DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/20/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
This action is in reply to the amendments filed on 12/30/2025.
Claims 9-27 are currently pending and have been examined.
Claims 9, 12, 16, and 21 are amended.
Claims 9-27 are currently rejected.
This action is made FINAL.
Response to Arguments
Applicant’s arguments filed 12/30/2025 have been fully considered but they are not fully persuasive.
Regarding the 101 rejection, applicant argues that the claims do not recite a mental process. Applicant argues that the steps cannot be performed entirely in the human mind citing a portion of the MPEP. However the MPEP states “In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); [MPEP 2106.04(a)(2)]”. The mental process identified is the analysis of the motion data to determine if they match up as shown in the rejection below. This can be done entirely in the mind if the person was presented this data. All the additional limitations are pre- and post- solution activities such as data gathering and presenting a notification. The MPEP allows for “performing a mental process using a generic computer” or “using a computer as a tool to perform a mental process”. Since the computer components are claimed at a high level of generality they are interpreted as a generic computer simply processing the mental process as a stand in for the person. The applicant argues that the data being processes is impractical to be performed in the human mind but the claims do not claim a specific amount of data or how it is processed that would render it impractical to be done by a person. The data could be as simple as comparing two graphs of speed/acceleration/location data to determine if the person and the car are travelling together. Applicant additionally argues that under step 2A, prong two that the claims as a whole integrate the mental process into a practical application of “an improvement to other technology or technical field”. This is not persuasive because the claims are not improving how a computer functions, simply using the computer to process data. The computer itself is not improved, simply being used as a generic tool to speed up data processing over a person performing the task. Applicant additionally argues that under step 2B are not well-understood, routine, and conventional. Applicant takes a selective reading of Kumar, which is argued below, to show that the claims are not well-understood. In light of the maintained rejection and additional arts cited in this rejection this is not persuasive. Additionally under the step 2B analysis, the claims merely recite insignificant extra-solution activity and is applying it to a general purpose computer. Therefore the additional elements do not remove the mental process from the 101 rejection which is being maintained.
Applicant’s arguments with regards to the art rejections have been considered and are not persuasive. Applicant argues that Kumar does not teach the “motion measurements” and argues that Kumar only uses information such as Bluetooth status and application use. While Kumar does use those factors the applicant is taking a selective reading of Kumar and ignoring the explicit teachings of “movement associated with movement of the mobile device may be received by the movement data evaluation and identification computing platform 110. The movement data may include movement over a predefined time period. The movement data may include location data (e.g., based on GPS) and may include or be used to identify speed of travel of the user in the vehicle [col 20, line 66 – col 21, line 5]” and “the vehicle data received may include vehicle operational data (e.g., engine RPM, speed, braking, swerving, acceleration, and the like) [col 21, lines 27-29]” which explicitly teach the gathered motion measurements of both the user and vehicle is steps 406-412 to be used in the analysis as performed in step 414. Applicant additionally argues the amended version of claim 12 which has been updated in light of the amendments. Therefore applicants arguments are not persuasive and the rejections are being maintained.
Claim Rejections - 35 USC § 101
Claims 9-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 9-27 are directed to a system, method, or product, which are one of the statutory categories of invention. (Step 1: YES)
The examiner has identified independent method Claim 9 as the claim that represents the claimed invention for analysis and is similar to independent method Claim 16 and system Claim 21. Claim 9 recites the limitations of:
receiving, by a server system from an electronic device affixed to a first vehicle, a first set of motion measurements generated by one or more motion sensors of the electronic device, the first set of motion measurements indicating that a first trip occurred in the first vehicle during a first time interval;
storing, in a data store of the server system, the first set of motion measurements in association with an indication of the first trip;
receiving, by the server system from a plurality of mobile devices, a plurality of sets of motion measurements each generated by one or more motion sensors of a respective mobile device of the plurality of mobile devices, the plurality of sets of motion measurements each indicating that a respective trip of a plurality of trips occurred during a respective time interval of a plurality of time intervals;
storing, in the data store of the server system, the plurality of sets of motion measurements in association with indications of the corresponding respective time interval of the corresponding respective trip;
querying, in the data store of the server system, the plurality of sets of motion measurements for a second set of motion measurements stored in association with a second time interval of a second trip for which the start times of the first time interval and the second time interval are within a first predefined threshold time difference of each other, the end times of the first time interval and the second time interval are within a second predefined threshold time difference of each other, or both;
storing, in the data store of the server system, a determined association between the first set of motion measurements and the second set of motion measurements, the association determined based on:
(i) comparing a first driving event time associated with the first set of motion measurements with a second driving event time associated with the second set of motion measurements;
(ii) comparing a first trip type associated with the first set of motion measurements with a second trip type associated with the second set of motion measurements; or
(iii) comparing a first characteristic associated with the first set of motion measurements with a second characteristic associated with the second set of motion measurements; and
causing, by the server system, the mobile device of the plurality of mobile devices that generated the second set of motion measurements to present a notification indicating that the first trip was determined to be the same trip as the second trip.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. Detecting, identifying, and matching data recites concepts performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The mobile device and server in Claim 9 is just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 16 and 21 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea.)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of: a mobile phone (including a processor), application, cache memory. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea without a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Additionally reciting data coming from multiple phones does not make the mental process unable to be performed in the human mind because the determination does not have the limitation that it is being performed in real time with an extensive amount of data. A person can be presented with location data and timing of a vehicle and multiple phones and be able to cross reference them to see if they are similar, either in tabular or map form. Therefore, claims 9, 16, and 21 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application.)
The claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Gathering and storing data is simply using generic computer components to carry out the mental process of matching data from two sources.
These limitations are insignificant pre-solution activity because installing a program is well known and not a core principle of the invention and obtaining data in an insignificant extra-solution activity. Applicant argues that saving phone energy corresponds to integration into a practical application, however that is an unclaimed element of the invention and the mental process of matching data can be applied to any set of data and is only being applied to vehicle telematics in this case. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 9, 16, and 21 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more.)
Dependent claims further define the abstract idea that is present in their respective independent claims 9, 16, and 21 and thus correspond to Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims 9-27 are not patent-eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 9, 14, 16, 18, 20-21, and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et. al. (US 10,785,604), herein Kumar in view of Kanevsky (US 10,902,521), herein Kanevsky, Siira et. al. (US 8,396,662), herein Siira, and Outwater et. al. (US 2017/0115125), herein Outwater.
Regarding claim 9:
Kumar teaches:
A method comprising:
receiving, by a server system from an electronic device affixed to a first vehicle (Telematics devices 263 can receive data from vehicle sensors 261, and can transmit the data to a mobile device 250 or movement data evaluation and identification server 210. [col 15, lines 16-18]; fig. 4, step 410), a first set of motion measurements generated by one or more motion sensors of the electronic device (sensors 261 can detect and store data corresponding to the vehicle's location (e.g., GPS coordinates), time, travel time, speed and direction, rates of acceleration or braking, gas mileage, and specific instances of sudden acceleration, braking, swerving, and distance traveled [col 13, lines 43-47]), the first set of motion measurements indicating that a first trip occurred in the first vehicle during a first time interval (Certain vehicle sensors 261 also can collect information regarding the vehicle's location, current and past driving routes, in order to classify the type of trip (e.g. work or school commute, shopping or recreational trip, unknown new route, etc.). [col 14, lines 32-36]; receiving the data may cause an initial processing of the movement data to occur. For instance, the movement data may be analyzed to identify one or more time periods associated with the movement data. In some examples, this may be based on time stamps associated with the received movement data. In some examples, the movement data may correspond to a single driving trip (e.g., a starting point, a destination point and travel between). [col 17, lines 16-23]);
storing, in a data store of the server system, the first set of motion measurements in association with an indication of the first trip (The movement data evaluation and identification server 210 can include one or more databases 212 configured to store data associated with driving behaviors, performance data, operational data, movement data, device usage data, and the like. [col 15, lines 46-50]);
receiving, by the server system from a plurality of mobile devices (the remote user computing devices 170, 175, may be mobile devices associated with one or more users [col 3, lines 31-32]), a plurality of sets of motion measurements each generated by one or more motion sensors of a respective mobile device of the plurality of mobile devices (The movement data may include sensor data including location data, such as global positioning system (GPS) data, accelerometer and/or gyroscope data, and the like [col 1, lines 47-50]), the plurality of sets of motion measurements each indicating that a respective trip of a plurality of trips occurred during a respective time interval of a plurality of time intervals (At step 408, mobile device data may be received. For example, data from the user's mobile device (e.g., remote user computing device 170) may be received. In some examples, the mobile device data may include application data (e.g., data associated with applications executing on the mobile device and times when executing, time when applications are disabled/enabled, and the like). In some arrangements, the mobile device data may include available connection data. For instance, the mobile device data may include data associated with a number of devices available for pairing (e.g., via BLUETOOTH) at one or more times or during one or more time periods. In some examples, the mobile device data may include image data captured by an image capture device of the mobile device. The image data may include an image of a user and associated surroundings within the vehicle. Various other types of mobile device data may be received as well. [col 21, lines 6-22]);
storing, in the data store of the server system, the plurality of sets of motion measurements in association with indications of the corresponding respective time interval of the corresponding respective trip (If the movement data evaluation and identification computing platform 110 determines that the movement data corresponds to movement of a driver, the data may be stored and further processed to generate one or more outputs [col 4, lines 5-8]; The movement data evaluation and identification server 210 can include one or more databases 212 configured to store data associated with driving behaviors, performance data, operational data, movement data, device usage data, and the like [col 15, lines 46-50]);
