Abstract:
Computerized systems distribute storage and analytical tasks between multiple storage devices and processors to analyze large amounts of automotive data. Reception of huge amounts of fine automotive data from large amounts of users and delivery of real-time and predictive analysis of the data can be handled across a distributed architecture. Configured storage devices can retain a universe of all reported/collected automotive data and advanced analytics can be performed on the data, before and after storage. The analyses can include real-time data extraction, batch-type complex predictive analysis, data management, machine learning on useful analytics, etc. The large and real-time data input may not interfere with analysis on the universe of accumulated data, permitting users and/or third-parties to readily access results of such analyses in real-time. Entry of automotive data and delivery of automotive analysis are conducted through unobtrusive delivery systems including attendant devices.

Description:
BACKGROUND 
       [0001]    Automotive conditions, including traffic data and individual car data, are known and available through conventional networks. For example, several services exist that report traffic conditions to mobile station users based on location, route, etc. Such systems may aggregate live data feed from municipal traffic monitors, eyewitness reports, automated vehicle volume sensors, current traffic conditions, GPS and navigation user density and movement, etc. Data can then be provided to users that reflects these conditions, and routing can be calculated based on these conditions. Data can be provided electronically to mobile station users while in or preparing for transit. Data in these types of services tend to include aggregate and live, incidental data that may be provided to users as is. 
         [0002]    Other services exist that report individual automobile history and traffic incidents. For example, municipal accident databases may gather post-accident vehicle information and accident location and type from reporting officers and the judicial system. Such databases can be provided to automotive purchasers to understand any accident history of a particular vehicle. Data in these types of services tend to include only post-severe-event data points on single automobiles and/or reported accidents. Further, data in individual automotive history reports are not conventionally available in real time or to mobile station users but are instead available as post-hoc, developed and sometimes purchasable reports reporting data as is. 
         [0003]    Users accessing existing sources sources to identify traffic incidents or find routing information based on as-is data may require a computer terminal or hand-held mobile device to view data output from existing systems. Users may need to manipulate, access, and/or otherwise obtrusively interact with the providing device to identify accident reports, traffic conditions, suggested routing, etc. Input of data, such as reporting a road hazard or requesting a route, is typically just as cumbersome, requiring a keyboard and/or visual review of spoken input and manual manipulation. 
       SUMMARY 
       [0004]    Example embodiments include computer systems that analyze automotive data using multiple networks and/or storage devices. One of the networks can receive the raw automotive data from users, and with optional formatting and addition of metadata regarding the data&#39;s context, pass it to another network. The receiving network may then accumulate a universal data set of the automotive data streaming in from the sending network on a larger database, such as a Big Data file system and analysis protocols. With properly-configured processing hardware, such as a high-efficiency distributed system architecture, the receiving network can strategically parse the incoming automotive data for real-time information that can instantaneously be sent back to the original network to supplement a database in the first network for user consumption on attendant devices. Incoming automotive data can also be saved on the larger database to amass a universal automotive data set for analysis by the processor. 
         [0005]    These analyses using universal automotive data can be extensive and involve extremely large amounts of input without interfering with data collection and real-time analytical distribution. Once generated, large universal analytical reports can be incrementally updated with real-time analysis and/or shipped to a user-interfaced network for immediate consumption by users or third-parties. Example systems are useable with mobile stations that can readily report fine levels of automotive data, including vehicle descriptions, behavior, and locations, road conditions, traffic patterns, route information, etc. Generated analyses can use these large inputs to create real-time and/or predictive analysis. Further, the analyses can be generated in response to user inquiries and/or machine learning on available data. Example methods can work on example embodiment systems to receive, format, analyze, meta-analyze, selectively store or move, and/or provide reports with automotive data. 
     
    
     
       BRIEF DESCRIPTIONS OF THE DRAWINGS 
         [0006]    Example embodiments will become more apparent by describing, in detail, the attached drawings, wherein like elements are represented by like reference numerals, which are given by way of illustration only and thus do not limit the example embodiments herein. 
           [0007]      FIG. 1  is an illustration of an example embodiment system. 
           [0008]      FIG. 2  is an illustration of an example method. 
