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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/30/2026 has been entered.
Claims 1-4, 6-20, and 22 are now pending in this application. Claims 1-4, 10-13, 19, and 20 have been amended. Claim 21 has been cancelled. New claim 22 has been added. Claims 1, 10, and 19 are independent claims.
Claim Objections
Examiner still recommends removing the comma for clarity should they decide to not repeat the words “method’ and “system” in the first line of each of independent claims 1 and 14.
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, 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.
Claims 1-4 and 6-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al., US Patent No. 11,248,925 B2 (hereinafter as Lee) in view of Ohmura, US Patent No. 10,994,726 B2 (hereinafter as Ohmura).
Regarding independent claim 1, Lee teaches a driver assistance system [see figs. 1 and 5 as well as col. 1, lines 40-41] comprising:
one or more vehicle sensors [see col. 1, lines 3-4 and the sensors shown in figs. 1 and 5]; and
one or more processors [see col. 1, line 3-5 as well as controller 100 shown in fig. 1] configured to:
receive a vehicle data from the one or more vehicle sensors, the vehicle data includes a location of a vehicle [see col. 1, lines 36-49 including various vehicle sensors; especially note the GPS providing location information];
causing generation of a spatio-temporal probability data stored in memory [note e.g. the external databases in col. 5, lines 62-64], the spatio-temporal data including historical driving event data aggregated across a plurality of vehicles and a plurality of trips [note the confidence level assignment to the inputs/information in the information fusion controller described in col. 4, lines 43-49, col. 7, lines 52-57 and col. 8, lines 1-19; especially note e.g. from col. 6, lines 11-27 the use of neighboring vehicles within a statistical model and from col. 6, lines 54-55 the input continuity across time intervals which indicates a plurality of trips; see also col. 5, lines 25-35 indicating real-time modifications and note the spatio-temporal data example e.g. in col. 5, lines 25-29], the historical driving event data including at least one of traffic events, near- miss events, or anomaly events occurring at corresponding geographic locations [note e.g. in col. 5, lines 25-29 the example of a predictions of bottle neck issues due to a disabled car, accident, etc. and monitoring real-time information along a certain path];
analyze the vehicle data and the spatio-temporal probability data using a machine learning predictive model to predict a future potentially dangerous driving condition at a geographic location prior to the vehicle reaching the geographical location [see e.g. col. 6, lines 3-27 indicating the use of a statistical model such as a Bayesian model to predict road lines/model; see also steps S203-S215 of fig. 2; especially note using the sensor data, building and validating a model based on environmental conditions along a certain driving path of the vehicle; see also col. 8, lines 37-58; note e.g. in col. 5, lines 6-11 and 25-29 indicating accidents and other potentially dangerous driving conditions being identified and avoided by redirection, which indicates predicting the situation prior to the vehicle reaching the congested point/location]; and
in response to predicting the future potentially dangerous driving condition at the geographic location and determining that the vehicle is approaching the geographic location, initiate a countermeasure to prevent the future potentially dangerous driving condition from occurring prior to the vehicle reaching the geographical location [note e.g. in S219 of fig. 2 the display of augmented road lines responsive to the prediction of the road line model and superimposed objects; see also e.g. col. 3, lines 5-12 indicating providing helpful guidance during driving; see again col. 5, lines 6-11 and 25-29 indicating accidents and other potentially dangerous driving conditions being identified and avoided by redirection (which is a countermeasure initiated prior to the vehicle arriving at the congested location)].
Lee does not explicitly teach that the countermeasure includes at least one of: adjusting a vehicle operating parameter, adjusting a driver assistance system threshold, or modifying availability of a vehicle feature.
Ohmura teaches a countermeasure that includes at least one of: adjusting a vehicle operating parameter, adjusting a driver assistance system threshold, or modifying availability of a vehicle feature [see col. 3, lines 29-34 and note adjusting an allowable upper limit for the speed of the vehicle as it approaches a certain object that could potentially be a traffic obstacle as in col. 12, lines 28-39].
It would have been obvious to one of ordinary skill in the art having the teachings of Lee and Ohmura, before the effective filing date of the claimed invention, to modify the countermeasure taught by Lee for dealing with certain predicted driving conditions by explicitly specifying that it includes at least one of: adjusting a vehicle operating parameter, adjusting a driver assistance system threshold, or modifying availability of a vehicle feature, as per the teachings of Ohmura. The motivation for this obvious combination of teachings would be to allow passengers to feel safe by making adjustments based on a vehicle-object distance, as suggested by Ohmura [again, see col. 3, lines 29-34; see also col. 2, lines 39-46].
