Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments with respect to 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant’s arguments hinge on the misconception that the claim performs actions that integrate the process into practical application.
However, the claim does not meaningfully integrate the process into practical application. The claim ultimately “generate[s] a vehicle operation plan…to operate the vehicle” and “update[s] the machine learning optimization model.” Neither of these reflect such integration.
The end result of the recited process is generating a path. Merely generating a path, by itself, is not a practical application of anything. It merely expresses a mental process that amounts to a drafting effort to monopolize the abstract idea without imposing a meaningful limit upon it. In other words, Applicant is claiming the idea of a solution, rather than applying the solution to a particular practical application. The fact that the path is “to operate the vehicle” is not a positive recitation of a practical application.
The final limitation of updating the machine learning model does not integrate into practical application either. The limitation is recited at a high level, amounting to merely applying the mental process to a computer. Moreover, the updating of the machine learning model does not appear to be the core inventive concept of claim 1; instead, the claim focuses on the path generation, and tacks on updating the model at the end.
To overcome the rejection, Examiner suggests amending the claim to positively recite either 1) controlling a vehicle in a specific manner based on the generated path (assuming it is supported in the specification), or 2) meaningfully outputting the path to a vehicle for the vehicle to follow. The first suggestion would be far stronger, as outputting a generated path can be mere post-solution activity. However, because here the claim describes a detailed navigation path generation process, it could arguably integrate into practical application by presenting the route to a vehicle so the vehicle can implement the path.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 1 is directed toward non-statutory subject matter, as shown below:
STEP 1: Does claim 1 fall within one of the statutory categories? Yes. The claim is directed toward a process which falls within one of the statutory categories.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim is directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Claim 1 recites:
A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:
identify input data set for a target vehicle operation, wherein the input data set is identified from the memory, and wherein the input data set comprises one or more candidate vehicle paths for the target vehicle operation;
determine, using a machine learning optimization model, an emissions impact-optimized path based on the input data set and historical emissions impact data associated with a plurality of historical vehicle operations;
evaluate the emissions impact-optimized path, based on one or more validation engines, to generate an evaluation output, wherein the evaluation comprises determining by the one or more validation engines whether the emissions impact-optimized path satisfies one or more operational key performance indicators that comprises at least safety, time, distance, and logistics;
determine a validated emissions impact-optimized path for the target vehicle operation based on the emissions impact-optimized path and the evaluation output, wherein the validated emissions impact-optimized path is associated with a positive evaluation output;
generate a vehicle operation plan based on the validated emissions impact-optimized path to operate the vehicle in accordance with the validated emissions impact-optimized path;
and update the machine learning optimization model with the validated emissions impact- optimized path and the evaluation output to re-train the machine learning optimization model for future emissions impact predictions.
The highlighted portion of claim 1 above is a mental process that can be practicably performed in the human mind and, therefore, an abstract idea. It merely consists of identifying input data, determining an emissions impact-optimized path, evaluating the emissions impact-optimized path, determining a validated emissions impact-optimized path for the target vehicle operation, generating a vehicle operation plan, and updating the model with the data. This is equivalent to a person identifying problem constraints and historical route data and using that data to determine a low-emission route to a destination, and remembering the path and how it performed for future decision-making. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). As such, a person can identify relevant data in order to determine a low emission route. The mere nominal recitation that the process is being executed by a computer or learning models does not take the limitation out of the mental process grouping. For example, the claim does not positively recite any limitations regarding actual use of the route in controlling the vehicle in a specific manner. Thus, the claim recites a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claim 1 recites:
A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:
identify input data set for a target vehicle operation, wherein the input data set is identified from the memory, and wherein the input data set comprises one or more candidate vehicle paths for the target vehicle operation;
determine, using a machine learning optimization model, an emissions impact-optimized path based on the input data set and historical emissions impact data associated with a plurality of historical vehicle operations;
evaluate the emissions impact-optimized path, based on one or more validation engines, to generate an evaluation output, wherein the evaluation comprises determining by the one or more validation engines whether the emissions impact-optimized path satisfies one or more operational key performance indicators that comprises at least safety, time, distance, and logistics;
determine a validated emissions impact-optimized path for the target vehicle operation based on the emissions impact-optimized path and the evaluation output, wherein the validated emissions impact-optimized path is associated with a positive evaluation output;
generate a vehicle operation plan based on the validated emissions impact-optimized path to operate the vehicle in accordance with the validated emissions impact-optimized path;
and update the machine learning optimization model with the validated emissions impact- optimized path and the evaluation output to re-train the machine learning optimization model for future emissions impact predictions.
Claim 1 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. As noted above, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. Here, the computer and learning models are recited at a high level of generality, such that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated into a practical application.
