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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on March 22, 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
This Office Action is in response to the claims filed on December 9, 2025.
Claims 1, 3-16, and 18-22 have been presented for examination.
Claims 1, 3-16, and 18-22 are currently rejected.
Claims 1, 3-5, 12-13, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Downs et al. (U.S. Patent Publication Number 2007/0208492) in view of Beaurepaire et al. (U.S. Patent Publication Number 2023/0039738).
Claims 6, 9-11, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Downs et al. (U.S. Patent Publication Number 2007/0208492) in view of Beaurepaire et al. (U.S. Patent Publication Number 2023/0039738), further in view of Glasgow et al. (U.S. Patent Publication Number 2017/0186315).
Claims 7-8 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Downs et al. (U.S. Patent Publication Number 2007/0208492) in view of Beaurepaire et al. (U.S. Patent Publication Number 2023/0039738), further in view of Nayak et al. (U.S. Patent Publication Number 2024/0144812).
Response to Arguments
35 U.S.C. 112
Applicant's arguments filed on December 9, 2025 have been fully considered but they are not persuasive. The Applicant argues that one having ordinary skill in the art would understand the meaning of a “highest individual contribution” and that the rejection should be withdrawn because the claim has been amended to define the “highest individual contribution” as being “predicted to have the greatest impact on the future traffic congestion levels.”
The Examiner has considered the arguments presented and respectfully disagrees. First, the recited “the greatest impact” lacks antecedent basis because such an “impact” is not previously described or defined. Second, a “greatest impact” as recited by the claim is not provided with measurable parameters. For these reasons, the Examiner maintains the 35 U.S.C. 112 rejection.
35 U.S.C. 101
Applicant's arguments filed on December 9, 2025 have been fully considered but they are not persuasive. The Applicant’s arguments appear to be primarily directed to the amended language, specifically the incorporation of “a supervised machine learning model” to perform the analysis of a driver composition. The Applicant concludes that for this reason, the 35 U.S.C. 101 rejection of claims 1-20 should be withdrawn.
The Examiner has considered the arguments presented and respectfully disagrees.
The limitation recites “predicting ... with a supervised machine learning model.” First, the claimed “supervised machine learning model” is recited at a high level of generality as merely a means to “apply” the otherwise mental process steps into a computational environment to automate the predicting and identifying steps. Therefore, even in combination, the “supervised machine learning model” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Second, the mere recitation of “with a supervised machine learning model” does not amount to significantly more than the judicial exception because the additional element does not add an inventive concept to the claim, as demonstrated in view of the prior art Downs et al. (U.S. Patent Publication Number 2007/0208492) and Beaurepaire et al. (U.S. Patent Publication Number 2023/0039738).
For these reasons, the Examiner maintains the 35 U.S.C. 101 rejection. The rejection has been updated to reflect the amended claim language.
35 U.S.C. 103
The Applicant’s arguments, see Applicant Remarks filed on December 9, 2025, appear to be primarily directed to the amended claim language. The Applicant’s arguments with respect to claim(s) 1, 3-16, and 18-20 have been considered but are moot because amendments shift the scope of claims and necessitate a new ground of rejection, which is made in view of Beaurepaire et al. (U.S. Patent Publication Number 2023/0039738).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-16, and 18-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The limitation “a highest individual contribution predicted to have the greatest impact on the future traffic congestion levels” in claims 1, 7-8, 16, and 20 contains relative terms (e.g., “highest” “greatest”) which render the claim indefinite. The term “highest individual contribution” and “greatest impact” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The standard for measuring an “individual contribution” and “greatest impact” are not defined. For example, under the broadest reasonable interpretation of the claim, the mere presence of a vehicle among traffic constitutes an individual contribution and may include a highest level of individual contribution to the congestion. Therefore, the characteristics quantifying a “highest individual contribution” and “greatest impact” are unclear.
Further, the limitation “the greatest impact” has insufficient antecedent basis. The limitation “the greatest impact” contains the first instance of the recitation of “impact,” therefore it is unclear what “the greatest impact” is in reference to, or how the impact is defined or measured.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-16, and 18-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Claim 1. A method of reducing traffic congestion for a road network, the method comprising:
predicting future traffic congestion levels for the road network based on historical traffic congestion levels for the road network;
predicting an individual contribution to the future traffic congestion levels for each of a plurality of drivers based on future routes corresponding to each of the plurality of drivers, wherein the future routes include at least one road segment through the road network and the individual contribution is predicted by analyzing a driver composition from historical driver route information for each of the plurality of drivers with a supervised machine learning model and the driver historical information includes at least one of a routine travel route or vehicle dynamic;
identifying at least one congested road segment based on the future traffic congestion levels for the road network;
identifying a set of the plurality of drivers with a highest individual contribution predicted to have the greatest impact on the future traffic congestion levels to the at least one congested road segment; and
providing the set of the plurality of drivers a first offer to avoid operating a vehicle on the road network.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. See MPEP 2106.03.