           [0009]      FIG. 3  is an illustration of an example method. 
           [0010]      FIG. 4  is an illustration of an example method. 
       
    
    
     DETAILED DESCRIPTION 
       [0011]    This is a patent document, and general broad rules of construction should be applied when reading it. Everything described and shown in this document is an example of subject matter falling within the scope of the claims, appended below. Any specific structural and functional details disclosed herein are merely for purposes of describing how to make and use example embodiments. Several different embodiments not specifically disclosed herein may fall within the claim scope; as such, the claims may be embodied in many alternate forms and should not be construed as limited to only example embodiments set forth herein. 
         [0012]    It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. 
         [0013]    It will be understood that when an element is referred to as being “connected,” “coupled,” “mated,” “attached,” or “fixed” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being “directly connected” or “directly coupled” to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a like fashion (e.g., “between” versus “directly between,” “adjacent” versus “directly adjacent,” etc.). Similarly, a term such as “communicatively connected” includes all variations of information exchange routes between two devices, including intermediary devices, networks, etc., connected wirelessly or not. 
         [0014]    As used herein, the singular forms “a”, “an” and “the” and the plural form “indicia” are intended to include both the singular and plural forms, unless the language explicitly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not themselves preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
         [0015]    It should also be noted that the structures and operations discussed below may occur out of the order described and/or noted in the figures. For example, two operations and/or figures shown in succession may in fact be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Similarly, individual operations within example methods described below may be executed repetitively, individually or sequentially, so as to provide looping or other series of operations aside from the single operations described below. It should be presumed that any embodiment having features and functionality described below, in any workable combination, falls within the scope of example embodiments. 
         [0016]    The inventor has recognized that input of massive amounts of fine automotive data from both individualized sources and aggregate reports, both in real-time and from historical data, requires minimal user interaction and burden in order to ensure maximum data delivery. Moreover, many users providing or seeking automotive data are often operating a motor vehicle and must be able to achieve these operations without obtrusively or distractingly interacting with a mobile device to do so in real time. If automotive data entry and retrieval are streamlined and made unobtrusive, it may be possible and safer to compile an even larger data set from vehicular operators to make more useful correlations and yield improved user data on traffic patterns, vehicle behavioral prediction, road conditions, vehicle demographics, and vehicular/location risk or accident probabilities. 
         [0017]    Although the combination of individual automotive data, such as the behavior of an individual car or an eyewitness report of a road condition, with aggregate automotive data, such as historic traffic patterns at given geographic locations may present a huge data set with which to work, strategic data handling and analysis of such near-universal data sets may provide relatively more compact and useable results for individual consumption in real or near-real time, if desired. Similarly, the inventor has recognized that individualized automotive data can be gathered and compiled in real-time and at very fine levels of information, such as from individual reports from mobile users, and, while this may represent a tremendous amount of data, can be usefully analyzed along with gathered historical or aggregate information using strategic data handling and analysis to provide fast, useable results from a huge and varied data set. 
         [0018]    The present invention is a system that analyzes automotive data using strategic data management between multiple storage regimes and users such as attendant devices that can capture automotive data and perform analyses on the same without distracting or requiring additional travel or accessing from human operators. Computer processor use of, and user access to, different file systems having different information permits useful analyses, such as predictive or real-time analyses, on universal automotive data from several sources along with real-time delivery of analyses. The present invention is also computerized methods of performing automotive analysis and strategic data processing for huge, universal automotive data sets with predictive or real-time application. One or more example embodiment and method of these inventions are described below in detail. It is understood that the example hardware configurations and actions with regard to data described below are merely options, and other options and arrangements are readily useable to practice the appended claims. 
         [0019]    U.S. patent application Ser. No. 13/601,224, “Systems and Methods for Analyzing and Predicting Automotive Data” to inventor Amit Sharma and filed Aug. 31, 2012, is herein incorporated by reference in its entirety. As used herein, “automotive data” and “automotive information” includes all data and inquiries, real-time or historical, raw, aggregate, formatted, compiled, or otherwise, relating to vehicles, locations, vehicle operators, road conditions, routing information, road environments, and any other road-based vehicular transportation information. 