Independent claims 10 and 19 are rejected analogous to the rejection of independent claim 1 above.
Regarding independent claim 10, Lee also discloses a method [see e.g. fig. 2] comprising the steps of claim 1.
Regarding independent claim 19, Lee also discloses a non-transitory computer-readable medium having stored contents that cause one or more computing systems to perform automated operations [see e.g. col. 8, lines 65-67], the automated operations including at least the steps of claim 1.
Regarding claims 2 and 11, the rejection of independent claims 1 and 10 are respectively incorporated. As per the independent claim rejections, Ohmura teaches that the countermeasure includes adjusting the driver assistance system threshold based on a proximity of the vehicle to the geographic location [again, see col. 3, lines 29-34 and note adjusting an allowable upper limit for the speed of the vehicle as it approaches a certain object that could potentially be a traffic obstacle as in col. 12, lines 28-39; especially note that the upper limit of the speed depends on the distance between the vehicle and the object/obstacle].
See the rejection of the independent claim for motivations to combine the art.
Regarding claims 3 and 12, the rejection of independent claims 1 and 10 are respectively incorporated. As per the independent claim rejections, Ohmura teaches that the countermeasure that includes adjusting the vehicle operating parameter [see col. 3, lines 29-34 and note adjusting an allowable upper limit for the speed of the vehicle as is approaches a certain object that could potentially be a traffic obstacle as in col. 12, lines 28-39].
See the rejection of the independent claim for motivations to combine the art.
Regarding claims 4 and 13, the rejection of independent claims 1 and 10 are respectively incorporated. Lee further teaches that the countermeasure includes sending a driver assistance message to a user interface in the vehicle to warn a driver of the vehicle of the future potentially dangerous driving condition while the vehicle is approaching the geographic location [note in col. 8, lines 20-58 indicating visual feedback alerting the driver of potential objects/obstructions to be aware of as the vehicle gets closer to the objects; note the change in color on the visual display].
Regarding claims 6 and 15, the rejection of claims 1 and 14 are respectively incorporated. Lee further teaches processing the vehicle data to determine at least one of: a geographic location of the vehicle where a pre-collision system of the vehicle was activated; a driver event; a traffic event; a near miss event; or an anomaly event [see e.g. col. 5, lines 25-35 and note the indication of a location of a disabled car or an accident causing traffic disruptions as examples of traffic information being integrated into the processing done in real time at certain locations].
Regarding claims 7 and 16, the rejection of claims 1 and 14 are respectively incorporated. Lee further teaches:
sending the vehicle data from the vehicle to a remote server, the creating the spatio-temporal probability data based upon the vehicle data is performed by the remote server [note the option of cloud computing in col. 10, lines 1-4 and the network controller 506 of fig. 5; see col. 9, lines 14-22; see again (for the creation of the spatio-temporal probability data) the confidence level assignment described in col. 4, lines 43-49, col. 7, lines 52-57 and col. 8, lines 1-19; col. 6, lines 11-27 for the use of neighboring vehicles and col. 6, lines 54-55 for the input continuity across time intervals; see also col. 5, lines 25-35 indicating real-time modifications]; and
receiving the spatio-temporal probability data from the remote server via a wireless network [note the wireless communication options among the servers and network controller as described in col. 9, lines 14-27; see again fig. 5].
Regarding claims 8 and 17, the rejection of claims 1 and 14 are respectively incorporated. Lee further teaches applying a machine learning framework to generate the machine learning predictive model [again, see e.g. col. 6, lines 3-27 indicating the use of a statistical model such as a Bayesian model to predict road lines/model; see also steps S203-S215 of fig. 2 including prediction model building and validation then input weight assignment and prediction model execution].
Regarding claims 9 and 18, the rejection of claims 8 and 17 are respectively incorporated. Lee further teaches:
sending the vehicle data from the vehicle to a remote server, the creating the spatio-temporal probability data based upon the vehicle data is performed by the remote server [note the option of cloud computing in col. 10, lines 1-4 and the network controller 506 of fig. 5; see col. 9, lines 14-22; see again (for the creation of the spatio-temporal probability data) the confidence level assignment described in col. 4, lines 43-49, col. 7, lines 52-57 and col. 8, lines 1-19; col. 6, lines 11-27 for the use of neighboring vehicles and col. 6, lines 54-55 for the input continuity across time intervals; see also col. 5, lines 25-35 indicating real-time modifications]; and
receiving the spatio-temporal probability data from the remote server via a wireless network [note the wireless communication options among the servers and network controller as described in col. 9, lines 14-27; see again fig. 5].
