DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after November 15 2021, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The Amendment filed on 10/16/2025 has been entered. Claims 1-2, 4, 6-7, 9-12, 14, 16-17 and 19-25 are pending in the instant patent application. Claims 1, 11 and 20 are amended. Claims 3, 5, 8, 13, 15 and 18 are cancelled. This Final Office Action is in response to the claims filed.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive.
As previously noted in the previous Office Action, Examiner found that during the re-evaluation of the claim language, that the claims recite abstract ideas, specifically Mental Processes and Certain Methods of Organizing Human Activity (business relations). Examiner will further note that the generic use of the machine learning model utilizing it in a generic capacity is not enough to remove it from the abstract idea groupings.
Furthermore, Examiner maintains that there isn’t an improvement to the technology or technical field. The training and retraining of the machine learning model is performed in a generic manner, in its generic capacity, performing its generic function. Furthermore, there is no connection in what is accomplished from the retraining of the RNN and how the resulting data is tied into the claim language or how the physics-related inter-relationships are further utilized in the claim language.
As noted in Ex Parte Desjardins, the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. There were clear improvements noted and further reflected in the claim language. The same cannot be said of the current claims in light of Ex Parte Desjardins. Thus for in at least these reasons, Examiner maintains that the claim language recites abstract ideas.
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.
Regarding Claims 1-2, 4, 6-7 and 9-10, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 1-2, 4, 6-7 and 9-10 are directed to the abstract idea of generating greenhouse gas emission estimations.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites obtaining multiple items of input data related to multiple aspects of at least one logistics context; deriving one or more contextual features from the multiple items of input data by processing at least a portion of the multiple items of input data using one or more data profiling techniques, wherein deriving one or more contextual features comprises generating at least one route profile and at least one vehicle profile; simulating greenhouse gas emissions data attributed to operation of multiple systems within the at least one logistics context by processing at least a portion of the multiple items of input data using thermodynamic equations comprising at least one heat transfer equation and at least one energy balance equation; identifying data cohorts based at least in part on the one or more contextual features, wherein identifying data cohorts comprises identifying data cohorts related to route information, topography information, and weather information, and identifying data cohorts related to vehicle information and driver information; generating at least one energy consumption estimate attributed to at least a portion of at least one logistics implementation by processing data pertaining to the at least one logistics implementation using the at least one trained machine learning model; generating at least one greenhouse gas emissions estimate attributed to the at least a portion of the at least one logistics implementation based at least in part on the at least one energy consumption estimate; and performing one or more automated actions based at least in part on the at least one generated greenhouse gas emissions estimate, wherein performing one or more automated actions comprises: automatically modifying, based at least in part on the at least one generated greenhouse gas emissions estimate, at least a portion of one or more systems within at least one transportation-related machine used in the at least one logistics implementation.
These claim limitations fall within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the commercial interactions taking place, notably business relations. In addition, Mental Processes for they are concepts that can be practically performed in the human mind and/or with pen/paper. Examiner will also note that the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Furthermore, the generic recitation of a machine learning model and at least one recurrent neural network does not take the claim out of the certain methods of organizing human activity for it is merely being used as a tool to carry out the abstract idea.
