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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07-22-2025 has been entered.
Response to Amendment
Pending claims: 1-5, 7-14, 16, 18-20, 22-24
Independent claims: 1, 14, 22
Amended claims: 1, 5, 11, 14, 16, and 22-24
No new claims have been introduced.
All canceled claims were previously cancelled.
The official correspondence below is a first action non-final after a request for continued examination.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5, 7-14, 16, 18-20, 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Buchholz (US 20160210852 A1) in view of Littlejohn (US 20220092971 A1) and in further view of Denninger (US 20230111908 A1).
REGARDING CLAIM 1, Buchholz discloses, one or more communication sensors (Buchholz: [0011] a cooperative vehicle is outfitted with surround sensors, advantageously with a camera, a front radar and/or a tail radar) communicatively coupled to an ad-hoc network (Buchholz: [0027] Vehicle 11, as well as vehicles 12, 15 and 18, are configured as cooperative vehicles. This means that they can take part in a method for the predicting of congestion parameters (examiner: when necessary or needed; ad-hoc)) formed by a plurality of communicatively connected vehicles (Buchholz: [0027] Vehicle 11, as well as vehicles 12, 15 and 18, are configured as cooperative vehicles. This means that they can take part in a method for the predicting of congestion parameters (examiner: when necessary or needed; ad-hoc)) and receiving vehicle data (Buchholz: [0014] For the prediction of at least one congestion parameter, a value [is determined?] by cooperative vehicles, also known as participating vehicles, for the traffic volume or the traffic density by means of weighted parameters, for example, the vehicle's own speed, the number of vehicles which can be detected with surround sensors, the speed of these vehicles and distances from these vehicles, the number of cooperative vehicles, also known as car2x-capable vehicles, in a given area; [0016] received traffic density values of the individual cooperative vehicles an approximation function), sensor data (Buchholz: [0014] the number of vehicles which can be detected with surround sensors; [0027] These vehicles 11, 12, 15, 18 are each outfitted with at least one detection unit 41-44 for the detecting of the traffic density), and roadway segment-wide traffic congestion predictions (Buchholz: [0034] the evaluation unit 60 can provide by a transmission unit 63 one or more congestion parameters to the cooperative vehicles 11, 12, 15, 18; [0013] a congestion prediction can be gathered from other vehicles and evaluated in one's own vehicle; [0014]) for a roadway segment (Buchholz: [0016] The central unit ascertains from the received traffic density values of the individual cooperative vehicles an approximation function. This approximation function shows the traffic volume over the stretch of road. Based on a digital road map, parameters can be used to correct a congestion prediction. One can further take account of information from on ramps and off ramps, such as highway intersections. The individual routes, i.e., the on ramps and off ramps, take account of the direction of the traffic flow and can be weighted with probabilities; [0018] ascertains the course of the traffic volume from one's own current position until the congestion end) generated by the plurality of communicatively connected vehicles via the ad-hoc network (Buchholz: [0014] For the prediction of at least one congestion parameter, a value [is determined?] by cooperative vehicles … The more cooperative vehicles taking part in a prediction of a congestion parameter, the more accurate the prediction can be); and a controller configured for: determining traffic density values at multiple individual locations on the roadway segment (Buchholz: [0014] ... a value [is determined?] by cooperative vehicles ...; [0016] The central unit ascertains from the received traffic density values of the individual cooperative vehicles an approximation function. This approximation function shows the traffic volume over the stretch of road. Based on a digital road map, parameters can be used to correct a congestion prediction. One can further take account of information from on ramps and off ramps, such as highway intersections. The individual routes, i.e., the on ramps and off ramps, take account of the direction of the traffic flow and can be weighted with probabilities; [0018] ascertains the course of the traffic volume from one's own current position until the congestion end) based on analyzing the received vehicle data (Buchholz: [0016] The central unit ascertains from the received traffic density values of the individual cooperative vehicles an approximation function. This approximation function shows the traffic volume over the stretch of road. Based on a digital road map, parameters can be used to correct a congestion prediction. One can further take account of information from on ramps and off ramps, such as highway intersections. The individual routes, i.e., the on ramps and off ramps, take account of the direction of the traffic flow and can be weighted with probabilities) and the sensor data (Buchholz: [0022] quality levels of the built-in sensor systems in the cooperative vehicles, a vehicle-specific quality factor can be relayed along with the traffic density value to a central unit; [0027] a sample method for predicting of congestion parameters is described from the viewpoint of vehicle 11. Vehicle 11, as well as vehicles 12, 15 and 18, are configured as cooperative vehicles. This means that they can take part in a method for the predicting of congestion parameters. These vehicles 11, 12, 15, 18 are each outfitted with at least one detection unit 41-44 for the detecting of the traffic density, such as a camera. Moreover, these vehicles 11, 12, 15, 18 are each outfitted with a transmission unit 51-54, which makes it possible to relay the ascertained traffic density and a position of the particular vehicle 11, 12, 15, 18 to a central evaluation unit 60 via a transmission link 61).
