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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/06/2026 has been entered.
Status of the Claims
Claims 1, 3-5, 9-11, 13-15 and 19-22 are pending. Claims 2, 6-8, 12, and 16-18 are cancelled. Claims 1 and 11 are amended.
Response to Arguments
Applicant’s arguments, filed 03/06/2026, with respect to the 101 rejection have been considered but are not persuasive.
Applicant argues, on pages 3-5, that the amended claims do not recite certain methods of organizing human activity or mathematical concepts as understood by the MPEP. Applicant also argues that under the 2025 USPTO guidance, limitations directed to training a machine learning model do not recite an abstract idea.
Examiner respectfully disagrees. The claim limitations as drafted, recite a concept, that, under broadest reasonable interpretation, is a certain method of organizing human activity. The limitations are analogous to managing personal behavior or interactions between people (interactions between people), or a commercial or legal interaction (sales activity) such as determining a transportation parameter (e.g. estimated delivery time, cost, or delay) based on historical transportation data of goods and services (see specification, par.0015). Therefore, this falls into the bucket of certain methods of organizing human activity. Additionally, the amended limitations of fuzzy inferencing training of a transportation classifier as a function of transportation parameter training data and the identifying of an outlier as a function of an average is also considered mathematical processes (mathematical formulas/equations) (See specification, Par. 0013 for a variety of different classification methods/formulas and par. 0017 discussing fuzzy inferencing rules/logic). The additional elements (apparatus, memory, processor, fuzzy inference engine, interface, training data) are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Examiner notes that the rejection has not categorized the training of the data as an abstract idea, rather it is considered under step 2A Prong two. Therefore, the claims recite an abstract idea.
Applicant argues, on pages 6-7, that the amended claims recite a specific improvement to machine learning training requiring the processor to generate a transportation data ranking, update transportation parameter training data by determining an average and removing identified outliers from historical transportation parameters, and iteratively train a layered transportation parameter classifier with adjusted node connections and weights and a fuzzy-inference compatibility threshold prior to estimating the transportation parameter. These limitations improve the accuracy and reliability of the trained transportation parameter classifier by refining training data and enhancing correlation-based prediction of transportation parameters. Applicant argues that the machine learning steps do not fall under abstract idea. Applicant also argues the consistent with Desjardins the claim as amended improves the operation of a machine-learning system itself, as opposed to merely applying a generic model to a specific technological environment or field of use.
Examiner respectfully disagrees. There is no improvement to machine learning and the limitations of training have been categorized/analyzed under step 2A Prong Two. As mentioned above, the additional elements are still recited at a high level of generality and amount to generally linking the judicial exception to a particular technological environment (e.g. trained classifier comprises fuzzy inference engine). Therefore, they do not integrate the judicial exception into a practical application, and they do not apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Additionally, it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Here, the alleged improvement is not to the machine learning model or training the transportation classifier or to improve the efficiency of the existing technology of tracking freight and remove susceptibility of human error and security risks based on computer pipeline architecture, rather it is an alleged improvement to accurately estimating parameters around the transportation of goods (See specification, par. 0002) and therefore, is an improvement to the abstract idea (commercial or legal interaction (sales activity)). Therefore, the claims recite an abstract idea.
Furthermore, with respect to Ex Parte Desjardins, the claims reflected an improvement to how the machine learning model itself operates. Here, as mentioned above, that is not the case. Examiner refers to Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212. Therefore, the claims do not provide an improvement to machine learning or machine learning training.
Applicant argues, on pages 7-9, that that the claims recite additional elements that amount to significantly more than the alleged judicial exception (i.e., inventive concept). Applicant also argues that no court cases, literature, or references are of record indicating that the above-described limitations are "well-understood, routine, [and] conventional," and furthermore asserts that neither the instant application nor the prosecution history in this matter contains any admission thereof. Applicant also argues that claim 1 recites an inventive concept at least because claims 1 contains limitation amounting to a non-conventional and non-generic arrangement of process steps.
Examiner respectfully disagrees. As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Furthermore, with respect to Berkheimer, At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B. Finally, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. (See MPEP 2106.05) Therefore, the claims are directed to ineligible subject matter. Therefore, the claims are ineligible.
Novelty/Non-obviousness
The claims would be considered allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 set forth in this action. The closest prior art of record is indicated in the office action mailed on 05/02/2024.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-5, 9-11, 13-15 and 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1, 3-5, 9-10, 21 are directed to a system with multiple components, and therefore are a machine.
Claims 11, 13-15, 19-20, 22 are directed to a series of steps, and therefore is a process.
