DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Examiner’s Note
Examiner has cited particular paragraphs/columns and line numbers or figures in the references as applied to the claims below for convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations with the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to the Applicant’s definition which is not specifically set forth in the claims.
Status of Application
The list of claims 1-6, 8-13 and 15-21 is pending in this application. In the claim set filed 10/10/2025:
Claim(s) 1, 8 and 15 is/are the independent claim(s) observed in the instant application.
Claim(s) 1, 3, 5, 8-10, 12, 15, 17 and 19 has/have been indicated as amended.
Claim(s) 2, 4, 6, 11, 13, 16, 18 and 20 has/have been indicated as originally presented.
Claim(s) 7 and 14 has/have been indicated as originally cancelled.
Claim(s) 21 has/have been indicated as newly presented.
Response to Arguments
With respect to Applicant’s remarks filed on 10/10/2025; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
With respect to the Objection(s) of claim(s) 3, 5, 10, 12, 17 and 19, Applicant’s “Amendments and Remarks” have been fully considered and have been found persuasive. Therefore the Objection(s) of claim(s) 3, 5, 10, 12, 17 and 19 is/are withdrawn.
With respect to the rejection(s) of claim(s) 1-6, 8-13 and 15-20 under 35 U.S.C. § 102(a)(1) and 35 U.S.C. § 103, Applicant’s “Amendments and Remarks” have been fully considered but have NOT been persuasive.
The Applicant argues: “The applied references do not disclose "wherein the updating the one or more sampling criteria includes restricting the one or more sampling criteria, and wherein the one or more sampling criteria include at least one of geolocation, vehicle model, vehicle manufacturer, and driver demographic."”
The Applicant further argues that “In claims 1 and 3, the Office Action interprets "sampling criteria" as nominal values or outlier values for determining when a driver has tampered with their vehicle” and subsequently “However, for claim 7, see the Office Action at page 12, which cites Tokman [0117]- [0142] (referring to "event type" and "timestamp" for example) as disclosing sampling criteria being the kind of data being sampled. Outliers of Tokman [0143] do not disclose the claimed sampling criteria. Also, the event type and timestamp of Tokman [01 19]-[0120] do not disclose the claimed "the one or more sampling criteria include at least one of geolocation, vehicle model, vehicle manufacturer, and driver demographic."”
The Examiner respectfully disagrees. First, pertaining to the point regarding inconsistent interpretation of the broadly recited, sampling criteria, the Examiner asserts that the interpretation is consistently outlined as follows. Tokman discloses a fleet management a reporting system selectively downloading, receiving, or otherwise retrieving data from a plurality of vehicles [Tokman; ¶: 0032]. Tokman further discloses one form of data collected is a disclosed “miles between event value (MBE)” [Tokman; ¶: 0143], which has been interpreted as patentably indistinct from the Applicant’s broadly recited “sampling criteria.” Tokman further discloses that the MBE value calculated using measured miles travelled over a particular timeframe [Tokman; ¶: 0061], wherein this data is provided using a plurality of sensors that are input into a machine-learning model includes: an event type, a timestamp, a GPS location, vehicle speed, vehicle odometer and vehicle location [Tokman; ¶: 0117-0142]. This data used to calculate the MBE has been interpreted as patentably indistinct from the Applicant’s broadly recited “geolocation.”
In view of at least the above, the Examiner asserts, that the interpretation of the MBE disclosed by Tokman in view of the broadly recited “sampling criteria” is consistent throughout.
