Patent Publication Number: US-2021192455-A1

Title: Methods and Systems for Submitting and/or Processing Insurance Claims for Damaged Motor Vehicle Glass

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is a continuation-in-part of U.S. patent application Ser. No. 17/016,097 filed Sep. 9, 2020, entitled “Methods and Systems for Submitting and/or Processing Insurance Claims for Damaged Motor Vehicle Glass”, which claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 62/897,746 filed Sep. 9, 2019, entitled “Methods and Systems for Submitting and/or Processing Insurance Claims for Damaged Motor Vehicle Glass”, the entirety of each of which is incorporated by reference herein. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to systems and methods for submitting and processing insurance claims, and more particularly to systems and methods for submitting and processing insurance claims for damaged motor vehicle glass. 
     BACKGROUND 
     When motor vehicle glass such as windshields are damaged, they are typically covered by insurance, and because they are covered by insurance, this necessitates the submission of a claim for insurance coverage. Currently, this can be done by having an insurance adjuster come out and look at your motor vehicle, or taking your motor vehicle in to an adjuster. This can require considerable time and effort, and slow the process of eventually getting your glass fixed through repair or replacement. The present disclosure provides automated methods and systems for submitting and/or processing an insurance claim for damaged motor vehicle glass. The methods can provide for greater efficiency and processing. 
     SUMMARY 
     Methods for submitting and/or processing insurance claims for damaged motor vehicle glass are provided that can include: receiving a plurality of images associated with motor vehicle glass at processing circuitry; performing image processing operations on each of the plurality of images to determine one or more of glass damage, glass type, and/or claim fraud; and submitting an insurance claim for glass repair or replacement based on the glass type or damage, or flagging the claim as fraud. 
     The present disclosure also provides a non-transitory computer readable storing instruction that when executed by a processor, causes a computer system to perform the following method. The method can include: prompting a user for initial claim submission information; prompting the user for a plurality of images of portions of motor vehicle glass; performing image processing operations on each of the plurality of images to train the computer system, determine one or more of glass damage, glass type, and/or claim fraud; and one of submit or reject an insurance claim for motor vehicle glass repair or replacement. 
     The present disclosure also provides a non-transitory computer-readable storage medium storing instruction that, when executed by a processor, causes a computer system to perform the following method: prompt a user for vehicle glass vendor information; process the information to determine if the user is in possession of the vehicle and/or whether the glass of the vehicle needs to be repaired or replaced; if the user is in possession of the vehicle and a replacement is determined, the user is: prompted for the VIN number of the vehicle; and the VIN number is processed through third party data to determine the required part numbers; if the user is not in possession of the vehicle, the user is: prompted for the VIN number of the vehicle; prompted for photos of the vehicle, vehicle glass, and damaged portions; the captured photos are proceed to determine repair or replace; if replace, the VIN number is processed through third party data to determine the required part numbers; and if repair, the method ends. 
    
    
     
       DRAWINGS 
       Embodiments of the disclosure are described below with reference to the following accompanying drawings. 
         FIG. 1  is a representation of motor vehicle glass having a long crack and a chip therein. 
         FIG. 2  is an example method according to an embodiment of the disclosure. 
         FIG. 3  is a representation of a process flow according to an embodiment of the disclosure. 
         FIG. 4A  is a represented portion of an overall method as part of a system for automatically generating and/or processing insurance claims. 
         FIG. 4B  is a represented portion of an overall method as part of a system for automatically generating and/or processing insurance claims. 
         FIG. 4C  is a represented portion of an overall method as part of a system for automatically generating and/or processing insurance claims. 
         FIG. 4D  is a represented portion of an overall method as part of a system for automatically generating and/or processing insurance claims. 
         FIG. 4E  is an example interface prompt for a use according to an embodiment of the disclosure. 
         FIG. 4F  is a represented portion of an overall method as part of a system for automatically generating and/or processing insurance claims 
         FIG. 5A  is a more detailed portion of the system and/or methods shown in  FIG. 4 . 
         FIG. 5B  is a depiction of motor vehicle glass image registration according to an embodiment of the disclosure. 
         FIG. 5C  is a depiction of motor vehicle identification points according to an embodiment of the disclosure. 
         FIG. 5D  is a depiction of a particular motor vehicle highlighting a specific motor vehicle tag. 
         FIG. 5E  is a depiction of motor vehicle Drivers Primary Viewing Area (DPVA) according to an embodiment of the disclosure. 
         FIG. 5F  is a depiction of an overview to be altered by touching the screen to indicate the location of the damage. 
         FIG. 6  is a depiction of a car windshield having at least two portions. 
         FIG. 7A  is a depiction of a car windshield having at least 9 portions, with each of the portions having a unique identifier. 
         FIG. 7B  is a depiction of the altered overview ( FIG. 5F ) with identifiers upon a windshield according to an embodiment of the disclosure. 
         FIG. 8  is portion  4  of  FIG. 7A . 
         FIG. 9  is portion  3  of  FIG. 7A . 
         FIG. 10A  is portion  3 ,  6  or  9  of  FIG. 7A . 
         FIG. 10B  is a depiction of an example glass identifier according to an embodiment of the disclosure. 
         FIGS. 11A-11F  are depictions of different forms of damaged motor vehicle glass breaks. 
         FIGS. 12A-12B  are more detailed portions of the overall system and method shown in  FIGS. 4A-4E . 
         FIG. 13  is a more detailed representation of the overall system shown in  FIGS. 4A-4E . 
         FIG. 14  is an even more in-depth depiction of the overall system and methods shown in  FIGS. 4A-4E . 
         FIG. 15  is a depiction of a group of portions of motor vehicle glass according to an embodiment of the disclosure. 
         FIG. 16  is a depiction of image augmentation according to an embodiment of the disclosure. 
         FIG. 17  is another depiction of image augmentation according to an embodiment of the disclosure. 
         FIGS. 18A-18D  are depictions of image augmentation according to an embodiment of the disclosure. 
         FIG. 19  is a depiction of an additional method or system that can be used in combination with the method or systems of the present disclosure according to an embodiment of the disclosure. 
     
