DETAILED ACTION
Claims 1-20 received on 12/18/2024 are considered in this office action. Claims 1-20 are pending for examination.
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 filed 09/16/2025 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because it lacks proper identification of all cited references, and further lacks a proper signature in accordance with 37 CFR 1.33(b). It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Claim Objections
Claim 1 is objected to because of the following informalities: in communication the one or more processors should read in communication with the one or more processors.
Claims 9 and 18 are objected to because of the following informalities: where in should read wherein.
Claim 10 is objected to because of the following informalities: one or more processor should read one or more processors.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “proximate” in claims 1, 10 and 19 is a relative term which renders the claim indefinite. The term “proximate” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The Examiner will interpret one or more vehicles proximate to a vehicle as one or more vehicles around a vehicle.
Claims 2-9, 11-18 and 20 are dependent on claims 1, 10 and 19, respectively, and fail to cure the deficiencies thereof, thus are rejected on the same basis.
Examiner’s Note - 35 USC § 101
The claim limitation of “display, to a driver of the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics” integrates the judicial exception, determine, in real-time based on the one or more first characteristics and the one or more second characteristics, at least one of: a driving behavior, a driver rating, occurrence of a collision, and vehicle diagnostics; and display, into a practical application, as an appropriate communication and determination of characteristics from the central server aims to improve a time consuming process which results on solely relying on central server, as supported by portions of the specification which is reproduced below:
[0026]-[0027] As described herein, processing driving and vehicle data, and determining, driving behavior, vehicle condition, and so forth, may be a time consuming process. Generally, data may be sent to a central processing server for further assessment and processing. Generally, data collection and data processing are sequential and may require multiple iterations to complete, thereby delaying feedback to drivers. This may incur additional expenses and cause delays in actions that drivers may need to take […] In some instances, driving location may be remote, and network connectivity may be intermittent. In some instances, the available network may not be able to support an upload of data. Accordingly, a driver may have to wait for better network connectivity before transmitting and/or receiving data.
[0070] Based on a real-time analysis of the driving data, edge-computing system 201 may cause vehicle sensors 208 to collect additional driving data. For example, a particular vehicle may be approaching at a high speed, and edge-computing system 201 may cause vehicle sensors 208 to capture a real-time video of the approaching car, and display the video over a vehicle's on-board display system. This may cause a driver to become aware of an impending hazard.
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 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 1-2, 4-6, 9-11, 13-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fields (US 20210166323 A1), in view of Rau (US 20190375416 A1).
Regarding claim 1, Fields teaches a computing device (para. [0007]: “the computing device traveling with the vehicle may be a mobile device associated with the insured or one or more processors associated with the vehicle”) comprising:
one or more processors (FIG. 3; para. [0080]: “Computing device 300 may include[…] a controller 340.”); and
an instruction storage device in communication the one or more processors and that stores instruction code that is executable by the one or more processor (para. [0083]: “program memory 302 may be implemented as a non-transitory tangible computer readable media configured to store computer-readable instructions, that when executed by controller 340, cause controller 340 to perform various acts”) to cause the computing device to:
collect, in real-time, vehicle driving event data comprising data indicative of driving characteristics associated with one or more vehicles proximate to a vehicle (para. [0276]-[0277]: “receive or determine an indication of a trigger event from computer analysis of telematics or sensor data gathered by one or more sensors […] detecting the vehicle following distance unexpectedly or rapidly decreasing”; para. [0038]: “To address these and other problems, telematics data (and/or driver behavior or vehicle information) may be captured in real-time, or near real-time, by a computing device, such as a vehicle-mounted computer, smart vehicle controller, or a mobile device of a vehicle driver (or passenger)”);
collect, in real-time, telematics information comprising one or more of driver data, vehicle data, and environmental data (para. [0276] -[0277]: “receive or determine an indication of a trigger event from computer analysis of telematics or sensor data gathered by one or more sensors […] detecting a brake pedal being engaged or otherwise triggered by brake system pressure or force applied to the brakes being determined to be above a predetermined threshold; detecting vehicle deceleration above a predetermined threshold”);