Regarding claim 14, the rejection of independent claim 10 is incorporated. Lee further teaches:
creating a spatio-temporal probability data based upon the vehicle data, the spatio-temporal data includes a history of traffic events at one or more geographic locations for a plurality of vehicles and a history of driver events at the one or more geographic locations for the vehicle [note the confidence level assignment described in col. 4, lines 43-49, col. 7, lines 52-57 and col. 8, lines 1-19; note e.g. from col. 6, lines 11-27 the use of neighboring vehicles and from col. 6, lines 54-55 the input continuity across time intervals; see also col. 5, lines 25-35 indicating real-time modifications]; and
that the machine learning predictive model uses the spatio-temporal probability data to predict the driving condition at the geographic location that the vehicle is approaching [again, see steps S203-S215 of fig. 2; especially note using the sensor data, building and validating a model based on environmental conditions along a certain driving path of the vehicle; see also col. 8, lines 37-58].
Regarding claim 20, the rejection of independent claim 19 is incorporated. Lee further teaches that the countermeasure includes at least one of: adjusting a threshold of the machine learning predictive model in response to the vehicle approaching the geographic location; or sending a driver assistance message to a user interface in the vehicle warning a driver of the vehicle of the predicted driving condition in response to the vehicle approaching the geographic location [note in col. 8, lines 20-58 indicating visual feedback alerting the driver of potential objects/obstructions to be aware of as the vehicle gets closer to the objects; note the change in color on the visual display].
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Ohmura, as applied to claim 1 above, and further in view of SENMYO, US PGPUB 2021/0261139 A1 ((hereinafter as Senmyo).
Regarding claim 22, the rejection of independent claim 1 is incorporated.
As per the rejection of claim 1, Lee teaches a spatio-temporal probability data including historical driving event data aggregated across a plurality of vehicles and a plurality of trips [note the confidence level assignment to the inputs/information in the information fusion controller described in col. 4, lines 43-49, col. 7, lines 52-57 and col. 8, lines 1-19; especially note e.g. from col. 6, lines 11-27 the use of neighboring vehicles within a statistical model and from col. 6, lines 54-55 the input continuity across time intervals which indicates a plurality of trips; see also col. 5, lines 25-35 indicating real-time modifications and note the spatio-temporal data example e.g. in col. 5, lines 25-29], the historical driving event data including traffic-related events occurring at corresponding geographic locations [note e.g. in col. 5, lines 25-29 the example of a predictions of bottle neck issues due to a disabled car, accident, etc. and monitoring real-time information along a certain path]; and
predicting the future potentially dangerous driving condition based on the spatio-temporal probability data [see e.g. col. 6, lines 3-27 indicating the use of a statistical model such as a Bayesian model to predict road lines/model; see also steps S203-S215 of fig. 2; especially note using the sensor data, building and validating a model based on environmental conditions along a certain driving path of the vehicle; see also col. 8, lines 37-58; note e.g. in col. 5, lines 6-11 and 25-29 indicating accidents and other potentially dangerous driving conditions being identified and avoided by redirection, which indicates predicting the condition/situation in relation to a congested point/location based on the probability data]; and
The previously combined art, however, does not explicitly teach that the future potentially dangerous driving condition is predicted based on a frequency or probability of historical near-miss events occurring at the geographic location, the historical near-miss events being derived from the historical driving event data aggregated across a plurality of vehicles and a plurality of trips.
Senmyo teaches future potentially dangerous driving conditions that are predicted based on a frequency or probability of historical near-miss events occurring at a geographic location, the historical near-miss events being derived from historical driving event data [see e.g. [0068] indicating prediction of a traffic situation based on a frequency of encountering a near-miss incident at a certain position; see also [0087] indicating predictions based on previous data involving near-miss incidents as well as fig. 1 and fig. 10; see also [0045]-[0046]].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Senmyo, before the effective filing date of the claimed invention, to further modify the prediction taught by Lee by explicitly specifying that the future potentially dangerous driving condition is predicted based on a frequency or probability of historical near-miss events occurring at the geographic location, the historical near-miss events being derived from the historical driving event data aggregated across a plurality of vehicles and a plurality of trips, as per the teachings of Senmyo. The motivation for this obvious combination of teachings would be to enable better driving assistance for collision avoidance in cases where the collision risk is high, as suggested by Senmyo [see e.g. [0003] as well as [0045]-[0050]].