         [0020]      FIG. 1  is a diagram of an example embodiment automotive tracking and analysis system  100 . As shown in  FIG. 1 , a user  101  is communicatively connected to an automotive information access network  120 . User  101  can be any device capable of receiving and processing automotive information for user consumption. For example, user  101  can be a mobile station such as a cellular telephone connected to a communications network, or a computer connected to the Internet, or a GPS device connected to a GPS network, etc. As used herein, the term “mobile station” is defined as any non-human device capable of communicative connections, either directly or indirectly. For example, mobile devices useable in example methods and embodiments include cellular telephones, pagers, Global Positioning System devices, personal data assistants, personal computers, radios, walkie-talkies, remote controls, etc. User  101  can be directly connected to automotive information access network  120  or may connect through another network, such as the Internet. 
         [0021]    In an example embodiment, user  101  may be or include an attendant device. As defined herein, an “attendant device” is a processor-driven device that is configured to receive or provide automotive data contemporaneously with a co-located human operator in an unobtrusive manner, the co-location being persistent through human operator movement, i.e., the device travels with the operator. Examples of attendant devices include wearable devices such as a headset, head-mounted display, and/or devices described in US Patent Applications 2013/0044215 to Rothkopf et al., 2013/0016070 to Starner et al., 2011/0254829 to Agevik et al., and 2013/0044042 to Olsson et al., these applications being incorporated by reference herein in their entireties, as well as non-wearable devices such as a hands-free smartphone carried by or a heads-up display on a vehicle windshield occupied by a human operator. Attendant devices may be joined with other components of user  101 , such as a headset communicatively connected with a separate mobile station, or constitute user  101  alone, such as glasses from the &#39;042 incorporated publication that are programmed to communicate with network  120  and perform other actions in example methods. 
         [0022]    Other users and/or community resources  102  can similarly be connected to automotive information access network  120 , directly or via another communications network such as Internet  110 . Community resources  102  can include any available information resource that may be serviced by, or drawn from network  120 , including public traffic reports, vehicle information websites, government census and traffic fatality information, Internet-based maps, geographic markers, roadside networks, user communities, third-party data vendors, web services, feedback and review sites, etc. 
         [0023]    Automotive information access network  120  is a system that retrieves, formats, sends, and receives automotive data to or from users  101 , community resources  102 , and/or any analytics engine  140 . For example, automotive information access network  120  may include an Internet-based server  125  configured for rapid data intake and delivery among several users  101  and/or community resources  102  accessing network  120  over the Internet, including standard communications protocols such as TCP/IP. Such a server  125  may include conventional domain and/or security protocols for access and authentication as well as processing capacities to retrieve, deliver, and/or format automotive data for use within example embodiment system  100 . Or, for example, automotive information access network  120  may be an intranet lacking Internet protocols, and user  101  may be an internal intranet user. Access network  120  may include a server  125  with its own transient data storage capabilities to handle and persist user inquiries and data input, regardless of when or whether such data is assigned to an ultimate data storage type or compiled in an analysis. Similarly, access network  120  may include its own processors and routines to gather or solicit information useful in example system  100  from external sources such as community  102  and/or users  101 . 
         [0024]    Access network  120  includes an interactive database  130  that is useable for near real-time or real-time data and analysis delivery to user  101  and/or community  102 . For example, interactive database  130  may include processed, extracted, and/or otherwise readily-accessible or real-time data for retrieval by access network  120  and deliverable to users  101  and/or community  102  with minimal querying, processing, and/or bandwidth. 
         [0025]    Access network  120  is connected to an analytics engine  140  that can strategically handle large volumes of data while producing real-time and fast batch-produced results. For example, analytics engine  140  may include one or more databases  160  in a cluster that store various forms of automotive data, and data from access network  120  can be delivered and retrieved to and from databases  160 . As shown in  FIG. 1 , multiple databases  160  can be used in order to more effectively manage exceedingly large and different types of automotive data sets. For example, database  160  may be a Big Data system capable of storing and managing extremely large data sets from several sources and having several different associations and attributes between entries. As used herein, Big Data is defined as an extremely-high capacity distributed storage and analysis system, including those found in U.S. Pat. No. 8,195,712 to McCormick and the Aug. 29, 2012 article “The Data Era—Moving from Big Data 1.0 to Big Data 2.0” by Kaskade in the Cloud Computing Journal, all of which are incorporated by reference herein in their entireties. An analytics engine  140  may include all or relatively large amounts of raw or unprocessed automotive data stored in databases  160  from entry by users  101  and/or collection from community resources  102 . 