Buchholz does not explicitly disclose, generating an initial roadway segment-wide predicted traffic congestion condition for the roadway segment based on the determined traffic density values at the multiple locations; validating the initial roadway segment-wide predicted traffic congestion condition by determining the roadway segment-wide traffic congestion predictions generated by the plurality of communicatively connected vehicles and the initial roadway segment-wide predicted traffic congestion condition converge to an agreed consensus.
However, in the same field of endeavor, Littlejohn discloses, generating an initial roadway segment-wide predicted traffic congestion condition for the roadway segment (Littlejohn: [0016] The output of the prediction module, e.g., a traffic-flow prediction, can be automatically verified by a verification module without requiring manual collection of “ground truth” data for verification ... a verification module can obtain traffic related information, some or all of which may have been used by the prediction module to make the initial traffic-flow prediction disseminated to end users ... dissemination of the initial traffic-flow prediction to one or more end-users or distribution systems can be delayed until after the verification process has been completed. [0017] Regardless of whether the initial traffic-flow prediction is disseminated before or after verification, the verification module can implement the same process ... the verification module can select particular traffic probe devices, and gather information from the selected traffic probe devices for use in performing traffic data verification/validation. As used herein, a “traffic data verification” includes verification of the accuracy of fully processed traffic data, such as traffic-flow predictions, included in traffic messages; [0021] QKZ.sub.1 can be considered to be a percentage of a roadway of interest that is properly identified by the initial the traffic-flow prediction as experiencing congestion; [0031] a predictive analysis ... squares or other regression analysis, a lookup table generated based on past traffic patterns for a particular area, or the like, can be applied to the initial estimate, so that the predicted traffic flow at 7:45 am may be different from the traffic flow determined at 7:40 am ... the predictive analysis can be varied depending on a time difference between making the initial traffic flow analysis and the anticipated dissemination of traffic data) based on the determined traffic density values at the multiple locations (Littlejohn: [0043-0045]; [FIG. 3(A)(B)(C)(D)(E)(F)(G)]); validating the initial roadway segment-wide predicted traffic congestion condition (Littlejohn: [0016] The output of the prediction module, e.g., a traffic-flow prediction, can be automatically verified by a verification module without requiring manual collection of “ground truth” data for verification ... a verification module can obtain traffic related information, some or all of which may have been used by the prediction module to make the initial traffic-flow prediction disseminated to end users ... dissemination of the initial traffic-flow prediction to one or more end-users or distribution systems can be delayed until after the verification process has been completed. [0017] Regardless of whether the initial traffic-flow prediction is disseminated before or after verification, the verification module can implement the same process ... the verification module can select particular traffic probe devices, and gather information from the selected traffic probe devices for use in performing traffic data verification/validation. As used herein, a “traffic data verification” includes verification of the accuracy of fully processed traffic data, such as traffic-flow predictions, included in traffic messages; [0021] QKZ.sub.1 can be considered to be a percentage of a roadway of interest that is properly identified by the initial the traffic-flow prediction as experiencing congestion; [0031] a predictive analysis ... squares or other regression analysis, a lookup table generated based on past traffic patterns for a particular area, or the like, can be applied to the initial estimate, so that the predicted traffic flow at 7:45 am may be different from the traffic flow determined at 7:40 am ... the predictive analysis can be varied depending on a time difference between making the initial traffic flow analysis and the anticipated dissemination of traffic data; [0032] Data validation module 115 can be used to validate the traffic-flow prediction generated by prediction module 112, or to validate a traffic-flow prediction received from one of the dataset sources. In at least some embodiments, data validation module 115 obtains traffic data from first dataset source 103, second dataset source 105, or N.sup.th dataset source 107, and processes the received traffic data to determine a “ground truth” traffic-flow estimate for a particular roadway at a particular time. The ground truth traffic flow estimate can be compared to the traffic-flow prediction generated by prediction module 112, to determine various quality measures. In at least one embodiment, the quality measures determined by data validation module 115 