DETAILED ACTION
Acknowledgement
This final office action is in response to the amendment filed on 07/31/2025.
Status of Claims
Claims 2-4 and 12-14 have been canceled.
Claims 1 and 11 have been amended.
Claims 1, 6-11, and 16-20 are now pending.
Response to Arguments
The previous 35 U.S.C. 112(a) rejection of claims 1-4, 6-14, and 16-20 is withdrawn in light of amendments. However, a new 35 U.S.C. 112(a) rejection is applied against the claims. See details below.
Applicant's arguments filed on 07/31/2025 regarding the 35 U.S.C. 101 rejection of the claims have been fully considered. The Applicant argues, in summary, that (i) the amended claims do not recite a mental process nor certain methods of organizing human activity and (ii) amended claim 1 in the instant application contain limitations that amount to more than mere instructions to apply an exception at least because the limitations include steps such as receiving data that includes data inputted by a user, but also the training of at least two machine learning models, wherein the output of the first machine learning model is an input to the second machine learning model. It is extremely beneficial to use the output of one machine learning model as input to another. This ensemble learning leads to improved overall performance by leveraging the strengths of multiple models. By combining the strengths of different models, stacking can often achieve higher accuracy than using any single model alone. The steps in claim 1 improves decision-making on operator selection, routing, and emissions policy, which constitute significant improvement based on what is commonly practiced in the trucking and transportation industries.
The Examiner respectfully disagrees. The Examiner maintains the position that claims 1 and 11 are directed to the abstract groupings of Mental Processes and Certain Methods of Organizing Human Activity because the claims describe a process of analyzing data using models and calculations, generating metrics (e.g. carbon emission datum, ranking, score, emission feedback), selecting operators based on the metrics, and displaying results of the analysis. Using models and calculations to generate metrics can be practically performed mentally by a human with pen and paper and thus is considered mental processes. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. The selection of or assigning an operator to a forecasted task and generating a greenhouse gas reduction plan for the operator to follow reflects certain methods of organizing human activity as the tasks and greenhouse gas reduction plan provides instructions for the operators to follow, thus managing their personal behavior. Certain Methods of Organizing Human Activity which encompasses managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. Per MPEP 2106.04(a), a claim recites a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The Examiner also maintains the position that the additional elements recited in amended claims 1 and 11 and listed in Steps 2A(2) and 2B do not integrate the abstract idea into a practical application nor provide significantly more because the additional elements do not improve the functioning of the computer itself nor improve upon another technology. The Examiner recognizes the training and use of multiple machine learning models as additional elements. However, the machine learning models are used to perform the abstract idea of generating metrics (e.g. carbon emission datum, ranking, score, emission feedback). This implementation does not improve the functioning of a computer or another technology. The use of the machine learning models as argued by the Applicant improves the accuracy of the output (i.e. metrics, classification) and overall steps of claims 1 and 11 improve the decision-making on operator selection, routing, and emissions policy. The Examiner submits that these are improvements in the abstract idea itself and does not represent an improvement in a specific technology or technological component. Per MPEP 2106.05(a), an improvement in the abstract idea itself is not an improvement in technology. Therefore, the 35 U.S.C. 101 rejection is maintained.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1, 6-11, and 16-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 11 include the limitation of “generate a forecasted carbon efficiency score of the at least an operator as a function of the operator data and the forecasted task datum using a forecasted task machine learning model which comprises: receiving operator training data, wherein the operator training data correlates a plurality of past operator data and past task data correlated to a plurality of carbon efficiency data; training a forecast machine-learning model using the operator training data; and generating the forecasted carbon efficiency score of the at least an operator as a function of the trained forecast machine-learning model”. The Applicant’s specification does not recite “a forecasted task machine learning model”. Paragraphs [0039] and [0083] recite training a “forecast machine-learning model”. However, the specification does not recite that training a “forecast machine-learning model” is associated with a “forecasted task machine learning model”. Therefore, claims 1 and 11 contain new matter and are rejected under 35 U.S.C. 112(a). Dependent claims 6-10 and 16-20 are also rejected under 35 U.S.C. 112(a).
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, 6-11, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention, “Method and Apparatus for Comparing the Efficiency of Operators”, is directed to an abstract idea, specifically Mental Processes and Certain Methods of Organizing Human Activity, without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1, 6-11, and 16-20 are directed to a statutory category, namely a machine (claims 1 and 6-10) and a process (claims 11 and 16-20).