Tokman further discloses that the system then implements a statistical method comparing “abnormal MBE values” compared to “MBE values” of other drivers to form a distribution. The system subsequently restricts the data by first omitting data pertaining to “drivers or vehicles having less than some minimum amount of data (e.g., less than 100 km traveled for selected timeframe)” [Tokman; ¶: 0064] and further comparing the per kilometer rate of events to a generated “vehicle average across the entire fleet” [Tokman; ¶: 0064] in order to identify instances of tampering. The subsequent instances of tampering occur when a potentially identified “abnormal MBE value” is found to be at least “3 standard deviations” from the calculated mean, the “abnormal MBE value” is determined to be an outlier and is therefore removed/discounted from the consideration as it is found to be indicative of tampering due to a determination that the driving behavior is “too good to be true” [Tokman; “Thus, abnormally large, too good to be true, deviations from an average or usual value is a further indication of possible tampering;” ¶: 0059]. After omitting this data, the system may perform recursive operations in order to update the calculations after removing the “suspicious MBE values” in particular through the equation disclosed in ¶: 0182, which demonstrates a recursive method of calculating a respective MBE value (denoted μn) based on a cumulative sum to identify discontinuities [Tokman; ¶: 0154, 0155, 0168, 0169, 0182-0184]. Tokman finally discloses transmitting tailored data to a remote backend server when a potential tampering event is detected [Tokman; ¶: 0185-0188].
In view of at least the above, the Examiner asserts that the transmitting data to a remote backend server based on a respective MBE value being at least “3 standard deviations” from the calculated mean as indicative of tampering, wherein the MBE value is based on geolocation information of the respective vehicle in order to perform score monitoring over time of a respective driver has been interpreted as patentably indistinct from the Applicant’s broadly recited “and updating the one or more sampling criteria based on the anomaly in the received data, wherein the updating the one or more sampling criteria includes restricting the one or more sampling criteria, and wherein the one or more sampling criteria include at least one of geolocation, vehicle model, vehicle manufacturer, and driver demographic.”
Therefore, the rejection(s) of claim(s) 1-6, 8-13 and 15-20 under 35 U.S.C. § 102(a)(1) and 35 U.S.C. § 103 has/have been maintained.
With respect to the rejection(s) of claim(s) 1-6, 8-13 and 15-20 under 35 U.S.C. § 101, Applicant’s “Amendments and Remarks” have been fully considered but have NOT been persuasive.
In particular, the Applicant argues that the amendments to independent claims 1, 8 and 15: represent “a technical improvement in a monitoring system, because less data is collected, and the collected data is more valuable. This reduces computer data collection operations, and it improves success in detecting anomalies.”
The Examiner respectfully disagrees. As currently filed, claim(s) 1, 8 and 15 do not recite what the Applicant considers the technical improvement in a monitoring system, namely that less data is collected when sampling data from the plurality of vehicles. This limitation has only been introduced in newly added claim 21, which depends from claim 1. Therefore, while the Examiner agrees that claim 21 as filed presents a technical improvement in a monitoring system, claim(s) 1-6, 8-13 and 15-20 do not similarly positively recite subsequently sampling data “from the plurality of vehicles based on the one or more sampling criteria after the restricting, thereby limiting a specific number of data samples collected at a server and reducing a number of data collection operations.” In order to overcome the rejections of claim(s) 1-6, 8-13 and 15-20 under 35 U.S.C. § 101, the Applicant should similarly amend independent claims 1, 8 and 15 to include the limitations as proposed in new claim 21.
In view of the above, the rejection(s) of claim(s) 1-6, 8-13 and 15-20 under 35 U.S.C. § 101 has/have been maintained.
Examiner’s: In view of the Applicant’s amended claim set filed, further claim rejection(s) appear in the Final Office Action below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-6, 8-13 and 15-20 is/are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1, 8 and 15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) using “A non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor” to perform the following: receiving data collected from the plurality of vehicles; generating a statistical model based on the received data; detecting an anomaly in the received data; and updating the one or more sampling criteria based on the anomaly in the received data.
The limitations of receiving data collected from the plurality of vehicles; generating a statistical model based on the received data; detecting an anomaly in the received data; and updating the one or more sampling criteria based on the anomaly in the received data, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “A non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “A non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor” language, in the context of this claim encompasses the user manually performing steps of receiving data collected from the plurality of vehicles; generating a statistical model based on the received data; detecting an anomaly in the received data; and updating the one or more sampling criteria based on the anomaly in the received data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – “A non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor” to perform receiving data collected from the plurality of vehicles; generating a statistical model based on the received data; detecting an anomaly in the received data; and updating the one or more sampling criteria based on the anomaly in the received data. The “A non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor” in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of generating, transmitting, receiving data from a generic sensor and outputting data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “A non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor” to perform receiving data collected from the plurality of vehicles; generating a statistical model based on the received data; detecting an anomaly in the received data; and updating the one or more sampling criteria based on the anomaly in the received data amount 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. The claim is not patent eligible.