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claim recites the limitation of predicting future traffic congestion levels for the road network based on historical traffic congestion levels for the road network; predicting an individual contribution to the future traffic congestion levels for each of a plurality of drivers based on future routes corresponding to each of the plurality of drivers, wherein the future routes include at least one road segment through the road network and the individual contribution is predicted by analyzing a driver composition from historical driver route information for each of the plurality of drivers with a supervised machine learning model and the driver historical information includes at least one of a routine travel route or vehicle dynamic; identifying at least one congested road segment based on the future traffic congestion levels for the road network; identifying a set of the plurality of drivers with a highest individual contribution predicted to have the greatest impact on the future traffic congestion levels to the at least one congested road segment; and providing the set of the plurality of drivers a first offer to avoid operating a vehicle on the road network.
These limitations, as drafted, are a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind, but for the recitation of “with a supervised machine learning model”.
For example, but for the recitation of “with a supervised machine learning model”, the claim encompasses a person looking at data collected and forming a simple judgement. Specifically, the claim encompasses a driver anticipating traffic congestion on a road based on past experiences of traffic congestion in that area, and visually and mentally identifying that a vehicle is contributing to the congestion of the local road segment. The process further encompasses anticipating future traffic congestion based on known historical traffic congestion trends (e.g., “the roads in this city are congested during rush hour and will likely be congested during rush hour again”), and further anticipating through observation that at least two drivers of the road will contribute most to the traffic congestion (e.g., observing two vehicles attempting to merge, thereby causing congestion). The claim may further encompass extending verbal offers to the drivers to avoid operating the vehicle on the road.
The mere nominal recitation of “with a supervised machine learning model” does not take the claim limitations out of the mental process grouping.
Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional element of “with a supervised machine learning model”
The recitation of “with a supervised machine learning model” merely describes how to generally “apply” the otherwise mental judgements using a generic or general-purpose modeling environment (e.g., a computer). The “supervised machine learning model” is recited at a high level of generality and is merely automates the predicting steps.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving steps and the displaying step were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background recites that the sensors are all conventional sensors mounted on the vehicle, and the specification does not provide any indication that the vehicle controller is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer.
Thus, the claim is ineligible.
Claim 16
Claim 16. A non-transitory computer-readable medium embodying programmed instructions which, when executed by a processor, are operable for performing a method comprising:
predicting future traffic congestion levels for the road network based on historical traffic congestion levels for the road network;
predicting an individual contribution to the future traffic congestion levels for each of a plurality of drivers based on future routes corresponding to each of the plurality of drivers, wherein the future routes include at least one road segment through the road network and the individual contribution is predicted by analyzing a driver composition from historical driver route information for each of the plurality of drivers with a supervised machine learning model and the driver historical information includes at least one of a routine travel route or vehicle dynamic;
identifying at least one congested road segment based on the future traffic congestion levels for the road network;
identifying a set of the plurality of drivers with a highest individual contribution predicted to have the greatest impact on the future traffic congestion levels to the at least one congested road segment; and
providing the set of the plurality of drivers a first offer to avoid operating a vehicle on the road network.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. See MPEP 2106.03.
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claim recites the limitation of predicting future traffic congestion levels for the road network based on historical traffic congestion levels for the road network; predicting an individual contribution to the future traffic congestion levels for each of a plurality of drivers based on future routes corresponding to each of the plurality of drivers, wherein the future routes include at least one road segment through the road network and the individual contribution is predicted by analyzing a driver composition from historical driver route information for each of the plurality of drivers with a supervised machine learning model and the driver historical information includes at least one of a routine travel route or vehicle dynamic; identifying at least one congested road segment based on the future traffic congestion levels for the road network; identifying a set of the plurality of drivers with a highest individual contribution predicted to have the greatest impact on the future traffic congestion levels to the at least one congested road segment; and providing the set of the plurality of drivers a first offer to avoid operating a vehicle on the road network.
These limitations, as drafted, are a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind, but for the recitation of “with a supervised machine learning model”.
For example, but for the recitation of “with a supervised machine learning model”, the claim encompasses a person looking at data collected and forming a simple judgement. Specifically, the claim encompasses a driver anticipating traffic congestion on a road based on past experiences of traffic congestion in that area, and visually and mentally identifying that a vehicle is contributing to the congestion of the local road segment. The process further encompasses anticipating future traffic congestion based on known historical traffic congestion trends (e.g., “the roads in this city are congested during rush hour and will likely be congested during rush hour again”), and further anticipating through observation that at least two drivers of the road will contribute most to the traffic congestion (e.g., observing two vehicles attempting to merge, thereby causing congestion). The claim may further encompass extending verbal offers to the drivers to avoid operating the vehicle on the road.
The mere nominal recitation of “with a supervised machine learning model” does not take the claim limitations out of the mental process grouping.
Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional element of with a supervised machine learning model.
The recitation of “with a supervised machine learning model” merely describes how to generally “apply” the otherwise mental judgements using a generic or general-purpose modeling environment (e.g., a computer). The “supervised machine learning model” is recited at a high level of generality and is merely automates the predicting steps. Further, while the preamble includes a non-transitory computer-readable medium executed by a processor, these components are recited at a high level of generality and merely describes how to “apply” the judicial exception.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving steps and the displaying step were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background recites that the sensors are all conventional sensors mounted on the vehicle, and the specification does not provide any indication that the vehicle controller is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer.