analyze, based on a first machine learning model, one or more first characteristics of the telematics information and the vehicle driving event data (para. [0278]: “The one or more processors may be configured to receive or generate telematics and sensor data from vehicle-mounted sensors, and input the telematics and sensor data into a machine learning program that is trained to identify a trigger event potentially associated with, or associated with, a vehicle collision, or trigger event that indicates an anomalous condition or a high risk of vehicle collision”; para. [0287]-[0289]: “Machine learning may involve identifying and recognizing patterns in existing data (such as telematics data; autonomous vehicle system, feature, or sensor data […] machine learning techniques may be used to extract the sensed items, such as driving behaviors or vehicle operation, generated by one or more sensors, and under what conditions those items were encountered”, wherein machine learning model usage involves identifying pattern such as driving behavior which indicates first characteristics);
receive, from a central server and based on a (para. [0301]: “The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines”; para. [0071]: “external computing device 206 may be configured to perform any suitable portion of the processing functions remotely that have been outsourced by one or more of mobile computing devices 204.1 and/or 204.2. For example, mobile computing device 204.1 and/or 204.2 may collect data (e.g., geographic location data and/or telematics data) as described herein, but may send the data to external computing device 206 for remote processing instead of processing the data locally”);
determine, in real-time based on the one or more first characteristics (para. [0280]: “machine learning program that is trained to identify a trigger event potentially associated with, or associated with, a vehicle collision, or a trigger event that indicates an anomalous condition or a high risk of vehicle collision”; para. [0166]: “the mobile computing device 110 or on-board computer 114 may process the data in real-time as it is received to determine whether an anomalous condition has occurred”); and
display, to a driver of the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics (FIGs. 4-17; para. [0079]: “generate one or more alerts indicative of the anomalous condition; and/or (6) broadcast one or more alert notifications to other devices, such as via wireless communication and/or data transmission”; para. [0116]: “Further continuing this example, upon determination of the anomalous condition, alert notification application 346 may broadcast a notification indicating the detected anomalous condition”, wherein anomalous condition indicates at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics, and FIGs. 4-17 comprise of driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics), but fails to specifically teach a second machine learning model and determine, in real-time based on the one or more first characteristics and the one or more second characteristics.
However, in the same field of endeavor, Rau teaches a second machine learning model (FIG. 14; para. [0066]: “The system 400 sends each data point to multiple isolation forest. Each isolation forest is different from the rest. The system 400 calculates a risk score based on the one or more labels generated by the one or machine learning classifiers”; para. [0065]: “The system can parallelize the computations of the machine learning classifiers (e.g., ensemble isolation forests) across as many processing cores that are available. For example, if a server has n processors, then each of the n processors can work to perform computations for one machine learning classifier (e.g., one isolation forest)”, which are isolation forests in the example of FIG. 14.”) and determine, in real-time based on the one or more first characteristics and the one or more second characteristics (FIG. 14; para. [0052]: “the system can combine the output of a group of isolation forests by using an ensemble learning process to generate a driver risk score”; para. [0073]: “A driver may receive feedback from the telematics device in real time about his driving behavior. The driver may receive a warning (e.g. visible warning light, audible message, vibration from vibrator) from the telematics device if his/her driver score is unsafe or a periodic compliment (e.g. visible positive reinforcement light, audible message, vibration from vibrator) if the driver score is safe or improves”).
Fields and Rau are considered analogous art to the claimed invention because they are in the same field of endeavor of identifying driving behavior and risk using machine learning based on telematics data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fields and incorporate usage of various models of Rau to calculates a risk score based on the one or more labels generated by the one or machine learning classifiers (Rau, para. [0066]). Doing so will improve accuracy by detecting anomalies from various models as individual unsafe data point may not indicate risk, but a collection of unsafe data points can start to indicate a risk (Rau, para. [0024]).