Dependent claim(s) 2-6, 9-13 and 16-20 when analyzed as a whole, is/are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional element(s), if any, in the dependent claim(s) is/are not sufficient to amount to significantly more than the judicial exception for the same reasons as with claim(s) 1, 8 and 15.
Examiner’s Note: In order to overcome this rejection, the Office suggests further defining the limitations of the independent claim(s), for example linking the claimed subject matter to a non-generic device and controlling a vehicle or an apparatus in a specific way based on the data comparison performed or further showing that the claimed subject matter is an improvement to a technical field, for example as is the case with claim 21. Limitations such as these suggested above would further bring the claimed subject matter out of the realm of abstract idea and into the realm of a statutory category.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 6, 8-11, 13, 15-18 and 20 is/are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by TOKMAN (United States Patent Publication 2020/0175782 A1), referenced as Tokman moving forward.
With respect to claim 1, Tokman discloses:
“A method for optimizing data collection from a plurality of vehicles, the method comprising: receiving data collected from the plurality of vehicles selected based on one or more sampling criteria” [Tokman; "One or more servers 140 of the fleet management and reporting system 100 are configured to selectively download, receive, or otherwise retrieve data either directly from the vehicles 112 via the service providers 130 or from collection servers 132 which may be third party servers from one or more various telematics suppliers;" Fig. 1; ¶: 0032;
"Still yet further, the event detection and reporting system 200 may also include a transmitter/receiver (transceiver) module 250 such as, for example, a radio frequency (RF) transmitter including one or more antennas 252 for wireless communication of the automated control requests, GPS data, one or more various vehicle configuration and/or condition data, or the like between the vehicles and one or more destinations such as, for example, to one or more services (not shown) having a corresponding receiver and antenna. The transmitter/receiver (transceiver) module 250 may include various functional parts of sub portions operatively coupled with a platoon control unit including for example a communication receiver portion, a global position sensor (GPS) receiver portion, and a communication transmitter;" Fig. 2; ¶: 0042];
“generating a statistical model based on the received data” [Tokman; "Outlier identification from distribution analysis and probability value assignment therefrom: With this statistical method, drivers with abnormal MBE values are identified and compared with other drivers, resulting in a distribution of MBE values. MBE values, for multiple drivers, for the fault of interest, e.g. lane departure warnings, may then be plotted and a model fit to the MBE values. This model might be a normal distribution, a lognormal distribution, etc." ¶: 0143, 0144];
“detecting, based on the statistical model, an anomaly in the received data” [Tokman; "The mean and variance of the distribution may then be determined. Those values further than at least, say, 3 standard deviations from the mean (to the good performance side) may be deemed to be outliers. The probability values of these ‘too far away to be true’ observations (e.g. 99th percentile) may be used as one input to the probability of tampering calculation. While this single probability may be useful, it is not necessarily sufficient to establish tampering. It is also possible that one has no observations further than 3 standard deviations from the mean, in which case the driver may be deemed to not be tampering;" ¶: 0145];
“and updating the one or more sampling criteria based on the anomaly in the received data, wherein the updating the one or more sampling criteria includes restricting the one or more sampling criteria” [Tokman; "With this, the measured, abnormally good, LDW MBE values may be replaced with more realistic ones on the vehicle or in the server-side driver scoring process as generated by a scoring algorithm which may use statistics or machine learning based on the normal vs tampering driver behavior to replace ‘too good to be true’ values with normal when tampering behavior is detected. This new, recomputed score absent of tampering could be displayed to the fleet manager on the fleet server to trigger a discussion when “actual” and “recomputed” score values deviate beyond Y points from each other;" ¶: 0168; See also: ¶: 0154, 0155, 0169, 0182-0184];
“and wherein the one or more sampling criteria include at least one of geolocation, vehicle model, vehicle manufacturer, and driver demographic” [Tokman; In at least paragraphs 0117-0142, Tokman discloses acquiring a multitude of types of vehicular data including but not limited to GPS location and vehicle location, for example].