         [0026]    Analytics engine  140  further includes a processor  150  that coordinates, analyzes, and/or creates data using database  160  and/or input data from access network  120 . Such analyses may be in useable form and stored in interactive database  130 . Processor  150  may further provide a number of data management functions to analytics engine  140 . For example, processor  150  may perform real-time analysis on input streams of automotive data received from access network  120 . Such real-time analysis may provide instantly-useable excerpts from received data, or predictive or suggestion-type data, storable on interactive database  130  for relatively immediate consumption by users  101  and/or community  102 . Processor  150  may perform such real-time analysis at desired interval points and/or for known types of inputs and outputs so as to incrementally build or supplement a real-time distributed analysis stored on analytics database  160  and/or interactive database  130 , for example. 
         [0027]    Processor  150  may also store data input from access network  120  into database  160 . In this way all data useable in analytics may be moved to database  160  in a useable format to create a near-universal data set, while interactive database  130  may be curated with only data ready for real-time delivery. Processor  150  may further perform specific suggestive analysis, analytics, forecasting, and/or machine learning on the contents of database  160  and data input from users  101  and community  102  via access network  120 . These processes may use Big Data storage and leveraging methods on data in databases  160 . For example, processor  150  may retrieve and process data from databases  160  in batches or in response to specific requests and provide useable results to access network  120  for storage in interactive database  130 . Processor  150  may further coordinate processes from databases  160 , which may be performed in batches or other times long after data storage in databases  160 , with real-time analysis performed on incoming data from access network  120 . Resulting outputs may be merged, correlated, or otherwise combined for useable results provided to access network  120  and storable on interactive database  130 , accessible for immediate querying. For example, processor  150  may compare underlying data for both a real-time and universal analysis and merge the real-time analysis into the most recent batch results of the universal analysis if new data was used in the real-time analysis over the batch analysis. 
         [0028]    Processor  150  may include several processors, machine clusters, and/or high-efficiency distributed network members with sufficient speeds and handling capacities to enable and work with Big Data methods. Processor  150  may be pre-programmed or be configurable based on desired analysis, including with instruction or requests from access database  120  with feedback as to what types of analytics or data storage preferences between multiple databases should be executed. Further, processor  150  may configure itself through machine learning based on received data and inquiries that may suggest desired or related predictive analysis available through such data. 
         [0029]    Although analytics engine  140  and access network  120  are shown in  FIG. 1  as individual systems with subcomponents, it is understood that these elements may be co-located in a single device having adequately differentiated file systems and processing configurations. Alternatively, the elements shown in  FIG. 1  may be remote and plural, each communicatively connected at adequate speeds to provide necessary data transfer and analysis, if, for example, more resources or better logistics are available in distinct locations. For example, analytics engine  140  may be a distributed file system across several individual storage devices, and/or processor  150  may be similarly distributed across several high-efficiency clusters. 
         [0030]    Example embodiment automotive tracking and analysis system  100  can be used in several ways to provide meaningful automotive information to users. As shown in  FIG. 1 , user  101  can provide user automotive data  201  to access network  120 . For example, a user  101  can log in over the Internet or other network and authenticate to access network  120 , or anonymous communication between user  101  and access network  120  can be provided. 
         [0031]    Users  101  can provide a large variety of user automotive data  201  to access network  120 , including vehicular, location, or environmental data, for example, in the form of queries or input. For example, user automotive data  201  can include vehicular information including vehicle license plate, color, make, model, condition, behavior, incident involvement, location, VIN, etc. Or, for example, automotive data  201  can include geo-location or geo-coded input, road conditions, traffic flow and conditions, weather, police enforcement, mile marker or road sign condition, including content, presence, etc. Automotive data  201  can be formatted as informational input, such as a simple user location or report of a missing road sign, or user automotive data  201  can be input as a query, such as an inquiry as to traffic at a particular position or history of an encountered vehicle. 