Thus, the claim is ineligible.
Claim 20
Claim 20. A system for reducing traffic congestion on a road network, the method comprising:
a vehicle having a plurality of sensors;
a controller in communication with the plurality of sensors, the controller having a processor and tangible, non-transitory memory on which instructions are recorded, the controller being configured to:
predict future traffic congestion levels for the road network based on historical traffic congestion levels for the road network;
predict an individual contribution to the future traffic congestion levels for each of a plurality of drivers based on future routes corresponding to each of the plurality of drivers, wherein the future routes include at least one road segment through the road network and the individual contribution is predicted by analyzing a driver composition from historical driver route information for each of the plurality of drivers with a supervised machine learning model and the driver historical information includes at least one of a routine travel route or vehicle dynamic;
identify at least one congested road segment based on the future traffic congestion levels for the road network;
identify a set of the plurality of drivers with a highest individual contribution predicted to have the greatest impact on the future traffic congestion levels to the at least one congested road segment; and
provide the set of the plurality of drivers a first offer to avoid operating a vehicle on the road network.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. See MPEP 2106.03.
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claim recites the limitation of predict future traffic congestion levels for the road network based on historical traffic congestion levels for the road network; predict an individual contribution to the future traffic congestion levels for each of a plurality of drivers based on future routes corresponding to each of the plurality of drivers, wherein the future routes include at least one road segment through the road network and the individual contribution is predicted by analyzing a driver composition from historical driver route information for each of the plurality of drivers with a supervised machine learning model and the driver historical information includes at least one of a routine travel route or vehicle dynamic; identify at least one congested road segment based on the future traffic congestion levels for the road network; identify a set of the plurality of drivers with a highest individual contribution predicted to have the greatest impact on the future traffic congestion levels to the at least one congested road segment; and provide the set of the plurality of drivers a first offer to avoid operating a vehicle on the road network.
These limitations, as drafted, are a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind, but for the recitation of “with a supervised machine learning model”.
For example, but for the recitation of “with a supervised machine learning model”, the claim encompasses a person looking at data collected and forming a simple judgement. Specifically, the claim encompasses a driver anticipating traffic congestion on a road based on past experiences of traffic congestion in that area, and visually and mentally identifying that a vehicle is contributing to the congestion of the local road segment. The process further encompasses anticipating future traffic congestion based on known historical traffic congestion trends (e.g., “the roads in this city are congested during rush hour and will likely be congested during rush hour again”), and further anticipating through observation that at least two drivers of the road will contribute most to the traffic congestion (e.g., observing two vehicles attempting to merge, thereby causing congestion). The claim may further encompass extending verbal offers to the drivers to avoid operating the vehicle on the road.
The mere nominal recitation of “with a supervised machine learning model” does not take the claim limitations out of the mental process grouping.
Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The claim recites additional elements or steps of a vehicle having a plurality of sensors; a controller in communication with the plurality of sensors, the controller having a processor and tangible, non-transitory memory on which instructions are recorded, the controller being configured to: predict ... with a supervised machine learning model.
The recitation of “with a supervised machine learning model” merely describes how to generally “apply” the otherwise mental judgements using a generic or general-purpose modeling environment (e.g., a computer). The “supervised machine learning model” is recited at a high level of generality and is merely automates the predicting steps. Further, the recited “vehicle having a plurality of sensors” and “controller” are recited at a high level of generality and merely describe how to “apply” the otherwise mental judgements using a generic or general-purpose computer to merely automate the predicting and identifying steps.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving steps and the displaying step were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background recites that the sensors are all conventional sensors mounted on the vehicle, and the specification does not provide any indication that the vehicle controller is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer.
Thus, the claim is ineligible.
Dependent Claims
Dependent claims 3-15 and 18-22 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 3-15 and 18-22 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1, 16, and 20.
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.
Claims 1, 3-5, 12-13, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Downs et al. (U.S. Patent Publication Number 2007/0208492) in view of Beaurepaire et al. (U.S. Patent Publication Number 2023/0039738).
Regarding claim 1, Downs discloses a method of reducing traffic congestion for a road network, the method comprising:
predicting future traffic congestion levels for the road network based on historical traffic congestion levels for the road network; (Downs ¶ 13 discloses “one or more predictive Bayesian or other models are automatically created for use in generating the future traffic condition predictions for each geographic area of interest, such as based on observed historical traffic conditions for those geographic areas,” wherein the traffic condition includes “traffic congestion,” see at least ¶ 2)
predicting an individual contribution to the future traffic congestion levels for each of a plurality of drivers based on future routes corresponding to each of the plurality of drivers, (Downs ¶ 50 discloses “The vehicle-based clients/data sources 384 in this example may each be a computing system located within a vehicle that provides data [i.e., a contribution to the traffic congestion] to one or more of the predictive traffic information systems,” including providing “information related to current traffic conditions for use in traffic prediction,” the traffic conditions including “traffic congestion,” see ¶ 2. One having ordinary skill in the art would recognize that any vehicle in the traffic is an individual contributor to the congestion.)
identifying at least one congested road segment based on the future traffic congestion levels for the road network; (Downs ¶ 16 discloses “The roads and/or road segments for which future traffic condition predictions and/or forecasts are generated may also be selected [i.e., identified] in various manners”)
Downs does not expressly disclose:
wherein the future routes include at least one road segment through the road network and the individual contribution is predicted by analyzing a driver composition from historical driver route information for each of the plurality of drivers with a supervised machine learning model and the driver historical information includes at least one of a routine travel route or vehicle dynamics;
identifying a set of the plurality of drivers with a highest individual contribution predicted to have the greatest impact on the future traffic congestion levels to the at least one congested road segment; and
providing the set of the plurality of drivers a first offer to avoid operating a vehicle on the road network.