    
    
     DESCRIPTION 
     This disclosure is submitted in furtherance of the constitutional purposes of the U.S. Patent Laws “to promote the progress of science and useful arts” (Article  1 , Section  8 ). 
     The present disclosure will be described with reference to  FIGS. 1-19 . Referring first to  FIG. 1 , example motor vehicle glass  10  is shown that includes a chip  12  as well as a crack  14 . Glass  10  can include multiple chips and/or multiple cracks. Glass  10  can be a windshield of a motor vehicle for example and it may or may not be constructed of Silicon. Glass  10  can also be a primarily polymeric construction such as a laminate. As can be seen, the crack extends a distance across the glass, and there is a single chip. The chip can be any one of a number of types of chips and occupy any place on the motor vehicle glass. Accordingly, the glass  10  of  FIG. 1  is damaged; hence it is designated as prior art. 
     Referring next to  FIG. 2 , an overall system is shown that includes a user  16  operating an image capturing device or camera  18 . This image capturing device or camera  18  can be any form of electronic image capturing device. It is not necessary that the image capturing device be a person digital assistant or cell phone, or even a tablet or other form of computer. It need not have a plethora of processing circuitry ability; the only requirement is that it is able to capture images. Accordingly, the device  18  can have at least some processing circuitry, at least a sufficient configuration to capture, store, and/or transfer one or more images. The image(s) captured utilizing camera  18  can be then transferred or uploaded to processing circuitry  20 , which includes a database  22  operably coupled to software  24  and hardware  26 . In accordance with example configurations, images can be captured and processed on the same or multiple devices connected via wire or wirelessly. The images may also be processed using cloud-based storage and/or processing software. 
     In accordance with example implementations, the images can be captured by following prompts or directions from an application on the computing device such as a tablet or smart phone having processing circuitry and a camera. These prompts or directions can specify the image to be captured and the order in which the images are captured, for example. As described in more detail in the following description, not only can the system prompt the capture of automobile glass, specific portions of automobile glass, but also specific portions of the automobile, such as, for example, specific section of the automobile glass, automobile identifiers, glass identifiers, license plates. 
     The processing circuitry can include personal computing system that includes a computer processing unit that can include one or more microprocessors, one or more support circuits, circuits that include power supplies, clocks, input/output interfaces, circuitry, and the like. Generally, all computer processing units described herein can be of the same general type. Application Platform Interface (API) that allows communication between different software applications in the system. The memory can include random access memory, read only memory, removable disc memory, flash memory, and various combinations of these types of memory. The memory can be referred to as a main memory and be part of a cache memory or buffer memory. The memory can store various software packages and components such as an operating system. 
     The computing system may also include a web server that can be of any type of computing device adapted to distribute data and process data requests. The web server can be configured to execute system application software such as the reminder schedule software, databases, electronic mail, and the like. The memory of the web server can include system application interfaces for interacting with users and one or more third party applications. Computer systems of the present disclosure can be standalone or work in combination with other servers and other computer systems that can be utilized, for example, with larger corporate systems such as financial institutions, insurance providers, and/or software support providers. The system is not limited to a specific operating system but may be adapted to run on multiple operating systems such as, for example, Linux and/or Microsoft Windows. The computing system can be coupled to a server and this server can be located on the same site as the computer system or at a remote location, for example. 
     In accordance with example implementations, these processes may be utilized in connection with the processing circuitry described. The processes may use software and/or hardware of the following combinations or types. For example, with respect to server-side languages, the circuitry may use Java, Python, PHP, .NET, Ruby, JavaScript, or Dart, for example. Some other types of servers that the systems may use include Apache/PHP, .NET, Ruby, NodeJS, Java, and/or Python. Databases that may be utilized are Oracle, MySQL, SQL, NoSQL, or SQLite (for Mobile). Client-side languages that may be used, this would be the user side languages, for example, are ASM, C, C++, C#, Java, Objective-C, Swift, ActionScript/Adobe AIR, or JavaScript/HTML5. Communications between the server and client may be utilized using TCP/UDP Socket based connections, for example, as Third-Party data network services that may be used include GSM, LTE, HSPA, UMTS, CDMA, WiMAX, WIFI, Cable, and DSL. The hardware platforms that may be utilized within processing circuitry include embedded systems such as (Raspberry PI/Arduino), (Android, iOS, Windows Mobile)—phones and/or tablets, or any embedded system using these operating systems, i.e., cars, watches, glasses, headphones, augmented reality wear etc., or desktops/laptops/hybrids (Mac, Windows, Linux). The architectures that may be utilized for software and hardware interfaces include x86 (including x86-64), or ARM. 
     The systems and/or processing circuitry  20  of the present disclosure can include a server or cluster of servers, one or more devices  18 , additional computing devices, several network connections linking devices  18  to server(s) including the network connections, one or more databases  22 , and a network connection between the server and the additional computing devices, such as those devices that may be linked to an adjuster. 