Regarding claim 2, Fields in view of Rau teaches the computing device of claim 1. Fields and Rau further teaches wherein the environmental data comprises at least one of traffic condition data, weather condition data, or road condition data (Fields para. [0042]: “If a traffic event is encountered, about to be encountered, and/or expected or anticipated to be encountered by the vehicle as it travels (e.g., road construction; heavy traffic; congestion; bad weather conditions; unlawful, unexpected or erratic operation of other vehicles; questionable or abnormal driving behavior of other drivers; irresponsible or overly aggressive drivers; un-attentive or tired drivers, etc.), the telematics (and/or data) data collected may indicate such”; Rau para. [0039]: “vehicle driver data (e.g., raw data collected by vehicle telematics device) can include, […] weather, […] The system can combine raw data from a vehicle telematics device with server-generated data, such as location, time, date, weather, and/or other data”).
Regarding claim 4, Fields in view of Rau teaches the computing device of claim 1, wherein the computing device is communicatively coupled to at least one sensor of a plurality of sensors arranged on the vehicle (para. [0278]: “The one or more processors may be configured to receive or generate telematics and sensor data from vehicle-mounted sensors”, wherein receive indicates communicatively coupled to at least one sensor of a plurality of sensors), and wherein the instruction code is executable to cause the computing device to: receive the vehicle driving event data from the at least one sensor (para. [0276] -[0277]: “receive or determine an indication of a trigger event from computer analysis of telematics or sensor data gathered by one or more sensors […] detecting a brake pedal being engaged or otherwise triggered by brake system pressure or force applied to the brakes being determined to be above a predetermined threshold; detecting vehicle deceleration above a predetermined threshold”).
Regarding claim 5, Fields in view of Rau teaches the computing device of claim 1. Fields further teaches wherein the computing device is communicatively coupled to an on-board telematics device of the vehicle (para. [0278]: “The one or more processors may be configured to receive or generate telematics and sensor data from vehicle-mounted sensors”, wherein receiving indicates communicatively coupled), and wherein the instruction code is executable to cause the computing device to: receive the telematics information from the telematics device (para. [0278]: “The one or more processors may be configured to receive or generate telematics and sensor data from vehicle-mounted sensors”; para. [0039]: “For instance, the vehicle-mounted computer or the mobile device may be equipped with (i) various sensors and/or meters capable of generating telematics data”).
Regarding claim 6, Fields in view of Rau teaches the computing device of claim 1. Fields further teaches wherein the instruction code is executable to cause the computing device to: determine, based on an available network, portions of the vehicle driving event data to be transmitted to a central server (FIG. 2; para. [0071]: “In some embodiments, external computing device 206 may be configured to perform any suitable portion of the processing functions remotely that have been outsourced by one or more of mobile computing devices 204.1 and/or 204.2. For example, mobile computing device 204.1 and/or 204.2 may collect data (e.g., geographic location data and/or telematics data) as described herein, but may send the data to external computing device 206 for remote processing instead of processing the data locally”; para. [0031]: “the vehicle telematics device 110 communicates with the remote server system 130 via the mobile communications device 116 over a network 120.”; para. [0042]: “Additionally, any of the data utilized in the system can be cached and transmitted once a network connection (such as a wireless network connection via the communications interface) becomes available.”).
Regarding claim 9, Fields in view of Rau teaches the computing device of claim 1. Rau further teaches where in the second machine learning model normalizes driving behavior patterns for a plurality of drivers (FIGs. 15-17; para. [0066]: “FIG. 15 is an example score distribution 1500, in accordance with an embodiment. In this example, the system filters data to consider a population of 313 sedans”; para. [0068]: “The numbers on the vertical axis represent normalized scores. […] sedan 1 is not that far away from the average speed. Accordingly, for sedan 1, the system calculates a driver risk score of 9, which indicates a relatively low level of risk. For sedan 2, the system calculates a driver risk score of 95, which indicates a relatively high level of risk. Note that magnitude and spectral differences. Isolation forests can learn spectral patterns as well as dynamic thresholds”).