querying, in the data store of the server system, the plurality of sets of motion measurements for a second set of motion measurements stored in association with a second time interval of a second trip (At step 414, the received data may be aggregated and analyzed. In some examples, analyzing the data may include applying one or more weighting factors to the data. For instance, certain types of data may be weighted more heavily than others since there is greater confidence in the accuracy of determining whether the user is a driver or non-driver passenger based on the data. For instance, data indicating a mode in which a vehicle is operating may be weighted heavily since if in an autonomous mode there is high confidence that a user is a non-driver passenger. Alternatively, data associated with number of available connections may be weighted less heavily than vehicle mode because there is less confidence in the determination of whether the user is a driver or non-driver passenger based on that factor alone (e.g., few connections may mean that the user is surrounded by people with mobile devices turned off or with connection capability disabled). In some examples, each factor or type of data (e.g., vehicle mode, application usage, image data, and the like) may be associated with a weighting factor that may be used to weight the factor in arrangements in which more than one factor is used to determine a user status as driver or non-driver passenger. [col 21, line 52 – col 22, line 6]; examiner notes that a duplication of mobile devices to match to the vehicle data is simply a duplication of parts and has not patentable significance unless an unexpected result is produced. The expected result of comparing multiple mobile devices to match to vehicle data would be to be able to identify and correlate which mobile device is in/driving the vehicle. See MPEP 2144.04(VI)(B).) for which the start times of the first time interval and the second time interval are within a first predefined threshold time difference of each other, the end times of the first time interval and the second time interval are within a second predefined threshold time difference of each other, or both (although not explicitly stated, examiner notes that Kumar is matching data from the user to potential explanations for the movement to be able to determine if the user is a driver or not. Kumar explicitly teaches matching the movement with published public transit routes and times which would inherently require matching of beginning and end times to match the posted schedule. This process although not explicitly taught would obviously also be applied to vehicle data provided to the system. Matching user data to vehicle data is the easiest way to place the user in the vehicle. Siira cited below explicitly teaches identifying a user time to a vehicle trip time beginning or ending within a threshold to determine if the user is the driver of a vehicle.);
storing, in the data store of the server system, an association between the first set of motion measurements and the second set of motion measurements (If, at step 416, the movement data corresponds to movement of a user as a driver of a vehicle, at step 420, the data may be further processed and/or analyzed to evaluate driving behaviors, vehicle operation, external factors (e.g., weather, time of day, and the like) to determine or adjust risk associated with the user as a driver. In some examples, a risk profile of the user may be generated or modified. The risk may then be used to determine or modify an insurance rate or premium, generate an insurance quote, determine a consumption rate of usage-based insurance, and the like, at step 422. [col 22, lines 42-52]);
the association determined based on:
(i) comparing a first driving event time associated with the first set of motion measurements with a second driving event time associated with the second set of motion measurements (Analyzing the data may include using machine learning to evaluate data, predict whether the user is a driver or non-driver passenger, or the like. In some examples, patterns, sequences, and the like, of data may be identified and used to determine the user status [col 22, lines 7-11]; the movement data, device usage data and/or extracted data may be analyzed. In some examples, as discussed herein, machine learning may be used to analyze the data and identify one or more patterns, sequences, or the like [col 19, lines 1-5]);
(ii) comparing a first trip type associated with the first set of motion measurements with a second trip type associated with the second set of motion measurements (examiner is interpreting the limitation in the alternative.); or
(iii) comparing a first characteristic associated with the first set of motion measurements with a second characteristic associated with the second set of motion measurements (Analyzing the data may include using machine learning to evaluate data, predict whether the user is a driver or non-driver passenger, or the like. In some examples, patterns, sequences, and the like, of data may be identified and used to determine the user status [col 22, lines 7-11]; the movement data, device usage data and/or extracted data may be analyzed. In some examples, as discussed herein, machine learning may be used to analyze the data and identify one or more patterns, sequences, or the like [col 19, lines 1-5]); and
causing, by the server system and based on the association (Analyzing the data may include using machine learning to evaluate data, predict whether the user is a driver or non-driver passenger, or the like. In some examples, patterns, sequences, and the like, of data may be identified and used to determine the user status [col 22, lines 7-11]), the mobile device of the plurality of mobile devices that generated the second set of motion measurements to present a notification (FIG. 6 illustrates one example user interface that may be generated according to one or more aspects described herein. The user interface 600 may be generated by, for example, movement data evaluation and identification computing platform 110 and transmitted to remote user computing device 170 for display. [col 23, line 66 – col 24, line 4]; Various other types of user interfaces and/or notifications may be generated and displayed without departing from the invention [col 24, lines 11-13]) indicating that the first trip was determined to be the same trip as the second trip (At step 416, a determination may be made as to whether, based on the analyzed data, the movement data corresponds to movement of a user as a driver of a vehicle [col 22, lines 28-31]).
Kumar does not explicitly teach, however Kanevsky teaches:
receiving, by the server system from a plurality of mobile devices (a plurality of mobile computing devices 220 [col 7, lines 55-67]), a plurality of sets of motion measurements each generated by one or more motion sensors of a respective mobile device of the plurality of mobile devices, the plurality of sets of motion measurements each indicating that a respective trip of a plurality of trips occurred during a respective time interval of a plurality of time intervals (The movement data/driving data analysis system 200 in these examples may also include a plurality of mobile computing devices 220. As discussed below, in some embodiments, mobile computing devices 220 may receive and execute a movement data analysis software application 222 from the server 210 or other application provider (e.g., an application store or third-party application provider). As part of the execution of the movement data analysis software application 222, or implemented as separate functionality, mobile computing device 220 may receive and analyze movement data from movement sensors 223 of the mobile device 220, identify driving patterns based on the received movement data, and use driving patterns to identify drivers associated with the movement data. [col 7, lines 55-67]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar to include the teachings as taught by Kanevsky with a reasonable expectation of success. Kanevsky teaches the benefits of “the ability to collect and analyze driving data and driving behaviors associated with vehicles and drivers has many valuable applications, for example, relating to vehicle and driver insurance, vehicle financing, product safety and marketing, government and law enforcement, and various other applications in other industries. For example, an insurance company may offer a safe driving discount, and a financial institution may offer financing incentives to customers based on driving behavior. Law enforcement or governmental personnel may collect and analyze driving data to identify dangerous driving roads or times, detect moving violations and other unsafe driving behaviors. In other cases, driving data may be used for navigation applications, vehicle tracking and monitoring applications, and vehicle maintenance applications, product sales and targeting advertisement applications, among others. [col 1, lines 15-30, Kanevsky]”.
Kumar and Kanevsky do not explicitly teach, however Siira teaches:
querying, in the data store of the server system, the plurality of sets of motion measurements for a second set of motion measurements stored in association with a second time interval of a second trip (by analyzing the logged out times of drivers registered in the system, and selecting the drivers that have logged-out times corresponding to the period of unauthorized use. Finally, in step 14, the system assigns a likelihood value to each candidate. The value may be higher, for example, if a given driver has logged back into the same vehicle after it has been used for a ghost trip, without any other intervening drivers being logged in [col 3, lines 43-50]) for which the start times of the first time interval (The EOBR may also include a clock and/or deduce time from GPS signals, in order to monitor the start time, end time and duration of each trip [col 3, lines 1-3]) and the second time interval are within a first predefined threshold time difference of each other, the end times of the first time interval and the second time interval are within a second predefined threshold time difference of each other, or both (This may be considered to be especially true if either or both of the second driver's log out time or subsequent log in time is within a certain threshold, respectively, of the start or end of the period in which the ghost trip occurred. Numerical values of the likelihood may be assigned depending on how close the log out and log in times match the start and end of the ghost trip respectively. [col 5, lines 56-62]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar and Kanevsky to include the teachings as taught by Siira with a reasonable expectation of success. Siira teaches the benefits of “to identify ghost trips, and identify drivers who may have engaged in such activity, the present invention discloses a system and method for detecting unauthorized use of vehicles, finding candidates who may have made such unauthorized use, and assigning a likelihood that each candidate actually made such use [Siira, col 1, lines 37-42]”.
Kumar implicitly teaches, however Outwater explicitly teaches:
the association determined based on:
(i) comparing a first driving event time associated with the first set of motion measurements with a second driving event time associated with the second set of motion measurements (the accelerometers of each of several smartphones may indicate a bump (from the speed bump) timestamped within a few milliseconds of each other, which in combination with similar GPS readings, indicates that they are passengers in the same vehicle, with the indication becoming stronger the longer the readings remain similar [0065]; the management system analyzes the accelerometer reports from multiple smartphones and the vehicle, the management system is able to identify a number of phones (and their corresponding owners) experiencing the same motion signature as the vehicle [0066]);
(ii) comparing a first trip type associated with the first set of motion measurements with a second trip type associated with the second set of motion measurements (examiner is interpreting the limitation in the alternative.); or
(iii) comparing a first characteristic associated with the first set of motion measurements with a second characteristic associated with the second set of motion measurements (the management system analyzes the accelerometer reports from multiple smartphones and the vehicle, the management system is able to identify a number of phones (and their corresponding owners) experiencing the same motion signature as the vehicle [0066]); and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar, Kanevsky, and Siira to include the teachings as taught by Outwater with a reasonable expectation of success. Siira teaches the benefits of “Tracking drivers is important, because it can matter who is driving the vehicle, in addition to how many miles are driven. The present invention gives insurance companies or fleet managers (or parents!) insight into who is really driving the vehicle and how many miles each driver logs. For example, a general expectation is that most families would not lend out a car for long periods of time to someone outside the family who is not intended to be covered by insurance, and the present invention allows confirmation of this. [Outwater, 0070]”.
Regarding claim 14:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 9, upon which this claim is dependent.
Kumar further teaches:
generating, from the first set of motion measurements, one or more characteristics of the first trip (At step 414, the received data may be aggregated and analyzed. In some examples, analyzing the data may include applying one or more weighting factors to the data. For instance, certain types of data may be weighted more heavily than others since there is greater confidence in the accuracy of determining whether the user is a driver or non-driver passenger based on the data. For instance, data indicating a mode in which a vehicle is operating may be weighted heavily since if in an autonomous mode there is high confidence that a user is a non-driver passenger. Alternatively, data associated with number of available connections may be weighted less heavily than vehicle mode because there is less confidence in the determination of whether the user is a driver or non-driver passenger based on that factor alone (e.g., few connections may mean that the user is surrounded by people with mobile devices turned off or with connection capability disabled). In some examples, each factor or type of data (e.g., vehicle mode, application usage, image data, and the like) may be associated with a weighting factor that may be used to weight the factor in arrangements in which more than one factor is used to determine a user status as driver or non-driver passenger. [col 21, line 52 – col 22, line 6]); and
updating, based on the one or more characteristics of the first trip, a score associated with a user identified by a user identifier associated with the mobile device (If, at step 416, the movement data corresponds to movement of a user as a driver of a vehicle, at step 420, the data may be further processed and/or analyzed to evaluate driving behaviors, vehicle operation, external factors (e.g., weather, time of day, and the like) to determine or adjust risk associated with the user as a driver. In some examples, a risk profile of the user may be generated or modified. The risk may then be used to determine or modify an insurance rate or premium, generate an insurance quote, determine a consumption rate of usage-based insurance, and the like, at step 422. [col 22, lines 42-52]).