The one or more data networks, one or more processors, one or more memories storing computer readable instructions, and the computer readable storage medium comprising computer-readable instructions merely describes how to generally “apply” the otherwise mental judgments in a generic or general-purpose computing environment. The one or more data networks, one or more processors, one or more memories storing computer readable instructions, and the computer readable storage medium comprising computer-readable instructions are recited at a high level of generality and merely automate the generating steps.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claim 1 does not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. 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 performance of an action is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
CONCLUSION
Thus, since claim 1 is: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claim 1 is directed towards non-statutory subject matter.
Independent claim 12 has similar limitations to claim 1 above, and is therefore ineligible for similar reasons.
Independent claim 20 has similar limitations to claim 1 above, but also has additional limitations. However, the “generating” and “selecting” steps of claim 20 similarly recite a mental process, and they are merely applied in a generic manner to a computer. Thus, claim 20 is also ineligible.
Dependent claims 2-11 and 13-19 are likewise ineligible. The claims generally add to the mental process (claims 2-4, 6-11, 13-16, and 17-19) or else further describe a computer or machine learning model generically (claims 5 and 16). Thus, the dependent claims are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US20250238813 by Russo et al. (hereinafter “Russo”), further in view of US20100088012 by O’Sullivan et al. (hereinafter “O’Sullivan”), and further in view of LU503564 by Jia (hereinafter “Jia”).
Regarding claim 1, Russo teaches A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to: identify input data set see for example Figure 1 for the system layout, including processor and memory. See also paragraphs [0021]-[0023], where the system a variety of input data in order to compute a route. See also paragraph [0075], where the system can suggest alternative routes (candidate vehicle paths), or likewise paragraph [0078] where the system considers alternate routes during planning that avoid higher costs, such as avoiding urban congestion routes.
determine, using a machine learning optimization model, an emissions impact-optimized path based on the input data set and historical emissions impact data associated with a plurality of historical vehicle operations; see for example paragraph [0054], where the system formulates a transportation plan based on historic data. Then paragraphs [0050]-[0051] describe using machine learning to predict the carbon emissions based on historical data. Again, the system uses historical data as described in paraph [0022], including “vehicle types, routes taken, fuel consumption” etc., reading on historical vehicle operations.
evaluate the emissions impact-optimized path, based on one or more validation engines, to generate an evaluation output, wherein the evaluation comprises determining by the one or more validation engines whether the emissions impact-optimized path satisfies one or more operational key performance indicators see for example paragraph [0055], where the system optimizes the transportation planning process to reduce carbon emissions based on its evaluation of the costs. Similarly, see paragraphs [0065] or [0089], where the system evaluates the routes to determine one with fewest carbon emissions (among other costs). See also paragraph [0078] for example, where the system optimizes routes based on estimated carbon emissions, predicted delivery times, cost estimates, etc. The routes output by the system are “optimized” in paragraph [0075], and can be compared to standard “benchmark” template routes to determine if they are improvements in paragraph [0065]; moreover, the plan can identify costs (like carbon emissions) in relation to pre-determined thresholds in e.g. paragraph [0084]. Either of such optimization or benchmark/threshold evaluation can read on satisfies one or more operational key performance indicators.
determine a validated emissions impact-optimized path see again paragraph [0055], where the system formulates a transportation plan that minimizes carbon emissions. See also again paragraphs [0075], [0065], and [0084] where the paths have to pass optimization, benchmarks, and thresholds.
generate a vehicle operation plan based on the validated emissions impact-optimized path to operate the vehicle in accordance with the validated emissions impact-optimized path; see additionally paragraphs [0053]-[0057], where the system generates routes (as part of an overall transportation plan). See also paragraphs [0022]-[0024] where the transportation plan can be for a “specific” or ”selected” vehicle, in real-time, etc.
and update the machine learning optimization model with the validated emissions impact- optimized path and the evaluation output to re-train the machine learning optimization model for future emissions impact predictions. See again paragraph [0085], or additionally [0097], where the system updates or retrains based on outliers and learned data. See also paragraph [0120], describing retraining the models to refine the model and improve performance generally.
Russo does not explicitly teach optimizing carbon emissions for a target vehicle operation. That is, Russo is interested in optimizing the transportation of a package, and considers any number of vehicles and their potential routes in finding the lowest emission means to deliver the package, rather than finding the lowest emission route for a single, specific vehicle.
However, O’Sullivan teaches a system which optimizes a route for a target vehicle operation. See for example Figure 4 or paragraphs [0037]-[0041], where the system calculates a route for the user’s vehicle based on user request.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the emissions-based transportation optimization system of Russo with the emissions-based vehicle navigation guidance of O’Sullivan with a reasonable expectation of success. Doing so allows the user to optimize their emissions impact for their own vehicle.