Regarding claim 10, it recites a non-transitory computer-readable medium having stored thereon instruction code that is executable by one or more processor of a computing device to cause the computing device to (Fields FIG. 3; Fields para. [0083]: “program memory 302 may be implemented as a non-transitory tangible computer readable media configured to store computer-readable instructions, that when executed by controller 340, cause controller 340 to perform various acts.”) perform claim limitations similar to those performed by the computing device of claim 1, and therefore is rejected on the same basis.
Regarding claim 11, it recites a non-transitory computer-readable medium having stored thereon instruction code that is similar to those performed by the computing device of claim 2, and therefore is rejected on the same basis.
Regarding claim 13, it recites a non-transitory computer-readable medium having stored thereon instruction code that is similar to those performed by the computing device of claim 4, and therefore is rejected on the same basis.
Regarding claim 14, it recites a non-transitory computer-readable medium having stored thereon instruction code that is similar to those performed by the computing device of claim 5, and therefore is rejected on the same basis.
Regarding claim 15, it recites a non-transitory computer-readable medium having stored thereon instruction code that is similar to those performed by the computing device of claim 6, and therefore is rejected on the same basis.
Regarding claim 18, it recites a non-transitory computer-readable medium having stored thereon instruction code that is similar to those performed by the computing device of claim 9, and therefore is rejected on the same basis.
Regarding claim 19, it recites a computer-implemented method claim reciting claim limitations similar to those performed by the computing device of claim 1, and therefore is rejected on the same basis.
Regarding claim 20, it recites a computer-implemented method claim reciting claim limitations similar to those performed by the computing device of claim 2, and therefore is rejected on the same basis.
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Fields, in view of Rau and further in view of Simoncini (US20210124961A1).
Regarding claim 3, Fields in view of Rau teaches the computing device of claim 1. Fields and Rau further teaches wherein the first machine learning model is trained to identify the telematics information and the vehicle driving event data which are relevant (Fields para. [0112]: “An anomalous condition may include any suitable condition that indicates a deviation from normal traffic patterns. For example, if an accident occurs, traffic may slow down due to a car pileup, a reduction in available lanes, and/or rerouting of traffic”; para. [0287]: “Machine learning may involve identifying and recognizing patterns in existing data (such as telematics data; autonomous vehicle system, feature, or sensor data”; Rau FIGs. 4, 8-13; Rau para. [0055]: “The machine learning system 300 includes vehicle driver data 302. The vehicle driver data 302 can include a variety of information such as speeding 304, hard acceleration 306, hard deceleration 308, and/or swerving 310, which is evidence about a driver's driving behavior or habits.”; Rau para. [0045]: “identify relationships in labeled vehicle telematics information. A known (or labeled) set of vehicle telematics device information, which can be referred to as a training set, can be used to train the machine learning classifier. Once the machine learning classifier is trained by using the labeled training set, the machine learning classifier can classify unknown sets of vehicle telematics device information”, wherein information with relationship indicates information and the vehicle driving event data which are relevant), but fails to specifically teach identify the telematics information and the vehicle driving event data to be collected.
However, in the same field of endeavor, Simoncini teaches wherein the first machine learning model is trained to identify the telematics information and the vehicle driving event data to be collected (FIG. 1E; para. [0029]: “with a feature selection model and based on the model outputs, to select sets of features from the plurality of features. In some implementations, when processing the plurality of features, vehicle platform 115 may estimate an importance (e.g., a Gini importance) of each of the plurality of features, and may select the sets of features from the plurality of features based on the importance estimated for each of the plurality of features”; para. [0034]: “For example, the standard deviation of the z-axis (e.g., a longitudinal acceleration) may indicate extreme braking and accelerations, the mean of the vehicle speed (e.g., an average speed) may be associated with a tendency of speeding, and the skewness of y-axis filtered acceleration (e.g., a lateral acceleration) may indicate harsh cornering in curves and turns. In this way, vehicle platform 115 may select a quantity of features that may be determined by a device with limited computing power, such as by user device 105 and/or a computing device associated with vehicle 110”).