Accordingly, the claim recites an abstract idea and dependent claims 2, 4, 6- 7 and 9-10 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of training at least one machine learning model related to energy consumption based at least in part on the one or more contextual features, wherein the at least one machine learning model comprises at least one recurrent neural network, and wherein training the at least one recurrent neural network comprises using at least a portion of the identified data cohorts and at least a portion of the one or more contextual features to learn physics-related inter-relationships between the simulated greenhouse gas emissions data; automatically retraining the at least one recurrent neural network based at least in part on the at least one generated greenhouse gas emissions estimate and resulting data from the at least one logistics implementation and at least one computing device. The training at least one machine learning model related to energy consumption based at least in part on the one or more contextual features, wherein the at least one machine learning model comprises at least one recurrent neural network, and wherein training the at least one recurrent neural network comprises using at least a portion of the identified data cohorts and at least a portion of the one or more contextual features to learn physics-based inter-relationships between the simulated greenhouse gas emissions data; automatically retraining the at least one recurrent neural network based at least in part on the at least one generated greenhouse gas emissions estimate and resulting data from the at least one logistics implementation and at least one computing device are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 1 includes various elements that are not directed to the abstract idea under 2A. These elements include training at least one machine learning model related to energy consumption based at least in part on the one or more contextual features, wherein the at least one machine learning model comprises at least one recurrent neural network, and wherein training the at least one recurrent neural network comprises using at least a portion of the identified data cohorts and at least a portion of the one or more contextual features to learn physics-based inter-relationships between the simulated greenhouse gas emissions data; automatically retraining the at least one recurrent neural network based at least in part on the at least one generated greenhouse gas emissions estimate and resulting data from the at least one logistics implementation, at least one computing device and the generic computing elements described in the Applicant's specification in at least Para 0048. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 1 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 11-12, 14 and 16-17, they are directed to a computer program product, however the claims are directed to a judicial exception without significantly more. Claims 11-12, 14 and 16-17 are directed to the abstract idea of generating greenhouse gas emission estimations.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 11, claim 11 recites obtain multiple items of input data related to multiple aspects of at least one logistics context; derive one or more contextual features from the multiple items of input data by processing at least a portion of the multiple items of input data using one or more data profiling techniques, wherein deriving one or more contextual features comprises generating at least one route profile and at least one vehicle profile; simulate greenhouse gas emissions data attributed to operation of multiple systems within the at least one logistics context by processing at least a portion of the multiple items of input data using thermodynamic equations comprising at least one heat transfer equation and at least one energy balance equation; identify data cohorts based at least in part on the one or more contextual features, wherein identifying data cohorts comprises identifying data cohorts related to route information, topography information, and weather information, and identifying data cohorts related to vehicle information and driver information; generate at least one energy consumption estimate attributed to at least a portion of at least one logistics implementation by processing data pertaining to the at least one logistics implementation using the at least one trained machine learning model; generate at least one greenhouse gas emissions estimate attributed to the at least a portion of the at least one logistics implementation based at least in part on the at least one energy consumption estimate; and perform one or more automated actions based at least in part on the at least one generated greenhouse gas emissions estimate, wherein performing one or more automated actions comprises: automatically modifying, based at least in part on the at least one generated greenhouse gas emissions estimate, at least a portion of one or more systems within at least one transportation-related machine used in the at least one logistics implementation.
These claim limitations fall within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the commercial interactions taking place, notably business relations. In addition, Mental Processes for they are concepts that can be practically performed in the human mind and/or with pen/paper. Examiner will also note that the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Furthermore, the generic recitation of a machine learning model and at least one recurrent neural network does not take the claim out of the certain methods of organizing human activity for it is merely being used as a tool to carry out the abstract idea.
Accordingly, the claim recites an abstract idea and dependent claims 12, 14 and 16-17 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of train at least one machine learning model related to energy consumption based at least in part on the one or more contextual features, wherein the at least one machine learning model comprises at least one recurrent neural network, and wherein training the at least one recurrent neural network comprises using at least a portion of the identified data cohorts and at least a portion of the one or more contextual features to learn physics-based inter-relationships between the simulated greenhouse gas emissions data and automatically retraining the at least one recurrent neural network based at least in part on the at least one generated greenhouse gas emissions estimate and resulting data from the at least one logistics implementation and a computing device. The train at least one machine learning model related to energy consumption based at least in part on the one or more contextual features, wherein the at least one machine learning model comprises at least one recurrent neural network, and wherein training the at least one recurrent neural network comprises using at least a portion of the identified data cohorts and at least a portion of the one or more contextual features to learn physics-based inter-relationships between the simulated greenhouse