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. 101 have been fully considered but they are not persuasive.
Applicant argues: “The Applicant respectfully submits that one or more features of amended independent claim 1 cannot be performed/executed by human mind. These steps are inextricably tied to a machine which requires computational processing performed interoperable by electronic components such as an apparatus with at least a processor, a memory, and cloud server as described at least at paragraphs [0004], [0049]-[0050] of published specification. The Applicant submits that the claimed invention is not directed to a mental process because they recite a specific, technology-based solution implemented through concrete computing components and resulting in the physical operation of a vehicle. The claimed invention requires processors operating as predictive and validation computing entities to identify emissions-related input data from memory, determine an emissions impact-optimized path using a machine-learning optimization model stored in the memory, and evaluate that optimized path using validation engines configured to assess operational KPIs that cannot be evaluated in the human mind. The claims further require determining a validated emissions impact-optimized path and generating a vehicle operation plan based on that validated path, followed by causing execution of the vehicle operation plan by a vehicle control system. This step ties the claim directly to real-world mechanical behavior and causes the computing system to physically operate a vehicle in accordance with the validated path. This is far beyond data manipulation or mental reasoning and constitutes a meaningful integration of any alleged abstract element into a practical application. Additionally, the claims recite updating and retraining the machine-learning optimization model using the validated emissions impact- optimized path and evaluation output so that the model's predictive accuracy improves over time. Such modification of model definition data stored in memory constitutes a technological improvement to the operation of the computing system itself. Taken together, the combination of machine-learning optimization, KPI-based validation, generation of a machine-executable vehicle operation plan, physical vehicle control, and closed-loop model retraining forms a specific technological pipeline that cannot be performed mentally and is rooted in computer and control technology. Therefore, the amended claims are not directed to an abstract idea. (see at least at paragraphs [0049], [0058], [0067]-[0080], and [OOO]-[0107] of published specification).
Therefore, the subject matter of independent claim 1 is not similar to alleged abstract idea. Clearly, the above-mentioned features cannot be regarded as certain methods that can be performed in human mind under Prong 1 of Step 2A.”
Examiner respectfully disagrees. 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)) (emphasis added).
Here, the claims recite the equivalent of mental processes in their steps. The steps can be performed mentally, or by a human using pen and paper. A human can identify candidate vehicle paths, determine an optimized route, evaluate the route, determine a path, generate a plan, and even “update” or “retrain” based on the path and evaluation to find a new path. No details are given about how the machine learning system operates or incorporates its new data into the updating and retraining step.
Applicant further argues: “Regarding Prong 2 of Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance, even if one were to arrive at a conclusion satisfying the Prong 1 of such analysis, assuming arguendo, to which the Applicant does not concede, the Applicant submits that the alleged abstract idea is integrated into a practical implementation. For instance, the subject matter of independent claim 1 can be practically realized in real-world transportation platforms, including the aviation industry, where emissions-optimized route planning, validation, and adaptive machine-learning refinement are executed through onboard avionics and fleet- management computing systems. The predictive computing entity, advisory computing entity, and efficiency computing entity may be deployed within aircraft operational environments, where emissions-related data, environmental conditions, operational constraints, and historical flight- performance information are stored in onboard or ground-based memory systems. The machine- learning optimization model is executed on dedicated processors that are part of aviation mission- planning computers or flight operations management systems. The validated emissions-impact optimized path is used to generate an aircraft operation plan, which directly informs actionable flight parameters such as altitude profiles, speed schedules, climb and descent behaviors, and lateral routing. This operation plan is then transmitted to and executed by an aircraft's flight management system (FMS) or autopilot control system, resulting in physical actuation of flight- control surfaces, throttle adjustments, and navigation decisions in accordance with the validated emissions-optimized path. Further, the system physically retrains the machine-learning optimization model by updating model-definition data stored within these aviation computing systems based on validated post-flight operational outcomes. See at least paragraphs [0016]- [0022], [0026]-[0027], [0043]-[0047], [0066]-[0080] of as published Specification. This accounts for practical implementation.”
Examiner respectfully disagrees. 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 computer system, processor, and memory merely describe how to generally “apply” the otherwise mental judgments in a generic or general-purpose computing environment. They are also recited at a high level of generality and merely automate the generating steps.
Examiner notes in particular that the claims (and even the specification, to Examiner’s knowledge) do not describe specific machine learning models, nor do they describe specifically how the models operate.
Applicant further argues: “Regarding Step 2B, even if one were to arrive at a conclusion satisfying the Step 2A of such analysis, assuming arguendo, to which the Applicant does not concede, the Applicant submits that elements of amended independent claim 1 provide an inventive concept and amount to significantly more than the exception itself.
The Applicant asserts that conventional route-planning and emissions estimation systems rely on static, rule-based calculations that do not adapt to changing operational conditions, lack the ability to validate optimization outputs against real-world constraints (such as safety, fuel- efficiency, or compliance KPIs), and cannot automatically generate executable vehicle operation plans. Further, existing systems also do not utilize historical emissions-impact data for predictive learning and do not incorporate feedback from real vehicle operations to improve future predictions. These limitations result in suboptimal emissions-impact decisions, inability to ensure operational feasibility, and a failure to adapt the optimization process to continuously improve accuracy and reliability over time. See at least paragraphs [0002], [0003], [0016J-[0022] of published Specification.
