shape of the vehicle (e.g., its front profile, rear profile, side profile, overhead profile, etc.), or similar characteristics. In various aspects the features can indicate characteristics of the image, such as a particular arrangement of pixels, a particular color shift, a bounding box size of an object in the image, a shape of an object in the image, or the like. 
     In step  1106 , based on the extracted features and using a machine learning algorithm, a component of system  100  (e.g., client device  190  or system  105 ) can determine attributes of the vehicle (e.g., as described above with regards to  FIGS. 5A and 6 ). In some embodiments, attributes of the vehicle can include make and model of the vehicle. In such embodiments, the attributes can further include a year of the vehicle and/or a trim of the vehicle. In some embodiments, image recognizer  110  may use the same machine learning algorithm as in step  1104  (e.g., a convolutional neural network that outputs attributes based on features). 
     In some embodiments, steps  1108 - 1112  may proceed as described above with regards to steps  910 - 914  of  FIG. 9 . For example, in step  1108 , a component of system  100  (e.g. client device  190  or system  105 ) can obtain user information relating to a vehicle financing request. In some embodiments, the user can create a user account and can submit an application to pre-qualify for vehicle financing. Additionally or alternatively, the component can display (or transmit for display on the client device) a vehicle financing application form to obtain the user information in step  1108 . 
     In step  1110 , a component of system  100  (e.g., client device  190  or system  105 ) can determine a real-time quote for the vehicle based on the make and the model obtained at step  1106  and the user information obtained at step  1108 . In some embodiments, the real-time quote includes pricing information relating to the identified vehicle. For example, the pricing information may include a vehicle price, monthly payment amount, APR, term length, and/or amount financed. In this manner, the real-time quote can be personalized based on the information obtained from the user in step  1108  (e.g., through pre-qualification and/or a questionnaire). As a non-limiting example, the real-time quote can reflect the user&#39;s credit, the amount desired to finance, and/or the preferred term length. 
     In step  1112 , a component of system  100  (e.g., client device  190  or system  105 ) can display (or transmit for display on the client device  190 ) the real-time quote. In some embodiments, the user may manipulate different factors to determine how the quote is affected. For example, the user may add, remove, or make changes to a service contract, gap insurance, or down payment to determine the effect on payment options, APR, and/or monthly payments relating to the quote. 
       FIG. 12  is a flowchart of another exemplary method  1200  for generating a real-time quote for a vehicle, according to disclosed embodiments. In some embodiments, method  1200  may be carried out by one or more components of system  100  (e.g., client device  190  or system  105 ). In some aspects, method  1200  can proceed similarly to the methods described above with regards to  FIGS. 4, 5A , and  8 . 
     In some embodiments, steps  1202 - 1206  may proceed as described above with regards to steps  1102 - 1106  of  FIG. 11 . For example, in step  1102 , a component of system  100  (e.g., client device  190  or system  105 ) can receive an image of a vehicle from image sensor  220 . In some embodiments, this image may then be transmitted to from client device  190  to image recognizer  110  through network  170 . In step  1204 , the component can extract one or more features from the image (e.g., as described with regards to  FIGS. 5A and 6 ). In step  1206 , the component may determine attributes of the vehicle (e.g., make and model) based on the extracted features and using a machine learning algorithm (e.g., as described with regards to  FIGS. 5A and 6 ). 
