DETAILED ACTION
This is a final rejection in response to amendments filed 7/1/2026. Claims 1-6 and 8-21 are pending.
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 .
Response to Arguments
Applicant’s amendments have overcome the double patenting rejection. Applicant's arguments regarding the prior art not teaching the amended features of the independent claims have been fully considered but they are not persuasive. Applicant argues that the prior art of Donderici does not teach the driving scenarios as machine learning training data. Examiner agrees that Donderici does not teach the driving scenarios as machine learning data. However, the teaching of Donderici is combined with Tao which does teach the scenarios as machine learning. Therefore, as clarified below, Donderici is teaching a driving scenario which Tao is using within its machine learning.
Applicant further argues that Tao does not teach training data for a machine learning model. However, Tao teaches the autonomous vehicle in communication with servers over a network where the servers are incorporate a machine learning engine which trains “a set of rules, algorithms, and/or predictive models 124” [0032].
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 6, 11-12, 14, 16-17, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Tao (US 20200031340) in view of Donderici (US 20230242153).
Regarding claim 1, Tao teaches a computer-implemented method ([0028] discusses the planning being implemented with processors) comprising:
determining, by a computing system, a first speed of traffic for a first portion of a road and a second speed of traffic for a second portion of the road ([0073] discusses determining a speed of traffic in a first line with [0061]-[0062] discussing determining speed of traffic in multiple adjacent lanes (first and second portions));
generating, by the computing system, an upper speed bound for a third portion of the road based on a machine learning model, the machine learning model trained based on training data including the first speed of traffic and the second speed of traffic ([0062] discusses determining a lower speed for the host vehicle based on the traffic flow of the adjacent lanes where [0031] discusses this data analysis being implemented with a machine learning engine); wherein
causing, by the computing system, a change in speed of a vehicle in the third portion based on the upper speed bound for the third portion ([0062] discusses determining a lower speed for the host vehicle based on the traffic flow of the adjacent lanes with [0067] discussing controlling the vehicle according to the new speed).
Donderici teaches ([0073] discusses road test data for a driving scenario associated with safe speed ranges of an upper bound of a speed limit of vehicle on the road).
Tao teaches the training data for a machine learning model based on scenarios including the speeds preferred by the user. Donderici teaches a driving scenario taking into consideration safe speed ranges for the user. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the driving scenarios of Tao with the safe speed ranges of Donderici as Donderici teaches that this ensures safe and efficient operation of the autonomous vehicle [0020].
Regarding claim 2, Tao teaches wherein the training data further includes at least one of road geometry, regional settings, or geographical location based on map data ([0030]-[0031] discuss the planning system using analytics data including MPOI for optimal routing with [0029] discussing MPOI as a map and point of interest server providing geographical location data).
Regarding claim 4, Tao teaches wherein the training data further includes at least one of increase in speed of traffic, decrease in speed of traffic, or difference in speeds of traffic in adjacent lanes ([0071]-[0072] discusses the training data including the average speed being lower or higher than the current speed of the ADV with [0073] discussing the training data including a difference in speeds of traffic in adjacent lanes).
Regarding claim 6, Tao teaches wherein the training data further includes at least one of high traffic scenarios, low traffic scenarios, or merging scenarios ([0062] discusses the system including a scenario of high traffic where the traffic flow causes the vehicles to slow down and further cause the ADV to lower its speed limit).
Regarding claim 11, Tao teaches a system (Figs. 1 & 2) comprising:
at least one processor; and a memory storing instructions that, when executed by the at least one processor ([0125] discusses the system as a non-transitory computer-readable storage medium executed by a processor), cause the system to perform operations comprising:
determining a first speed of traffic for a first portion of a road and a second speed of traffic for a second portion of the road ([0073] discusses determining a speed of traffic in a first line with [0061]-[0062] discussing determining speed of traffic in multiple adjacent lanes (first and second portions));
generating an upper speed bound for a third portion of the road based on a machine learning model, the machine learning model trained based on training data including the first speed of traffic and the second speed of traffic ([0062] discusses determining a lower speed for the host vehicle based on the traffic flow of the adjacent lanes where [0031] discusses this data analysis being implemented with a machine learning engine); wherein
causing a change in speed of a vehicle in the third portion based on the upper speed bound for the third portion ([0062] discusses determining a lower speed for the host vehicle based on the traffic flow of the adjacent lanes with [0067] discussing controlling the vehicle according to the new speed).
Donderici teaches ([0073] discusses road test data for a driving scenario associated with safe speed ranges of an upper bound of a speed limit of vehicle on the road).
Tao teaches the training data for a machine learning model based on scenarios including the speeds preferred by the user. Donderici teaches a driving scenario taking into consideration safe speed ranges for the user. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the driving scenarios of Tao with the safe speed ranges of Donderici as Donderici teaches that this ensures safe and efficient operation of the autonomous vehicle [0020].
Regarding claim 12, Tao teaches wherein the training data further includes at least one of road geometry, regional settings, or geographical location based on map data ([0030]-[0031] discuss the planning system using analytics data including MPOI for optimal routing with [0029] discussing MPOI as a map and point of interest server providing geographical location data).
Regarding claim 14, Tao teaches wherein the training data further includes at least one of increase in speed of traffic, decrease in speed of traffic, or difference in speeds of traffic in adjacent lanes ([0071]-[0072] discusses the training data including the average speed being lower or higher than the current speed of the ADV with [0073] discussing the training data including a difference in speeds of traffic in adjacent lanes).