However, Beaurepaire discloses:
wherein the future routes include at least one road segment through the road network and the individual contribution is predicted by analyzing a driver composition from historical driver route information for each of the plurality of drivers with a supervised machine learning model and the driver historical information includes at least one of a routine travel route or vehicle dynamics; (Beaurepaire ¶ 66 discloses a traffic impact module 305 that uses “a machine learning model to predict the estimated impact of the vehicle on the traffic flow [i.e., predict the individual contribution], to compute the traffic impact index,” such that “the traffic impact module 305 can apply a weighted average algorithm to determine the overall traffic impact caused by a driver,” wherein the “estimated impact of the vehicle on the traffic flow can be based on determining a deviation of the traffic flow from a reference traffic state (e.g., based on historical traffic flow data) [i.e., driver historical information including vehicle dynamics],” see ¶ 63. Also see ¶ 33.)
identifying a set of the plurality of drivers with a highest individual contribution predicted to have the greatest impact on the future traffic congestion levels to the at least one congested road segment; and (Beaurepaire ¶ 57 discloses that “the system 100 can determine an overall traffic impact (e.g., disturbance) caused by a group of drivers (e.g., within a map tile), compute an average traffic impact index for the drivers within the map tile”)
providing the set of the plurality of drivers a first offer to avoid operating a vehicle on the road network. (Beaurepaire ¶ 32 discloses that the system 100 can “leverage vehicle sensor data to assess a traffic impact index of one or more concerning driving behaviors of one user (e.g., determining the overall traffic disturbance caused by the user), and recommend the user to take action(s) in order to adjust traffic impact index (e.g., to reduce a traffic disturbance index). For instance, the system 100 can recommend ... the user to pull over after a few meters after the exit ramp [i.e., avoid operating the vehicle on the road network]”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the neural network predictive models of Downs with using a supervised machine learning for analyzing a driver composition from historical driver route information for each of the plurality of drivers for identifying a set of the plurality of drivers with a highest individual contribution predicted to have the greatest impact on the future traffic congestion levels, as disclosed by Beaurepaire, with reasonable expectation of success, to reduce traffic disturbance (Beaurepaire ¶ 32), and to determine optimal action(s) to take for different concerning driving behavior(s) on different road links/lanes (Beaurepaire ¶ 53), rendering the limitation to be an obvious modification.
Regarding claim 2, Downs in combination with Beaurepaire discloses the method of claim 1, wherein:
the individual contribution to the future traffic congestion levels is determined based on historical driver route information. (Downs ¶ 13 discloses “one or more predictive Bayesian or other models are automatically created for use in generating the future traffic condition predictions for each geographic area of interest, such as based on observed historical traffic conditions for those geographic areas,” wherein the models are based on “individual data sources,” such as “individual users,” see ¶¶ 29 and 50)
Regarding claim 3, Downs in combination with Beaurepaire discloses the method of claim 1, wherein:
the historical driver route information includes the routine travel route, travel route timing, the vehicle dynamics, or parking information recorded for each of the plurality of drivers associated with a route segment that corresponds to the future routes. (Downs ¶ 88 discloses “the usual total travel times for a route in FIG. 7H may be determined in various ways in various embodiments, including based on historical averages, by reference to a predictive model that can be used to determine expected long-term traffic condition forecasts based on historical observations”)
Regarding claim 4, Downs in combination with Beaurepaire discloses the method of claim 3, wherein:
the route segment corresponds to a new route segment not found within the historical driver route information, (Downs ¶ 24 discloses that “the user could be directed toward a new optimal route of ABDF.” One having ordinary skill in the art would recognize that a “new” route is one that has not been found historically as according to the definition of “new” in Merriam-Webster of being different from one of the same category that has existed previously. See Merriam-Webster, “new.”)