     In some embodiments, steps  1208 - 1212  may proceed in a manner similar to that described above with regards to steps  1010 - 1014  of  FIG. 10 . For example, in step  1208 , a component of system  100  (e.g., client device  190  or system  105 ) can obtain comparison information based at least in part on the attributes determined in step  1106 . In some embodiments, the component can be configured to obtain comparison information from comparison resource  107 . In some aspects, the component can be configured to retrieve pricing information from comparison resource  107  based on at least one of vehicle-related information and personal information. For example, the component may retrieve pricing information for vehicles having the same or similar attributes as those determined. Further, as described above in relation to  FIG. 5 , in some embodiments the component may be configured to receive at least some user information from the user (e.g., via client device  190 ), and/or retrieve such information from a user account (e.g., a user account of system  105 ). In such embodiments, the component may be configured to retrieve pricing information for vehicles having the same or similar attributes as those determined in step  1206  that were purchased or leased by individuals similar to the user. The similarity of the individuals to the user can be determined based on the received user information and the personal information stored by comparison resource  107 . In this manner, as described above with regards to step  1010 , the obtained subset of comparison information can be defined by recency, geography, or personal characteristics of the users leasing or purchasing the similar or matching vehicles 
     In step  1210 , a component of system  100  (e.g., client device  190  or system  105 ) can estimate a quote for the vehicle based on specific vehicle information and the comparison information obtained at step  1208  (e.g., as described with regards to  FIG. 8 ). Step  1210  may be performed in a manner similar to that described above in relation to step  1012 . For example, the estimated quotes may be specific to particular financing options such as auto purchase loans of different durations and/or auto lease arrangements of different durations, and may leverage statistics of the retrieved pricing information. As described above in step  1012 , in some embodiments, the component can be configured to estimate one or more quotes in response to input received from client device  190 . 
     In step  1212 , a component of system  100  (e.g., client device  190  or system  105 ) can display (or transmit for display on the client device  190 ) one or more quotes. Step  1212  may be performed in a manner similar to that described above in relation to step  1014 . For example, as above, the one or more estimated quotes may be transmitted via network  170  and may be displayed on a user interface of client device  190 . In some embodiments, the user interface may provide an augmented reality (AR) display. 
       FIG. 13  depicts an illustration of an exemplary client device capturing an image of a vehicle, in accordance with disclosed embodiments. As disclosed above, the client device  190  may be a mobile device, a tablet, a laptop, a cellular device, a personal computer, a smartphone (e.g., Apple iPhone, Blackberry, Android-based phones, etc.), and/or a wearable smart device such as a smart watch. The client device  190  may include an image sensor  220  such as a camera. A user of the client device may capture an image of a vehicle  1302  using image sensor  220  of client device  190 . The vehicle image may be displayed on a user interface  1304  of the client device display. 
       FIG. 14  depicts a group of exemplary graphical user interfaces capturing vehicle images, according with disclosed embodiments. In some embodiments, the exemplary graphical user interfaces may be displayed on client device  190  and may take the image from image sensor  220  and modify it to include elements displayed in a user interface on client device  190 . In some embodiments, the exemplary graphical user interfaces may be displayed when a user opens an application running on client device  190 , activating the image sensor  220 . 
     Graphical user interface  1401  shows an interface with a vehicle within a rectangular guide  1403 , a picture guide  1404 , and a displayed message  1402 . Rectangular guide  1403  may have color selected to highly contrast the original image. Similarly, picture guide  1404  may be generated when server system  105  determines that the picture quality is poor and cannot be processed. In some embodiments, picture guide  1404  may be displayed when server system  105  determines that the captured image is below an acceptability threshold and displays an error message and/or a message instructing the user to capture another image of the vehicle in a better position so the image can be accepted. 
     While graphical user interface shows a guide  1404  and message  1402  in the bottom, the guide and message may also be on the top like guide  1413  and message  1412 , or also in the side, like guide  1422 , or any combination, such as the guide  1422  on the side with message  1423  on the top. Therefore graphical user interfaces  1411  and  1421  show alternative graphical user interfaces with similar elements arranged in different positions. Server system  105  can be configured to select the position of elements in the graphical user interface to facilitate interactions with the user. 
     In other embodiments, the exemplary graphical user interfaces can modify an image of a vehicle including a first guide  1435  and a second guide  1434 . For example, graphical user interface  1431  presents the side of the vehicle and a displayed message  1432  to position the front and back of the vehicle between the first guide  1435  and second guide  1434 . 