include QK.sub.1 and QK.sub.2) by determining the roadway segment-wide traffic congestion predictions generated by the plurality of communicatively connected vehicles and the initial roadway segment-wide predicted traffic congestion condition converge to an agreed consensus (Littlejohn: [0031] a predictive analysis ... squares or other regression analysis, a lookup table generated based on past traffic patterns for a particular area, or the like, can be applied to the initial estimate, so that the predicted traffic flow at 7:45 am may be different from the traffic flow determined at 7:40 am ... the predictive analysis can be varied depending on a time difference between making the initial traffic flow analysis and the anticipated dissemination of traffic data ... [0032] Data validation module 115 can be used to validate the traffic-flow prediction generated by prediction module 112, or to validate a traffic-flow prediction received from one of the dataset sources. In at least some embodiments, data validation module 115 obtains traffic data from first dataset source 103, second dataset source 105, or N.sup.th dataset source 107, and processes the received traffic data to determine a “ground truth” traffic-flow estimate for a particular roadway at a particular time. The ground truth traffic flow estimate can be compared to the traffic-flow prediction generated by prediction module 112, to determine various quality measures. In at least one embodiment, the quality measures determined by data validation module 115 include QK.sub.1 and QK.sub.2; [0034] data validation module 115 can be used to validate a traffic-flow prediction obtained from one of the dataset sources, rather than verifying the output of prediction module 112. For example, if N.sup.th dataset source 107 includes fully processed traffic data, for example a traffic prediction related to a particular roadway on a particular date, the data validation module 115 can generate quality metrics for some or all elements of the N.sup.th dataset source; [0036] the verification techniques described herein can be applied to the third party traffic-flow predictions, allowing the quality of datasets obtained from outside sources to be compared against other outside sources, and/or against traffic-flow predictions generated by prediction module 112. In some such embodiments, traffic-flow predictions generated by prediction module 112, and meeting a particular quality threshold, can be treated as the “actual estimated traffic-flow” for validation of third party traffic data. In other embodiments, however, the third party traffic-flow predictions are treated in the same manner as predictions generated by prediction module; [0046-0055]; (see at least ([0011] one or more traffic probe devices to determine whether a traffic probe device associated with a vehicle is to be used, or excluded from use, in determining traffic data quality measures; [0029] For example, N.sup.th dataset source 107 can include navigation devices, smart phones, tablets, computers, or various other communication-capable devices carried by or included in vehicles moving along various roadways) for collecting data from vehicles)), for the benefit of disseminating accurate traffic messages such as estimated travel times, delays, traffic flow, detours, and the like, to end users.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Buchholz to include confirming initial models taught by Littlejohn. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to disseminate accurate traffic messages such as estimated travel times, delays, traffic flow, detours, and the like, to end users.
Buchholz, as modified, discloses alternate routing based upon prediction based upon aggregated data ([0034], [0036]). Buchholz does not explicitly “executing actions to at least partially autonomously maneuver the vehicle based on the validated traffic congestion prediction”.
However, in the same field of endeavor Denninger discloses, data sharing via V2I, V2V, I2V (i.e. aggregated data sent to other vehicles and a server [FIG. 2]), predicting route congestion, and autonomously controlling a vehicle to re-route (Denninger: [0010]; [0024]; [0039]), for the benefit of increase efficiency by autonomously avoiding the problem area and saving overall drive time.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by a modified Buchholz to include autonomous maneuvering taught by Denninger. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to increase efficiency by autonomously avoiding the problem area and saving overall drive time.
REGARDING CLAIM 2, Buchholz, as modified, remains as applied above to claim 1, and further, Buchholz also discloses, the vehicle comprises a plurality of sensors generating the sensor data (Buchholz: [0010]); [0011]).
REGARDING CLAIM 3, Buchholz, as modified, remains as applied above to claim 2, and further, Buchholz also discloses, the received vehicle data is communicated by the plurality of communicatively connected vehicles (Buchholz: [0027]).
REGARDING CLAIM 4, Buchholz, as modified, remains as applied above to claim 3, and further, Buchholz also discloses, the plurality of communicatively connected vehicles comprise one or more vehicle sensors generating the vehicle data (Buchholz: [0022]).