With respect to claim 2, Tokman discloses: “The method of claim 1, wherein the updating the one or more sampling criteria is performed using a machine learning model” [Tokman; "Another aspect of the invention is to detect and classify tampering using machine learning technology. One machine learning approach is a probabilistic detection that tampering has occurred on a vehicle using, for example, Naive Bayes. This detection may be carried out on the vehicle in real-time or shortly after suspected tampering has occurred. As noted above, this detection may trigger a potential tampering event that transmits typical vehicle data along with tailored data useful for tampering analysis and evidence based on one or more suspected tampering type(s), such as radar and camera targets over a specific window of time for camera or radar tampering, to a backend server when said tampering is suspected;" ¶: 0108].
With respect to claim 3, Tokman discloses: “wherein the restricting comprises restricting the one or more sampling criteria to match entries in the statistical model which exceed a standard deviation” [Tokman; In at least the paragraphs and figures cited, Tokman discloses identifying potential instances of tampering by identifying abnormal statistical instances related to the normal distribution of miles between event(MBE) values based on deeming these abnormal statistical instances as outliers. Tokman further discloses that the criteria for determining an outlier is for example "values further than at least, say, 3 standard deviations from the mean (to the good performance side) may be deemed to be outliers;" ¶: 0143-0145].
With respect to claim 4, Tokman discloses: “The method of claim 1, further comprising: updating logic for anomaly detection in one of the vehicles of the plurality of vehicles based on the statistical model using a machine learning model” [Tokman; "Another aspect of the invention is to detect and classify tampering using machine learning technology. One machine learning approach is a probabilistic detection that tampering has occurred on a vehicle using, for example, Naive Bayes. This detection may be carried out on the vehicle in real-time or shortly after suspected tampering has occurred. As noted above, this detection may trigger a potential tampering event that transmits typical vehicle data along with tailored data useful for tampering analysis and evidence based on one or more suspected tampering type(s), such as radar and camera targets over a specific window of time for camera or radar tampering, to a backend server when said tampering is suspected;" ¶: 0108].
With respect to claim 6, Tokman discloses: “The method of claim 1, wherein the detecting the anomaly in the received data is further based on receiving a report of anomalous data from one of the vehicles of the plurality of vehicles” [Tokman; "As further detailed below, the in-vehicle event detection and reporting system 200 may be adapted to detect a variety of operational parameters and conditions of the vehicle and the driver's interaction therewith and, based thereon, to determine if a driving or vehicle event has occurred (e.g., if one or more operational parameter/condition thresholds has been exceeded). Data related to detected events (i.e., event data) may then be stored and/or transmitted to a remote location/server, as described in more detail below;" Fig. 2A; ¶: 0035;
"This is why the relevant data is transmitted to a server when a potential tampering event is detected on the vehicle;" ¶: 0188; See also: Claim 1, Claim 4 and Claim 5].
With respect to claim 8, Tokman discloses:
“An apparatus for optimizing data collection from a plurality of vehicles, the apparatus comprising: at least one memory storing computer-executable instructions; and at least one processor configured to execute the computer-executable instructions to: receive data collected from the plurality of vehicles selected based on one or more sampling criteria” [Tokman; "One or more servers 140 of the fleet management and reporting system 100 are configured to selectively download, receive, or otherwise retrieve data either directly from the vehicles 112 via the service providers 130 or from collection servers 132 which may be third party servers from one or more various telematics suppliers;" Fig. 1; ¶: 0032;
"Still yet further, the event detection and reporting system 200 may also include a transmitter/receiver (transceiver) module 250 such as, for example, a radio frequency (RF) transmitter including one or more antennas 252 for wireless communication of the automated control requests, GPS data, one or more various vehicle configuration and/or condition data, or the like between the vehicles and one or more destinations such as, for example, to one or more services (not shown) having a corresponding receiver and antenna. The transmitter/receiver (transceiver) module 250 may include various functional parts of sub portions operatively coupled with a platoon control unit including for example a communication receiver portion, a global position sensor (GPS) receiver portion, and a communication transmitter;" Fig. 2; ¶: 0042];