the machine learning model analyzes the historical congestion data to generate a baseline driver model (Beaurepaire ¶ 42 discloses “the training data can include ground truth data taken from historical driving behavior data and/or historical traffic impact data” and estimating the impact of the vehicle on the traffic flow based on historical traffic flow data caused by one or more driving behaviors, see ¶ 63) that does not contribute to congestion and compares the driver composition from historical driver route information for each of the plurality of drivers to determine a distance that each of the plurality of drivers are from the baseline model and the highest individual contribution for the set of the plurality of drivers corresponds drivers with the greatest distance to the baseline driver model. (Beaurepaire ¶ 66 discloses that a “traffic impact module 305 can then train the machine learning model to automatically determine a traffic impact index (TII) level as high or low [i.e., a comparison] depending on the feature values of the items in the traffic impact index,” wherein the training data for the machine learning model includes “ground truth data taken from historical driving behavior and/or historical traffic impact data,” see ¶ 42, and the “traffic impact index can be an overall traffic impact index computed for the geographic boundary based on respective traffic impact indexes computed for the plurality of vehicles,” see ¶ 70. One having ordinary skill in the art would recognize that determining that a traffic index level is high or low indicates a distance from the ground truth or baseline.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the neural network predictive models of Downs with that analyzing the historical congestion data to generate a baseline driver model does not contribute to congestion and compares the driver composition from historical driver route information for each of the plurality of drivers, as disclosed by Beaurepaire, with reasonable expectation of success, to reduce traffic disturbance (Beaurepaire ¶ 32), and to determine optimal action(s) to take for different concerning driving behavior(s) on different road links/lanes (Beaurepaire ¶ 53), rendering the limitation to be an obvious modification.
Regarding claim 5, Downs in combination with Beaurepaire discloses the method of claim 4, including:
predicting driver route information for the new route segment based on the historical driver route information for other route segments in the road network. (Downs ¶ 54 discloses that “the Traffic Prediction Model Generator component utilizes historical observation case data to ... make predictions of future traffic flow on a particular road segment for a particular future time”)
Regarding claim 12, Downs in combination with Beaurepaire discloses the method of claim 1, wherein:
the future traffic congestion levels are defined in terms of a number of vehicles traveling per unit time over a predetermined road segment in the road network. (Downs ¶ 29 discloses that the traffic conditions on a particular road segment at a present time represent “the percentage of individual data sources (e.g., traffic sensors or other data sources) for that road segment [i.e., a number of vehicles traveling] that are reporting black (e.g., highly congested) traffic conditions at the time [i.e., per unit time] being represented”)
Regarding claim 13, Downs in combination with Beaurepaire discloses the method of claim 12, wherein:
the individual contribution to the future traffic congestion levels for each of a plurality of drivers is defined in terms of a number of vehicles traveling per unit time over a predetermined driver road segment in the road network. (Downs ¶ 29 discloses that the traffic conditions on a particular road segment at a present time represent “the percentage of individual data sources (e.g., traffic sensors or other data sources) for that road segment [i.e., a number of vehicles traveling] that are reporting black (e.g., highly congested) traffic conditions at the time [i.e., per unit time] being represented”)
Regarding claim 16, Downs in combination with Beaurepaire discloses the parallel limitations contained in parent claim 1 for the reasons discussed above. In addition, Downs further discloses a non-transitory computer-readable medium embodying programmed instructions which, when executed by a processor, are operable for performing the method of claim 1 (Downs in at least ¶ 45).
Regarding claim 18, Downs in combination with Beaurepaire discloses the parallel limitations contained in parent claim 3 for the reasons discussed above. In addition, Downs further discloses a computer-readable medium (Downs in at least ¶ 45).
Regarding claim 19, Downs in combination with Beaurepaire discloses the parallel limitations contained in parent claim 4 for the reasons discussed above. In addition, Downs further discloses a computer-readable medium (Downs in at least ¶ 45).
Regarding claim 20, Downs in combination with Beaurepaire discloses the parallel limitations contained in parent claim 1 for the reasons discussed above. In addition, Downs further discloses a vehicle having a plurality of sensors (Downs ¶ 50 discloses that “each vehicle may include a GPS” device and “other geo-location device capable of determining geographic location, speed, direction, [and] other data related to the vehicle’s travel.” One having ordinary skill in the art would recognize that a GPS and other geo-location devices constitute a plurality of sensors, see Innovative Solutions & Support, “GPS Sensor Unit”); a controller (Downs ¶ 50 discloses a computing system located within a vehicle) in communication with the plurality of sensors (Downs ¶ 50 discloses that the vehicles provide data to the predictive traffic information systems, the vehicles including a GPS and other geo-location devices for determining geographic location, speed, and direction data. Therefore, in order to provide the data to the predictive traffic information system, the computing system first receives geographic location data, thereby communicating with the sensors), the controller having a processor and tangible, non-transitory memory on which instructions are recorded (Downs in at least ¶ 45).
Claims 6, 9-11, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Downs et al. (U.S. Patent Publication Number 2007/0208492) in view of Beaurepaire et al. (U.S. Patent Publication Number 2023/0039738), further in view of Glasgow et al. (U.S. Patent Publication Number 2017/0186315).
Regarding claim 6, Downs in combination with Beaurepaire does not expressly disclose the method of claim 1, wherein:
the first offer includes at least one of monetary compensation or an alternative mode of transportation.
However, Glasgow discloses:
the first offer includes at least one of monetary compensation or an alternative mode of transportation. (Glasgow ¶ 26 discloses that a “detour suggestion may further include a monetary incentive such as a coupon ... corresponding to the alternative destination,” also see ¶ 44 “Detour suggestions provided by the re-routing module 204 may further include one or more incentives (e.g., monetary incentives) to encourage vehicle occupants to accept detour suggestions and route their vehicles accordingly”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the offer of Downs, of the combination of Downs and Beaurepaire, to include monetary compensation, as disclosed by Glasgow, with reasonable expectation of success, to aid vehicles and their occupants in avoiding heavy traffic (Glasgow ¶ 3), rendering the modification to be obvious.