       FIG. 15  is an exemplary flow chart illustrating an identification process, in accordance with disclosed embodiments. In some embodiments, identification process  1500  may be executed by server system  105 . 
     Server system  105  may receive a plurality of images to generate an attribute identification function in step  1502 . These images may be collected by image classifier  130 . In some aspects these images may be scraped from, for example, websites and/or inventories of vehicle dealers or vehicle manufacturers. The collected images may be associated with metadata describing the type of image and whether the image is exterior or interior. 
     In step  1504 , server system  105  may categorize images into at least two clusters. For example, server system  105  may extract features from images and cluster the images based on the extracted features in a binary fashion, that is, into two clusters. For example, image classifier  130  may implement clustering techniques, such as DBSCAN, to classify images. In some embodiments, this classification may cluster images of vehicle interiors and images of vehicle exteriors into two different clusters. In some embodiments, server system  105  may eliminate images that cannot be classified. 
     In step  1505 , server system  105  may modify metadata associated with images based on their classification in step  1504 . For example, image classifier  130  may insert comments like “exterior” or “interior” in the metadata associated with images based on the clustering. In some embodiments, image classifier  130  may implement a convention that later facilitates sorting the labeled images. For instance, instead of adding notes to the metadata such as “interior” and “exterior,” the file name of the images may be changed with a convention such as “1” for interior and “0” for exterior. 
     In step  1506 , server system  105  may determine a classification function for the clustered images. For example, image classifier  130  may generate a logistic regression classifier based on image features and the labels created in step  1505 . In some embodiments, the classification may be binary, and the linear regression may be configured to categorize images into one of the two clusters. For example, the classification function may classify images of vehicle exteriors into a “vehicle exterior” cluster. As an additional example, the classifier may classify vehicle interiors into another cluster. In some embodiments, this other cluster may include all images not classified as a vehicle exterior (e.g., images of vehicle interiors, images of subject matter other than vehicles, etc.). 
     In step  1508 , server system  105  may receive a second group of images and classify them using the classification function determined in step  1506 . For example, a logistic regression classifier determined in step  1506  may be used to classify the second group of images. In some embodiments, the second group of images may include associated metadata. For example, the metadata of the second group of images may include information about attributes of the vehicle such as vehicle make, model, and/or trim. In some embodiments, server system  105  may classify the second group of images as images of vehicle interior or vehicle exterior based on the classification function. In such embodiments, server system  105  may use the images classified as vehicle exterior for training a machine learning algorithm. By using the images classified as vehicle exterior to generate a model, server system  105  can reduce the resources devoted for image preprocessing and improve model generation speed. 
     In step  1510 , server system  105  may extract features from labeled images. For example, image classifier  130  may apply a pre-trained convolution network to extract features from the labeled images, which may comprise images from the second group labeled with vehicle “exterior” (that is, images classified as exterior by the classification function). 
     In step  1512 , server system  105  may categorize the labeled images based on one or more vehicle attributes (e.g., vehicle make, model, year, and trim). Furthermore, in step  1512  server system  105  may generate a new file with categorized images. For example, server system  105  may generate one or more files indicating the categories of the images. As a non-limiting example, the one or more files can include one or more binary tables. These binary tables can include images as rows (or columns) and categories as columns (or rows). In this non-limiting example, membership of images in categories can be indicated by the values of the element at the tables. 
     In step  1514 , based on the categorized images, server system  105  may generate an attribute function, consistent with disclosed embodiments. The attribute function can be configured to use features extracted from an image of the exterior of a vehicle to identify attributes of the vehicle. For example, the attribute function can be configured to identify the make and model of the depicted vehicle. As an additional example, the attribute function can be configured to further identify the trim and/or year of the depicted vehicle. 
     The attribute function may comprise one or more layers in a convolutional neural network that also extracts the features from the image. Alternatively, the attribute function may comprise a second convolutional distinct neural network configured to receive the output (or the output of a particular middle layer) of a first convolutional neural network that extracts the features from the image. 