“generate a statistical model based on the received data” [Tokman; "Outlier identification from distribution analysis and probability value assignment therefrom: With this statistical method, drivers with abnormal MBE values are identified and compared with other drivers, resulting in a distribution of MBE values. MBE values, for multiple drivers, for the fault of interest, e.g. lane departure warnings, may then be plotted and a model fit to the MBE values. This model might be a normal distribution, a lognormal distribution, etc." ¶: 0143, 0144];
“detect, based on the statistical model, an anomaly in the received data by restricting the one or more sampling criteria” [Tokman; "The mean and variance of the distribution may then be determined. Those values further than at least, say, 3 standard deviations from the mean (to the good performance side) may be deemed to be outliers. The probability values of these ‘too far away to be true’ observations (e.g. 99th percentile) may be used as one input to the probability of tampering calculation. While this single probability may be useful, it is not necessarily sufficient to establish tampering. It is also possible that one has no observations further than 3 standard deviations from the mean, in which case the driver may be deemed to not be tampering;" ¶: 0145];
“and update the one or more sampling criteria based on the anomaly in the received data by restricting the one or more sampling criteria” [Tokman; "With this, the measured, abnormally good, LDW MBE values may be replaced with more realistic ones on the vehicle or in the server-side driver scoring process as generated by a scoring algorithm which may use statistics or machine learning based on the normal vs tampering driver behavior to replace ‘too good to be true’ values with normal when tampering behavior is detected. This new, recomputed score absent of tampering could be displayed to the fleet manager on the fleet server to trigger a discussion when “actual” and “recomputed” score values deviate beyond Y points from each other;" ¶: 0168; See also: ¶: 0154, 0155, 0169, 0182-0184];
“wherein the one or more sampling criteria include at least one of geolocation, vehicle model, vehicle manufacturer, and driver demographic” [Tokman; In at least paragraphs 0117-0142, Tokman discloses acquiring a multitude of types of vehicular data including but not limited to GPS location and vehicle location, for example].
With respect to claim 9, Tokman discloses: “The apparatus of claim 8, wherein the update of the one or more sampling criteria is performed using a machine learning model” [Tokman; "Another aspect of the invention is to detect and classify tampering using machine learning technology. One machine learning approach is a probabilistic detection that tampering has occurred on a vehicle using, for example, Naive Bayes. This detection may be carried out on the vehicle in real-time or shortly after suspected tampering has occurred. As noted above, this detection may trigger a potential tampering event that transmits typical vehicle data along with tailored data useful for tampering analysis and evidence based on one or more suspected tampering type(s), such as radar and camera targets over a specific window of time for camera or radar tampering, to a backend server when said tampering is suspected;" ¶: 0108].
With respect to claim 10, Tokman discloses: “wherein the update of the one or more sampling criteria is comprises restricting the one or more sampling criteria to match entries in the statistical model which exceed a standard deviation” [Tokman; In at least the paragraphs and figures cited, Tokman discloses identifying potential instances of tampering by identifying abnormal statistical instances related to the normal distribution of miles between event(MBE) values based on deeming these abnormal statistical instances as outliers. Tokman further discloses that the criteria for determining an outlier is for example "values further than at least, say, 3 standard deviations from the mean (to the good performance side) may be deemed to be outliers;" ¶: 0143-0145].
With respect to claim 11, Tokman discloses: “The apparatus of claim 8, wherein the at least one processor is further configured to execute the computer-executable instructions to: update logic for anomaly detection in one of the vehicles of the plurality of vehicles based on the statistical model using a machine learning model” [Tokman; "Another aspect of the invention is to detect and classify tampering using machine learning technology. One machine learning approach is a probabilistic detection that tampering has occurred on a vehicle using, for example, Naive Bayes. This detection may be carried out on the vehicle in real-time or shortly after suspected tampering has occurred. As noted above, this detection may trigger a potential tampering event that transmits typical vehicle data along with tailored data useful for tampering analysis and evidence based on one or more suspected tampering type(s), such as radar and camera targets over a specific window of time for camera or radar tampering, to a backend server when said tampering is suspected;" ¶: 0108].