Regarding claim 9, Downs in combination with Beaurepairedoes not expressly disclose the method of claim 1, including:
determining a first set of the plurality of drivers that accepted the first offer and updating the future traffic congestion levels by removing the individual contribution to the future traffic congestion levels for the first set of the plurality of drivers to determine updated future traffic congestion levels.
However, Glasgow dislcoses:
determining a first set of the plurality of drivers that accepted the first offer and updating the future traffic congestion levels by removing the individual contribution to the future traffic congestion levels for the first set of the plurality of drivers to determine updated future traffic congestion levels. (Glasgow ¶ 84 discloses that “the detour suggestion is provided to remove,” thereby updating, “the vehicle from its current route.” One having ordinary skill in the art would recognize that the users transmit data for the network in a data exchange, and that by removing the vehicle from the current route, the individual’s contribution would be removed for the current route. Also see ¶ 44, “reduces the aggregated amount of value in the participation pool by a participatory amount.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the offer of Downs, of the combination of Downs and Beaurepaire, with additionally removing the contribution to the future traffic congestion levels for the first set of the plurality of drivers to determine updated future traffic congestion levels, as disclosed by Glasgow, with reasonable expectation of success, to reduce the aggregated amount of value in the participation pool by a participatory amount (Glasgow ¶ 44), which one having ordinary skill in the art would recognize would prevent overutilization of computational resources (Glasgow ¶ 3 and MPEP 2143.01(G)), rendering the modification to be obvious.
Regarding claim 10, Downs in combination with Beaurepaire does not expressly disclose the method of claim 9, including:
identifying a remaining set of the plurality of drivers that did not accept the first offer having the highest individual contribution to the updated future traffic congestion levels.
However, Glasgow dislcoses:
identifying a remaining set of the plurality of drivers that did not accept the first offer having the highest individual contribution to the updated future traffic congestion levels. (Glasgow ¶ 127 discloses “the incentive module 220 updates the user account record of a vehicle occupant to reflect an increase in account balance based on participation in the rerouting service,” but “If the user selects the “DECLINE” button, no further action is taken and the vehicle continues along the current route 1402.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the offer of Downs, of the combination of Downs and Beaurepaire, with identifying a remaining set of the plurality of drivers that did not accept the first offer having the highest individual contribution to the updated future traffic congestion levels, as disclosed by Glasgow, with reasonable expectation of success, to prevent inefficient load balancing of traffic, and thus, additional delays for occupants in vehicles trying to reach their intended destinations (Glasgow ¶ 2 and MPEP 2143.01(G)), rendering the modification to be obvious.
Regarding claim 11, Downs in combination with Beaurepaire does not expressly disclose the method of claim 10, including:
providing the remaining set of the plurality of drivers a second offer if the corresponding future travel routes correspond to at least one congested road segment in the updated future traffic congestion levels.
However, Glasgow dislcoses:
providing the remaining set of the plurality of drivers a second offer if the corresponding future travel routes correspond to at least one congested road segment in the updated future traffic congestion levels. (Glasgow ¶ 44 discloses “one or more incentives” which one having ordinary skill in the art would recognize includes at least second incentive. and ¶ 127 discloses that the user may decline to travel on the alternative route.)
The Examiner notes that this limitation contains a contingent limitation. Under the broadest reasonable interpretation, a method (or process) claim having contingent limitations (e.g., “if”) requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. If the claimed invention may be practiced without the condition happening, then the contingent step is not required by the broadest reasonable interpretation of the claim. Ex parte Schulhauser, 2013-007847 (PTAB 2016) (precedential) where the board held that when method steps are to be carried out only upon the occurrence of a condition precedent, the broadest reasonable interpretation holds that those steps are not required to be performed. (id. at *7). See, e.g., Ex parte Sheinfeld Appeal No. 2018-007091 (PTAB 2019) at *13; Ex Parte Vdovjak 2018-007087 (PTAB 2019) at 18; Ex parte Ionescu 2018-002662 (PTAB 2018) at *4; Ex parte Shier 2017-011168 (PTAB 2019) at *23; and Ex parte Blight 2017-006004 (PTAB 2018) at *12 (supporting the interpretation that “upon” limitations are conditional).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the offer of Downs, of the combination of Downs and Beaurepaire, to include a second offer, as disclosed by Glasgow, with reasonable expectation of success, to aid vehicles and their occupants in avoiding heavy traffic (Glasgow ¶ 3), rendering the modification to be obvious.
Regarding claim 14, Downs in combination with Beaurepaire does not expressly disclose the method of claim 1, wherein:
the first offer is determined based on a driver utility of travel for each of the plurality of drivers traveling along the at least one congested road segment.