     In step  1514  server system  105  may, in some embodiments, determine the attribute identification function using training images and a result file. Server system  105  may partition the categorized images of step  1512  into at least training images and validation images. Server system  105  can train the attribute function using an input file including extracted features for the training images and a result file including the binary table entries for the training images. Training can include determining or adjusting parameters of the attribute identification function. For example, when the attribute identification function includes a neural network, training can include updating weights and offsets of the neural network using to a backpropagation algorithm. Server system  105  can train the attribute function until a cost function based on prediction error is below a threshold value. Server system  105  can validate the attribute identification function using the validation images. 
     In step  1516 , a component of system  100  (e.g., system  105  or client device  190 ) may use the attribute function determined in step  1514  to identify at least one attribute associated with a first image received from client device  190  (e.g., as described above with regard to  FIG. 6 ). In some embodiments, server system  105  may receive an image or a video feed from client device  190 . In such embodiments, server system may identify attributes from the images using the attribute identification function. For example, server system  105  may receive an image of a vehicle or a video feed showing a vehicle from client device  190 . Server system  105  may then extract features from the image, using for example, image recognizer  110  (or a convolutional neural network thereof) and input the features to the attribute identification function (which may comprise an output layer of the convolutional neural network or a separate neural network). The attribute identification function may then return attributes of the vehicle in the image such as the vehicle&#39;s make and model. 
     In some embodiments, once attributes of the image are identified in step  1516 , server system  105  may retrieve additional information. For example, using the identified information, server system  105  may search in database  180  for information relating to the identified vehicle given the identified attributes, including price information. In other embodiments, server system  105  may search for cost and condition information in online resource  106  For example, server system  105  may send an information query to websites such as Kelly Blue Book® to collect cost and condition information. In yet other embodiments, server system  105  may send queries to dealer or vehicle manufacturer websites to collect additional information of the vehicle. 
       FIG. 16  is an exemplary flow chart illustrating a training data set generation process, in accordance with disclosed embodiments. In some embodiments generation process  1600  may be carried out by server system  105 . In such embodiments, generation process  1600  may be carried out by image classifier  130 . 
     In step  1602 , server system  105  may initiate an image collection tool. In some embodiments, the image collection tool may be a web scraper configured to collect images from websites. In other embodiments, collection tool may include a file synchronization tool that is connected to, for example, online resource  106 . In such embodiment, the image collection tool may copy image files in a memory such as memory  160 . 
     In step  1604 , server system  105  may receive a plurality of images. In step  1604 , server system  105  may compress images or create a new file to efficiently store the plurality of collected images. For example, in step  1604  server system  105  may store images in a new file in that reduces the color space to the most common colors in the image. Alternatively, server system  105  may modify pixels in the image so they only reference an index color, or to combine pixels with dithering. Other compression techniques to generate new files are also possible once the plurality of images is received in step  1604 . 
     In some embodiments, the newly generated file at step  1604  may change brightness by averaging or dropping some of the chrominance information in the image. In yet other embodiments, the new file may be generated by employing a Fourier-related transform such as the Discrete Cosine Transform (DCT), wavelet transform, or Fractal compression to generate a new file that more efficiently store the collected images to generate the models. 
     In step  1606 , server system  105  may perform a preliminary search in metadata associated with the collected images. For example, server system  105  may search for keywords in the metadata or search for XML information. Because the number of collected images may be very large to train the machine learning algorithm, performing a preliminary search, instead of a full search in the metadata, may save computer resources and accelerate the classification process. 