REGARDING CLAIM 5, Buchholz, as modified, remains as applied above. Further, Littlejohn also discloses, determining the roadway segment-wide traffic congestion predictions generated by the plurality of communicatively connected vehicles and the initial roadway segment-wide predicted traffic congestion condition converge to the agreed consensus indicates a presence of traffic congestion on the roadway segment (Littlejohn: see at least [0016-0017], [0021], [0031] for initial, and [0031-0036] for combining information for verification/validation).
REGARDING CLAIM 7, Buchholz, as modified, remains as applied above to claim 3, and further, Buchholz also discloses, the controller generates a notification in response to the validated traffic congestion prediction (Buchholz: [0030]).
REGARDING CLAIM 8, Buchholz, as modified, remains as applied above to claim 3, and further, Buchholz also discloses, the one or more communication sensors receive the vehicle data via an ad-hoc network between the plurality of communicatively connected vehicles (Buchholz: [0027]).
REGARDING CLAIM 9, Buchholz, as modified, remains as applied above to claim 8, and further, Buchholz also discloses, the ad-hoc network comprises vehicle-to-vehicle (V2V) communication between the plurality of communicatively connected vehicles (Buchholz: [0022]; [0014]).
REGARDING CLAIM 10, Buchholz, as modified, remains as applied above to claim 9, and further, Buchholz also discloses, the vehicle data comprises at least one of: motion data, direction data, road data, and lane data (Buchholz: [0036]).
Buchholz does not explicitly recite the terminology "motion data, direction data, road data, and lane data". However, the examiner respectfully submits, the central unit providing information pertaining to congestion start and end and an alert that the vehicle is approaching a start or end in 11 second suggests or implies "the vehicle data comprises at least one of: motion data, direction data, road data, and lane data", because of information sent to a particular vehicle.
REGARDING CLAIM 11, Buchholz, as modified, remains as applied above to claim 3. Further, Littlejohn also discloses, the initial roadway segment-wide predicted traffic congestion condition is communicated to the plurality of communicatively connected vehicles (Littlejohn: [0016] The output of the prediction module, e.g., a traffic-flow prediction, can be automatically verified by a verification module without requiring manual collection of “ground truth” data for verification ... a verification module can obtain traffic related information, some or all of which may have been used by the prediction module to make the initial traffic-flow prediction disseminated to end users ... dissemination of the initial traffic-flow prediction to one or more end-users or distribution systems can be delayed until after the verification process has been completed. [0017] Regardless of whether the initial traffic-flow prediction is disseminated before or after verification, the verification module can implement the same process ... the verification module can select particular traffic probe devices, and gather information from the selected traffic probe devices for use in performing traffic data verification/validation. As used herein, a “traffic data verification” includes verification of the accuracy of fully processed traffic data, such as traffic-flow predictions, included in traffic messages; [0021] QKZ.sub.1 can be considered to be a percentage of a roadway of interest that is properly identified by the initial the traffic-flow prediction as experiencing congestion; [0031] a predictive analysis ... squares or other regression analysis, a lookup table generated based on past traffic patterns for a particular area, or the like, can be applied to the initial estimate, so that the predicted traffic flow at 7:45 am may be different from the traffic flow determined at 7:40 am ... the predictive analysis can be varied depending on a time difference between making the initial traffic flow analysis and the anticipated dissemination of traffic data).
REGARDING CLAIM 12, Buchholz, as modified, remains as applied above to claim 7, and further, Buchholz also discloses, the notification is communicated to the plurality of communicatively connected vehicles as a warning of detected traffic congestion (Buchholz: [0030]).
In this case, a "warning" is interpreted as an indication of a possible situation or notice of something.
REGARDING CLAIM 13, Buchholz, as modified, remains as applied above to claim 5, and further, Denninger also discloses, the controller executes actions to fully autonomously maneuver the vehicle based on the validated traffic congestion prediction (Denninger: [0010]; [0024]; [0039]).
REGARDING CLAIM 14, Buchholz, receiving vehicle data (Buchholz: [0014]; [0016]), sensor data (Buchholz: [0014] the number of vehicles which can be detected with surround sensors; [0027]), and road segment-wide traffic congestion predictions (Buchholz: [0034]; [0013-0014]) for a roadway segment (Buchholz: [0016-0018] ascertains the course of the traffic volume from one's own current position until the congestion end) generated by a plurality of communicatively connected vehicles, (Buchholz: [0014-0016]), wherein the plurality of communicatively connected vehicles and the vehicle are connected via an ad-hoc network (Buchholz: [0014-0016], [0027]); determining traffic density values at multiple locations on the roadway segment (Buchholz: [0014]; [0016]; [0018]) based on analyzing the received vehicle data (Buchholz: [0016]) and the sensor data (Buchholz: [0022]; [0027]).