The claimed invention provides a multi-stage, machine-learning-driven emissions optimization system implemented by specialized computing components, including a predictive computing entity, advisory computing entity, efficiency computing entity, and memory storing emissions-related training data and model definition data. The system identifies emissions-related input data from memory, determines an emissions impact-optimized path using a machine-learning optimization model, and evaluates the optimized path using validation engines configured to assess operational KPIs such as safety, efficiency, or regulatory constraints. Based on this evaluation, the system determines a validated emissions impact-optimized path and generates a vehicle operation plan configured for execution by a vehicle control system. The system further updates and retrains the machine-learning optimization model using the validated optimized path and evaluation output, enabling continuous improvement of future emissions-impact prediction. See at least paragraphs [0016]-[0022], [0066]-[0099], of published Specification.
The claimed invention provides significant technical advantages by enabling a closed- loop, machine-learning-driven emissions optimization system that continuously improves its predictive accuracy and operational performance. Further, the system not only determines an emissions impact-optimized path using a machine-learning optimization model, but also evaluates that path against operational KPls using validation engines to ensure safety, feasibility, and efficiency. By generating a vehicle operation plan based on the validated emissions impact- optimized path and causing execution of that plan by a vehicle control system, the invention directly improves real-world vehicle operation in a manner that traditional static route-planning systems cannot achieve. Furthermore, by updating and retraining the machine-learning optimization model using the validated path and evaluation output, the system enhances model definition data stored in memory and continuously refines its future emissions-impact predictions, resulting in more accurate, efficient, and operationally compliant vehicle behavior over time. This combination of optimized prediction, validated decision-making, physical vehicle execution, and adaptive model improvement yields a technical effect that improves both computational system performance and the real-world operation of vehicles.”
Examiner respectfully disagrees. The claims do 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).
Here, generating and evaluating a path is well known and understood in the art, along with key performance indicators and other cost threshold metrics.
Applicant's arguments with respect to 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant argues: “The Applicant submits that the combination of Russo and O'Sullivan does not teach, suggest, or render obvious one or more features of amended independent claim 1. For instance, Russo and O'Sullivan either alone or in combination fails to describe, for example, the feature(s) of "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; 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" as recited in amended independent claim 1.
Russo describes a machine-learning system for optimizing carbon emissions in logistics. It collects and integrates order, transportation, and environmental data, then projects carbon emissions using trained models. The system generates transportation plans and continuously refines them in real time as new logistics data arrives. It detects carbon emission outliers, determines offsets (like route changes or cleaner vehicles), and updates both the plan and the model. The approach uses vector space analysis and clustering to compare logistics scenarios and spot anomalies, aiming to minimize carbon impact while maintaining operational efficiency. See Russo at Abstract, and paras [0021]-[0023], [0050]-[005], [0054], [0065], [0089].
That is, Russo describes a system that operates at a broader logistics level, where transportation plans for multiple vehicles are generated and iteratively refined using projected carbon emissions derived from integrated logistics and environmental data. However, Russo does not teach a system that selects and validates an emissions-optimized path for a specific vehicle operation by explicitly evaluating candidate paths against operational key performance indicators (KPIs) before execution. Russo's disclosure focuses on projecting and minimizing carbon emissions at the logistics plan level, using integrated data and machine learning to iteratively refine transportation plans and address emission outliers through offset actions. However, it does not describe or suggest a process where individual vehicle paths are optimized, validated using KPI criteria, and then used to retrain the model based on the outcome of that validation. In particular, Russo does not teach the combination of path-level selection, KPI-based validation, and targeted model updating for single vehicle operations.
Further, O'Sullivan describes a travel routing system that calculates multiple alternative routes for a vehicle between two locations, estimates the emissions and fuel consumption for each route using vehicle, environmental, and traffic data, and presents these options to the user. The user then selects a preferred route, and the system provides guidance for that route. The process is based on predefined constraints such as minimizing environmental impact, fuel consumption, travel time, or distance, and can update recommendations dynamically if conditions change. See O'Sullivan at Abstract, fig 4 or paras [0037]-[0041].
That is, O'Sullivan describes a machine learning optimization model that automatically selects an emissions impact-optimized path from candidate routes, rather than relying on user choice. O'Sullivan fails to evaluate the selected path against operational KPIs using validation engines and update the machine learning model with the results of this evaluation to improve future predictions. This closed-loop, KPI-driven, and learning-based optimization is not taught or suggested by O'Sullivan, which relies on static calculations and user selection without model retraining or automated validation.
Accordingly, Russo and O'Sullivan either alone or in combination fail to describe, for example, the feature of "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; 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" as recited by amended independent claim 1.”
Examiner respectfully disagrees. While Russo does operate at a logistics level, Russo also considers specific routes for a given vehicle as part of the logistics optimization. For example, in paragraphs [0021]-[0024] Russo generates routes for a vehicle type, or a “specific” or “selected” vehicle, and also considers low level route characteristics such as “traffic congestion”, “construction”, “weather forecasts.” See also paragraph [0075], where the system can suggest alternative routes, or likewise paragraph [0078] where the system considers alternate routes during planning that avoid higher costs, such as avoiding urban congestion routes.
Moreover, Russo measures routes against evaluation criteria. See for example paragraphs [0065] or [0089], where the system evaluates the routes to determine one with fewest carbon emissions (among other costs). In paragraph [0078] 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 evaluations can read on satisfies one or more operational key performance indicators.
Russo also retrains the model based on the past or new data. See again paragraph [0085], or additionally [0097], where the system updates or retrains based on outliers and learned data associated with the routes. See also paragraph [0120], describing retraining the models to refine the model and improve performance generally. Further, the claimed updating and retraining step is broad, only generally using the path and evaluation output to “retrain” the model.
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;
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;
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”).
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.
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:
US20240202610 by Sanzone et al. teaching path creation via machine learning with value targets.
US20210256781 by Jorn et al. teaching trip sustainability evaluations.
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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/JORDAN T SMITH/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666