     In step  1610 , server system  105  may determine whether the associated metadata includes a word of interest based on the preliminary search. If the metadata does not include a word of interest (step  1610 : No), server system  105  may continue to step  1612 , classifying the image as unlabeled, and/or discarding it. If instead the metadata includes a word of interest (step  1610 : Yes), server system  105  may continue to step  1614  and perform a second search for attributes in metadata. A “word of interest” may include any vehicle-related word such as “exterior,” “interior,” “car,” “vehicle,” “front view,” “side view,” “rear view,” “Ford,” “Honda,” “2006,” “2007,” “XL,” “4 L,” “horsepower,” or the like. Such staggered search for metadata improves computer functionality by minimizing the resources that are used to classify images creating a stratified approach that quickly identifies images that are not of interest and then devoting more resources to images that overcome an initial threshold. Indeed, because the determination in step  1610  may be performed with specialized computers, such as FPGAs, specifically programmed to perform the keyword search in metadata, process  1600  may improve the overall speed of the server system  105 . 
     In step  1620 , server system  105  may determine whether the metadata includes information about a classification of the vehicle and/or attributes of the vehicle. For example, in step  1620  server system  105  may determine that the word “exterior” is in the comments section of the metadata associated with a group of images and determine that the metadata includes a classification. Similarly, server system  105  may determine that the word “Accord” is in the comments section of the metadata associated with a group of images and determine that the metadata includes an attribute (in this case, that the “model” attribute has the value “Accord”). Alternatively or additionally, server system  105  may identify that the metadata has information about the image source (e.g., from “www.ford.com”) and correlate it to a vehicle maker attribute (e.g., a make of “Ford”). 
     If server system  105  determines the metadata does not include a classification and/or attributes (step  1620 : No), server system  105  may continue to step  1622  and classify the image as attribute-less. However, if server system  105  determines that the metadata includes a classification and/or attributes (step  1620 : yes), server system  105  may continue to step  1624  and classify the images in a labeled trained data set that may later be used to generate a machine learning classifier, such as a convolutional neural network or a logistic regression classifier. 
       FIG. 17  is an exemplary flow chart illustrating a process for generating an identification model, in accordance with disclosed embodiments. Process  1700  may be performed by server system  105  and configured to generate classification models, such as convolutional neural networks, traditional neural networks, or logistic regression classifiers, to identify images. 
     In step  1702 , server system  105  may partition model data in a training data set and a validation data set. For example, server system  105  may receive a plurality of images of the exterior of vehicles. The images may be associated with metadata describing attributes of the vehicle in the image. Server system  105  may divide the images of the exterior of the vehicles and generate two groups, one to train the model and a second to validate the model. 
     In step  1704 , server system  105  may generate an input array based on features of the training data set. For example, server system  105  may generate a variable comprising feature information of images in the training data set. For example, the feature information for an image may be extracted from the image using a first convolutional neural network. 
     In step  1706 , server system  105  may generate output vectors based on metadata of the training data set. For example, based on the images in the training data set, the server system may generate a desired output vector identifying vehicle make and model that is included in the training data set. 
     In step  1708 , server system  105  may determine one or more parameters (e.g., weights) and/or activation functions to initialize the model to be created. For example, server system  105  may receive, from a trainer, the initializing values as well as a number of layers and nodes, and whether the network will be fully or partially connected. Alternatively, server system  105  may determine the initializing values for a number of layers and nodes that were previously determined by the trainer. In any of the embodiments above, the trainer may also have determined the dimensionality of the network and/or a pixel sampling size. 
     Moreover, in step  1708  server system  105  may receive, from a trainer, an indication of one or more activation functions for use in the model. For example, server system  105  may receive, from a trainer, an indication of that the model should use sigmoidal activation functions. Moreover, in step  1710  server system  105  may receive, from a trainer, initial values or an indication of initial values for parameters (e.g., weights, offsets, etc.) of the model. 
     In step  1712 , server system  105  may apply the input array based on features of training data set of step  1704  to calculate an estimated output in step  1714  and a cost function. In step  1720 , server system  105  may determine whether the cost function is below a threshold of required accuracy, which may be specified by the user. If server system  105  determines that the cost function is not below a threshold and the required accuracy has not been achieved, server system  105  may continue to step  1722  and determine a gradient to modify weights in synapsis and/or modify the activation functions in the different nodes. However, if the cost function if below a threshold (step  1720 : yes), server system  105  may accept the model in step  1724 . 