With respect to claim 13, Tokman discloses: “The apparatus of claim 8, wherein detecting the anomaly in the received data is further based on receiving a report of anomalous data from one of the vehicles of the plurality of vehicles” [Tokman; "As further detailed below, the in-vehicle event detection and reporting system 200 may be adapted to detect a variety of operational parameters and conditions of the vehicle and the driver's interaction therewith and, based thereon, to determine if a driving or vehicle event has occurred (e.g., if one or more operational parameter/condition thresholds has been exceeded). Data related to detected events (i.e., event data) may then be stored and/or transmitted to a remote location/server, as described in more detail below;" Fig. 2A; ¶: 0035;
"This is why the relevant data is transmitted to a server when a potential tampering event is detected on the vehicle;" ¶: 0188; See also: Claim 1, Claim 4 and Claim 5].
With respect to claim 15, Tokman discloses:
“A non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor to cause the processor to perform a method comprising: receiving data collected from a plurality of vehicles selected based on one or more sampling criteria” [Tokman; "One or more servers 140 of the fleet management and reporting system 100 are configured to selectively download, receive, or otherwise retrieve data either directly from the vehicles 112 via the service providers 130 or from collection servers 132 which may be third party servers from one or more various telematics suppliers;" Fig. 1; ¶: 0032;
"Still yet further, the event detection and reporting system 200 may also include a transmitter/receiver (transceiver) module 250 such as, for example, a radio frequency (RF) transmitter including one or more antennas 252 for wireless communication of the automated control requests, GPS data, one or more various vehicle configuration and/or condition data, or the like between the vehicles and one or more destinations such as, for example, to one or more services (not shown) having a corresponding receiver and antenna. The transmitter/receiver (transceiver) module 250 may include various functional parts of sub portions operatively coupled with a platoon control unit including for example a communication receiver portion, a global position sensor (GPS) receiver portion, and a communication transmitter;" Fig. 2; ¶: 0042;
"In accordance with the descriptions herein, the term “computer readable medium,” as used herein, refers to any non-transitory media that participates in providing instructions to the processor 230 for execution;" ¶: 0198];
“generating a statistical model based on the received data” [Tokman; "Outlier identification from distribution analysis and probability value assignment therefrom: With this statistical method, drivers with abnormal MBE values are identified and compared with other drivers, resulting in a distribution of MBE values. MBE values, for multiple drivers, for the fault of interest, e.g. lane departure warnings, may then be plotted and a model fit to the MBE values. This model might be a normal distribution, a lognormal distribution, etc." ¶: 0143, 0144];
“detecting, based on the statistical model, an anomaly in the received data” [Tokman; "The mean and variance of the distribution may then be determined. Those values further than at least, say, 3 standard deviations from the mean (to the good performance side) may be deemed to be outliers. The probability values of these ‘too far away to be true’ observations (e.g. 99th percentile) may be used as one input to the probability of tampering calculation. While this single probability may be useful, it is not necessarily sufficient to establish tampering. It is also possible that one has no observations further than 3 standard deviations from the mean, in which case the driver may be deemed to not be tampering;" ¶: 0145];
“and updating the one or more sampling criteria based on the anomaly in the received data, wherein the updating the one or more sampling criteria includes restricting the one or more sampling criteria” [Tokman; "With this, the measured, abnormally good, LDW MBE values may be replaced with more realistic ones on the vehicle or in the server-side driver scoring process as generated by a scoring algorithm which may use statistics or machine learning based on the normal vs tampering driver behavior to replace ‘too good to be true’ values with normal when tampering behavior is detected. This new, recomputed score absent of tampering could be displayed to the fleet manager on the fleet server to trigger a discussion when “actual” and “recomputed” score values deviate beyond Y points from each other;" ¶: 0168; See also: ¶: 0154, 0155, 0169, 0182-0184];
“and wherein the one or more sampling criteria include at least one of geolocation, vehicle model, vehicle manufacturer, and driver demographic” [Tokman; In at least paragraphs 0117-0142, Tokman discloses acquiring a multitude of types of vehicular data including but not limited to GPS location and vehicle location, for example].