Buchholz does not explicitly disclose, one or more processors; and non-transitory computer readable medium comprising instructions, validating the initial road segment-wide predicted traffic congestion condition by determining the road segment-wide traffic congestion predictions generated by the plurality of communicatively connected vehicles and the initial road segment- wide predicted traffic congestion condition converge to an agreed consensus.
However, in the same field of endeavor, Littlejohn discloses, one or more processors (Littlejohn: [0076]); and non-transitory computer readable medium (Littlejohn: [0076]) comprising instructions (Littlejohn: [0076]), validating the initial road segment-wide predicted traffic congestion condition (Littlejohn: [0016-0017]; [0021]; [0031-0032]) by determining the road segment-wide traffic congestion predictions generated by the plurality of communicatively connected vehicles and the initial road segment- wide predicted traffic congestion condition converge to an agreed consensus (Littlejohn: [0031-0032]; [0034]; [0036]; [0046-0055]; (see at least ([0011] one or more traffic probe devices to determine whether a traffic probe device associated with a vehicle is to be used, or excluded from use, in determining traffic data quality measures; [0029] For example, N.sup.th dataset source 107 can include navigation devices, smart phones, tablets, computers, or various other communication-capable devices carried by or included in vehicles moving along various roadways) for collecting data from vehicles)), for the benefit of disseminating accurate traffic messages such as estimated travel times, delays, traffic flow, detours, and the like, to end users.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Buchholz to include confirming initial models taught by Littlejohn. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to disseminate accurate traffic messages such as estimated travel times, delays, traffic flow, detours, and the like, to end users.
Buchholz, as modified, discloses alternate routing based upon prediction based upon aggregated data (Buchholz: [0034], [0036]), and confirming initial evaluations (Littlejohn: [0016-0017], [0021]). Buchholz, as modified, does not explicitly disclose “applying a learning-based model to the received vehicle data and sensor data to generate an initial predicted traffic congestion condition; and executing actions to at least partially autonomously maneuver the vehicle based on the validated traffic congestion prediction”.
However, in the same field of endeavor Denninger discloses, applying a learning-based model to the determined traffic density values (Denninger: [ABS]; [0010]) to generate an initial roadway segment-wide predicted traffic congestion condition (Denninger: [0010]); and executing actions to at least partially autonomously maneuver the vehicle based on the validated road segment-wide traffic congestion prediction (Denninger: [0010]; [0024]; [0039]), for the benefit of increase efficiency by autonomously avoiding the problem area and saving overall drive time.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by a modified Buchholz to include autonomous maneuvering taught by Denninger. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to increase efficiency by autonomously avoiding the problem area and saving overall drive time.
REGARDING CLAIM 16, Buchholz, as modified, remains as applied above to claim 15, and further, Littlejohn also discloses, determining the roadway segment-wide traffic congestion predictions generated by the plurality of communicatively connected vehicles and the initial roadway segment-wide predicted traffic congestion converge to the agreed consensus indicates a presence of traffic congestion on the roadway segment (Littlejohn: see at least [0016 - 0017], [0021], [0031] for initial, and [0031-0036] for combining information for verification/ validation).
REGARDING CLAIM 18, Buchholz, as modified, remains as applied above to claim 14, and further, Buchholz also discloses, the ad-hoc network comprises vehicle-to-vehicle (V2V) communication (Buchholz: [0022]; [0014]).
REGARDING CLAIM 19, Buchholz, as modified, remains as applied above to claim 14, and further, Buchholz also discloses, generating a notification in response to the validated traffic congestion prediction (Buchholz: [0030]).
REGARDING CLAIM 20, Buchholz, as modified, remains as applied above to claim 14, and further, Denninger also discloses, executing actions to fully autonomously maneuver the vehicle based on the validated traffic congestion prediction (Denninger: [0010]; [0024]; [0039]).