         [0032]    Example system  100  is configured to in real-time receive and manage huge amounts of user automotive data  201 , both real-time and historical. As such, several users  101  may each provide user automotive data  201  at very fine levels. Users  101  can run an application that easily and safely captures automotive data  201  for input into access network  120  to encourage and enable fine levels of input. For example, users  101  that include attendant devices may observe and receive automotive data as a part of their travel and/or incidental use. Automotive data such as vehicle identification, speed, congestion, road markings, and/or road conditions, for example, may be unobtrusively observed, recorded and transmitted to access network  120  with minimal or no human operator intervention by attendant devices. Alternatively, for example, human operators may be able to speak simple commands or use simple gestures/facial expressions to attendant devices to identify, capture, and/or request certain automotive data observed by the human operator. Because an attendant device is co-located with a human operator, no additional travel or procuring may be required for user input of automotive data by such devices, permitting increased focus on travel and vehicular operations as well as increased automotive data gathering. 
         [0033]    Or, for example, an application interface for user  101  may permit user  101  to capture an image of a road condition and easily tag it under a general category like “hazard” or “heavy traffic” or “incorrect road sign” and automatically geocode the image or provide other context information for the image and provide all image and input data to access network  120 . Or user  101  may speak a license plate number for a vehicle driving erratically and also speak the erratic behavior, and an application for user  101  may translate such speech into text, with automatic context information addition, for transmission to access network  120  as automotive data  201 . Or, for example, user  101  may set or program a device to automatically report conditions like location, movement, signal strength, phone usage, etc. that requires no additional human interaction in order to report the same as user automotive data  201  to access network  120 . 
         [0034]    If user  101  is in a mobile situation, automatic and simplified input may aid in increased and safer user automotive information gathering and reporting. In an example where user  101  includes an attendant device, automotive information gathering may be wholly subsumed within the vehicular operation and road observing inherent in driving, without additional user operation or input required. Similarly, if user  101  is in a non-mobile and safer situation, such as at a computer terminal in a library or on a smartphone while waiting for an appointment, user  101  may be configured to provide a more involved interface that gathers more automotive data, such as a detailed description of a road problem or driver erratic behavior. User automotive data  201  provided in example system  100  can be real-time data or historical data input post-hoc. 
         [0035]    Access network  120  provides automotive data  201  to analytics engine  140  as a real-time input stream  230  compatible with analytics engine  140 , storage on databases  160 , and/or analysis with processor  150 . Access network  120  may provide some degree of formatting to user automotive data  201  before providing stream  230 , such formatting based on type and content. Additionally, user automotive data  201  may possess some uniformity or other desired formatting by user  101 . For example, user  101  may include a processor that is specifically configured to transmit input information in a specific format with automatically-included and formatted geocoding including time and location. Or, for example, user  101  may include an attendant device whose processor identifies optical observations as automotive data and transmits the automotive data to an intermediary mobile station with the user  101  for additional image processing and/or classification, or transmits the data with any desired formatting directly to access network  120 . Also, for example, access network  120  may perform some post-processing on received data  201 , such as image analysis of an input image to extract a car make and model and/or license plate number. Any and all such formatted data can be provided through stream  230  to analytics engine  140 . 
         [0036]    Analytics engine  140  can provide a wide variety of calculations, routines, analytics, etc. on database  160  with processor  150  and provide the results  240  to access network  120 , which can in real-time interact with and fetch a manageable data set. Moreover, analytics engine  140  can conduct such analyses at specific intervals or other times based on resource availability and/or data completeness. As discussed above, results  240  can include any analysis of data from database  160  and/or real-time data from stream  230  that may require no additional storage before being processed and merged into results  240 . Storage of comprehensive amounts of fine automotive data in databases  160  may not interfere with analysis and creation of results  240  for access network  120  through Big Data management and proper resource outlaying to processor  150 . 