     Device  18  and/or processing circuitry  20  and/or plurality of devices  18  and the additional computing device can be any type of communication devices that support network communication, including a telephone, a mobile phone, a smart phone, a personal computer, a laptop computer, a smart watch, a personal digital assistant (PDA), a wearable or embedded digital device(s), a network-connected vehicle, etc. In some embodiments, the devices  18  and the computing device can support multiple types of networks. For example, the devices  18  and the computing device may have wired or wireless network connectivity using IP (Internet Protocol) or may have mobile network connectivity allowing over cellular and data networks. 
     The various networks may take the form of multiple network topologies. For example, networks can include wireless and/or wired networks. Networks can link the server and the devices  18 . Networks can include infrastructure that support the links necessary for data communication between at least one device  18  and a server. Networks may include a cell tower, base station, and switching network as well as cloud-based networks. 
     As described in greater detail herein, devices  18  can be used to capture one or more images of damaged glass. The images are transmitted over a network connection to a server. The server can process the images to assess damage, obtain information to assist with determination of repair costs, process a claim, detect fraud, and/or train the system to better review future images. The features can be transmitted over network connection to another computer device for approval or adjustment. 
     In accordance with example implementations, device  18  can have the following functional components; one or more processors, memory, network interfaces, storage devices, power source, one or more output devices, one or more input devices, and software modules—operating the system and a motor vehicle glass claims application—stored in memory. The software modules can be provided as being contained in memory, but in certain embodiments, the software modules can be contained in storage devices or a combination of memory and storage devices. Each of the components including the processor, memory, network interfaces, storage devices, power source, output devices, input devices, operating system, the network monitor, and the data collector can be interconnected physically, communicatively, and/or operatively for inter-component communications. 
     The processor can be configured to implement functionality and/or process instructions for execution within device  18 . For example, the processor can execute instructions stored in the memory or instructions stored on a storage device. Memory can be a non-transient, computer-readable storage medium, and configured to store information within device  18  during operation. In some embodiments, memory can include a temporary memory, an area for information not to be maintained when the device  18  is turned off. Examples of such temporary memory include volatile memories such as Random Access Memory (RAM), dynamic random access memories (DRAM), and Static Random Access Memory (SRAM). Memory can also maintain program instructions for execution by the processor. 
     Device  18  can also include one or more non-transient computer-readable storage media. The storage device can be generally configured to store larger amounts of information than memory. The storage device can further be configured for long-term storage of information. In some embodiments, the storage device can include non-volatile storage elements. Non-limiting examples of non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. 
     Device  18  can use network interfaces to communicate with external devices or server(s) via one or more networks, and other types of networks through which a communication with the device  18  may be established. Network interfaces may be a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information. Other non-limiting examples of network interfaces include Bluetooth, 3G LTE and Wi-Fi radios in client computing devices, and Universal Serial Bus (USB). In specific implementations, device  18  may not have access to an entirety of the system. For example, the system will have a database that includes a myriad of captured as well as generated images and certain implementations will not allow access to these images by the prompted operator. 
     Device  18  can include one or more power sources to provide power to the device. Non-limiting examples of power sources can include single-use power sources, rechargeable power sources, and/or power sources developed from nickel-cadmium, lithium-ion, or other suitable material. 
     One or more output devices can also be included in device  18 . Output devices can be configured to provide output to a user using tactile, audio, and/or video stimuli. Output devices can include a display screen (part of the presence-sensitive screen), a sound card, a video graphics adapter card, or any other type of device for converting a signal into an appropriate form understandable to humans or machines. Additional examples of output devices can include a speaker such as headphones, a Cathode Ray Tube (CRT) monitor, a Liquid Crystal Display (LCD), or any other type of device that can generate intelligible output to a user. 
     Device  18  can include one or more input devices. Input devices can be configured to receive input from a user or a surrounding environment of the user through tactile, audio, and/or video feedback. Non-limiting examples of input devices can include a photo and video camera, presence-sensitive screen, a mouse, a keyboard, a voice responsive system, microphone or any other type of input device. In some examples, a presence-sensitive screen includes a touch-sensitive screen. 
     Device  18  can include an operating system. The operating system can control operations of the components of the device  18 . For example, the operating system can facilitate the interaction of the processors, memory, network interface, storage device(s), input device, output device, and power source. 
     Device  18  can be configured to use a claims application to capture one or more images of damaged glass. In some embodiments, the claims application may guide a user of device  18  as to which views should be captured. In some embodiments, the claims application can interface with and receive inputs from a GPS transceiver and/or accelerometer. 