Response to Arguments
Applicant's prior art arguments regarding the amended independent claims have been fully considered but they are not persuasive.
Regarding arguments that “Lee’s “Confidence Levels” do not constitute spatio-temporal probability data” [see pp. 13-14 of Applicant’s response], Examiner respectfully disagrees and reiterates that Lee clearly and explicitly teaches spatio-temporal probability data that is generated and stored in memory [note e.g. the external databases in col. 5, lines 62-64] and that includes historical driving event data aggregated across a plurality of vehicles and a plurality of trips [especially note e.g. from col. 6, lines 11-27 the use of neighboring vehicles within a statistical model and from col. 6, lines 54-55 the input continuity across time intervals which indicates a plurality of trips; see also col. 5, lines 25-35 indicating real-time modifications and note the spatio-temporal data example e.g. in col. 5, lines 25-29], the historical driving event data including traffic-related events occurring at corresponding geographic locations [note e.g. in col. 5, lines 25-29 the example of a predictions of bottle neck issues due to a disabled car, accident, etc. and monitoring real-time information along a certain path]. Lee further explicitly teaches utilizing the spatio-temporal probability data using a machine learning predictive model to predict a future potentially dangerous driving condition at a geographic location prior to the vehicle reaching the geographical location [see e.g. col. 6, lines 3-27 indicating the use of a statistical model such as a Bayesian model to predict road lines/model; see also steps S203-S215 of fig. 2; especially note using the sensor data, building and validating a model based on environmental conditions along a certain driving path of the vehicle; see also col. 8, lines 37-58; note e.g. in col. 5, lines 6-11 and 25-29 indicating accidents and other potentially dangerous driving conditions being identified and avoided by redirection, which indicates predicting the situation prior to the vehicle reaching the congested point/location].
Examiner respectfully reiterates that while Lee discloses an augmented road line detection and display system, Lee essentially discloses a system and accompanying method for doing so that comprises receives input from sensors of the vehicle of interest and a sub-system of the vehicle then builds and validates a road line model to accordingly detect or predict a road line utilizing the inputs as well as associated weighted environmental conditions. Examiner also respectfully notes that Lee clearly and explicitly discloses at least in col. 5, lines 25-35 the use of time-based and location-based data collected using vehicle sensors from neighboring vehicle as can be seen in col. 6, lines 11-27. Lee further teaches that the input is continuous across time intervals as seen at least in col. 6, lines 54-55. Examine especially notes that fig. 2 clearly shows a method that builds and validates a model, but also uses weighted inputs based on environmental conditions with the model to detect objects along a driving path. Therefore, Examiner respectfully asserts that Lee clearly teaches each and every element of the limitations argued by Applicant.
Next, Examiner also reiterates that fig. 2 clearly shows a method that builds and validates a model as well as uses weighted inputs based on environmental conditions with the model to detect objects along a driving path. Therefore, Examiner respectfully asserts that Lee clearly teaches the utilization of current inputs as well as generated probability data to predict a future driving condition contrary to Applicant’s allegation.
Thus, Examiner respectfully reasserts that Lee clearly teaches each and every element of the limitations including those argued by Applicant, namely: (1) generation of the recited spatio-temporal probability data comprising historical multi-vehicle event histories and (2) analysis of both vehicle data and that probability data using a machine learning predictive model to predict a driving condition at a geographic location that the vehicle is approaching.
Regarding Applicant’s arguments regarding the amended limitations [see pp. 10-12 and 14-15]. Examiner respectfully refers Applicant to the teachings of the secondary reference, Ohmura and the full updated rejections of the entire amended claim set under 35 U.S.C. 103 as being unpatentable over Lee in view of Ohmura, as presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Examiner notes from the cited art:
Dabell, US 11,508,022 B1, which teaches crowdsourcing records across devices for traffic event modeling and producing statistical estimates of patrol locations over a certain time duration [see e.g. figs. 1-3 and fig. 5 as well as the corresponding description].
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/MARIA S AYAD/Primary Examiner, Art Unit 2172
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