However, Glasgow discloses:
the first offer is determined based on a driver utility of travel for each of the plurality of drivers traveling along the at least one congested road segment. (Glasgow ¶ 46 discloses “The tributary amount may be a fixed amount for all participating users of the navigation system 100 or a variable amount determined by the incentive module 220 based on a number of vehicles traveling along the primary route that have occupants that are users of the navigation system 100. For example, the incentive module 220 may determine the tributary amount by dividing the participatory amount by the number of vehicles traveling along the primary route that have occupants that are users of the navigation system 100.” Also see ¶ 47.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the offer of Downs, of the combination of Downs and Beaurepaire, with the first offer being determined based on a driver utility of travel for each of the plurality of drivers traveling along the at least one congested road segment, as disclosed by Glasgow, with reasonable expectation of success, to prevent inefficient load balancing of traffic, and thus, additional delays for occupants in vehicles trying to reach their intended destinations (Glasgow ¶ 2 and MPEP 2143.01(G)), rendering the modification to be obvious.
Regarding claim 15, Downs in combination with Beaurepaire does not expressly disclose the method of claim 14, wherein:
the first offer exceeds the driver utility of travel for each of the plurality of drivers traveling along the at least one congested road segment.
However, Glasgow discloses:
the first offer exceeds the driver utility of travel for each of the plurality of drivers traveling along the at least one congested road segment. (Glasgow in ¶¶ 127 and 130 disclose that the driver may accept travel on an alternative route, wherein the incentives for taking the alternate route are “provided to vehicle occupants,” see ¶ 35. One having ordinary skill in the art would recognize that “vehicle occupants” encompasses each of the plurality of drivers. One having ordinary skill in the art would recognize that the driver accepting the alternate route indicates that the offer exceeds a driver utility of travel, wherein the driver utility of travel is defined in ¶ 52 of the instant specification as quantifying a level of utility a driver places on operating the vehicle along the travel route.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the revised route of Downs, of the combination of Downs and Beaurepaire, with an offer exceeding the driver utility of travel for each of the plurality of drivers traveling along the at least one congested road segment, as disclosed by Glasgow, with reasonable expectation of success, to select an alternative route in accordance with a user preference (Glasgow ¶ 42), rendering the modification to be obvious.
Claims 7-8 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Downs et al. (U.S. Patent Publication Number 2007/0208492) in view of Beaurepaire et al. (U.S. Patent Publication Number 2023/0039738), further in view of Nayak et al. (U.S. Patent Publication Number 2024/0144812).
Regarding claim 7, Downs in combination with Beaurepaire does not expressly disclose the method of claim 1, wherein:
identifying the set of the plurality of drivers with the highest individual contribution to the at least one congested road segment includes ranking the plurality of drivers by their individual contribution and providing at least one having a highest ranking the first offer.
However, Nayak discloses:
identifying the plurality of drivers with the highest individual contribution to the at least one congested road segment includes ranking the plurality of drivers by their individual contribution and providing at least one having a highest ranking the first offer. (Nayak ¶ 55 discloses selecting, thereby identifying, one or more vehicles 107, wherein the selection may be based on “a proximity of the one or more vehicles 107 with the location of the traffic incident,” see ¶ 53, such that “the one or more vehicles 107 are selected to maximize the initial confidence score associated with the detected traffic incident, see ¶ 87. Also see ¶ 101.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the identification of contributing drivers of Downs, of the combination of Downs and Beaurepaire, with ranking the plurality of drivers by their individual contribution and providing at least one having a highest ranking the first offer, as disclosed by Nayak, with reasonable expectation of success, to facilitate the verification of the traffic incident in an improved manner (Nayak ¶ 48), rendering the modification to be obvious.
Regarding claim 8, Downs in combination with Beaurepaire does not expressly disclose the method of claim 7, wherein:
the first offer is provided to the plurality of drivers having a highest ranking having corresponding future routes through the road network having a highest level of congestion as determined by the future traffic congestion levels.
However, Nayak dislcoses:
the first offer is provided to the plurality of drivers having a highest ranking having corresponding future routes through the road network having a highest level of congestion as determined by the future traffic congestion levels. (Nayak ¶ 55 discloses “The vehicle reroute module 201d may be configured to reroute the selected [i.e., identified] one or more vehicles 107,” wherein the selection may be based on “a proximity of the one or more vehicles 107 with the location of the traffic incident,” see ¶ 53, such that “the one or more vehicles 107 are selected to maximize the initial confidence score associated with the detected traffic incident, see ¶ 87. Also see ¶ 101.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the identification of contributing drivers of Downs, of the combination of Downs and Beaurepaire, with ranking the plurality of drivers such that drivers having a highest ranking having corresponding future routes through the road network having a highest level of congestion as determined by the future traffic congestion levels, as disclosed by Nayak, with reasonable expectation of success, to enable reception of the data of the traffic incident from multiple sources (such as the one or more reroute vehicles) to obtain the reliable data of the traffic incident (Nayak ¶ 5), rendering the modification to be obvious.