     The servers can be at least one computing machine that can assess and accurately identify vehicle glass repair, replacement or a no damage disposition, glass part number, ADAS calibration requirements and supported moldings required based on images provided from device  18 . The server can have access to one or more databases and other facilities that provide the features described herein. 
     Servers, according to certain aspects of the disclosure can include one or more processors, memory, network interface(s), storage device(s), and software modules—image processing engines, damage estimation engines, and database query and edit engines can be stored in the memory. The software modules are provided as being stored in memory, but in certain embodiments, the software modules are stored in storage devices or a combination of memory and storage devices. In certain embodiments, each of the components including the processor(s), memory, network interface(s), storage device(s), media manager, connection service router, data organizer, and database editor are interconnected physically, communicatively, and/or operatively for inter-component communications. 
     Processor(s), analogous to processor(s) in device  18 , can be configured to implement functionality and/or process instructions for execution within the server. For example, processor(s) can execute instructions stored in memory or instructions stored on storage devices. Memory, which may be a non-transient, computer-readable storage medium, is configured to store information within the server during operation. In some embodiments, memory includes a temporary memory, i.e., an area for information not to be maintained when the server is turned off. Examples of such temporary memory include volatile memories such as Random Access Memory (RAM), dynamic random access memories (DRAM), and Static Random Access Memories (SRAM). Memory also maintains program instructions for execution by processor(s). 
     The server uses network interface(s) to communicate with external devices via one or more networks. Such networks may also include one or more wireless networks, wired networks, fiber optics networks, and other types of networks through which communication between the server and an external device may be established. Network interface(s) may be a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information. 
     Storage devices of the processing circuitry of the present disclosure can be provided as part of a server to include one or more non-transient computer-readable storage media. Storage devices are generally configured to store larger amounts of information than memory. Storage devices can be configured for long-term storage of information. In some examples, storage devices can include non-volatile storage elements. Examples of non-volatile storage elements can include, but are not limited to, magnetic hard discs, optical discs, floppy discs, flash memories, resistive memories, or forms of electrically programmable memory (EPROM) or electrically erasable and programmable (EEPROM) memory. 
     Servers can include instructions that implement an image processing engine configured to receive images of damaged glass from one or more devices  18  and perform image processing on the images. The server can further include instructions that implement a damage estimation engine that receives the images processed by the image processing engine and, in conjunction with a database query an edit engine that has access to a database storing parts and labor costs, calculates an estimate for repair or replacement of the damaged motor vehicle glass. 
     Accordingly, user  16  can prepare or capture a plurality of images of motor vehicle glass  10 , and these images can be uploaded to processing circuitry  20 , and this processing circuitry can operate in accordance with the methods and systems disclosed herein, including interacting with a third-party processing circuitry  28  to process a claim  30 . 
     Referring next to  FIG. 3 , processing circuitry  20  and methods used therein can include generally three method components that operate in most circumstances together to process claim  30 . This module  32  can include module  34  that can acquire information for first notice of loss; module  36 , which is machine learning and training, and module  38 , which is fraud detection. 
     Module  34  is entitled “FNOL” (First Notice of Loss) can be filed using but not limited to a carrier web site, carrier app, or NCS interactive voice response system, for example. Additionally, the FNOL can be acquired using TPA as method and the NCS APP may interface with an insurance carrier&#39;s system. Accordingly, during First Notice of Loss, there is a series of proprietary questions; a “survey” can be initiated, including but not limited to: “Are you aware a claim has been filed on your policy?” “Did you notice the damage, or did a glass shop employee point it out?” “Has the work been done?” “If yes, why did the shop proceed without authorization?” Then, a description of the damages is requested; appearance, size, and quantity. Each filing of the FNOL method can utilize security features as part of the claim reporting process, including personal identification number, authorization, claim number, policy number and insured, multi-factor authorization to access policy information to avoid claim recycling (refiling claims within 180 days or per carrier specific guidelines) and verification of claim. Once the claim has been verified, the methods of the present disclosure can prompt the insured for required photos, including but not limited to those photos requested as will be described below. The Machine Learning and Training module  36  can utilize the information gained regarding specifics of a specific claimant, specifics of a specific car, specifics relating to glass type; also the Machine Learning and Training module  36  can utilize images acquired to determine the presence of a repairable chip; the presence of a repairable crack; or the presence of a chip and/or crack that cannot be repaired and requires glass replacement. Module  36  can be considered an artificial intelligence module. For example, the module can be software that includes and/or applies a set or sets of business rules that are used to develop and train fraud models and/or glass damage models under supervised learning. For example, models can be customized to detect fraud via prediction models that are monitored based on pre-defined business rules including but not limited to vehicle type, damage reported, vehicle location, owner of the vehicle and coverage type etc., as will be detailed herein. Also, the module can be configured to apply these same learning techniques to image capture and/or augmentation that is used to initiate vehicle glass damage identification and/or repair determination. For example, the preparation of additional images from single images of damage and the comparison of those images to image rules. The image rules including but not limited to previously categorized images of window damage. 