gas emissions data and automatically retraining the at least one recurrent neural network based at least in part on the at least one generated greenhouse gas emissions estimate and resulting data from the at least one logistics implementation and a computing device are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 11 includes various elements that are not directed to the abstract idea under 2A. These elements include train at least one machine learning model related to energy consumption based at least in part on the one or more contextual features, wherein the at least one machine learning model comprises at least one recurrent neural network, and wherein training the at least one recurrent neural network comprises using at least a portion of the identified data cohorts and at least a portion of the one or more contextual features to learn physics-based inter-relationships between the simulated greenhouse gas emissions data and automatically retraining the at least one recurrent neural network based at least in part on the at least one generated greenhouse gas emissions estimate and resulting data from the at least one logistics implementation, a computing device and the generic computing elements described in the Applicant's specification in at least Para 0048. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 11 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 20-25, they are directed to a system, however the claim is directed to a judicial exception without significantly more. Claims 20-25 are directed to the abstract idea of generating greenhouse gas emission estimations.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 20, claim 20 recites obtain multiple items of input data related to multiple aspects of at least one logistics context; derive one or more contextual features from the multiple items of input data by processing at least a portion of the multiple items of input data using one or more data profiling techniques, wherein deriving one or more contextual features comprises generating at least one route profile and at least one vehicle profile; simulate greenhouse gas emissions data attributed to operation of multiple systems within the at least one logistics context by processing at least a portion of the multiple items of input data using thermodynamic equations comprising at least one heat transfer equation and at least one energy balance equation; identify data cohorts based at least in part on the one or more contextual features, wherein identifying data cohorts comprises identifying data cohorts related to route information, topography information, and weather information, and identifying data cohorts related to vehicle information and driver information; generate at least one energy consumption estimate attributed to at least a portion of at least one logistics implementation by processing data pertaining to the at least one logistics implementation using the at least one trained machine learning model; generate at least one greenhouse gas emissions estimate attributed to the at least a portion of the at least one logistics implementation based at least in part on the at least one energy consumption estimate; and perform one or more automated actions based at least in part on the at least one generated greenhouse gas emissions estimate, wherein performing one or more automated actions comprises: automatically modifying, based at least in part on the at least one generated greenhouse gas emissions estimate, at least a portion of one or more systems within at least one transportation-related machine used in the at least one logistics implementation.
These claim limitations fall within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the commercial interactions taking place, notably business relations. In addition, Mental Processes for they are concepts that can be practically performed in the human mind and/or with pen/paper. Examiner will also note that the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Furthermore, the generic recitation of a machine learning model and at least one recurrent neural network does not take the claim out of the certain methods of organizing human activity for it is merely being used as a tool to carry out the abstract idea.
Accordingly, the claim recites an abstract idea and dependent claims 21-25 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a memory, a processor, train at least one machine learning model related to energy consumption based at least in part on the one or more contextual features, wherein the at least one machine learning model comprises at least one recurrent neural network, and wherein training the at least one recurrent neural network comprises using at least a portion of the identified data cohorts and at least a portion of the one or more contextual features to learn physics-based inter-relationships between the simulated greenhouse gas emissions data and automatically retraining the at least one recurrent neural network based at least in part on the at least one generated greenhouse gas emissions estimate and resulting data from the at least one logistics implementation and a computing device. The memory, processor, train at least one machine learning model related to energy consumption based at least in part on the one or more contextual features, wherein the at least one machine learning model comprises at least one recurrent neural network, and wherein training the at least one recurrent neural network comprises using at least a portion of the identified data cohorts and at least a portion of the one or more contextual features to learn physics-based inter-relationships between the simulated greenhouse gas emissions data and automatically retraining the at least one recurrent neural network based at least in part on the at least one generated greenhouse gas emissions estimate and resulting data from the at least one logistics implementation and a computing device are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 20 includes various elements that are not directed to the abstract idea under 2A. These elements include train at least one machine learning model related to energy consumption based at least in part on the one or more contextual features, wherein the at least one machine learning model comprises at least one recurrent neural network, and wherein training the at least one recurrent neural network comprises using at least a portion of the identified data cohorts and at least a portion of the one or more contextual features to learn physics-based inter-relationships between the simulated greenhouse gas emissions data and automatically retraining the at least one recurrent neural network based at least in part on the at least one generated greenhouse gas emissions estimate and resulting data from the at least one logistics implementation, a memory, a processor and the generic computing elements described in the Applicant's specification in at least Para 0048. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 20 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.E.S./ Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623