Regarding claim 16, Tao teaches a non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system ([0125] discusses the system as a non-transitory computer-readable storage medium executed by a processor), cause the computing system to perform operations comprising:
determining a first speed of traffic for a first portion of a road and a second speed of traffic for a second portion of the road ([0073] discusses determining a speed of traffic in a first line with [0061]-[0062] discussing determining speed of traffic in multiple adjacent lanes (first and second portions));
generating an upper speed bound for a third portion of the road based on a machine learning model, the machine learning model trained based on training data including the first speed of traffic and the second speed of traffic ([0062] discusses determining a lower speed for the host vehicle based on the traffic flow of the adjacent lanes where [0031] discusses this data analysis being implemented with a machine learning engine); wherein
causing, by the computing system, a change in speed of a vehicle in the third portion based on the upper speed bound for the third portion ([0062] discusses determining a lower speed for the host vehicle based on the traffic flow of the adjacent lanes with [0067] discussing controlling the vehicle according to the new speed).
Donderici teaches ([0073] discusses road test data for a driving scenario associated with safe speed ranges of an upper bound of a speed limit of vehicle on the road).
Tao teaches the training data for a machine learning model based on scenarios including the speeds preferred by the user. Donderici teaches a driving scenario taking into consideration safe speed ranges for the user. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the driving scenarios of Tao with the safe speed ranges of Donderici as Donderici teaches that this ensures safe and efficient operation of the autonomous vehicle [0020].
Regarding claim 17, Tao teaches wherein the training data further includes at least one of road geometry, regional settings, or geographical location based on map data ([0030]-[0031] discuss the planning system using analytics data including MPOI for optimal routing with [0029] discussing MPOI as a map and point of interest server providing geographical location data).
Regarding claim 19, Tao teaches wherein the training data further includes at least one of increase in speed of traffic, decrease in speed of traffic, or difference in speeds of traffic in adjacent lanes ([0071]-[0072] discusses the training data including the average speed being lower or higher than the current speed of the ADV with [0073] discussing the training data including a difference in speeds of traffic in adjacent lanes).
Regarding claim 21, Tao teaches wherein the training data further includes at least one of high traffic scenarios, low traffic scenarios, or merging scenarios ([0062] discusses the system including a scenario of high traffic where the traffic flow causes the vehicles to slow down and further cause the ADV to lower its speed limit).
Claims 3, 5, 13, 15, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tao in view of Donderici and further in view of Beaurepaire (US 20230256977).
Regarding claim 3, Tao teaches the road types as including highways [0061] but does not explicitly teach wherein the regional settings include at least one of urban, suburban, highway, or rural.
Beaurepaire teaches wherein the regional settings include at least one of urban, suburban, highway, or rural ([0060] discusses the training data including regional settings such as urban or rural settings).
Tao teaches determining a speed limit of a vehicle. Beaurepaire teaches taking into consideration the type of road. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Tao with the consideration of Beaurepaire as this provides further information to the learning system making the system more accurate for the user.
Regarding claim 5, Tao teaches determining a speed limit of a vehicle but does not explicitly teach wherein the training data further includes a class of vehicle including at least one of truck, sedan, or SUV.
Beaurepaire teaches wherein the training data further includes a class of vehicle including at least one of truck, sedan, or SUV ([0044] discusses the training data including class of vehicle including trucks and sedans).
Tao teaches determining a speed limit of a vehicle. Beaurepaire teaches taking into consideration the type of road. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Tao with the consideration of Beaurepaire as this provides further information to the learning system making the system more accurate for the user.
Regarding claim 13, Tao teaches the road types as including highways [0061] but does not explicitly teach wherein the regional settings include at least one of urban, suburban, highway, or rural.
Beaurepaire teaches wherein the regional settings include at least one of urban, suburban, highway, or rural ([0060] discusses the training data including regional settings such as urban or rural settings).
Tao teaches determining a speed limit of a vehicle. Beaurepaire teaches taking into consideration the type of road. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Tao with the consideration of Beaurepaire as this provides further information to the learning system making the system more accurate for the user.
Regarding claim 15, Tao teaches determining a speed limit of a vehicle but does not explicitly teach wherein the training data further includes a class of vehicle including at least one of truck, sedan, or SUV.
Beaurepaire teaches wherein the training data further includes a class of vehicle including at least one of truck, sedan, or SUV ([0044] discusses the training data including class of vehicle including trucks and sedans).
Tao teaches determining a speed limit of a vehicle. Beaurepaire teaches taking into consideration the type of road. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Tao with the consideration of Beaurepaire as this provides further information to the learning system making the system more accurate for the user.
Regarding claim 18, Tao teaches the road types as including highways [0061] but does not explicitly teach wherein the regional settings include at least one of urban, suburban, highway, or rural.
Beaurepaire teaches wherein the regional settings include at least one of urban, suburban, highway, or rural ([0060] discusses the training data including regional settings such as urban or rural settings).
Tao teaches determining a speed limit of a vehicle. Beaurepaire teaches taking into consideration the type of road. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Tao with the consideration of Beaurepaire as this provides further information to the learning system making the system more accurate for the user.
Regarding claim 20, Tao teaches determining a speed limit of a vehicle but does not explicitly teach wherein the training data further includes a class of vehicle including at least one of truck, sedan, or SUV.
Beaurepaire teaches wherein the training data further includes a class of vehicle including at least one of truck, sedan, or SUV ([0044] discusses the training data including class of vehicle including trucks and sedans).
Tao teaches determining a speed limit of a vehicle. Beaurepaire teaches taking into consideration the type of road. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Tao with the consideration of Beaurepaire as this provides further information to the learning system making the system more accurate for the user.
Allowable Subject Matter
Claims 8-10 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIELLE M JACKSON whose telephone number is (303)297-4364. The examiner can normally be reached Monday-Friday 7:00-4:30 MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Lin can be reached at (571) 270-3976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.M.J./Examiner, Art Unit 3657 /ABBY LIN/Supervisory Patent Examiner, Art Unit 3657