       FIG. 18  is an exemplary flow chart illustrating a process for handling a request for identification (e.g., similar to the method described with respect to  FIG. 6 ). In some embodiments, process  1800  may be performed by a component of system  100  (e.g., server system  105  or client device  190 ). 
     In step  1802 , the component may receive an image or a video feed from image sensor  220  (or from client device  190 ). In other embodiments, server system  105  may receive the image or video from online resource  106 . 
     In step  1804 , server system  105  may determine whether the image is acceptable for processing. For example, server system  105  may perform a validity assessment of whether the contrast in the image is high enough or whether the format of the image can be processed. In some embodiments, server system  105  may determine if the image displays a full vehicle by, for example, analyzing contrast in the image. 
     If the image is not acceptable for processing (step  1804 : No), server system  105  may continue to step  1806  and return instructions to display an error message in client device  190 . In some embodiments, server system  105  may additionally return instructions to display an onscreen guide to help the user to take an acceptable image for processing. For example, server system  105  may generate an image to superimpose on the client screen indicating an error and presenting a guide to take a subsequent image. 
     However, if the image is acceptable for processing (step  1804 : yes), server system may continue to step  1808  and extract image features. For example image classifier  130  may import layers of a first convolutional neural network; determine features described in a target layer of the first network, and initialize a multi class fitting model (e.g., a second convolutional neural network) using the features in the target layer. 
     In step  1810 , server system  105  may request an identification model to identify attributes in the image. In some embodiments, image recognizer  110  may use the identification model to identify attributes in the image. For example, the convolutional neural network of step  1808  may further include layers for determining a vehicle make and model. Alternatively or additionally, image recognizer  110  may apply a second convolutional neural network to determine a vehicle make and model from the features extracted by the first convolutional neural network. In step  1812 , the server system  105  may fully identify the vehicle based on the determined make and model (and/or other determined attributes such as year or trim). 
     The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to precise forms or embodiments disclosed. Modifications and adaptations of the embodiments will be apparent from consideration of the specification and practice of the disclosed embodiments. For example, the described implementations include hardware and software, but systems and methods consistent with the present disclosure can be implemented with hardware alone. In addition, while certain components have been described as being coupled to one another, such components may be integrated with one another or distributed in any suitable fashion. 
     Moreover, while illustrative embodiments have been described herein, the scope includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations and/or alterations based on the present disclosure. The elements in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application, which examples are to be construed as nonexclusive. Further, the steps of the disclosed methods can be modified in any manner, including reordering steps and/or inserting or deleting steps. 
     Instructions or operational steps stored by a computer-readable medium may be in the form of computer programs, program modules, or codes. As described herein, computer programs, program modules, and code based on the written description of this specification, such as those used by the controller, are readily within the purview of a software developer. The computer programs, program modules, or code can be created using a variety of programming techniques. For example, they can be designed in or by means of Java, C, C++, assembly language, or any such programming languages. One or more of such programs, modules, or code can be integrated into a device system or existing communications software. The programs, modules, or code can also be implemented or replicated as firmware or circuit logic. 
     The features and advantages of the disclosure are apparent from the detailed specification, and thus, it is intended that the appended claims cover all systems and methods falling within the true spirit and scope of the disclosure. As used herein, the indefinite articles “a” and “an” mean “one or more.” Similarly, the use of a plural term does not necessarily denote a plurality unless it is unambiguous in the given context. Words such as “and” or “or” mean “and/or” unless specifically directed otherwise. Further, since numerous modifications and variations will readily occur from studying the present disclosure, it is not desired to limit the disclosure to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope of the disclosure. 
     Other embodiments will be apparent from consideration of the specification and practice of the embodiments disclosed herein. It is intended that the specification and examples be considered as example only, with a true scope and spirit of the disclosed embodiments being indicated by the following claims.