Russo does not explicitly teach measuring a path’s key performance indicators including at least safety, time, distance, and logistics. Although Russo generally suggests considering all these factors (see, e.g., ¶ [0077] for “safety”; ¶ [0055] for “time”; ¶ [0094] for “distance”; and any of those same paragraphs for the broad term “logistics”), Russo does not explicitly teach refining the paths on all of these together.
However, Jia teaches refining a path based on metrics including at least safety, time, distance, and logistics. See page 6, lines 18-26, where the routes are optimized based on criteria including travel time, carbon emission, fuel cost, road risk, and the maximum vehicle driving distance constraints, where logistics is a broad term and is taught here by any one of travel time, fuel cost, and vehicle driving distance constraints.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the emissions-based transportation optimization system of Russo, modified by the emissions-based vehicle navigation guidance of O’Sullivan, with the optimization goals of Jia with a reasonable expectation of success. Doing so allows the system to optimize routes based on sensible criteria.
Claim 12 has similar limitations to claim 1 above, and is therefore rejected using a similar rationale.
Regarding claim 2, Russo teaches wherein the historical emissions impact data comprises (i) historical operational data for each historical vehicle operation of one or more historical vehicle operations historical vehicle operations. Similarly, see paragraph [0042], where the system accounts for vehicle efficiency or fuel type in its predictions.
Russo does not explicitly teach (ii) historical seasonal-based emissions impact data for each historical vehicle operation of the one or more historical vehicle operations.
However, O’Sullivan teaches using (ii) historical seasonal-based emissions impact data for each historical vehicle operation of the one or more historical vehicle operations. See for example paragraph [0029], where the system considers season-related data, like “environmental event data”, “historic traffic patterns”, “weather along the travel route”, etc. In paragraph [0031], this type of data is used, along with other vehicle-related data, to calculate the environmental impact along the route.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the emissions-based transportation optimization system of Russo with the emissions-based vehicle navigation guidance of O’Sullivan with a reasonable expectation of success. Doing so allows the user to optimize their emissions impact for their own vehicle.
Claim 13 has similar limitations to claim 2 above, and is therefore rejected using a similar rationale.
Regarding claim 3, Russo teaches wherein the historical operational data for each historical vehicle operation comprises (i) historical resource usage data associated with one or more high-density emissions zones along historical vehicle path for the historical vehicle operation and (ii) historical resource usage data associated with one or more low-density emissions zones along the historical vehicle path for the historical vehicle operation. See for example paragraphs [0051] and [0054], where the system analyzes historical route and emission data in order to predict future emissions, included remissions from specific routes. See also [0078] or [0094], where the system observes increased emissions in urban settings, for example due to congestion.
Claim 14 has similar limitations to claim 3 above, and is therefore rejected using a similar rationale.
Regarding claim 4, Russo teaches wherein the historical operational data for each historical vehicle operation further comprises (i) duration of the historical vehicle operation in the one or more high-density emissions zones and (ii) duration of the historical vehicle operation in the one or more low-density emissions zones. See for example paragraphs [0051] and [0054], where the system analyzes historical route and emission data in order to predict future emissions, included remissions from specific routes. See also [0078] or [0094], where the system observes increased emissions in urban settings, for example due to congestion or timing of peak traffic.
Claim 15 has similar limitations to claim 4 above, and is therefore rejected using a similar rationale.
Regarding claim 5, Russo teaches wherein the machine learning optimization model is a reinforcement learning-based machine learning model. See for example paragraphs [0118]-[0121], where the system can include numerous machine learning methods.
Claim 16 has similar limitations to claim 5 above, and is therefore rejected using a similar rationale.
Regarding claim 6, Russo teaches wherein the one or more processors are configured to determine the emissions impact-optimized path by: generating, based on the input data set and the historical emissions impact data, predicted emissions impact data for each candidate vehicle path of one or more candidate vehicle paths; and selecting the emissions impact-optimized path from the one or more candidate vehicle paths based on the predicted emissions impact data for each candidate vehicle path. See for example paragraphs [0035] and [0055], where the system estimates emissions along the path and can select the path with minimized emissions.
Claim 17 has similar limitations to claim 6 above, and is therefore rejected using a similar rationale.
Regarding claim 7, Russo teaches wherein the one or more processors are configured to evaluate the emissions impact-optimized path based on the one or more validation engines by determining whether the emissions impact-optimized path satisfies the one or more operational key performance indicators. See again paragraph [0055], where the system evaluates the path in order to find a path with minimized emissions. Similarly, see paragraphs [0065] or [0089], where the system evaluates the routes to determine one with fewest carbon emissions (among other costs).
Claim 18 has similar limitations to claim 7 above, and is therefore rejected using a similar rationale.
Regarding claim 8, Russo teaches wherein the one or more processors are further configured to evaluate the emissions impact-optimized path to generate the evaluation output based on one or more efficiency engines. See again paragraph [0055], where the system evaluates the path in order to find a path with minimized emissions. Similarly, see paragraphs [0065] or [0089], where the system evaluates the routes to determine one with fewest carbon emissions (among other costs).