Regarding claim 16:
Kumar teaches:
A system (a movement data evaluation and identification system in accordance with one or more aspects described herein [col 3, lines 11-14]) comprising:
A server system (fig. 1, movement data evaluation and identification computing platform 110 and fig. 2, movement data evaluation and identification server 210) comprising: one or more processors (movement data evaluation and identification computing platform 110 may include one or more processors 111, memory 112, and communication interface 113 [col 7, lines 8-12]); and
a non-transitory computer-readable medium storing instructions which, when executed by the one or more processors (movement data evaluation and identification computing platform 110 may include one or more processors 111, memory 112, and communication interface 113 [col 7, lines 8-12]), configure the server system to perform operations comprising:
receiving, from an electronic device affixed to a first vehicle, a first vehicle (Telematics devices 263 can receive data from vehicle sensors 261, and can transmit the data to a mobile device 250 or movement data evaluation and identification server 210. [col 15, lines 16-18]; fig. 4, step 410), a first set of motion measurements generated by one or more motion sensors of the electronic device (sensors 261 can detect and store data corresponding to the vehicle's location (e.g., GPS coordinates), time, travel time, speed and direction, rates of acceleration or braking, gas mileage, and specific instances of sudden acceleration, braking, swerving, and distance traveled [col 13, lines 43-47]), the first set of motion measurements indicating that a first trip occurred in the first vehicle during a first time interval (Certain vehicle sensors 261 also can collect information regarding the vehicle's location, current and past driving routes, in order to classify the type of trip (e.g. work or school commute, shopping or recreational trip, unknown new route, etc.). [col 14, lines 32-36]; receiving the data may cause an initial processing of the movement data to occur. For instance, the movement data may be analyzed to identify one or more time periods associated with the movement data. In some examples, this may be based on time stamps associated with the received movement data. In some examples, the movement data may correspond to a single driving trip (e.g., a starting point, a destination point and travel between). [col 17, lines 16-23]);
storing, in a data store of the server system, the first set of motion measurements in association with an indication of the first trip (The movement data evaluation and identification server 210 can include one or more databases 212 configured to store data associated with driving behaviors, performance data, operational data, movement data, device usage data, and the like. [col 15, lines 46-50]);
receiving, from a plurality of mobile devices, (the remote user computing devices 170, 175, may be mobile devices associated with one or more users [col 3, lines 31-32]), a plurality of sets of motion measurements each generated by one or more motion sensors of a respective mobile device of the plurality of mobile devices (The movement data may include sensor data including location data, such as global positioning system (GPS) data, accelerometer and/or gyroscope data, and the like [col 1, lines 47-50]), the plurality of sets of motion measurements each indicating that a respective trip of a plurality of trips occurred during a respective time interval of a plurality of time intervals (At step 408, mobile device data may be received. For example, data from the user's mobile device (e.g., remote user computing device 170) may be received. In some examples, the mobile device data may include application data (e.g., data associated with applications executing on the mobile device and times when executing, time when applications are disabled/enabled, and the like). In some arrangements, the mobile device data may include available connection data. For instance, the mobile device data may include data associated with a number of devices available for pairing (e.g., via BLUETOOTH) at one or more times or during one or more time periods. In some examples, the mobile device data may include image data captured by an image capture device of the mobile device. The image data may include an image of a user and associated surroundings within the vehicle. Various other types of mobile device data may be received as well. [col 21, lines 6-22]);
storing, in the data store of the server system, the plurality of sets of motion measurements in association with indications of the corresponding respective time interval of the corresponding respective trip (If the movement data evaluation and identification computing platform 110 determines that the movement data corresponds to movement of a driver, the data may be stored and further processed to generate one or more outputs [col 4, lines 5-8]; The movement data evaluation and identification server 210 can include one or more databases 212 configured to store data associated with driving behaviors, performance data, operational data, movement data, device usage data, and the like [col 15, lines 46-50]);
querying, in the data store of the server system, the plurality of sets of motion measurements for a second set of motion measurements stored in association with a second time interval of a second trip (At step 414, the received data may be aggregated and analyzed. In some examples, analyzing the data may include applying one or more weighting factors to the data. For instance, certain types of data may be weighted more heavily than others since there is greater confidence in the accuracy of determining whether the user is a driver or non-driver passenger based on the data. For instance, data indicating a mode in which a vehicle is operating may be weighted heavily since if in an autonomous mode there is high confidence that a user is a non-driver passenger. Alternatively, data associated with number of available connections may be weighted less heavily than vehicle mode because there is less confidence in the determination of whether the user is a driver or non-driver passenger based on that factor alone (e.g., few connections may mean that the user is surrounded by people with mobile devices turned off or with connection capability disabled). In some examples, each factor or type of data (e.g., vehicle mode, application usage, image data, and the like) may be associated with a weighting factor that may be used to weight the factor in arrangements in which more than one factor is used to determine a user status as driver or non-driver passenger. [col 21, line 52 – col 22, line 6]; examiner notes that a duplication of mobile devices to match to the vehicle data is simply a duplication of parts and has not patentable significance unless an unexpected result is produced. The expected result of comparing multiple mobile devices to match to vehicle data would be to be able to identify and correlate which mobile device is in/driving the vehicle. See MPEP 2144.04(VI)(B).) for which the start times of the first time interval and the second time interval are within a first predefined threshold time difference of each other, the end times of the first time interval and the second time interval are within a second predefined threshold time difference of each other, or both (although not explicitly stated, examiner notes that Kumar is matching data from the user to potential explanations for the movement to be able to determine if the user is a driver or not. Kumar explicitly teaches matching the movement with published public transit routes and times which would inherently require matching of beginning and end times to match the posted schedule. This process although not explicitly taught would obviously also be applied to vehicle data provided to the system. Matching user data to vehicle data is the easiest way to place the user in the vehicle. Siira cited below explicitly teaches identifying a user time to a vehicle trip time beginning or ending within a threshold to determine if the user is the driver of a vehicle.);
storing, in the data store of the server system, a determined association between the first set of motion measurements and the second set of motion measurements (If, at step 416, the movement data corresponds to movement of a user as a driver of a vehicle, at step 420, the data may be further processed and/or analyzed to evaluate driving behaviors, vehicle operation, external factors (e.g., weather, time of day, and the like) to determine or adjust risk associated with the user as a driver. In some examples, a risk profile of the user may be generated or modified. The risk may then be used to determine or modify an insurance rate or premium, generate an insurance quote, determine a consumption rate of usage-based insurance, and the like, at step 422. [col 22, lines 42-52]), the association determined based on:
(i) comparing a first driving event time associated with the first set of motion measurements with a second driving event time associated with the second set of motion measurements (Analyzing the data may include using machine learning to evaluate data, predict whether the user is a driver or non-driver passenger, or the like. In some examples, patterns, sequences, and the like, of data may be identified and used to determine the user status [col 22, lines 7-11]; the movement data, device usage data and/or extracted data may be analyzed. In some examples, as discussed herein, machine learning may be used to analyze the data and identify one or more patterns, sequences, or the like [col 19, lines 1-5]);
(ii) comparing a first trip type associated with the first set of motion measurements with a second trip type associated with the second set of motion measurements (examiner is interpreting the limitation in the alternative.); or
(iii) comparing a first characteristic associated with the first set of motion measurements with a second characteristic associated with the second set of motion measurements (Analyzing the data may include using machine learning to evaluate data, predict whether the user is a driver or non-driver passenger, or the like. In some examples, patterns, sequences, and the like, of data may be identified and used to determine the user status [col 22, lines 7-11]; the movement data, device usage data and/or extracted data may be analyzed. In some examples, as discussed herein, machine learning may be used to analyze the data and identify one or more patterns, sequences, or the like [col 19, lines 1-5]); and
Causing, based on the association (Analyzing the data may include using machine learning to evaluate data, predict whether the user is a driver or non-driver passenger, or the like. In some examples, patterns, sequences, and the like, of data may be identified and used to determine the user status [col 22, lines 7-11]), the mobile device of the plurality of mobile devices that generated the second set of motion measurements to present a notification (FIG. 6 illustrates one example user interface that may be generated according to one or more aspects described herein. The user interface 600 may be generated by, for example, movement data evaluation and identification computing platform 110 and transmitted to remote user computing device 170 for display. [col 23, line 66 – col 24, line 4]; Various other types of user interfaces and/or notifications may be generated and displayed without departing from the invention [col 24, lines 11-13]) indicating that the first trip was determined to be the same trip as the second trip (At step 416, a determination may be made as to whether, based on the analyzed data, the movement data corresponds to movement of a user as a driver of a vehicle [col 22, lines 28-31]).
Kumar does not explicitly teach, however Kanevsky teaches:
receiving, by the server system from a plurality of mobile devices (a plurality of mobile computing devices 220 [col 7, lines 55-67]), a plurality of sets of motion measurements each generated by one or more motion sensors of a respective mobile device of the plurality of mobile devices, the plurality of sets of motion measurements each indicating that a respective trip of a plurality of trips occurred during a respective time interval of a plurality of time intervals (The movement data/driving data analysis system 200 in these examples may also include a plurality of mobile computing devices 220. As discussed below, in some embodiments, mobile computing devices 220 may receive and execute a movement data analysis software application 222 from the server 210 or other application provider (e.g., an application store or third-party application provider). As part of the execution of the movement data analysis software application 222, or implemented as separate functionality, mobile computing device 220 may receive and analyze movement data from movement sensors 223 of the mobile device 220, identify driving patterns based on the received movement data, and use driving patterns to identify drivers associated with the movement data. [col 7, lines 55-67]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar to include the teachings as taught by Kanevsky with a reasonable expectation of success. Kanevsky teaches the benefits of “the ability to collect and analyze driving data and driving behaviors associated with vehicles and drivers has many valuable applications, for example, relating to vehicle and driver insurance, vehicle financing, product safety and marketing, government and law enforcement, and various other applications in other industries. For example, an insurance company may offer a safe driving discount, and a financial institution may offer financing incentives to customers based on driving behavior. Law enforcement or governmental personnel may collect and analyze driving data to identify dangerous driving roads or times, detect moving violations and other unsafe driving behaviors. In other cases, driving data may be used for navigation applications, vehicle tracking and monitoring applications, and vehicle maintenance applications, product sales and targeting advertisement applications, among others. [col 1, lines 15-30, Kanevsky]”.
Kumar and Kanevsky do not explicitly teach, however Siira teaches:
querying, in the data store of the server system, the plurality of sets of motion measurements for a second set of motion measurements stored in association with a second time interval of a second trip (by analyzing the logged out times of drivers registered in the system, and selecting the drivers that have logged-out times corresponding to the period of unauthorized use. Finally, in step 14, the system assigns a likelihood value to each candidate. The value may be higher, for example, if a given driver has logged back into the same vehicle after it has been used for a ghost trip, without any other intervening drivers being logged in [col 3, lines 43-50]) for which the start times of the first time interval (The EOBR may also include a clock and/or deduce time from GPS signals, in order to monitor the start time, end time and duration of each trip [col 3, lines 1-3]) and the second time interval are within a first predefined threshold time difference of each other, the end times of the first time interval and the second time interval are within a second predefined threshold time difference of each other, or both (This may be considered to be especially true if either or both of the second driver's log out time or subsequent log in time is within a certain threshold, respectively, of the start or end of the period in which the ghost trip occurred. Numerical values of the likelihood may be assigned depending on how close the log out and log in times match the start and end of the ghost trip respectively. [col 5, lines 56-62]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar and Kanevsky to include the teachings as taught by Siira with a reasonable expectation of success. Siira teaches the benefits of “to identify ghost trips, and identify drivers who may have engaged in such activity, the present invention discloses a system and method for detecting unauthorized use of vehicles, finding candidates who may have made such unauthorized use, and assigning a likelihood that each candidate actually made such use [Siira, col 1, lines 37-42]”.