Step 2A (1): Independent claims 1 and 11 are directed to an abstract idea of Mental Processes based on the following claim limitations: “comparing an efficiency of operators; receive/receiving… fuel consumption data and mileage data associated with a first trip; preprocess/preprocessing… the fuel consumption data and the mileage data using a distance metric to cluster the fuel consumption data as fuel consumption data input; calculate/calculating… at least a carbon emission datum…; generating the at least a carbon emission datum as a function of the carbon emission…model; receive/receiving… operator data, wherein the operator data comprises at least an operator associated with at least a carbon emission datum; and a task datum and a forecasted task datum associated with at least a carbon emission datum; classify/classifying…the task datum into one or more task categories; calculate/calculating… a carbon emission rate of the at least an operator as a function of the at least a carbon emission datum and the classified one or more task categories; calculate/calculating…an ideal greenhouse gas metric associated with the classified one or more task categories…; generate/generating…a greenhouse gas ratio to apportion a greenhouse gas emission with a pollutant source as a function of the ideal greenhouse gas metric, wherein generating the greenhouse gas ratio further comprises determining a conversion factor; generate/generating…a carbon efficiency score of the at least an operator…; generate/generating…an operator ranking as a function of the carbon efficiency score of the at least an operator; select/selecting…an operator of the at least an operator as a function of the operator ranking; generate a forecasted carbon efficiency score of the at least on operator as a function of the operator data and the forecasted task datum…; generating the forecasted carbon efficiency score of the at least an operator…; select an operator of the at least an operator for a forecasted task as a function of the forecasted carbon efficiency score; generate/generating…a greenhouse gas emission feedback for the selected operator, wherein the greenhouse gas emission feedback comprises historical trends, daily emissions, and a comparison of at least two operator’s gas efficiency and steps to increase gas efficiency of a lower ranked operator; generate a greenhouse gas reduction plan based on a tracking of the selected operators to prevent excessive pollutant emissions and the comparison of the at least two operator’s gas efficiency; and display/displaying… an estimated amount of greenhouse gas emissions reduced based on the operator selection and the greenhouse gas emission feedback as a function of the greenhouse gas ratio…”. These claims describe a process of analyzing data using models and calculations, generating metrics (e.g. carbon emission datum, ranking, score, emission feedback), selecting operators based on the metrics, and displaying results of the analysis. Dependent claims 6-10 and 16-20 further describes the operator data and the process of calculating the metrics of carbon emissions rate and carbon efficiency score using various data inputs and models. These steps can be practically performed mentally by a human with pen and paper and thus is considered mental processes. The limitations of “select an operator of the at least an operator as a function of the operator ranking; selecting an operator of the at least an operator for a forecasted task as a function of the forecasted carbon efficiency score; generate a greenhouse gas emission feedback for the selected operators…; generate a greenhouse gas reduction plan based on a tracking of the selected operator…; and display an estimated amount of greenhouse gas emissions reduced based on the operator selection and the greenhouse gas emission feedback…”, are considered certain methods of organizing human activity as the selection of or assigning an operator to a forecasted task and generating a greenhouse gas reduction plan for the operator reflects providing instructions for them to follow, thereby managing their behavior. Therefore, under the broadest reasonable interpretation, these limitations fall within the abstract groupings of Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions and Certain Methods of Organizing Human Activity which encompasses managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping). Therefore, claims 1, 6-11, and 16-20 are directed to an abstract idea and are not patent eligible.
Step 2A (2): This judicial exception is not integrated into a practical application. In particular, claims 1 and 11 recite additional elements of “an apparatus comprising at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to; training a carbon emission machine learning model wherein the carbon emission machine learning model is trained using carbon emission training data comprises a plurality of correlations between the preprocessed fuel consumption data input and a carbon emission data output; using a task classifier trained on the operator data comprising historical performance of the operator; train/training, by the processor, a carbon efficiency machine learning model using carbon efficiency training data comprising example carbon emission rates and the classified one or more task categories correlated to an example carbon efficiency score; using a forecasted task machine learning model; training a forecast machine-learning model using the operator training data; display…on a display device; and …by a processor”. These additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing devices that are used to perform the analysis, generate metrics, select operators, and display results. The use of the machine learning and trained models/algorithms are considered instructions to apply or implement a model on a computer. Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Also, limitations that amount to merely indicating a field of use or technological environment (e.g. machine learning) in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)). Therefore, claims 1, 6-11, and 16-20 do not include individual or a combination of additional elements that integrate the judicial exception into a practical application and thus are not patent eligible.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1 and 11 recite additional elements of “an apparatus comprising at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to; training a carbon emission machine learning model wherein the carbon emission machine learning model is trained using carbon emission training data comprises a plurality of correlations between the preprocessed fuel consumption data input and a carbon emission data output; using a task classifier trained on the operator data comprising historical performance of the operator; train/training, by the processor, a carbon efficiency machine learning model using carbon efficiency training data comprising example carbon emission rates and the classified one or more task categories correlated to an example carbon efficiency score; using a forecasted task machine learning model; training a forecast machine-learning model using the operator training data; display…on a display device; and …by a processor”. As per the Applicant’s specification, an apparatus is a computing device which may include a mobile device such as a mobile telephone or smartphone [0010]; processor may be a microcontroller, digital signal processor (DSP) and/or system on a chip (SoC) [0010]; memory may include a ROM device, a RAM device, a magnetic card, an optical card, a solid-state memory device, etc. [0085]; machine learning models may be created and trained using a machine-learning module ([0021] and [0029]); a machine learning process is a process that automatedly uses training data to generate an algorithm that will be performed by a computing device/module [0069]; and a display device is a device that is capable of displaying data in a visual manner and may include a television, a computer monitor, an LCD screen, an OLED screen, a CRT screen, and the like [0036]. These additional elements evaluated individually and in combination are viewed as mere instructions to apply or implement the abstract idea on a computer. As stated above, the use of the machine learning and trained models/algorithms are considered instructions to apply or implement a model/algorithm on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1, 6-11, and 16-20 do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the judicial exception and thus are not patent eligible.
Conclusion
The closest prior arts to the claimed invention include Rani et al. (US 2017/0323244 A1) “Method and Apparatus for Evaluating Driver Performance and Determining Driver Rewards”, Dickman et al. (WO 2022/251352 A1) “System and Method for Vehicle Transportation Scenario Generation and Searching” , Shi (US 2020/0372588 A1) “Methods and Systems for Machine Learning for Prediction of Grid Carbon Emissions”, Bellowe (US 2017/0351978 A1) “Dynamic Recommendation Platform with Artificial Intelligence” and NPL: X. Lu, K. Ota, M. Dong, C. Yu and H. Jin, "Predicting Transportation Carbon Emission with Urban Big Data". However, none of the prior arts alone or in combination teach the claimed invention as detailed in independent claims 1 and 11.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ayanna Minor whose telephone number is (571)272-3605. The examiner can normally be reached M-F 9am-5 pm.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624