         [0037]    Because databases  160  can store comprehensive and extensive automotive data, both individual- and aggregate-type automotive data from both real-time and historical input, whereas interactive database  130  may store more compact results  240  for rapid, real-time access, processor  150  can perform any type or number of requested, programmed, and/or desired analysis as an ongoing or batch-type background process so as to provide smaller, accessible results  240  to access network  120 . An endless number of analyses are possible with appropriate programming of analytics engine  140 , including previously-installed algorithms, analyses developed through machine learning, and routines input on the fly. For example, analytics engine  140  may perform data associations or perform analysis based on user inquiries, data entry  201 , and/or other machine learning. More user inquiries or entries regarding or relevant to a particular type of analysis may result in analytics engine  140  performing batches of corresponding analyses in a distributed manner. 
         [0038]    An example analysis may include analytics engine  140  coupling multiple tuples of datasets in database  160 , such as accident report and location, or erratic driving incident and car type, to calculate a recommendation and/or prediction relating to such data. Another example analysis may include analytics engine  140  performing historical regression on traffic density at a particularly-requested location verses date to produce predicted heavy traffic times/days at the position. Another example analysis may include analytics engine  140  comparing data of a particular data entry against other verified sources to determine the accuracy of particular automotive data. Another example analysis may include analytics engine  140  compiling vehicle and location data, including vehicle damage or history, for example, on a single vehicle, identified by appearance, registration, VIN, license plate, etc., into a single report or safety metric for that vehicle. Another example analysis may include analytics engine  140  correlating a road condition, such as a broken traffic signal or pothole, with a specific location and providing a map where such hazards exist. Another example analysis may include analytics engine  150  determining a number of similar inputs and their frequency, identifying a shared rationale or keyword for the report, and determining popular or urgent automotive issues based on the frequency and shared rationales. 
         [0039]    As seen, because of the fine granularity of data input from users  101  and storable in database  160 , which can be a Big Data system specifically configured to handle large amounts of diverse data, an infinite variety of different comparisons, collections, analyses, predictions, etc. may be made by analytics engine  140 . Operators may program analytics engine  140  to provide any desired type of analysis through appropriate machine learning configurations. 
         [0040]    Results  240  provided by analytics engine  140  may compact, real-time, and/or ready for user consumption, such as in the form of a prediction or recommendation. Analytics engine  140  may provide results  240  in regular batches and/or in real-time to access network  120  in a format for fast delivery, such as events and automotive issues graphed by map, simple issue alerts, separated string fields listing all associations for a particular category/vehicle, etc. Results  240  may additionally be stored on analytics engine  140  for additional analysis, machine learning, and/or backup. 
         [0041]    Community  102  may then access results  206  on access network  120  in real-time, based on request or otherwise as desired. Community  102  may be connected to access network  120  in much the same way as users  101 , such as through a login through the Internet, or through conventional report delivery from a paid subscription service, for example. Delivered results  206  may be delivered nearly instantly in response to a query. Delivered results  206  may further be delivered based on relevance to characteristics of community  102 . Although user  101  and community  102  are shown as separate entities in  FIG. 1  for purposes of input  201 , it is understood that users  101  may become a part of community  102  and vice versa. For example, delivered results  206  may be a recommendation, prediction, or other real-time analysis particularly relevant or associated with a user  101  based on their input automotive data  201 , in which case user  101  can be a part of community  102  consuming delivered results  206 . 
       Example Methods 
       [0042]    Example system  100  being described with several example configurations, example methods are now described that are uniquely enabled by example systems. As shown in  FIG. 2 , a user, such as a mobile device user with a smartphone loaded with an automotive data application configured in accordance with example embodiments, inputs data about a speeding vehicle in S 100 . The input may be in the form of a query about the vehicle, a picture taken of the vehicle as it sped by, an input later describing the vehicle speed and location, etc. Input in S 100  may require very little or no user attention and/or interaction, such that input S 100  can be repeated in example methods for very small pieces of information and without distracting a mobile user needing to concentrate on driving. 