Claim 19 has similar limitations to claim 8 above, and is therefore rejected using a similar rationale.
Regarding claim 9, Russo teaches wherein the one or more processors are configured to evaluate the emissions impact-optimized path based on the one or more efficiency engines by determining whether the emissions impact-optimized path satisfies one or more efficiency key performance indicators. See for example paragraphs [0035] and [0055], where the system estimates emissions along the path and can select the path with minimized emissions. Paragraph [0078] also discusses minimizing various costs in generating the route.
Regarding claim 10, Russo teaches wherein the one or more processors are further configured to reject the emissions impact-optimized path if the emissions impact-optimized path. See again paragraph [0055], where the system formulates a transportation plan that minimizes carbon emissions. See also paragraphs [0084]-[0085], where outliers higher than a threshold are flagged, or paragraph [0065], where routes are evaluated against “benchmark” routes.
Russo does not explicitly teaches generating a route for the target vehicle. That is, Russo is interested in optimizing the transportation of a package, and considers any number of vehicles and their potential routes in finding the lowest emission means to deliver the package, rather than finding the lowest emission route for a single, specific vehicle.
However, O’Sullivan teaches a system which optimizes a route for the target vehicle. See for example Figure 4 or paragraphs [0037]-[0041], where the system calculates a route for the user’s vehicle based on user request.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the emissions-based transportation optimization system of Russo with the emissions-based vehicle navigation guidance of O’Sullivan with a reasonable expectation of success. Doing so allows the user to optimize their emissions impact for their own vehicle.
Regarding claim 11, Russo teaches wherein the input data set comprises an environmental model, wherein the one or more processors are configured to determine the emissions impact-optimized path by: generating predicted emissions data for one or more candidate vehicle paths; and analyzing the predicted emissions data with the input data set and the historical emissions impact data to determine the emissions impact-optimized path. See for example paragraph [0054], where the system formulates a transportation plan based on historic data. Then paragraphs [0050]-[0051] describe using machine learning to predict the carbon emissions based on historical data. Again, the system uses historical data as described in paraph [0022], including “vehicle types, routes taken, fuel consumption” etc. See also example paragraph [0055], where the system optimizes the transportation planning process to reduce carbon emissions based on its evaluation of the costs. Similarly, see paragraphs [0065] or [0089], where the system evaluates the routes to determine one with fewest carbon emissions (among other costs).
Regarding claim 20, Russo teaches A computer-implemented method comprising: identifying, by one or more processors, input data set see for example Figure 1 for the system layout, including processor and memory. See also paragraphs [0021]-[0023], where the system a variety of input data in order to compute a route.
generating, by the one or more processors and using a machine learning optimization model, predicted emissions impact data for each candidate vehicle path of one or more candidate vehicle paths based on the input data set and historical emissions impact data; see for example paragraph [0054], where the system formulates a transportation plan based on historic data. Then paragraphs [0050]-[0051] describe using machine learning to predict the carbon emissions based on historical data. The system uses historical data as described in paraph [0022], including “vehicle types, routes taken, fuel consumption” etc.
and selecting, by the one or more processors and using the machine learning optimization model, an emissions impact-optimized path from the one or more candidate vehicle paths based on the predicted emissions impact data for each candidate vehicle path. See for example paragraph [0055], where the system optimizes the transportation planning process to reduce carbon emissions based on its evaluation of the costs. Similarly, see paragraphs [0065] or [0089], where the system evaluates the routes to determine one with fewest carbon emissions (among other costs).
Russo does not explicitly teach optimizing carbon emissions for a target vehicle operation. That is, Russo is interested in optimizing the transportation of a package, and considers any number of vehicles and their potential routes in finding the lowest emission means to deliver the package, rather than finding the lowest emission route for a single, specific vehicle.
However, O’Sullivan teaches a system which optimizes a route for a target vehicle operation. See for example Figure 4 or paragraphs [0037]-[0041], where the system calculates a route for the user’s vehicle based on user request.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the emissions-based transportation optimization system of Russo with the emissions-based vehicle navigation guidance of O’Sullivan with a reasonable expectation of success. Doing so allows the user to optimize their emissions impact for their own vehicle.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
BR102013021698 by Yoshizaki et al. teaching route optimization based on obvious parameters.
US20160054736 by Kolhouse et al. teaching evaluating vehicle costs for logistics.
US20200201357 by Zeng et al. teaching evaluating fleet cost metrics.
US20250238805 by Russo et al. generating routes in accordance with emissions and other metrics.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN THOMAS SMITH whose telephone number is (571)272-0522. The examiner can normally be reached Monday - Friday, 9am - 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Antonucci can be reached at (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN T SMITH/ Examiner, Art Unit 3666