Independent Claims
Step 2A Prong One
The limitation of Claim 1 recites:
receive transport data from at least a transport entity associated with a transport of a payload comprising at least an arrival time of the payload;
generate a transport data ranking for each element of the transport data, wherein the transport data ranking indicates an importance of each element of the transport data;
receive transportation parameter training data correlating a plurality of transport data to a plurality of historical transportation parameters, wherein the plurality of historical transportation parameters is identified as a function of previous transports having at least a similar transportation parameter;
update the transportation parameter training data, wherein updating the transportation parameter training data comprises:
identifying at least an outlier within the historical transportation parameters, wherein the at least an outlier is identified as a function of an average of the identified historical transportation parameters; and
removing the at least an outlier from within the historical transportation parameters;
…
…
determine an estimated transportation parameter as a function of the classification of the transport data and the compatibility threshold, the transport data ranking and the one or more historical transportation parameters, wherein the estimated transportation parameter comprises at least a transport delay associated with the payload;
display the estimated transportation parameter … .
The limitations of Claim 11 recites:
A method for estimating a transportation parameter, wherein the method comprises:
receiving, …, transport data from at least a transport entity associated with a transport of a payload comprising at least an arrival time of the payload;
generating, …, a transport data ranking for each element of the transport data, wherein the transport data ranking indicates an importance of each element of the transport data;
receiving, …, transportation parameter training data correlating a plurality of transport data to a plurality of historical transportation parameters, wherein the plurality of historical transportation parameters are identified as a function of received from previous transports having at least a similar transportation parameter;
update, …, the transportation parameter training data, wherein updating the transportation parameter training data comprises:
identifying at least an outlier within the historical transportation parameters, wherein the at least an outlier is identified as a function of an average of the identified historical transportation parameters; and
removing the at least an outlier from within the historical transportation parameters;
…
…
determining, …, an estimated transportation parameter as a function of the classification of the transport data and the compatibility threshold, the transport data ranking and the one or more historical transportation parameters;
displaying, …, the estimated transportation parameter … .
The claim limitations as drafted, recite a concept, that, under broadest reasonable interpretation, is a certain method of organizing human activity. The limitations are analogous to managing personal behavior or interactions between people (interactions between people), or a commercial or legal interaction (sales activity) such as determining a transportation parameter (e.g. estimated delivery time, cost, or delay) based on historical transportation data. Additionally, the fuzzy inferencing/compatibility threshold and training of a transportation classifier as a function of transportation parameter training data and the identifying of an outlier as a function of an average is also considered mathematical processes (mathematical formulas/ equations) (See specification, Par. 0013 for classification methods). The generic computer implementations (see below) do not change the character of the limitations. Accordingly, the claims recite an abstract idea.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
Claim 1:
An apparatus for estimating a transportation parameter, wherein the apparatus comprises:
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:
train a transportation parameter classifier as a function of the updated transportation parameter training data, wherein training the transportation parameter classifier further comprises:
using the transportation parameter training data applied to an input layer of nodes comprising the plurality of historical transportation parameters, one or more intermediate layers of nodes, and an output layer of nodes comprising the plurality of historical transport parameters;
adjusting one or more connections and one of weights between nodes in adjacent layers of the transportation parameter classifier;
detecting additional correlations between the output layer of nodes and the input layer of nodes; and
iteratively train the transportation parameter classifier as a function of the additional correlations;
classify the transport data to one or more historical transportation parameters of the plurality of historical transportation parameters as a function of the trained transportation parameter classifier, wherein the trained transportation parameter classifier comprises a fuzzy inference engine configured to determine a compatibility threshold using the transport data as an input and output the one or more historical transportation parameters
graphical user interface
Claim 11:
Processor
training, using the at least a processor, a transportation parameter classifier as a function of the transportation parameter training data, wherein training the transportation parameter classifier further comprises:
using the transportation parameter training data applied to an input layer of nodes comprising the plurality of historical transportation parameters, one or more intermediate layers of nodes, and an output layer of nodes comprising the plurality of historical transport parameters;
adjusting one or more connections and one of weights between nodes in adjacent layers of the transportation parameter classifier;
detecting additional correlations between the output layer of nodes and the input layer of nodes; and
iteratively train the transportation parameter classifier as a function of the additional correlations;
classifying, using the at least a processor, the transport data to one or more historical transportation parameters as a function of the trained transportation parameter classifier, wherein the trained transportation parameter classifier comprises a fuzzy inference engine configured to determine a compatibility threshold using the transport data as an input and output the one or more historical transportation parameters
graphical user interface
These additional elements are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h))
Therefore, the claims recite an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
Dependent Claims
Dependent claims 3-5, 9-10 and 13-15, 19-22 further narrow the same abstract ideas recited in Claims 1 and 11, respectively. Therefore, claims 3-5, 9-10 and 13-15, 19-22 are directed to an abstract idea for the reasons given above.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the dependent claims recite the following additional elements:
Claim 21:
Classification algorithm
Clustering algorithm
Claim 22:
Classification algorithm
Clustering algorithm
These additional elements are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h))
Therefore, the claims recite an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISMAIL A MANEJWALA whose telephone number is (571)272-8904. The examiner can normally be reached M-F 8-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached on 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ISMAIL A MANEJWALA/Primary Examiner, Art Unit 3628
Prosecution Insights