Regarding claim 21, Downs in combination with Beaurepaire discloses the method of claim 1, including:
determining a first set of the plurality of drivers that accepted the first offer (Beaurepaire ¶ 34 “determining some driving behaviors (e.g., after some of all users accept the recommendation(s))”) and updating the future traffic congestion levels by removing the individual contribution to the future traffic congestion levels for the first set of the plurality of drivers to determine updated future traffic congestion levels; (Beaurepaire ¶ 34 “the system 100 can update the recommendations to the user group when determining some driving behaviors (e.g., after some of all users accept the recommendation(s)),” wherein the recommendation includes recommending the user to “pull over after a few meters after the exit ramp,” wherein the mapping data is updated in real-time as inputs to a mapping database, see Beaurepaire ¶ 24. One having ordinary skill in the art would recognize that updating recommendations after the plurality of users has accepted the recommendation includes having determined updated anticipated traffic congestion in order to provide the updated recommendation.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the future traffic condition predictions of Downs with determining a first set of the plurality of drivers that accepted the first offer and updating the future traffic congestion levels by removing the individual contribution to the future traffic congestion levels for the first set of the plurality of drivers to determine updated future traffic congestion levels, as disclosed by Beaurepaire, with reasonable expectation of success, to reduce the overall traffic impact caused by a group of drivers (Beaurepaire ¶ 57), rendering the limitation to be an obvious modification.
Downs in combination with Beaurepaire does not expressly disclose:
wherein identifying the set of the plurality of drivers with the highest individual contribution to the at least one congested road segment includes ranking the plurality of drivers by their individual contribution and providing at least one having a highest ranking the first offer.
However, Nayak dislcoses:
wherein identifying the set of the plurality of drivers with the highest individual contribution to the at least one congested road segment includes ranking the plurality of drivers by their individual contribution and providing at least one having a highest ranking the first offer. (Nayak ¶ 55 discloses “The vehicle reroute module 201d may be configured to reroute the selected [i.e., identified] one or more vehicles 107,” wherein the selection may be based on “a proximity of the one or more vehicles 107 with the location of the traffic incident,” see ¶ 53, such that “the one or more vehicles 107 are selected to maximize the initial confidence score associated with the detected traffic incident, see ¶ 87. Also see ¶ 101.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the identification of contributing drivers of Downs, of the combination of Downs and Beaurepaire, with ranking the plurality of drivers such that drivers having a highest ranking having corresponding future routes through the road network having a highest level of congestion as determined by the future traffic congestion levels, as disclosed by Nayak, with reasonable expectation of success, to enable reception of the data of the traffic incident from multiple sources (such as the one or more reroute vehicles) to obtain the reliable data of the traffic incident (Nayak ¶ 5), rendering the modification to be obvious.
Regarding claim 22, Downs in combination with Beaurepaire discloses the computer-readable medium of claim 16, including:
determining a first set of the plurality of drivers that accepted the first offer (Beaurepaire ¶ 34 “determining some driving behaviors (e.g., after some of all users accept the recommendation(s))”) and updating the future traffic congestion levels by removing the individual contribution to the future traffic congestion levels for the first set of the plurality of drivers to determine updated future traffic congestion levels; (Beaurepaire ¶ 34 “the system 100 can update the recommendations to the user group when determining some driving behaviors (e.g., after some of all users accept the recommendation(s)),” wherein the recommendation includes recommending the user to “pull over after a few meters after the exit ramp,” wherein the mapping data is updated in real-time as inputs to a mapping database, see Beaurepaire ¶ 24. One having ordinary skill in the art would recognize that updating recommendations after the plurality of users has accepted the recommendation includes having determined updated anticipated traffic congestion in order to provide the updated recommendation.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the future traffic condition predictions of Downs with determining a first set of the plurality of drivers that accepted the first offer and updating the future traffic congestion levels by removing the individual contribution to the future traffic congestion levels for the first set of the plurality of drivers to determine updated future traffic congestion levels, as disclosed by Beaurepaire, with reasonable expectation of success, to reduce the overall traffic impact caused by a group of drivers (Beaurepaire ¶ 57), rendering the limitation to be an obvious modification.
Downs in combination with Beaurepaire does not expressly disclose:
wherein identifying the set of the plurality of drivers with the highest individual contribution to the at least one congested road segment includes ranking the plurality of drivers by their individual contribution and providing at least one having a highest ranking the first offer.
However, Nayak dislcoses:
wherein identifying the set of the plurality of drivers with the highest individual contribution to the at least one congested road segment includes ranking the plurality of drivers by their individual contribution and providing at least one having a highest ranking the first offer. (Nayak ¶ 55 discloses “The vehicle reroute module 201d may be configured to reroute the selected [i.e., identified] one or more vehicles 107,” wherein the selection may be based on “a proximity of the one or more vehicles 107 with the location of the traffic incident,” see ¶ 53, such that “the one or more vehicles 107 are selected to maximize the initial confidence score associated with the detected traffic incident, see ¶ 87. Also see ¶ 101.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the identification of contributing drivers of Downs, of the combination of Downs and Beaurepaire, with ranking the plurality of drivers such that drivers having a highest ranking having corresponding future routes through the road network having a highest level of congestion as determined by the future traffic congestion levels, as disclosed by Nayak, with reasonable expectation of success, to enable reception of the data of the traffic incident from multiple sources (such as the one or more reroute vehicles) to obtain the reliable data of the traffic incident (Nayak ¶ 5), rendering the modification to be obvious.
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.
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/STEPHANIE T SU/Patent Examiner, Art Unit 3662