DETAILED ACTION
This is the first office action in response to U.S. application 18/979,408. All claims 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 .
Claim Rejections - 35 USC § 102
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 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.
Claims 1-2, 4, 6, 11-12, 14, 16-17 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tao (US 20200031340).
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); and
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).
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); and
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).
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); and
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).
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).
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 3, 5, 13, 15, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tao 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.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Tao in view of Donderici (US 20230242153).
Regarding claim 7, Tao teaches the training data including the speeds preferred by the user but does not explicitly teach wherein the training data further includes road test data for a driving scenario associated with at least one of speed ranges that are too fast, speed ranges that are too slow, safe speed ranges, or comfortable speed ranges, and the upper speed bound for the third portion of the road is within the safe speed ranges or the comfortable speed ranges.
Donderici teaches wherein the training data further includes road test data for a driving scenario associated with at least one of speed ranges that are too fast, speed ranges that are too slow, safe speed ranges, or comfortable speed ranges, and the upper speed bound for the third portion of the road is within the safe speed ranges or the comfortable speed ranges ([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 including the speeds preferred by the user. Donderici teaches 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 system of Tao with the safe speed ranges of Donderici as Donderici teaches that this ensures safe and efficient operation of the autonomous vehicle [0020].
Double Patenting
Claims 1-7 and 11-20 of this application are patentably indistinct from claims 1-20 of U.S. Patent 12,187,278. Pursuant to 37 CFR 1.78(f), when two or more applications filed by the same applicant or assignee contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-7 and 11-20 rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-12 of U.S. Patent 12,187,278 (herein referred to as ‘278) in view of Beaurepaire and Donderici. Although the claims at issue are not identical, they are not patentably distinct from each other. As shown in the table below claim 1 is rejected by ‘278’s claims 1 and 3.
Claim
This Application’s Claim
‘278 Claims
1
A computer-implemented method comprising:
1. A computer-implemented method comprising:
1
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;
1. determining, by a computing system, first speeds of first objects in a first lane and second speeds of second objects in a second lane;
1
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; and
1. generating, by the computing system, a desired upper speed bound for a third lane based on the first speed of traffic and the second speed of traffic; and.
3. wherein the desired upper speed bound is generated by a machine learning model
1
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.
1. causing, by the computing system, a change in speed of the vehicle in the third lane based on the desired upper speed bound for the third lane
‘278’s claim 1 is the same as the present application’s claim 1 except that in the present application the upper speed bound is generated by a machine learning model. It would be obvious to utilize a machine learning model to make the system more robust. It then follows that this application’s claim 1 would be broader, and with the obviousness statement to include the machine learning model in ‘278, this would be obviousness double patenting.
As shown in the table below claim 11 is rejected by ‘278’s claim 11 and 13.
11
A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
11. A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
11
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;
11. determining first speeds of first objects in a first lane and second speeds of second objects in a second lane;
11
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;
11. generating a desired upper speed bound for a third lane based on the first speed of traffic and the second speed of traffic;
13. wherein the desired upper speed bound is generated by a machine learning model
11
and causing a change in speed of a vehicle in the third portion based on the upper speed bound for the third portion.
11. and causing a change in speed of the vehicle in the third lane based on the desired upper speed bound for the third lane.
‘278’s claim 11 is the same as the present application’s claim 11 except that in the present application the upper speed bound is generated by a machine learning model. It would be obvious to utilize a machine learning model to make the system more robust. It then follows that this application’s claim 11 would be broader, and with the obviousness statement to include classifying the object in ‘278, this would be obviousness double patenting.
As shown in the table below claim 11 is rejected by ‘278’s claims 16 and 18.
16
A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations comprising:
16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations comprising:
16
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;
16. determining first speeds of first objects in a first lane and second speeds of second objects in a second lane
16
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;
16. generating a desired upper speed bound for a third lane based on the first speed of traffic and the second speed of traffic;
18. wherein the desired upper speed bound is generated by a machine learning model
16
and causing a change in speed of a vehicle in the third portion based on the upper speed bound for the third portion.
16. and causing a change in speed of the vehicle in the third lane based on the desired upper speed bound for the third lane.
‘278’s claim 16 is the same as the present application’s claim 16 except that in the present application the upper speed bound is generated by a machine learning model. It would be obvious to utilize a machine learning model to make the system more robust. It then follows that this application’s claim 16 would be broader, and with the obviousness statement to include classifying the object in ‘278, this would be obviousness double patenting.
While application ‘294 does not cite the limitations of claims 3, 5, 13, 15, 18, and 20, prior art Beaurepaire teaches the limitations of claims 3, 5, 13, 15, 18, and 20. ([0060] discusses the training data including regional settings such as urban or rural settings and [0044] discusses the training data including class of vehicle including trucks and sedans). 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.
While application ‘294 does not cite the limitations of claim 7, prior art Buehler teaches the limitations of claims 7. ([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). Donderici teaches 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 system of Tao with the safe speed ranges of Donderici as Donderici teaches that this ensures safe and efficient operation of the autonomous vehicle [0020].
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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Jean (US 20240253624) teaches determining speed limits for lanes based on visual cues; Abad (US 20240017726) teaches adjusting a speed limit based on a slow lead car; and Matus (US 20190005812) teaches training a machine learning model with traffic characteristics.
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/D.M.J./ Examiner, Art Unit 3657 /ABBY LIN/Supervisory Patent Examiner, Art Unit 3657