REGARDING CLAIM 22, Buchholz discloses, receiving vehicle data (Buchholz: [0014-0016]), sensor data (Buchholz: [0014]; [0027]), and roadway segment-wide traffic congestion predictions (Buchholz: [0034]; [0013-0014]) for a roadway segment (Buchholz: [0016-0018]) generated by a plurality of communicatively connected vehicles, wherein the plurality of communicatively connected vehicles and the vehicle are connected via an ad-hoc network (Buchholz: [0014]; [0016]); determining traffic density values at multiple locations on the roadway segment (Buchholz: [0014]; [0016]; [0018]) based on analyzing the received vehicle data (Buchholz: [0016]) and the sensor data (Buchholz: [0022]; [0027]).
Buchholz does not explicitly disclose, generating an initial roadway segment-wide predicted traffic congestion condition on the roadway segment based on the determined traffic density values at the multiple locations; validating the initial roadway segment-wide predicted traffic congestion condition by determining the roadway segment-wide traffic congestion predictions generated by the plurality of communicatively connected vehicles and the initial roadway segment-wide predicted traffic congestion condition converge to an agreed consensus.
However, in the same field of endeavor, Littlejohn discloses, generating an initial roadway segment-wide predicted traffic congestion condition on the roadway segment (Littlejohn: [0016-0017]; [0021]; [0031]) based on the determined traffic density values at the multiple locations (Littlejohn: [0043-0045]; [FIG. 3(A)(B)(C)(D)(E)(F)(G)]); validating the initial roadway segment-wide predicted traffic congestion condition (Littlejohn: [0016-0017]; [0021]; [0031]; [0032]) by determining the roadway segment-wide traffic congestion predictions generated by the plurality of communicatively connected vehicles and the initial roadway segment-wide predicted traffic congestion condition converge to an agreed consensus (Littlejohn: [0031-0034]; [0036]; [0046-0055]; (see at least ([0011] one or more traffic probe devices to determine whether a traffic probe device associated with a vehicle is to be used, or excluded from use, in determining traffic data quality measures; [0029] For example, N.sup.th dataset source 107 can include navigation devices, smart phones, tablets, computers, or various other communication-capable devices carried by or included in vehicles moving along various roadways) for collecting data from vehicles)), for the benefit of disseminating accurate traffic messages such as estimated travel times, delays, traffic flow, detours, and the like, to end users.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Buchholz to include confirming initial models taught by Littlejohn. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to disseminate accurate traffic messages such as estimated travel times, delays, traffic flow, detours, and the like, to end users.
Buchholz, as modified, discloses that which is claimed including alternate routing based upon prediction based upon aggregated data ([0034], [0036]). Buchholz does not explicitly “executing actions to at least partially autonomously maneuver the vehicle based on the validated traffic congestion prediction”.
However, in the same field of endeavor Denninger discloses, data sharing via V2I, V2V, I2V (i.e. aggregated data sent to other vehicles and a server [FIG. 2]), predicting route congestion, and autonomously controlling a vehicle to re-route (Denninger: [0010]; [0024]; [0039]), for the benefit of increase efficiency by autonomously avoiding the problem area and saving overall drive time.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by a modified Buchholz to include autonomous maneuvering taught by Denninger. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to increase efficiency by autonomously avoiding the problem area and saving overall drive time.
REGARDING CLAIM 23, Buchholz, as modified, remains as applied above to claim 22, and further, Littlejohn also discloses, determining the roadway segment-wide traffic congestion predictions generated by the plurality of communicatively connected vehicles and the initial roadway segment-wide predicted traffic congestion condition converge to the agreed consensus indicates a presence of traffic congestion on the roadway (Littlejohn: see at least [0016-0017], [0021], [0031] for initial, and [0031-0036] for combining information for verification/ validation).
REGARDING CLAIM 24, Buchholz, as modified, remains as applied above to claim 22, and further, Littlejohn also discloses, communicating the initial roadway segment-wide predicted traffic congestion condition to the plurality of communicatively connected vehicles (Littlejohn: [0016-0017]; [0021]; [0031]).
Response to Arguments
Applicant’s arguments, beginning on page seven, filed 07-22-2025, with respect to the §112(a) rejection of record, have been fully considered and are persuasive. The §112(a) rejection of record has been withdrawn.
Applicant’s arguments with respect to the rejection of the independent claim(s) under 35 USC §103, obviousness, have been considered but are moot because the new ground of rejection does not rely on the same reference combination applied in the prior rejection of record for matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Tengler (US 20070083296 A1)
Ramalho de Oliveira (US 20180376305 A1)
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/A.S./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663