Simoncini is considered analogous art to the claimed invention because it is in the same field of endeavor of identifying driver behavior using machine learning based on telematics data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fields in view of Rau and incorporate the teachings of Simoncini and select sets of features from the plurality of features with a feature selection model. Doing so will result in efficient use by reducing the number of features to input to the model thus may be used by device with limited computing power (Simoncini, para. [0034]).
Regarding claim 12, it recites a non-transitory computer-readable medium having stored thereon instruction code that is similar to those performed by the computing device of claim 3, and therefore is rejected on the same basis.
Claims 7-8 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Fields, in view of Rau and further in view of Anthony (US20180315260A1).
Regarding claim 7, Fields in view of Rau teaches the computing device of claim 1, but fails to specifically teach wherein the instruction code is executable to cause the computing device to:
receive, from a central server, configuration data (para. [0081]: “The cloud system may attempt to diagnose the anomaly by processing the data further and then analyze it against the global population of data from other deployments and test data (45). Once a diagnosis is found the result may be sent back to the end user, and the local system is updated with an improved ML model (48)”, wherein update indicates configuration data); and
dynamically update, based on the configuration data, a configuration of the computing device (para. [0081]: “The cloud system may attempt to diagnose the anomaly by processing the data further and then analyze it against the global population of data from other deployments and test data (45). Once a diagnosis is found the result may be sent back to the end user, and the local system is updated with an improved ML model (48)”, wherein improved ML model indicates a configuration of the computing device).
Anthony is considered analogous art to the claimed invention because it is in the same field of endeavor of identifying anomalies using machine learning based on telematics data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fields in view of Rau and incorporate the teachings of Anthony and updating the local machine learning model based on updates sent via a central server. Doing so will improve the machine learning model’s result and accuracy by updating with global data received from various different local devices, thus strengthen or correct the machine learning model (Anthony, para. [0084]).
Regarding claim 8, Fields in view of Rau teaches the computing device of claim 1, but fails to specifically teach wherein the instruction code is executable to cause the computing device to: receive, from a central server, trained data; and dynamically update, based on the trained data, the first machine learning model.
However, in the same field of endeavor, Anthony teaches wherein the instruction code is executable to cause the computing device to: receive, from a central server, trained data (para. [0081]: “The cloud system may attempt to diagnose the anomaly by processing the data further and then analyze it against the global population of data from other deployments and test data (45). Once a diagnosis is found the result may be sent back to the end user, and the local system is updated with an improved ML model (48)”); and
dynamically update, based on the trained data, the first machine learning model (para. [0081]: “The cloud system may attempt to diagnose the anomaly by processing the data further and then analyze it against the global population of data from other deployments and test data (45). Once a diagnosis is found the result may be sent back to the end user, and the local system is updated with an improved ML model (48)”).
Anthony is considered analogous art to the claimed invention because it is in the same field of endeavor of identifying anomalies using machine learning based on telematics data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fields in view of Rau and incorporate the teachings of Anthony and updating the local machine learning model based on updates sent via a central server. Doing so will improve the machine learning model’s result and accuracy by updating with global data received from various different local devices, thus strengthen or correct the machine learning model (Anthony, para. [0084]).
Regarding claim 16, it recites a non-transitory computer-readable medium having stored thereon instruction code that is similar to those performed by the computing device of claim 7, and therefore is rejected on the same basis.
Regarding claim 17, it recites a non-transitory computer-readable medium having stored thereon instruction code that is similar to those performed by the computing device of claim 8, and therefore is rejected on the same basis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hallac (US20230237335A1) teaches iterative repetition to update the dynamic component of the vehicle fingerprint, which can be employed to determine the health of the vehicle and generate insights about future vehicle component and systems failures.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW S KIM whose telephone number is (571)272-7356. The examiner can normally be reached Mon - Fri 8AM - 5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James J Lee can be reached on (571) 270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW SANG KIM/Examiner, Art Unit 3668
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