Kumar implicitly teaches, however Outwater explicitly teaches:
the association determined based on:
(i) comparing a first driving event time associated with the first set of motion measurements with a second driving event time associated with the second set of motion measurements (the accelerometers of each of several smartphones may indicate a bump (from the speed bump) timestamped within a few milliseconds of each other, which in combination with similar GPS readings, indicates that they are passengers in the same vehicle, with the indication becoming stronger the longer the readings remain similar [0065]; the management system analyzes the accelerometer reports from multiple smartphones and the vehicle, the management system is able to identify a number of phones (and their corresponding owners) experiencing the same motion signature as the vehicle [0066]);
(ii) comparing a first trip type associated with the first set of motion measurements with a second trip type associated with the second set of motion measurements (examiner is interpreting the limitation in the alternative.); or
(iii) comparing a first characteristic associated with the first set of motion measurements with a second characteristic associated with the second set of motion measurements (the management system analyzes the accelerometer reports from multiple smartphones and the vehicle, the management system is able to identify a number of phones (and their corresponding owners) experiencing the same motion signature as the vehicle [0066]); and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar, Kanevsky, and Siira to include the teachings as taught by Outwater with a reasonable expectation of success. Siira teaches the benefits of “Tracking drivers is important, because it can matter who is driving the vehicle, in addition to how many miles are driven. The present invention gives insurance companies or fleet managers (or parents!) insight into who is really driving the vehicle and how many miles each driver logs. For example, a general expectation is that most families would not lend out a car for long periods of time to someone outside the family who is not intended to be covered by insurance, and the present invention allows confirmation of this. [Outwater, 0070]”.
Regarding claim 18:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 16, upon which this claim is dependent.
Kumar further teaches:
generate, from the first set of motion measurements, one or more characteristics of the first trip (At step 414, the received data may be aggregated and analyzed. In some examples, analyzing the data may include applying one or more weighting factors to the data. For instance, certain types of data may be weighted more heavily than others since there is greater confidence in the accuracy of determining whether the user is a driver or non-driver passenger based on the data. For instance, data indicating a mode in which a vehicle is operating may be weighted heavily since if in an autonomous mode there is high confidence that a user is a non-driver passenger. Alternatively, data associated with number of available connections may be weighted less heavily than vehicle mode because there is less confidence in the determination of whether the user is a driver or non-driver passenger based on that factor alone (e.g., few connections may mean that the user is surrounded by people with mobile devices turned off or with connection capability disabled). In some examples, each factor or type of data (e.g., vehicle mode, application usage, image data, and the like) may be associated with a weighting factor that may be used to weight the factor in arrangements in which more than one factor is used to determine a user status as driver or non-driver passenger. [col 21, line 52 – col 22, line 6]); and
update, based on the one or more characteristics of the first trip, a score associated with a user identifier associated with the mobile device (If, at step 416, the movement data corresponds to movement of a user as a driver of a vehicle, at step 420, the data may be further processed and/or analyzed to evaluate driving behaviors, vehicle operation, external factors (e.g., weather, time of day, and the like) to determine or adjust risk associated with the user as a driver. In some examples, a risk profile of the user may be generated or modified. The risk may then be used to determine or modify an insurance rate or premium, generate an insurance quote, determine a consumption rate of usage-based insurance, and the like, at step 422. [col 22, lines 42-52]).
Regarding claim 20:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 16, upon which this claim is dependent.
Kumar further teaches:
wherein the first set of motion measurements and the second set of motion measurements are received in response to activation of an application on the mobile device (Memory 112 may include one or more program modules having instructions that when executed by processor(s) 111 cause movement data evaluation and identification computing platform 110 to perform one or more functions described herein and/or one or more databases that may store and/or otherwise maintain information which may be used by such program modules and/or processor(s) 111. In some instances, the one or more program modules and/or databases may be stored by and/or maintained in different memory units of movement data evaluation and identification computing platform 110 and/or by different computing devices that may form and/or otherwise make up movement data evaluation and identification computing platform 110. [col 7, lines 17-30]).
Regarding claim 21:
Kumar teaches:
One or more non-transitory computer-readable storage media storing instructions that, upon execution by one or more processors of a system, cause the system to perform operations (Memory 112 may include one or more program modules having instructions that when executed by processor(s) 111 cause movement data evaluation and identification computing platform 110 to perform one or more functions described herein and/or one or more databases that may store and/or otherwise maintain information which may be used by such program modules and/or processor(s) 111 [col 7, lines 17-24]) comprising:
receiving, by a server system from an electronic device affixed to a first vehicle (Telematics devices 263 can receive data from vehicle sensors 261, and can transmit the data to a mobile device 250 or movement data evaluation and identification server 210. [col 15, lines 16-18]; fig. 4, step 410), a first set of motion measurements generated by one or more motion sensors of the electronic device (sensors 261 can detect and store data corresponding to the vehicle's location (e.g., GPS coordinates), time, travel time, speed and direction, rates of acceleration or braking, gas mileage, and specific instances of sudden acceleration, braking, swerving, and distance traveled [col 13, lines 43-47]), the first set of motion measurements indicating that a first trip occurred in the first vehicle during a first time interval (Certain vehicle sensors 261 also can collect information regarding the vehicle's location, current and past driving routes, in order to classify the type of trip (e.g. work or school commute, shopping or recreational trip, unknown new route, etc.). [col 14, lines 32-36]; receiving the data may cause an initial processing of the movement data to occur. For instance, the movement data may be analyzed to identify one or more time periods associated with the movement data. In some examples, this may be based on time stamps associated with the received movement data. In some examples, the movement data may correspond to a single driving trip (e.g., a starting point, a destination point and travel between). [col 17, lines 16-23]);
storing, in a data store of the server system, the first set of motion measurements in association with an indication of the first trip (The movement data evaluation and identification server 210 can include one or more databases 212 configured to store data associated with driving behaviors, performance data, operational data, movement data, device usage data, and the like. [col 15, lines 46-50]);
receiving, by the server system from a plurality of mobile devices (the remote user computing devices 170, 175, may be mobile devices associated with one or more users [col 3, lines 31-32]), a plurality of sets of motion measurements each generated by one or more motion sensors of a respective mobile device of the plurality of mobile devices (The movement data may include sensor data including location data, such as global positioning system (GPS) data, accelerometer and/or gyroscope data, and the like [col 1, lines 47-50]), the plurality of sets of motion measurements each indicating that a respective trip of a plurality of trips occurred during a respective time interval of a plurality of time intervals (At step 408, mobile device data may be received. For example, data from the user's mobile device (e.g., remote user computing device 170) may be received. In some examples, the mobile device data may include application data (e.g., data associated with applications executing on the mobile device and times when executing, time when applications are disabled/enabled, and the like). In some arrangements, the mobile device data may include available connection data. For instance, the mobile device data may include data associated with a number of devices available for pairing (e.g., via BLUETOOTH) at one or more times or during one or more time periods. In some examples, the mobile device data may include image data captured by an image capture device of the mobile device. The image data may include an image of a user and associated surroundings within the vehicle. Various other types of mobile device data may be received as well. [col 21, lines 6-22]);
storing, in the data store of the server system, the plurality of sets of motion measurements in association with indications of the corresponding respective time interval of the corresponding respective trip (If the movement data evaluation and identification computing platform 110 determines that the movement data corresponds to movement of a driver, the data may be stored and further processed to generate one or more outputs [col 4, lines 5-8]; The movement data evaluation and identification server 210 can include one or more databases 212 configured to store data associated with driving behaviors, performance data, operational data, movement data, device usage data, and the like [col 15, lines 46-50]);
querying, in the data store of the server system, the plurality of sets of motion measurements for a second set of motion measurements stored in association with a second time interval of a second trip (At step 414, the received data may be aggregated and analyzed. In some examples, analyzing the data may include applying one or more weighting factors to the data. For instance, certain types of data may be weighted more heavily than others since there is greater confidence in the accuracy of determining whether the user is a driver or non-driver passenger based on the data. For instance, data indicating a mode in which a vehicle is operating may be weighted heavily since if in an autonomous mode there is high confidence that a user is a non-driver passenger. Alternatively, data associated with number of available connections may be weighted less heavily than vehicle mode because there is less confidence in the determination of whether the user is a driver or non-driver passenger based on that factor alone (e.g., few connections may mean that the user is surrounded by people with mobile devices turned off or with connection capability disabled). In some examples, each factor or type of data (e.g., vehicle mode, application usage, image data, and the like) may be associated with a weighting factor that may be used to weight the factor in arrangements in which more than one factor is used to determine a user status as driver or non-driver passenger. [col 21, line 52 – col 22, line 6]; examiner notes that a duplication of mobile devices to match to the vehicle data is simply a duplication of parts and has not patentable significance unless an unexpected result is produced. The expected result of comparing multiple mobile devices to match to vehicle data would be to be able to identify and correlate which mobile device is in/driving the vehicle. See MPEP 2144.04(VI)(B).) for which the start times of the first time interval and the second time interval are within a first predefined threshold time difference of each other, the end times of the first time interval and the second time interval are within a second predefined threshold time difference of each other, or both (although not explicitly stated, examiner notes that Kumar is matching data from the user to potential explanations for the movement to be able to determine if the user is a driver or not. Kumar explicitly teaches matching the movement with published public transit routes and times which would inherently require matching of beginning and end times to match the posted schedule. This process although not explicitly taught would obviously also be applied to vehicle data provided to the system. Matching user data to vehicle data is the easiest way to place the user in the vehicle. Siira cited below explicitly teaches identifying a user time to a vehicle trip time beginning or ending within a threshold to determine if the user is the driver of a vehicle.);
storing, in the data store of the server system, an association between the first set of motion measurements and the second set of motion measurements (If, at step 416, the movement data corresponds to movement of a user as a driver of a vehicle, at step 420, the data may be further processed and/or analyzed to evaluate driving behaviors, vehicle operation, external factors (e.g., weather, time of day, and the like) to determine or adjust risk associated with the user as a driver. In some examples, a risk profile of the user may be generated or modified. The risk may then be used to determine or modify an insurance rate or premium, generate an insurance quote, determine a consumption rate of usage-based insurance, and the like, at step 422. [col 22, lines 42-52]), the association determined based on:
(i) comparing a first driving event time associated with the first set of motion measurements with a second driving event time associated with the second set of motion measurements (Analyzing the data may include using machine learning to evaluate data, predict whether the user is a driver or non-driver passenger, or the like. In some examples, patterns, sequences, and the like, of data may be identified and used to determine the user status [col 22, lines 7-11]; the movement data, device usage data and/or extracted data may be analyzed. In some examples, as discussed herein, machine learning may be used to analyze the data and identify one or more patterns, sequences, or the like [col 19, lines 1-5]);
(ii) comparing a first trip type associated with the first set of motion measurements with a second trip type associated with the second set of motion measurements (examiner is interpreting the limitation in the alternative.); or
(iii) comparing a first characteristic associated with the first set of motion measurements with a second characteristic associated with the second set of motion measurements (Analyzing the data may include using machine learning to evaluate data, predict whether the user is a driver or non-driver passenger, or the like. In some examples, patterns, sequences, and the like, of data may be identified and used to determine the user status [col 22, lines 7-11]; the movement data, device usage data and/or extracted data may be analyzed. In some examples, as discussed herein, machine learning may be used to analyze the data and identify one or more patterns, sequences, or the like [col 19, lines 1-5]); and
Causing, by the server and based on the association (Analyzing the data may include using machine learning to evaluate data, predict whether the user is a driver or non-driver passenger, or the like. In some examples, patterns, sequences, and the like, of data may be identified and used to determine the user status [col 22, lines 7-11]), the mobile device of the plurality of mobile devices that generated the second set of motion measurements to present a notification (FIG. 6 illustrates one example user interface that may be generated according to one or more aspects described herein. The user interface 600 may be generated by, for example, movement data evaluation and identification computing platform 110 and transmitted to remote user computing device 170 for display. [col 23, line 66 – col 24, line 4]; Various other types of user interfaces and/or notifications may be generated and displayed without departing from the invention [col 24, lines 11-13]) indicating that the first trip was determined to be the same trip as the second trip (At step 416, a determination may be made as to whether, based on the analyzed data, the movement data corresponds to movement of a user as a driver of a vehicle [col 22, lines 28-31]).
Kumar does not explicitly teach, however Kanevsky teaches:
receiving, by the server system from a plurality of mobile devices (a plurality of mobile computing devices 220 [col 7, lines 55-67]), a plurality of sets of motion measurements each generated by one or more motion sensors of a respective mobile device of the plurality of mobile devices, the plurality of sets of motion measurements each indicating that a respective trip of a plurality of trips occurred during a respective time interval of a plurality of time intervals (The movement data/driving data analysis system 200 in these examples may also include a plurality of mobile computing devices 220. As discussed below, in some embodiments, mobile computing devices 220 may receive and execute a movement data analysis software application 222 from the server 210 or other application provider (e.g., an application store or third-party application provider). As part of the execution of the movement data analysis software application 222, or implemented as separate functionality, mobile computing device 220 may receive and analyze movement data from movement sensors 223 of the mobile device 220, identify driving patterns based on the received movement data, and use driving patterns to identify drivers associated with the movement data. [col 7, lines 55-67]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar to include the teachings as taught by Kanevsky with a reasonable expectation of success. Kanevsky teaches the benefits of “the ability to collect and analyze driving data and driving behaviors associated with vehicles and drivers has many valuable applications, for example, relating to vehicle and driver insurance, vehicle financing, product safety and marketing, government and law enforcement, and various other applications in other industries. For example, an insurance company may offer a safe driving discount, and a financial institution may offer financing incentives to customers based on driving behavior. Law enforcement or governmental personnel may collect and analyze driving data to identify dangerous driving roads or times, detect moving violations and other unsafe driving behaviors. In other cases, driving data may be used for navigation applications, vehicle tracking and monitoring applications, and vehicle maintenance applications, product sales and targeting advertisement applications, among others. [col 1, lines 15-30, Kanevsky]”.
Kumar and Kanevsky do not explicitly teach, however Siira teaches:
querying, in the data store of the server system, the plurality of sets of motion measurements for a second set of motion measurements stored in association with a second time interval of a second trip (by analyzing the logged out times of drivers registered in the system, and selecting the drivers that have logged-out times corresponding to the period of unauthorized use. Finally, in step 14, the system assigns a likelihood value to each candidate. The value may be higher, for example, if a given driver has logged back into the same vehicle after it has been used for a ghost trip, without any other intervening drivers being logged in [col 3, lines 43-50]) for which the start times of the first time interval (The EOBR may also include a clock and/or deduce time from GPS signals, in order to monitor the start time, end time and duration of each trip [col 3, lines 1-3]) and the second time interval are within a first predefined threshold time difference of each other, the end times of the first time interval and the second time interval are within a second predefined threshold time difference of each other, or both (This may be considered to be especially true if either or both of the second driver's log out time or subsequent log in time is within a certain threshold, respectively, of the start or end of the period in which the ghost trip occurred. Numerical values of the likelihood may be assigned depending on how close the log out and log in times match the start and end of the ghost trip respectively. [col 5, lines 56-62]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar and Kanevsky to include the teachings as taught by Siira with a reasonable expectation of success. Siira teaches the benefits of “to identify ghost trips, and identify drivers who may have engaged in such activity, the present invention discloses a system and method for detecting unauthorized use of vehicles, finding candidates who may have made such unauthorized use, and assigning a likelihood that each candidate actually made such use [Siira, col 1, lines 37-42]”.
Kumar implicitly teaches, however Outwater explicitly teaches:
the association determined based on:
(i) comparing a first driving event time associated with the first set of motion measurements with a second driving event time associated with the second set of motion measurements (the accelerometers of each of several smartphones may indicate a bump (from the speed bump) timestamped within a few milliseconds of each other, which in combination with similar GPS readings, indicates that they are passengers in the same vehicle, with the indication becoming stronger the longer the readings remain similar [0065]; the management system analyzes the accelerometer reports from multiple smartphones and the vehicle, the management system is able to identify a number of phones (and their corresponding owners) experiencing the same motion signature as the vehicle [0066]);
(ii) comparing a first trip type associated with the first set of motion measurements with a second trip type associated with the second set of motion measurements (examiner is interpreting the limitation in the alternative.); or
(iii) comparing a first characteristic associated with the first set of motion measurements with a second characteristic associated with the second set of motion measurements (the management system analyzes the accelerometer reports from multiple smartphones and the vehicle, the management system is able to identify a number of phones (and their corresponding owners) experiencing the same motion signature as the vehicle [0066]); and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar, Kanevsky, and Siira to include the teachings as taught by Outwater with a reasonable expectation of success. Siira teaches the benefits of “Tracking drivers is important, because it can matter who is driving the vehicle, in addition to how many miles are driven. The present invention gives insurance companies or fleet managers (or parents!) insight into who is really driving the vehicle and how many miles each driver logs. For example, a general expectation is that most families would not lend out a car for long periods of time to someone outside the family who is not intended to be covered by insurance, and the present invention allows confirmation of this. [Outwater, 0070]”.
Regarding claim 26:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 21, upon which this claim is dependent.
Kumar further teaches:
generating, from the first set of motion measurements, one or more characteristics of the first trip (At step 414, the received data may be aggregated and analyzed. In some examples, analyzing the data may include applying one or more weighting factors to the data. For instance, certain types of data may be weighted more heavily than others since there is greater confidence in the accuracy of determining whether the user is a driver or non-driver passenger based on the data. For instance, data indicating a mode in which a vehicle is operating may be weighted heavily since if in an autonomous mode there is high confidence that a user is a non-driver passenger. Alternatively, data associated with number of available connections may be weighted less heavily than vehicle mode because there is less confidence in the determination of whether the user is a driver or non-driver passenger based on that factor alone (e.g., few connections may mean that the user is surrounded by people with mobile devices turned off or with connection capability disabled). In some examples, each factor or type of data (e.g., vehicle mode, application usage, image data, and the like) may be associated with a weighting factor that may be used to weight the factor in arrangements in which more than one factor is used to determine a user status as driver or non-driver passenger. [col 21, line 52 – col 22, line 6]); and
updating, based on the one or more characteristics of the first trip, a score associated with a user identified by a user identifier associated with the mobile device (If, at step 416, the movement data corresponds to movement of a user as a driver of a vehicle, at step 420, the data may be further processed and/or analyzed to evaluate driving behaviors, vehicle operation, external factors (e.g., weather, time of day, and the like) to determine or adjust risk associated with the user as a driver. In some examples, a risk profile of the user may be generated or modified. The risk may then be used to determine or modify an insurance rate or premium, generate an insurance quote, determine a consumption rate of usage-based insurance, and the like, at step 422. [col 22, lines 42-52]).
Claim(s) 10-12, 17, and 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et. al. (US 10,785,604), herein Kumar in view of Kanevsky (US 10,902,521), herein Kanevsky, Siira et. al. (US 8,396,662), herein Siira, and Outwater et. al. (US 2017/0115125), herein Outwater and in further view of Zhao et. al. (CN 111144446), herein Zhao.
Regarding claim 10:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 9, upon which this claim is dependent.
Kumar in view of Kanevsky, Siira, and Outwater does not explicitly teach, however Zhao teaches:
determining, for each respective time interval of the plurality of time intervals, a first difference between a first start time of the first time interval and a second start time of the respective time interval, and a second difference between first end time of the first time interval and second end time of the respective time interval (The main factors affecting the judgment accuracy is similarity (matching threshold), the higher the threshold, the higher the accuracy, but the corresponding recall rate will be reduced, so it can be adjusted according to the specific application scene to a confirmed data identity serial number of the driver as reference, dynamically adjusting the threshold value to obtain the required accuracy and recall rate…selecting matching degree portion part of the user exceeds the threshold value as positive samples (labeled data as positive samples), at the same time, randomly extracting amount of non-matched user (does not match the vehicle of the user and/or not matched by the user) as negative sample (labeled as negative sample data), combining to obtain the sample set. For positive samples, can be controlled by means of variable and sampling verifying, selecting rational screening threshold value, ensuring the sample quality. [page 9]; examiner notes that in order to compare to a threshold value, Zhao must inherently determine a difference between the time of data points it is attempting to match.); and
comparing the first difference and the second difference with a predefined threshold time difference (wherein the step of mobile phone data of the user and vehicle data constructing space grid, determining the matching result of mobile phone track and vehicle track, comprising: according to the time threshold value and spatial threshold. respectively performing segmentation of the time dimension and the space dimension of mobile phone data and vehicle data of the user to obtain mobile phone track grid set and vehicle track the grid set, using improved Jaccard distance, calculating the matching degree of each vehicle track grid each mobile phone track grid and vehicle track grid set mobile phone track grid in the set, obtaining the matching degree of the mobile phone the track and vehicle track. [claim 2]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar in view of Kanevsky, Siira, and Outwater to include the teachings as taught by Zhao with a reasonable expectation of success. Zhao teaches the benefits of “a driver identification method and system based on space-time grid, comprising: constructing the space grid according to mobile phone and vehicle data, determining the matching result of mobile phone track and vehicle track; mobile phone data not matching by using matching result with as the sample set mapped to the spatial grid, counting the grid of positive and negative sample the number of accesses according to the access times of the positive and negative sample of each grid determining the discrimination of each grid, selecting a plurality of key network to compressed feature space according to the distinguishing degree; and training the decision model according to the judging model and matching result to determine driver identity of the user. data by mobile phone and vehicle data such as current data constructing the space grid, to determine mobile phone track and vehicle track of the matching result according to the distinguishing degree of each grid selection key network, judging model training according to the judging model and matching result, determining the driver identity of user, the method is simple and capable of identifying the driver identity according to the existing data. [Zhao, abstract]” which solves the “need to provide a simple method, capable of identifying the driver identity according to the existing data. [Zhao, page 2]”
Regarding claim 11:
Kumar in view of Kanevsky, Siira, Outwater, and Zhao teaches all the limitations of claim 10, upon which this claim is dependent.
Kanevsky further teaches:
the association between the first set of motion measurements and the second set of motion measurements is stored in response to (The movement data/driving data analysis system 200 in these examples may also include a plurality of mobile computing devices 220. As discussed below, in some embodiments, mobile computing devices 220 may receive and execute a movement data analysis software application 222 from the server 210 or other application provider (e.g., an application store or third-party application provider). As part of the execution of the movement data analysis software application 222, or implemented as separate functionality, mobile computing device 220 may receive and analyze movement data from movement sensors 223 of the mobile device 220, identify driving patterns based on the received movement data, and use driving patterns to identify drivers associated with the movement data. [col 7, lines 55-67])
Zhao further teaches:
determining that the first difference for the second trip and the second difference for the second trip are less than the predefined threshold time difference (wherein the step of mobile phone data of the user and vehicle data constructing space grid, determining the matching result of mobile phone track and vehicle track, comprising: according to the time threshold value and spatial threshold. respectively performing segmentation of the time dimension and the space dimension of mobile phone data and vehicle data of the user to obtain mobile phone track grid set and vehicle track the grid set, using improved Jaccard distance, calculating the matching degree of each vehicle track grid each mobile phone track grid and vehicle track grid set mobile phone track grid in the set, obtaining the matching degree of the mobile phone the track and vehicle track. [claim 2]).
Regarding claim 12:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 9, upon which this claim is dependent.
Kumar further teaches:
and the first event includes at least one of hard braking, speeding, or a collision (sensors 261 can detect and store data corresponding to the vehicle's location (e.g., GPS coordinates), time, travel time, speed and direction, rates of acceleration or braking, gas mileage, and specific instances of sudden acceleration, braking, swerving, and distance traveled [col 13, lines 43-47])
and the second event includes at least one of the hard braking, the speeding, or the collision (The sensors 253 and/or GPS receiver or LBS component 254 of a mobile device 250 can also be used to determine driving speeds, routes, stoppage points, accident force and angle of impact, and other accident characteristics and accident-related data [col 11, lines 58-62])
Kanevsky further teaches:
and the second event (The movement data/driving data analysis system 200 in these examples may also include a plurality of mobile computing devices 220. As discussed below, in some embodiments, mobile computing devices 220 may receive and execute a movement data analysis software application 222 from the server 210 or other application provider (e.g., an application store or third-party application provider). As part of the execution of the movement data analysis software application 222, or implemented as separate functionality, mobile computing device 220 may receive and analyze movement data from movement sensors 223 of the mobile device 220, identify driving patterns based on the received movement data, and use driving patterns to identify drivers associated with the movement data. [col 7, lines 55-67]) includes at least one of the hard braking, the speeding, or the collision (use the corresponding driving data to detect accidents [col 20, lines 12-13])
Kumar in view of Kanevsky, Siira, and Outwater does not explicitly teach, however Zhao teaches:
obtaining an indication of a first event of the first trip, wherein the first event occurred at a first time during the first time interval (The statistic time slices of vehicle track grid, each mobile phone track grid and it does the matching degree calculation of performing statistic time slice division; [page 6]); and
obtaining an indication of a second event of the second trip, wherein the second event occurred at a second time during the first time interval (The mobile phone data of each user in the position (latitude and longitude), time and other data to obtain mobile phone track of each user according to the vehicle data transmitted by each terminal in the position and time data, vehicle trajectory determination which corresponds to each terminal. [page 7]);
wherein the association between the first set of motion measurements and the second set of motion measurements is further based on a comparison of the first event with the second event (pace grid module used for constructing the space grid and vehicle data according to mobile phone data of the user, determining the matching result of mobile phone track and vehicle track [page 8]; the matching degree (similarity) satisfies the determination of matching threshold value for the driver relationship between people and vehicle, at the same time, it can get the driver identity of the user. [page 8]; Assuming a target vehicle A, the vehicle track data of target vehicle A according to said method, divided into multiple statistic time slices. The space grid of the position point in each statistic time slice of the time grid and statistic time slices where, for matching with mobile phone track of each user, calculating the similarity. [page 8]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar in view of Kanevsky, Siira, and Outwater to include the teachings as taught by Zhao with a reasonable expectation of success. Zhao teaches the benefits of “a driver identification method and system based on space-time grid, comprising: constructing the space grid according to mobile phone and vehicle data, determining the matching result of mobile phone track and vehicle track; mobile phone data not matching by using matching result with as the sample set mapped to the spatial grid, counting the grid of positive and negative sample the number of accesses according to the access times of the positive and negative sample of each grid determining the discrimination of each grid, selecting a plurality of key network to compressed feature space according to the distinguishing degree; and training the decision model according to the judging model and matching result to determine driver identity of the user. data by mobile phone and vehicle data such as current data constructing the space grid, to determine mobile phone track and vehicle track of the matching result according to the distinguishing degree of each grid selection key network, judging model training according to the judging model and matching result, determining the driver identity of user, the method is simple and capable of identifying the driver identity according to the existing data. [Zhao, abstract]” which solves the “need to provide a simple method, capable of identifying the driver identity according to the existing data. [Zhao, page 2]”
Regarding claim 17:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 16, upon which this claim is dependent.
Kumar in view of Kanevsky, Siira, and Outwater does not explicitly teach, however Zhao teaches:
determining that a first difference between a first start time of the first trip and a second start time of the second trip is less than a predefined threshold time difference (wherein the step of mobile phone data of the user and vehicle data constructing space grid, determining the matching result of mobile phone track and vehicle track, comprising: according to the time threshold value and spatial threshold. respectively performing segmentation of the time dimension and the space dimension of mobile phone data and vehicle data of the user to obtain mobile phone track grid set and vehicle track the grid set, using improved Jaccard distance, calculating the matching degree of each vehicle track grid each mobile phone track grid and vehicle track grid set mobile phone track grid in the set, obtaining the matching degree of the mobile phone the track and vehicle track. [claim 2]); and
determining that a second difference between a first end time of the first trip and a second end time of the second trip is less than the predefined threshold time difference (wherein the step of mobile phone data of the user and vehicle data constructing space grid, determining the matching result of mobile phone track and vehicle track, comprising: according to the time threshold value and spatial threshold. respectively performing segmentation of the time dimension and the space dimension of mobile phone data and vehicle data of the user to obtain mobile phone track grid set and vehicle track the grid set, using improved Jaccard distance, calculating the matching degree of each vehicle track grid each mobile phone track grid and vehicle track grid set mobile phone track grid in the set, obtaining the matching degree of the mobile phone the track and vehicle track. [claim 2]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar in view of Kanevsky, Siira, and Outwater to include the teachings as taught by Zhao with a reasonable expectation of success. Zhao teaches the benefits of “a driver identification method and system based on space-time grid, comprising: constructing the space grid according to mobile phone and vehicle data, determining the matching result of mobile phone track and vehicle track; mobile phone data not matching by using matching result with as the sample set mapped to the spatial grid, counting the grid of positive and negative sample the number of accesses according to the access times of the positive and negative sample of each grid determining the discrimination of each grid, selecting a plurality of key network to compressed feature space according to the distinguishing degree; and training the decision model according to the judging model and matching result to determine driver identity of the user. data by mobile phone and vehicle data such as current data constructing the space grid, to determine mobile phone track and vehicle track of the matching result according to the distinguishing degree of each grid selection key network, judging model training according to the judging model and matching result, determining the driver identity of user, the method is simple and capable of identifying the driver identity according to the existing data. [Zhao, abstract]” which solves the “need to provide a simple method, capable of identifying the driver identity according to the existing data. [Zhao, page 2]”
Regarding claim 22:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 21, upon which this claim is dependent.
Kumar in view of Kanevsky and Siira does not explicitly teach, however Zhao teaches:
determining, for each respective time interval of the plurality of time intervals, a first difference between a first start time of the first time interval and a second start time of the respective time interval, and a second difference between first end time of the first time interval and second end time of the respective time interval (The main factors affecting the judgment accuracy is similarity (matching threshold), the higher the threshold, the higher the accuracy, but the corresponding recall rate will be reduced, so it can be adjusted according to the specific application scene to a confirmed data identity serial number of the driver as reference, dynamically adjusting the threshold value to obtain the required accuracy and recall rate…selecting matching degree portion part of the user exceeds the threshold value as positive samples (labeled data as positive samples), at the same time, randomly extracting amount of non-matched user (does not match the vehicle of the user and/or not matched by the user) as negative sample (labeled as negative sample data), combining to obtain the sample set. For positive samples, can be controlled by means of variable and sampling verifying, selecting rational screening threshold value, ensuring the sample quality. [page 9]; examiner notes that in order to compare to a threshold value, Zhao must inherently determine a difference between the time of data points it is attempting to match.); and
comparing the first difference and the second difference with a predefined threshold time difference (wherein the step of mobile phone data of the user and vehicle data constructing space grid, determining the matching result of mobile phone track and vehicle track, comprising: according to the time threshold value and spatial threshold. respectively performing segmentation of the time dimension and the space dimension of mobile phone data and vehicle data of the user to obtain mobile phone track grid set and vehicle track the grid set, using improved Jaccard distance, calculating the matching degree of each vehicle track grid each mobile phone track grid and vehicle track grid set mobile phone track grid in the set, obtaining the matching degree of the mobile phone the track and vehicle track. [claim 2]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar in view of Kanevsky, Siira, and Outwater to include the teachings as taught by Zhao with a reasonable expectation of success. Zhao teaches the benefits of “a driver identification method and system based on space-time grid, comprising: constructing the space grid according to mobile phone and vehicle data, determining the matching result of mobile phone track and vehicle track; mobile phone data not matching by using matching result with as the sample set mapped to the spatial grid, counting the grid of positive and negative sample the number of accesses according to the access times of the positive and negative sample of each grid determining the discrimination of each grid, selecting a plurality of key network to compressed feature space according to the distinguishing degree; and training the decision model according to the judging model and matching result to determine driver identity of the user. data by mobile phone and vehicle data such as current data constructing the space grid, to determine mobile phone track and vehicle track of the matching result according to the distinguishing degree of each grid selection key network, judging model training according to the judging model and matching result, determining the driver identity of user, the method is simple and capable of identifying the driver identity according to the existing data. [Zhao, abstract]” which solves the “need to provide a simple method, capable of identifying the driver identity according to the existing data. [Zhao, page 2]”
Regarding claim 23:
Kumar in view of Kanevsky, Siira, Outwater, and Zhao teaches all the limitations of claim 22, upon which this claim is dependent.
Kanevsky further teaches:
the association between the first set of motion measurements and the second set of motion measurements is stored in response to (The movement data/driving data analysis system 200 in these examples may also include a plurality of mobile computing devices 220. As discussed below, in some embodiments, mobile computing devices 220 may receive and execute a movement data analysis software application 222 from the server 210 or other application provider (e.g., an application store or third-party application provider). As part of the execution of the movement data analysis software application 222, or implemented as separate functionality, mobile computing device 220 may receive and analyze movement data from movement sensors 223 of the mobile device 220, identify driving patterns based on the received movement data, and use driving patterns to identify drivers associated with the movement data. [col 7, lines 55-67])
Zhao further teaches:
determining that the first difference for the second trip and the second difference for the second trip are less than the predefined threshold time difference (wherein the step of mobile phone data of the user and vehicle data constructing space grid, determining the matching result of mobile phone track and vehicle track, comprising: according to the time threshold value and spatial threshold. respectively performing segmentation of the time dimension and the space dimension of mobile phone data and vehicle data of the user to obtain mobile phone track grid set and vehicle track the grid set, using improved Jaccard distance, calculating the matching degree of each vehicle track grid each mobile phone track grid and vehicle track grid set mobile phone track grid in the set, obtaining the matching degree of the mobile phone the track and vehicle track. [claim 2]).
Regarding claim 24:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 21, upon which this claim is dependent.
Kumar in view of Kanevsky, Siira, and Outwater does not explicitly teach, however Zhao teaches:
obtaining an indication of a first event of the first trip, wherein the first event occurred at a first time during the first time interval (The statistic time slices of vehicle track grid, each mobile phone track grid and it does the matching degree calculation of performing statistic time slice division; [page 6]); and
obtaining an indication of a second event of the second trip, wherein the second event occurred at a second time during the first time interval (The mobile phone data of each user in the position (latitude and longitude), time and other data to obtain mobile phone track of each user according to the vehicle data transmitted by each terminal in the position and time data, vehicle trajectory determination which corresponds to each terminal. [page 7]);
wherein the association between the first set of motion measurements and the second set of motion measurements is further based on a comparison of the first event with the second event (pace grid module used for constructing the space grid and vehicle data according to mobile phone data of the user, determining the matching result of mobile phone track and vehicle track [page 8]; the matching degree (similarity) satisfies the determination of matching threshold value for the driver relationship between people and vehicle, at the same time, it can get the driver identity of the user. [page 8]; Assuming a target vehicle A, the vehicle track data of target vehicle A according to said method, divided into multiple statistic time slices. The space grid of the position point in each statistic time slice of the time grid and statistic time slices where, for matching with mobile phone track of each user, calculating the similarity. [page 8]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar in view of Kanevsky, Siira, and Outwater to include the teachings as taught by Zhao with a reasonable expectation of success. Zhao teaches the benefits of “a driver identification method and system based on space-time grid, comprising: constructing the space grid according to mobile phone and vehicle data, determining the matching result of mobile phone track and vehicle track; mobile phone data not matching by using matching result with as the sample set mapped to the spatial grid, counting the grid of positive and negative sample the number of accesses according to the access times of the positive and negative sample of each grid determining the discrimination of each grid, selecting a plurality of key network to compressed feature space according to the distinguishing degree; and training the decision model according to the judging model and matching result to determine driver identity of the user. data by mobile phone and vehicle data such as current data constructing the space grid, to determine mobile phone track and vehicle track of the matching result according to the distinguishing degree of each grid selection key network, judging model training according to the judging model and matching result, determining the driver identity of user, the method is simple and capable of identifying the driver identity according to the existing data. [Zhao, abstract]” which solves the “need to provide a simple method, capable of identifying the driver identity according to the existing data. [Zhao, page 2]”
Claim(s) 13, 15, 25, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et. al. (US 10,785,604), herein Kumar in view of Kanevsky (US 10,902,521), herein Kanevsky, Siira et. al. (US 8,396,662), herein Siira, and Outwater et. al. (US 2017/0115125), herein Outwater and in further view of Abramson et. al. (US 2017/0279957), herein Abramson.
Regarding claim 13:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 9, upon which this claim is dependent.
Kumar in view of Kanevsky, Siira, and Outwater does not explicitly teach, however Silver teaches:
identifying, based on the querying, a third set of motion measurements for a third trip of the plurality of trips is the same trip as the first trip and the second trip, wherein the third set of motion measurement was generated by a second mobile device different from the first mobile device (Upon determining that multiple occupants are present within the vehicle (thus allowing for the possibility that a device present within the vehicle is being operated by a passenger), one or more aspects of the functionality of the device can be adjusted, changed etc. (e.g., restricted in one or more ways) that may allow for authentication/unlocking by a passenger (e.g., using one or more of the techniques described herein, such as those which may be relatively easy for a passenger to perform but relatively harder or impossible for a driver to perform) or which may occur passively, without the active involvement of the operator of the device). [0879]);
determining, based on the first set of motion measurements and the second set of motion measurements, that a user identified by the user identifier associated with the first mobile device was driving the first vehicle during the first time interval (At step 230, processor 110 executing one or more of software modules 130, including, preferably, determination module 170, computes one or more determination factors, such as a probability, based on the various determination characteristics, that the in-vehicle role of the user of mobile device 105 is a driver and/or a probability that the in-vehicle role of the user of the mobile device 105 is a passenger, substantially in the manner described in detail above with regard to step 230. [0206]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar in view of Kanevsky, Siira, and Outwater to include the teachings as taught by Abramson with a reasonable expectation of success. Abramson teaches the benefits of “if the computed probability indicates that the in-vehicle role of a user of mobile device 105 is likely to be a driver, processor 110 can coordinate the disabling of one or more features of the mobile device 105, such as the disabling of any and/or all features that enable the entry of text into mobile device 105. In doing so, existing safety risks can be reduced by preventing a user who has been determined to be likely to be a driver of a vehicle from using various regular functions of mobile device 105 that are likely to distract the user and increase safety risks while driving and/or are restricted and/or prohibited based on the vehicle's current (or most recently known) location, as preferably determined in conjunction with GPS 145C. In other arrangements, one or more other transformations to the operation state of mobile device can be similarly applied based on the computed probability. For example, notifications (such as warning notifications) can be provided at the mobile device 105, notifications can be transmitted to third parties (notifying a third party, such as a law enforcement agency, of the in-vehicle role of the user of mobile device 105 and/or of the particular operation of the mobile device 105, such as that typing is being performed upon mobile device 105), instructions can be provided to third parties (such as a cellular service provider) to change an operation state of mobile device 105 (such as temporarily disabling the communication ability of mobile device 105), and/or one or more applications executing or executable on mobile device 105 can be disabled (such as a text messaging application) [Abramson, 0189]”.
Regarding claim 15:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 9, upon which this claim is dependent.
Kumar in view of Kanevsky, Siira, and Outwater does not explicitly teach, however Abramson teaches:
receiving, in response to presenting the notification (mobile device 105 preferably prompts one or more users to initiate and/or provide one or more stimuli that can be received as inputs at mobile device 105 and/or receives one or more second inputs in response to the prompting, and/or receives one or more third inputs from vehicle data system 164, and/or receives one or more fourth inputs from at least one of the second mobile device 160, all in the manner described in detail herein. [0528]; By way of further illustration, a transportation mode can be determined relatively actively, such as by prompting a user to identify (e.g., with voice, touch, haptic, etc. inputs) the mode of transportation being used, such as prior to the start of (or during) a trip, and/or by virtue of the fact that the information provided in this system can be packaged differently for different use cases (e.g., bicycles vs. drivers) and the transportation mode of the device user can be determined from the application used. [1081]), a user input confirming that a user of the mobile device was driving the first vehicle during the first time interval (By way of further illustration, a transportation mode can be determined relatively actively, such as by prompting a user to identify (e.g., with voice, touch, haptic, etc. inputs) the mode of transportation being used, such as prior to the start of (or during) a trip, and/or by virtue of the fact that the information provided in this system can be packaged differently for different use cases (e.g., bicycles vs. drivers) and the transportation mode of the device user can be determined from the application used. [1081]; At step 707, mobile device 105 preferably prompts one or more users to initiate and/or provide one or more stimuli that can be received as inputs at mobile device 105. By way of example, mobile device 105 can prompt each of the one or more users in a vehicle to repeat a particular word or series of words projected by mobile device 105. It should be understood that in certain arrangements such a prompt can request for the words to be repeated sequentially while in other arrangements such a prompt can request for the words to be repeated simultaneously, while in yet other arrangements the timing of the repetition is of no consequence. It should be appreciated that such prompting can request practically any stimulus that can be received and/or analyzed as an input in the manner described herein. [0455]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar in view of Kanevsky, Siira, and Outwater to include the teachings as taught by Abramson with a reasonable expectation of success. Abramson teaches the benefits of “if the computed probability indicates that the in-vehicle role of a user of mobile device 105 is likely to be a driver, processor 110 can coordinate the disabling of one or more features of the mobile device 105, such as the disabling of any and/or all features that enable the entry of text into mobile device 105. In doing so, existing safety risks can be reduced by preventing a user who has been determined to be likely to be a driver of a vehicle from using various regular functions of mobile device 105 that are likely to distract the user and increase safety risks while driving and/or are restricted and/or prohibited based on the vehicle's current (or most recently known) location, as preferably determined in conjunction with GPS 145C. In other arrangements, one or more other transformations to the operation state of mobile device can be similarly applied based on the computed probability. For example, notifications (such as warning notifications) can be provided at the mobile device 105, notifications can be transmitted to third parties (notifying a third party, such as a law enforcement agency, of the in-vehicle role of the user of mobile device 105 and/or of the particular operation of the mobile device 105, such as that typing is being performed upon mobile device 105), instructions can be provided to third parties (such as a cellular service provider) to change an operation state of mobile device 105 (such as temporarily disabling the communication ability of mobile device 105), and/or one or more applications executing or executable on mobile device 105 can be disabled (such as a text messaging application) [Abramson, 0189]”.
Regarding claim 25:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 21, upon which this claim is dependent.
Kumar in view of Kanevsky, Siira, and Outwater does not explicitly teach, however Silver teaches:
identifying, based on the querying, a third set of motion measurements for a third trip of the plurality of trips is the same trip as the first trip and the second trip, wherein the third set of motion measurement was generated by a second mobile device different from the first mobile device (Upon determining that multiple occupants are present within the vehicle (thus allowing for the possibility that a device present within the vehicle is being operated by a passenger), one or more aspects of the functionality of the device can be adjusted, changed etc. (e.g., restricted in one or more ways) that may allow for authentication/unlocking by a passenger (e.g., using one or more of the techniques described herein, such as those which may be relatively easy for a passenger to perform but relatively harder or impossible for a driver to perform) or which may occur passively, without the active involvement of the operator of the device). [0879]);
determining, based on the first set of motion measurements and the second set of motion measurements, that a user identified by the user identifier associated with the first mobile device was driving the first vehicle during the first time interval (At step 230, processor 110 executing one or more of software modules 130, including, preferably, determination module 170, computes one or more determination factors, such as a probability, based on the various determination characteristics, that the in-vehicle role of the user of mobile device 105 is a driver and/or a probability that the in-vehicle role of the user of the mobile device 105 is a passenger, substantially in the manner described in detail above with regard to step 230. [0206]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar in view of Kanevsky, Siira, and Outwater to include the teachings as taught by Abramson with a reasonable expectation of success. Abramson teaches the benefits of “if the computed probability indicates that the in-vehicle role of a user of mobile device 105 is likely to be a driver, processor 110 can coordinate the disabling of one or more features of the mobile device 105, such as the disabling of any and/or all features that enable the entry of text into mobile device 105. In doing so, existing safety risks can be reduced by preventing a user who has been determined to be likely to be a driver of a vehicle from using various regular functions of mobile device 105 that are likely to distract the user and increase safety risks while driving and/or are restricted and/or prohibited based on the vehicle's current (or most recently known) location, as preferably determined in conjunction with GPS 145C. In other arrangements, one or more other transformations to the operation state of mobile device can be similarly applied based on the computed probability. For example, notifications (such as warning notifications) can be provided at the mobile device 105, notifications can be transmitted to third parties (notifying a third party, such as a law enforcement agency, of the in-vehicle role of the user of mobile device 105 and/or of the particular operation of the mobile device 105, such as that typing is being performed upon mobile device 105), instructions can be provided to third parties (such as a cellular service provider) to change an operation state of mobile device 105 (such as temporarily disabling the communication ability of mobile device 105), and/or one or more applications executing or executable on mobile device 105 can be disabled (such as a text messaging application) [Abramson, 0189]”.
Regarding claim 27:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 21, upon which this claim is dependent.
Kumar in view of Kanevsky, Siira, and Outwater does not explicitly teach, however Abramson teaches:
receiving, in response to presenting the notification (mobile device 105 preferably prompts one or more users to initiate and/or provide one or more stimuli that can be received as inputs at mobile device 105 and/or receives one or more second inputs in response to the prompting, and/or receives one or more third inputs from vehicle data system 164, and/or receives one or more fourth inputs from at least one of the second mobile device 160, all in the manner described in detail herein. [0528]; By way of further illustration, a transportation mode can be determined relatively actively, such as by prompting a user to identify (e.g., with voice, touch, haptic, etc. inputs) the mode of transportation being used, such as prior to the start of (or during) a trip, and/or by virtue of the fact that the information provided in this system can be packaged differently for different use cases (e.g., bicycles vs. drivers) and the transportation mode of the device user can be determined from the application used. [1081]), a user input confirming that a user of the mobile device was driving the first vehicle during the first time interval (By way of further illustration, a transportation mode can be determined relatively actively, such as by prompting a user to identify (e.g., with voice, touch, haptic, etc. inputs) the mode of transportation being used, such as prior to the start of (or during) a trip, and/or by virtue of the fact that the information provided in this system can be packaged differently for different use cases (e.g., bicycles vs. drivers) and the transportation mode of the device user can be determined from the application used. [1081]; At step 707, mobile device 105 preferably prompts one or more users to initiate and/or provide one or more stimuli that can be received as inputs at mobile device 105. By way of example, mobile device 105 can prompt each of the one or more users in a vehicle to repeat a particular word or series of words projected by mobile device 105. It should be understood that in certain arrangements such a prompt can request for the words to be repeated sequentially while in other arrangements such a prompt can request for the words to be repeated simultaneously, while in yet other arrangements the timing of the repetition is of no consequence. It should be appreciated that such prompting can request practically any stimulus that can be received and/or analyzed as an input in the manner described herein. [0455]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar in view of Kanevsky, Siira, and Outwater to include the teachings as taught by Abramson with a reasonable expectation of success. Abramson teaches the benefits of “if the computed probability indicates that the in-vehicle role of a user of mobile device 105 is likely to be a driver, processor 110 can coordinate the disabling of one or more features of the mobile device 105, such as the disabling of any and/or all features that enable the entry of text into mobile device 105. In doing so, existing safety risks can be reduced by preventing a user who has been determined to be likely to be a driver of a vehicle from using various regular functions of mobile device 105 that are likely to distract the user and increase safety risks while driving and/or are restricted and/or prohibited based on the vehicle's current (or most recently known) location, as preferably determined in conjunction with GPS 145C. In other arrangements, one or more other transformations to the operation state of mobile device can be similarly applied based on the computed probability. For example, notifications (such as warning notifications) can be provided at the mobile device 105, notifications can be transmitted to third parties (notifying a third party, such as a law enforcement agency, of the in-vehicle role of the user of mobile device 105 and/or of the particular operation of the mobile device 105, such as that typing is being performed upon mobile device 105), instructions can be provided to third parties (such as a cellular service provider) to change an operation state of mobile device 105 (such as temporarily disabling the communication ability of mobile device 105), and/or one or more applications executing or executable on mobile device 105 can be disabled (such as a text messaging application) [Abramson, 0189]”.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et. al. (US 10,785,604), herein Kumar in view of Kanevsky (US 10,902,521), herein Kanevsky, Siira et. al. (US 8,396,662), herein Siira, and Outwater et. al. (US 2017/0115125), herein Outwater in further view of Chintakindi et. al. (US 2019/0027038), herein Chintakindi.
Regarding claim 19:
Kumar in view of Kanevsky, Siira, and Outwater teaches all the limitations of claim 16, upon which this claim is dependent.
Kumar in view of Kanevsky, Siira, and Outwater does not explicitly teach, however Chintakindi teaches:
wherein the first set of motion measurements are received from the electronic device via the mobile device (vehicle sensors 212 may be configured to transmit the above-mentioned data to one or more external computing systems including mobile device 215 [0023]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Kumar in view of Kanevsky, Siira, and Outwater to include the teachings as taught by Chintakindi with a reasonable expectation of success. Chintakindi teaches the benefits of “determining that an adverse driving event is likely to occur and utilizing accident calculus algorithms to determine and cause vehicle driving actions necessary to mitigate consequences of the adverse driving event. After determining that an adverse driving event is likely to occur, a computing device my forecast consequences of the driving event. The computing device may determine potential evasive maneuvers that may be taken responsive to the adverse driving event. Additionally, the computing device may determine consequences associated with the potential evasive maneuvers and assign a weight relative to the consequence. The computing device may compare the potential driving maneuvers based on the weighted consequences to determine a driving maneuver to take. [Chintakindi , abstract]”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hassib (US 2013/0289819) discloses Certain example embodiments of the disclosed technology may include systems and methods for telematics monitoring. An example method is provided that includes receiving, at a mobile computing device, and from a Vehicle Identification Unit (VIU), identification (ID) data representing a first vehicle. The method further includes receiving, by the mobile computing device, sensor data from one or more sensors associated with the mobile computing device. Certain embodiments may further include receiving, at an Operational Measurement Unit (OMU), an operation indication associated with the first vehicle. The OMU may include an operational measurement component configured to advance an operational count in response to receiving the operation indication. Certain example embodiments may include transmitting telematics data by the mobile computing device. In certain embodiments, the telematics data may include least a portion of one or more of the ID data, the sensor data, and/or the operational count data.
Peng (CN 110837600) discloses constructing a basic database, establishing the best matching model, obtaining personal information and consumption data of the user, vehicle obtaining user travel data; analyzing the user of vehicle running data, obtaining the home address of the user, work address and consumption location, home address, work address and consuming place respectively comparing with the personal information, consumer data, if comparison result is accordant, the user information checking qualified; for the user information verification is qualified, forming a user analysis report according to the user analysis report, using the best matching model recommended best matched object for the user. This method can according to the vehicle running data analysis the home address, work location and so on, realizing the verification of the user information, ensuring the authenticity of the user information. but also the passenger information and driving habit for analyzing, evaluating marriage status and character type, avoid the marriage industry of all kinds of random phase.
Mungo (US 2023/0008460) discloses a method for asseverating video images, photos, audio-video and/or data in general, acquired from different sources (10, 11, 12, 13), wherein the images are first validated to verify the absence of tampering and then subjected to a step of certifying, in which they are encrypted. The encrypted images are accessible only to authorized and identified users, who are provided with the decryption code. The invention also comprises a system for implementing the method.
Davis (US 2021/0142419) discloses Methods and systems for managing user accounts based upon the detection of various usage events associated with mobile or other electronic devices are provided. An electronic device may be located within a vehicle. During operation of the vehicle, the electronic device is configured to detect and record various usage events. Based upon the usage events or absence of usage events, either the electronic device or a remote server may determine how one or more user accounts are affected or impacted. The usage events may be related to type of smart phone usage, such as texting, web-surfing, telephone calls, etc., and/or simultaneous vehicle usage (e.g., vehicle movement or rest). The remote server may process the one or more user accounts accordingly and notify the electronic device of the processing. The electronic device may present any detected usage events or account changes via a user interface.
Tsai (US 2020/0342235) discloses A baseline event detection system to detect events by performing operations that include: generating a baseline data set; accessing a data stream; performing a comparison of the baseline data set and the data stream; and detecting an event based on the comparison.
Niu (CN 107492251) discloses a driver identity identification and driving state monitoring method based on machine learning and deep learning collecting the motion data of the automobile through the intelligent mobile phone sensor; identifying the vehicle driving element action. The driving element action sequence is divided into driving operation by using fuzzy pattern recognition. then combining road traffic information and camera device through computer vision technology to identify the obstacle and congestion condition in front of vehicle driving, and dividing the different driving scene. combining the driving operation to extract the statistical characteristic; and forming the characteristic vector as the input of the deep neural network; by constructing the personal driving characteristic library and training the corresponding deep neural network model to identify the identity of the driver. after confirming the identity of the driver, by recursion neural network to identify the driving state of each time of the driver. The invention uses multi-source data, based on driving operation and scene, using the method of deep learning to improve the identification accuracy.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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Scott R. Jagolinzer
Examiner
Art Unit 3665
/S.R.J./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665