         [0043]    In S 200 , the input data is formatted for compatibility, enhancement, and/or addition of data. For example, the picture taken by the user can be analyzed by image processing software or programs on the user&#39;s device or on an access network receiving the image to determine car make, model, color, speed, license plate number, etc., geocoded with location and date, associated with the particular user, etc. The processing in S 200  may thus add additional information to the basic input with little or no further user interaction. Additionally, a user may be able to add additional meta or context data in S 200 , such as mark the car as “speeding” or “erratic driver” or input visual characteristics of the car. 
         [0044]    In S 300  the analytics engine receives the formatted data from the access network and may perform any applicable real-time distributed analysis on this relatively smaller data stream. In S 400 , the received data may be concurrently stored in an analytics database for batch-type larger-scale analysis. Analysis on an analytics database, such as a Big Data Cluster, can be on-going throughout an incoming data stream and storage and/or real-time analysis of the same without interruption. S 300  and S 310  may be skipped if no applicable real-time analysis is available for a particular portion of input; similarly, S 400  and S 410  may be skipped if data does not require analytics or storage in an analytics database, such as if the data is fully redundant with previous data. 
         [0045]    In S 310 , results of the real-time analysis in S 300  are provided. The results can be any analysis or excerpted data; for example data of the vehicle being pulled over and ticketed may be analyzed and passed on instantly in S 310 . In S 410 , any available and relevant existing results from analytics on a Big Data database may also be identified and provided. The results can be a suggestive or predictive analysis; for example, formatted geo-coded speeding received from S 200  may be associated with an existing record for the identified vehicle in a Big Data database that is drawn from prior user or community input, including public vehicle registrations, prior user complaints, ticket citation information, etc. Or, for example, the received speeding information and other relevant input details may be compared across different car makes and models, associated with time of day and used in predictive road danger assessment, etc., stored in a Big Data database in an analytics engine, based on analytical need and programming. 
         [0046]    In S 500 , the real-time and batch analytics data can be merged in a meaningful way if both were provided. Real-time data and results based thereon calculated in S 400  may represent an update to Big Data computations provided in S 410 , and a merge of the two in S 500  can provide a more complete and up-to-date result. For example, the stop and ticketing information passed through in S 310  may be combined with a compiled report from S 410  of incidents for that vehicle in S 500 . A user providing the initial speeding data may thus have a real-time and comprehensive analysis based on the data provided. Merged results from S 500  may be stored in the analytics database as well, and any results from S 310  can then be discarded. 
         [0047]    In S 600  merged information from S 500  may be formatted and provided to an access network. Users or community resources can be provided results from interacting database instantaneously in S 700 . For example, a user inputting a speeding picture in S 100  may be provided with results in S 700  about whether the car was ever ticketed for speeding. This information can be provided to the user in S 700  as the result of a pre-compiled analytical association from S 410 , and/or as a real-time report of the vehicle being subsequently pulled over from another user input in S 310 . In this way, example systems and methods can intake huge amounts of automotive data from a variety of sources and deliver real time responses to user requests. 
         [0048]      FIG. 3  is an illustration of another example method. As shown in  FIG. 3 , user input in the form of a request is received in S 1000  by an example embodiment system. The request may be from a community service, such as a Department of Transportation database or official determining road repairs, about missing or damaged road signs in a particular area/route, for example. In S 2000 , the request can be formatted in a way that provides compatibility and best request servicing impression for the requestor and processed by the access network. The access network may compare the request against an interactive database to see if any responsive data is available in real time in S 2000 . 
         [0049]    In S 3000 , a background process may run at regular intervals in batches for analysis of Big Data in an analytics engine. For example, the process may invoke machine learning that provides suggestive analysis on automotive data, responsive to requests for particular data or analyses. This process may regularly compile results at desired intervals and provide the same to an interactive database for real-time fetching and faster reads in S 4000 . 
         [0050]    In S 5000 , the results of the analysis, which may include, for example geocoded road sign damage or absence from collection and analysis of user inputs, online map markers, government records, etc., are provided to the requestor form the interactive database. It is understood that S 3000  and S 4000  may be executed independently of each other depending on big data batches run at desired times or in response to particular inputs or operator commands determined by an example embodiment access network itself based on user inquiries and/or frequency. 