With respect to claim 16, Tokman discloses: “The non-transitory computer-readable recording medium of claim 15, wherein the updating the one or more sampling criteria is performed using a machine learning model” [Tokman; "Another aspect of the invention is to detect and classify tampering using machine learning technology. One machine learning approach is a probabilistic detection that tampering has occurred on a vehicle using, for example, Naive Bayes. This detection may be carried out on the vehicle in real-time or shortly after suspected tampering has occurred. As noted above, this detection may trigger a potential tampering event that transmits typical vehicle data along with tailored data useful for tampering analysis and evidence based on one or more suspected tampering type(s), such as radar and camera targets over a specific window of time for camera or radar tampering, to a backend server when said tampering is suspected;" ¶: 0108].
With respect to claim 17, Tokman discloses: “The non-transitory computer-readable recording medium of claim 15, wherein the restricting comprises restricting the one or more sampling criteria to match entries in the statistical model which exceed a standard deviation.” [Tokman; In at least the paragraphs and figures cited, Tokman discloses identifying potential instances of tampering by identifying abnormal statistical instances related to the normal distribution of miles between event(MBE) values based on deeming these abnormal statistical instances as outliers. Tokman further discloses that the criteria for determining an outlier is for example "values further than at least, say, 3 standard deviations from the mean (to the good performance side) may be deemed to be outliers;" ¶: 0143-0145].
With respect to claim 18, Tokman discloses: “The non-transitory computer-readable recording medium of claim 15, wherein the method further comprises: updating logic for anomaly detection in one of the vehicles of the plurality of vehicles based on the statistical model using a machine learning model” [Tokman; "Another aspect of the invention is to detect and classify tampering using machine learning technology. One machine learning approach is a probabilistic detection that tampering has occurred on a vehicle using, for example, Naive Bayes. This detection may be carried out on the vehicle in real-time or shortly after suspected tampering has occurred. As noted above, this detection may trigger a potential tampering event that transmits typical vehicle data along with tailored data useful for tampering analysis and evidence based on one or more suspected tampering type(s), such as radar and camera targets over a specific window of time for camera or radar tampering, to a backend server when said tampering is suspected;" ¶: 0108].
With respect to claim 20, Tokman discloses: “The non-transitory computer-readable recording medium of claim 15, wherein the detecting the anomaly in the received data is further based on receiving a report of anomalous data from one of the vehicles of the plurality of vehicles” [Tokman; "As further detailed below, the in-vehicle event detection and reporting system 200 may be adapted to detect a variety of operational parameters and conditions of the vehicle and the driver's interaction therewith and, based thereon, to determine if a driving or vehicle event has occurred (e.g., if one or more operational parameter/condition thresholds has been exceeded). Data related to detected events (i.e., event data) may then be stored and/or transmitted to a remote location/server, as described in more detail below;" Fig. 2A; ¶: 0035;
"This is why the relevant data is transmitted to a server when a potential tampering event is detected on the vehicle;" ¶: 0188; See also: Claim 1, Claim 4 and Claim 5].
With respect to claim 21, Tokman discloses: “The method of claim 1, further comprising sampling data from the plurality of vehicles based on the one or more sampling criteria after the restricting, thereby limiting a specific number of data samples collected at a server and reducing a number of data collection operations” [Tokman; In at least the paragraphs and figures cited, Tokman discloses: "this detection may trigger a potential tampering event that transmits typical vehicle data along with tailored data useful for tampering analysis and evidence based on one or more suspected tampering type(s), such as radar and camera targets over a specific window of time for camera or radar tampering, to a backend server when said tampering is suspected;" ¶: 0108; wherein, the assessment of whether tampering has occurred is based on assessment of an MBE compared to cumulative mean for a particular period of time in order to reduce a number of incorrect detections on the server side; See also: 0109, 0177, 0182-0188].
Claim Rejections - 35 USC § 103
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claim(s) 5, 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tokman in view of Ellis et al. (United States Patent Publication 2019/0102659 A1), referenced as Ellis moving forward.
With respect to claim 5, Tokman does not specifically state: “wherein the updating logic for anomaly detection is based on adjusting a predefined range of values which are considered the anomaly based on entries in the statistical model which exceed a standard deviation.”
Ellis, which is in the same field of invention of systems/methods data classification using machine learning, teaches: “wherein the updating logic for anomaly detection is based on adjusting a predefined range of values which are considered the anomaly based on entries in the statistical model which exceed a standard deviation” [Ellis; "The standard deviations may be used to generate the minimum and/or maximum threshold(s) for the feature classifications in the AFD-based model. At block 618, the example AFD 210 updates the AFD-based model (e.g., the feature classification threshold ranges) based on the moving average and/or moving standard deviation;" Fig. 6; ¶: 0056; See also: Fig. 7; ¶: 0055, 0064, 0065].
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 system/method for classifying vehicle events in order to identify instances of tampering as disclosed by Tokman to incorporate the teachings regarding updating the logic for data classification by adjusting feature classification threshold ranges based on a moving standard deviation as taught by Ellis with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for classifying vehicle events in order to identify instances of tampering that is more robust in its ability to correctly classify events with a higher degree of accuracy using by implementing an auto-feature discrimination function to generate local-based classifications [Ellis; ¶: 0020].
With respect to claim 12, Tokman does not specifically state: “wherein updating logic for anomaly detection is based on adjusting a predefined range of values which are considered the anomaly based on entries in the statistical model which exceed a standard deviation.”
Ellis teaches: “wherein updating logic for anomaly detection is based on adjusting a predefined range of values which are considered the anomaly based on entries in the statistical model which exceed a standard deviation” [Ellis; "The standard deviations may be used to generate the minimum and/or maximum threshold(s) for the feature classifications in the AFD-based model. At block 618, the example AFD 210 updates the AFD-based model (e.g., the feature classification threshold ranges) based on the moving average and/or moving standard deviation;" Fig. 6; ¶: 0056; See also: Fig. 7; ¶: 0055, 0064, 0065].
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 system/method for classifying vehicle events in order to identify instances of tampering as disclosed by Tokman to incorporate the teachings regarding updating the logic for data classification by adjusting feature classification threshold ranges based on a moving standard deviation as taught by Ellis with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for classifying vehicle events in order to identify instances of tampering that is more robust in its ability to correctly classify events with a higher degree of accuracy using by implementing an auto-feature discrimination function to generate local-based classifications [Ellis; ¶: 0020].
With respect to claim 19, Tokman does not specifically state: “wherein the updating logic for anomaly detection is based on adjusting a predefined range of values which are considered the anomaly based on entries in the statistical model which exceed a standard deviation.”
Ellis teaches: “wherein the updating logic for anomaly detection is based on adjusting a predefined range of values which are considered the anomaly based on entries in the statistical model which exceed a standard deviation” [Ellis; "The standard deviations may be used to generate the minimum and/or maximum threshold(s) for the feature classifications in the AFD-based model. At block 618, the example AFD 210 updates the AFD-based model (e.g., the feature classification threshold ranges) based on the moving average and/or moving standard deviation;" Fig. 6; ¶: 0056; See also: Fig. 7; ¶: 0055, 0064, 0065].
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 system/method for classifying vehicle events in order to identify instances of tampering as disclosed by Tokman to incorporate the teachings regarding updating the logic for data classification by adjusting feature classification threshold ranges based on a moving standard deviation as taught by Ellis with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for classifying vehicle events in order to identify instances of tampering that is more robust in its ability to correctly classify events with a higher degree of accuracy using by implementing an auto-feature discrimination function to generate local-based classifications [Ellis; ¶: 0020].
Prior Art (Not relied upon)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached form 892.
Chen et al. (United States Patent Publication 2021/0390850 A1) discloses: A traffic condition detection method comprises: obtaining a plurality of traffic parameters associated with a monitoring area and obtaining a normal parameter range based on the traffic parameters, wherein at least half of the traffic parameters fall within the normal parameter range; and performing a monitoring procedure on the monitoring area, wherein the monitoring procedure comprises: determining whether a real-time traffic parameter falls within the normal parameter range; and outputting a traffic abnormality notification associated with the monitoring area when the real-time traffic parameter does not fall within the normal parameter range.
Conclusion
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 extension fee 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 date of this final action.
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/R.N.B./Examiner, Art Unit 3666C
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666
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