     Machine Learning module or AI  36  can be configured to utilize or incorporate Deepomatic&#39;s TensorFlow (See, for example Deepomatic.com and/or tensorflow.org) image recognition technology for image recognition and/or analysis. This image recognition can capture the glass morphology and structural damage, for example. In accordance with example implementations, computer vision can be used to prepare extracts of glass damage information that describes the morphology and structure of glass damage via tagging tasks that provide for labeling of specific features (size and shape) of a given image. Classification of a given image can be based on specific detection tasks that are tied to pixel-level precision for image detection. Detection is followed with segmentation into pre-defined glass damage buckets based on ROLAGS standards. 
     Accordingly, module  32  also includes Fraud Detection module  38 . Fraud Detection module  38  can include classification logic, which identifies correlations of various physical, vision, computing and data parameters to determine indicators of fraud. This module can proceed to either alert, reject, or pass or accept the claim per predefined requirements that improve over time with data collection and utilizing machine learning in accordance with module  36 . Fraud Detection module  38  can run concurrently with the systems and methods herein, by cross-referencing photo indications with information gathered by third-party vendors  28 , including but not limited to Chrome Data, Carfax, Comp9, NAGS, etc., to identify characteristics including but not limited to Image Related Parameters, for example: color of vehicle different between photos; interior is a different color between photos; lighting is different from photo to photo; photos show vehicle inside a structure, then outside; shade band in some photos and not in others; frit band different from photo to photo; stickers showing in some photos and not in others; surroundings different photo to photo; photos identified to be from library or photo from the internet; as well as Vehicle Information, such as: glass LOGO doesn&#39;t match the type of vehicle; date and time stamp off; date and time stamp before Date of Loss (D.O.L.); geo tag is on some photos and not on others, geo tag is off; geo tag greater than a predefined number of miles from insured&#39;s address; photos from different phone number than that of the policyholder, for example. 
     Referring next to  FIGS. 4A-4E , an example overall method is shown as part of a system for submitting an insurance claim for the damaged motor vehicle glass in  FIGS. 4A-4E . Clearly this overall method is too large to be printed on a single sheet as a diagram; therefore, it is provided in components. First, we will address the steps in  FIG. 4A . In step  40 , a software application or “app” is initiated by the user. This could be a non-transitory computer readable storage medium storing instruction that executes a prompt for the user to initial claim submission information. In accordance with example implementations, this processing can include a mobile device registering sequence. This can be a requirement to register a “mobile number” with the system to obtain a unique ID registration code for the user. In this system, the next step  42 , the claim submission information can be web initiated or initiated through a website or via cloud based system. In step  44 , the carrier or third-party agent can be initiated, and then in step  45 , an automated phone system can be initiated. At step  34 , the First Notice of Loss module is initiated or entered. From there, in step  46  there is an inspection protocol, and following from that inspection protocol can be a proof of loss concept  48 , and then a photo request that also provides from inspection protocol  50 . Additionally, these steps as well as other steps in the system can be completed with UI/UX tools that guide a user the methods and/or systems. For example, image guidance, instruction guidance, voice guidance, video guidance, video tools, FAQ pages, and/or a Chat Bot. 
     Referring next to  FIG. 4B , upon completing step  48  for the proof of loss, a vehicle identification and policy coverage deductible can be entered by the user at  52  and third-party data is acquired at  54 . Third-party data acquisition can include a VIN decode, NAGS part information including labor kits and molding, or ADAS manufacturer calibration guidelines in  56 ,  58 , and  60 . From there, a decision regarding the presence or absence of an ADAS component can be made at  61  and from there a type of glass decision can be made at  63  and rendered by the system. Entering the third party data  54  is not always performed. For example, where glass does not need to be replaced, but can be repaired, third party data processing  54  can be by passed and the system can proceed to the methods of  FIG. 4D . Accordingly, not all VIN&#39;s received by the system necessarily enter third party data processing  54 . This determination can be made during the processing of step  74 . For example, during AI determination, repair or replace can be determined. If repair, then step  53  allows third party data step  54  to be bypassed. If replace, then step  53  can require third party data step  54  processing. 
     Referring next to  FIG. 4C , from step  80  ( FIG. 4D ), either or both of fraud detection or photo failure can be made to proceed to failure photo step  68  and/or fraud detection  70 . Accordingly, there is a failure photo decision at  68  that can include a part of a fraud detection component  70 , which is at least one part of the link between the FNOL fraud detection component and the machine learning component. From there, during this acquisition, a determination can be made whether the photos are sufficient or not at a decision  64  (requiring technical assistance), and if necessary, technical support can be provided at  66 . Automated prompts can be given by the system for the acquisition of photos at step  62  and a return to photo request at step  50  of  FIG. 4A . 
     Referring next to  FIG. 4D , an example machine learning system is described with reference to the damaged motor vehicle glass claim submission systems and methods of the present disclosure. Accordingly, after step  50 , the photos can be provided to a machine learning at step  72 . The photos include, but are not limited to, as will be described later, the glass ID, ADAS camera, the VIN number, the glass overview with indicators  500  or  700 , see for example,  FIG. 5E  and/or  FIG. 7B , close up measured photo of each damaged area, mileage, and vehicle capture information such as images of the four corners of the vehicle and/or rear of vehicle including license plate. 
     Prior to proceeding to step  74 , upload failures where photos were captured by the user, but failed to be received by the system for processing prior to step  74 , can be validated and retried through a job batch scheduling method at step  73  that operates when system capacity is favorable. In accordance with example implementations, photos can be requested in step  50  and provided, however, the photos acquired in step  72  failed to upload for processing in  74 . For example, cameras placed within about 10 inches from the glass when capturing images have proven effective to provide the images, but these images may fail to be uploaded. Accordingly, the uploading of these photos can be attempted again at step  73 . 
     Proceeding next to step  74 , motor vehicle glass can be analyzed for count of damage areas, classify each damaged area, measure size of each damaged area, and an AI determination of repair, replacement or wear &amp; tear. For example, a measured photo can include, but is not limited to, the inclusion of certain known reference materials, such as currency, ruler, any other objective reference matter. During this step, the images, particularly the images of the damaged glass can be augmented. For example, an overview image can be taken such as  FIG. 5F . The glass itself can be augmented with physical markers such as the adhesive tags  500  shown in  FIG. 5E . The system can be configured to recognize these tags by color or oddity (for example, they don&#39;t belong) and zoom in to these portions creating images for processing. Alternatively, the glass can be digitally augmented in accordance with  FIG. 7B , wherein the user displays the image on an interface then touches or marks the image at the location of the damage  700 . The marked or augmented image can then be processed focusing on the marked or augmented portions. 
     Additionally, upon completion of the AI determination of the steps of  74 , a threshold for using third party data can be established. Accordingly, where the AI determination is to “replace”, the system can proceed between  52  to  54  of  FIG. 4B . Where the AI determination does not include a “replace” conclusion, the system may not access or use third party data at step  53 . 
     Following step  74  can be step  75 , which includes VIN number and glass ID, whether or not the glass ID matches the VIN number, and whether or not the VIN number indicates the presence or absence of an ADAS and/or whether or not the VIN number matches the VIN number submitted by the user. Referring next to step  77 , the Unrelated Prior Damage (UPD) or commercial use can be determined, and vehicle capture information such as the four corners of the motor vehicle or rear and/or vehicle including license plate can be captured information in step  78 . This information can be provided to the fraud detection determination in step  80  referenced in  FIG. 12A-12B . 
     Additionally, step  79  customer effort survey can provide feedback on tools used for UI “User Interface”/UX “User Experience” for example Image Guidance, Instructional Guidance, Voice Guidance, Video Tools, FAQ Information and Chat Bot. Questions for example; Was it Easy to Retrieve APP, Ease of Login &amp; Registration Process, Was APP easy to Navigate, and Option to send additional Survey Request. Subject matter of  74 ,  75 ,  77  and/or  79  can be used in decision step  80 , and example prompt screen for which is shown in  FIG. 4E . 
     Referring next to  FIG. 4F , a determination of fraud detection is made in step  82  after AI determination of pass in step  81 , and this decision can determine repairable in  83  or replace in  84 , or no damage in  85 . This can be done on an autoglass-by-autoglass basis, or on a photo-by-photo basis. If there is a replacement window, NAGS identified part and calibration requirements can be sent to the vendor in  86 . If there is no damage, then the claim is sent back to the carrier in  87 . If there is a replace or repairable determination, a work order is generated and released in  88 . 
     Referring next to  FIG. 5A , an example method of the first notice of loss is described in more detail or with reference to another example implementation, wherein an interface  100  can include as described herein a camera or a personal digital assistant or cell phone or laptop computer, for example. Survey questions are initiated to the user in step  102 , and security information is initiated to the user in step  104 . The system prompts the user to capture images as described in step  72  of  FIG. 4D . Referring to  FIG. 5B , an example depiction of VIN # image registration is shown. In accordance with example implementations a user can launch an NCS VIN scanner Application. The NCS VIN scanner Application utilizes a barcode reader, QR code reader and includes a built-in OCR text reader with autofocus. The Mobile APP can prompt the user to align the vehicle VIN within a rendered bracket frame as shown. VIN registration can be completed after alignment and autofocus criteria are met. In accordance with additional implementations, and with reference to  FIG. 5C , the user can acquire a VIN # image. An example VIN # image is shown in  FIG. 5D . The system can use this image to identify the year, make, model of vehicle, build features and part selection. 
     Referring to  FIG. 5E  an example motor vehicle Drivers Primary Viewing Area (DPVA) is shown. In accordance with example implementations, a user can launch an NCS windshield photo Application which launches a windshield image capture Mobile APP. The NCS windshield Mobile APP can prompt the user to align the motor vehicle glass image within a rendered frame as shown. Motor vehicle Drivers Primary Viewing Area (DPVA) is complete after alignment and autofocus criteria are met. In accordance with another implementation, and with reference to  FIG. 5F , an NCS APP photo of an overview to be altered by touching the screen to indicate the location of the damage. 
     Accordingly, and with reference to  FIG. 6 , the windshield  10  can be divided into portions, for example, A and B at the very least, but these portions are commensurate with the prompting of portions by the systems and methods of the present disclosure. 
     Referring to  FIG. 7A , an even more detailed depiction of portions  10 A and  10 B is shown wherein there are 9 portions, each of the 9 portions having a specific designation. Accordingly, within these “damage zones” the system can request that you enter each of these pictured portions in the designated order in order to place them with the proper identifier. Referring to  FIG. 7B , identifiers are placed manually or are created electronically via the Mobile APP touch screen process to identify damages. The identifiers are placed manually or are created electronically via the Mobile APP touch screen to identify areas of damage. 
     Accordingly, and with reference to  FIG. 8 , portion  4  in  FIG. 7A  may include a depiction of an ADAS  112 . A portion  3  in  FIG. 7A  can include VIN  114  as shown in  FIG. 9 . A portion  3 ,  6  or  9  in  FIG. 7A , can include glass ID  116  as shown in  FIGS. 10A and 10B . 
     In accordance with example implementations, and with reference to  FIGS. 11A-11E , example windshield chips are shown, with  FIG. 11A  representing a half moon chip of the size shown;  FIG. 11B  representing a star chip of the size shown;  FIG. 11C  representing a bullseye chip of the size shown;  FIG. 11D  showing a combo chip of the size shown, and  FIG. 11E  showing a batwing chip of the size shown; and  FIG. 11F  showing a crack of the size shown. Accordingly, and with reference to  FIGS. 12A and 12B , a more detailed example of fraud detection is shown, wherein the instances are weighed to determine whether or not fraud exists, and each of these instances or inconsistencies can be given a certain weight, and then totaled, and this totaled weight can be given an amount that is either above or below the fraud threshold. Accordingly, in step  200 , details are received, and in step  202 , the individual details are processed. In step  204 , the vehicle color is compared between the photos and in  206 , the vehicle interior color is compared between the photos. The lighting of the photos is compared for consistency in  208 ; the setting of or pose of the motor vehicle, particularly the windshield, is compared in  210 ; the presence of shade bands in the photographs is compared in  212 ; the frit band associated with the photographs is compared in  214 ; and the stickers across the different portions of the photographs are compared in  216 . The surroundings to the vehicle are compared in  218 ; historical photos of the vehicle, if available, are compared in  220 ; and the windshield logo, if any, is compared with that which should exist in  224 ; and the date and time stamp of the photos is compared in  226 , for example. 
     With reference to  FIG. 12B , continuing on, the GEO tag of the photos is compared in  228 , and whether or not the GEO tag was on or off is determined in  230 . The GEO tag distance of a predetermined number of miles from the insured&#39;s address in comparison to that of the photos is made in  232 , and a determination of whether the photos were from different phone numbers is made in  234 . Raw fraud counts are made in  236 , and if it achieves a threshold number in  238 , then fraud is determined in  240 , or no fraud is determined in  242 . 
     Referring next to  FIG. 13 , an example implementation of a machine learning relating to the images is shown, wherein images are received in step  300 , and the damage classified in step  302  by the type of break at  304 , the size of the break in  306 , the number of breaks per image in  308 , and the location of the breaks on the windshield itself in  310 . This machine learning is done first with data augmentation at  312  (example implementations are described with reference to  FIGS. 5E and 7B ), and then the system fits received data to model in  314  and compared to the truth table as described, for example, in  FIGS. 12A and 12B , at step  316 , for a final disposition regarding fraud and/or replacement or repair of the windshield at  318 . 
     Referring next to  FIG. 14 , data augmentation example step  312  is shown with an HPT driver or programming  320  preparing rotated image at  322 , flipped images at  324 , and brightness or contrast changed images at  326 , and color jittering at  328 . 
     In accordance with example implementations and with reference to  FIG. 15 , as part of data augmentation, the images that are next to each other, for example, 9, 6, and 3 can have the perimeters associated with those images, for example, perimeter  400  and  402 , compared to ensure that they are overlapping with existing images as part of fraud detection. 
     Referring next to  FIG. 16 , an example image, in this case  6  can be rotated or flipped as part of data augmentation to create additional images  404 ,  406 ,  408 , and  410 . These images can be stored for later use as representative of a certain chip  12 . 
     With reference to  FIG. 17 , portion  6  with chip  12  can have color jittering performed at  412  and/or brightness contrast changed to produce additional images at  414 . 
       FIGS. 18A-18D  show images that have been augmented such as flipped, rotated, brightness/contrast changed, or color jittered, for example. Finally, this information, along with additional information shown below in the Table 1 can be included as part of the systems and methods of the present disclosure. For example, additional details relating to the size of the chip, the location of the chip can be entered into the system and be part of the machine learning. This can provide for additional efficiency in the system and method. 
     
       
         
           
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
             
            
               
                 Location 
                 YES 
                 NO 
               
               
                   
               
               
                 DPVA: (12″ in width 
                 See DPVA 
                 See Other than DPVA 
               
               
                 within the wiper sweep) 
                 Classification 
                 Classification Step Below 
               
               
                   
                 Step Below 
                   
               
               
                 ADAS (Rear View 
                 Replace 
                 See Other than DPVA 
               
               
                 Mirror Area 
                   
                 Classification Step Below 
               
               
                   
               
            
           
           
               
            
               
                 DPVA (12 inches wide in wiper sweep) 
               
            
           
           
               
               
               
               
               
            
               
                 Type 
                 Size 
                 Crush Zone 
                 YES 
                 NO 
               
               
                   
               
               
                 Bullseye chip 
                 1″ or less in 
                  3/16ths inch 
                 Repair 
                 Replace 
               
               
                   
                 diameter 
                 or less 
                   
                   
               
               
                 Halfmoon chip 
                 1″ or less in 
                  3/16ths inch 
                 Repair 
                 Replace 
               
               
                   
                 diameter 
                 or less 
                   
                   
               
               
                 Star chip 
                 1″ or less in 
                  3/16ths inch 
                 Repair 
                 Replace 
               
               
                   
                 diameter 
                 or less 
                   
                   
               
               
                 Combo chip 
                 1″ or less in 
                  3/16ths inch 
                 Repair 
                 Replace 
               
               
                   
                 diameter 
                 or less 
                   
                   
               
               
                 Batwing chip 
                 less than 6″ in 
                  3/16ths inch 
                 Repair 
                 Replace 
               
               
                   
                 diameter 
                 or less 
                   
                   
               
               
                 Multiple chips 
                 Greater than 4″ 
                  3/16ths inch 
                 Repair 
                 Replace 
               
               
                   
                 between chips 
                 or less 
               
               
                   
               
            
           
           
               
            
               
                 Other than DPVA: (Anywhere other than DPVA) 
               
            
           
           
               
               
               
               
               
            
               
                 Type 
                 Size 
                   
                 YES 
                 NO 
               
               
                   
               
               
                 Bullseye chip 
                 1″ or less in 
                 ⅜ths inch or 
                 Repair 
                 Replace 
               
               
                   
                 diameter 
                 less 
                   
                   
               
               
                 Halfmoon chip 
                 1″ or less in 
                 ⅜ths inch or 
                 Repair 
                 Replace 
               
               
                   
                 diameter 
                 less 
                   
                   
               
               
                 Star chip 
                 3″ in diameter 
                 ⅜ths inch or 
                 Repair 
                 Replace 
               
               
                   
                 or less 
                 less 
                   
                   
               
               
                 Combo chip 
                 2″ in diameter 
                 ⅜ths inch or 
                 Repair 
                 Replace 
               
               
                   
                 or less 
                 less 
                   
                   
               
               
                 Batwing chip 
                 less than 6″ in 
                 ⅜ths inch or 
                 Repair 
                 Replace 
               
               
                   
                 diameter 
                 less 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 Option 
                 Count 
                 YES 
                 NO 
               
               
                   
               
               
                   
                 Option 1 
                 1 
                 Repair 
                 Replace 
               
               
                   
                 Option 2 
                 Less than 2 
                 Repair 
                 Replace 
               
               
                   
                 Option 3 
                 Less than 3 
                 Repair 
                 Replace 
               
               
                   
                 Option 4 
                 Less than 4 
                 Repair 
                 Replace 
               
               
                   
                 Option 5 
                 Less than 5 
                 Repair 
                 Replace 
               
               
                   
                 Option 6 
                 Less than 6 
                 Repair 
                 Replace 
               
               
                   
                 Option 7 
                 Less than 7 
                 Repair 
                 Replace 
               
               
                   
                 Option 8 
                 Less than 8 
                 Repair 
                 Replace 
               
               
                   
                 Option + 
                 Less than + 
                 Repair 
                 Replace 
               
               
                   
               
            
           
         
       
     
     Referring next to  FIG. 19 , a third party vendors method and/or system  500  is provided that can be used alone or in combination with the methods and systems described herein is provided. At step  501  a determination of glass vendor need is made. Where the vehicle is at the glass vendor and repair rather than replacement is needed, the method or system is completed. Where the vehicle is not at the glass vendor and/or the vehicle is with the glass vendor and replacement is needed, a glass work order is logged at, for example, using the application at step  502 . This application can use one or more of the processing steps of  FIG. 4A , such as  40 ,  42 ,  44 , and/or  45 . 
     Upon logging a work order at  502 , a determination is made regarding the glass vendor having the vehicle or not. If the glass vendor has the vehicle the system can proceed to step  504 , if not the system can proceed to  505 . 
     The method or system can allow glass vendors to utilize the AI application developed and maintained by the methods described herein via at least two separate functions  504  or  505 . 
     Where the vehicle is at the glass vendor or glass salesperson and replacement is determined, at  511  access to the systems and/or methods of the present disclosure can be granted via an application to process the VIN (Vehicle Identification Number). After step  511 , a decision of repair or replace can be determined, and the VIN may be processed at  512 , match confirmed at  513 , and part numbers provided at  514 . Accordingly, the system or method herein at  FIG. 4B  at steps  54 - 63  can process information (VIN, for example), access glass/vehicle feature codes via third party applications, and provide accurate parts via NAGS which will give the glass vendor results including glass parts, type of glass, pricing, kits and molding requirements at  514 , for example. 
     Alternatively, where the vehicle is not at the glass vendor or salesperson, at  505 , the glass vendor can obtain permission via communication with a system monitor, such as the system or method owner or licensee. This permission can be requested at  505 , via a text, call, e-mail, instant message, etc., then granted or denied. When granted, access authorization can be granted via a link or password as for example at  506 . The granted user, such as Vehicle owner can log into the above system or method at step  507  and scan vehicle information such as a bar or QR code or enters VIN into the system at  508 . At step  509 , the user can be prompted to or provide a series of photos which are captured at  509 , information is uploaded to the A.I. at  510  and then the method proceeds to the repair/replace decision. Steps  508  and  509  can utilize the processing of step  50  as well as steps  72 ,  74 , and/or  75 . 
     In compliance with the statute, embodiments of the invention have been described in language more or less specific as to structural and methodical features. It is to be understood, however, that the entire invention is not limited to the specific features and/or embodiments shown and/or described, since the disclosed embodiments comprise forms of putting the invention into effect.