         [0051]      FIG. 4  is an illustration of another example method using an attendant device. As shown in  FIG. 4 , user input in the form of optical data of a road sign or of a nearby vehicle is observed by a processor-based eyewear mobile station that is configured to execute example methods in S 1001 . The example embodiment configured eyewear may include a frame and substantially transparent lenses covering a human operator&#39;s field of vision, an image projection and/or holographic lens mechanism that adds images for perception on the lenses, and/or an auditory input/output device. 
         [0052]    A camera or other lensed device in the eyewear may properly receive, record, and/or process such incident optical data. The capture of the optical automotive data may be in response to an explicit operator command, such as a spoken “identify sign” or a gesture such as a point at the vehicle. The eyewear in S 1001  may require confirmation or otherwise respond to an explicit operator command, such as by zooming in on or highlighting the captured data. Alternatively, the capture of the optical automotive data may be automatic with no additional operator instruction required, such as a glance at the road sign tracked by the eyewear, or simple encountering of the vehicle by the eyewear regardless of operator awareness of the same. 
         [0053]    The eyewear may process the observed optical data or other input into automotive data compatible with a request to an access network in S 2001 . This may include sign recognition and content extraction or driver identification of the observed vehicle, for example, as described using the eyewear image processing from the publication “InSight: Recognizing Humans without Face Recognition” by Wang et al. (Duke University 2013), incorporated by reference in its entirety. In S 2001 , the request can be formatted in a way that provides compatibility and best request servicing impression for the requestor and processed by the access network. An incidental smartphone or other mobile station locally connected to the eyewear may also perform any required formatting, information supplementation such as adding geocoding for example, and communication with an access network. Alternatively, all capturing, processing, and communications in S 2001  may be completed by an appropriately-configured eyewear mobile station. 
         [0054]    The access network may compare the received request against an interactive database to see if any responsive data is available in real time in S 2001 . For example, the eyewear may transmit an image of the captured road sign or extracted license plate information from the passing car to the access network, which may in turn further process the image for content or directly compare the vehicle tag against available analytics, for example. 
         [0055]    In S 3001 , a background process may run at regular intervals in batches for analysis of Big Data in an analytics engine. For example, the process may invoke machine learning that provides suggestive analysis on automotive data, responsive to requests for particular data or analyses. This process may regularly compile results at desired intervals and provide the same to an interactive database for real-time fetching and faster reads in S 4001 . For example, in S 3001 , a database of automotive data including criminal reports associated with license tags may be regularly updated and provided, or histories of users requesting information about particular road signs may be associated with a confusing road juncture in an analysis provided in S 3001 . 
         [0056]    In S 5001 , the results of the analysis, which may include, for example, a map showing where the road sign is located or updated routing information based on the map content, or information including traffic reports and incidents involving the observed vehicle, are provided to the requestor from the interactive database. 
         [0057]    In S 5001 , analysis results may be displayed to a human operator through the attendant device with minimal obtrusiveness. For example, eyewear may display a semi-transparent notification or hologram on a lens or frame of the eyewear within a human operator&#39;s field of vision indicating additional information regarding the road sign or vehicle are available, and the operator may view or be read such information upon a spoken confirmation. Alternatively, the eyewear may immediately deliver the analysis alongside the observed automotive data, such as a visual highlight transmitted on a lens in a manner that follows the image of the observed car incident on the operator&#39;s eye, indicating “dangerous driver” or “stolen car” for example. In this way, automotive data delivered through example methods and devices may appear as additional context information for normally observed objects without obscuring or interfering with vehicle operation or sight. 
         [0058]    It is understood that S 3001  and S 4001  may be executed independently of each other depending on big data batches run at desired times or in response to particular inputs or operator commands determined by an example embodiment access network itself based on user inquiries and/or frequency. 
         [0059]    Example methods and embodiments thus being described, it will be appreciated by one skilled in the art that example embodiments may be varied through routine experimentation and without further inventive activity. For example, although a first and second network are shown dividing analytical and reception/providing tasks to manage large streams of automotive data, it is understood that multiple additional networks, hardware components, and/or users can perform desired analyses and task-division in example embodiments. Variations are not to be regarded as departure from the spirit and scope of the exemplary embodiments, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims.