DETAILED ACTION
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 .
Application Status
This final action is in response to applicant’s amendments of 22 October 2025. Claims 1-6 and 16-20 are examined and pending. Claims 1 and 16 are currently amended and claims 7-15 are withdrawn.
Election/Restrictions
Claims 1-6 and 16-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Group II, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on21 October 2024.
Response to Arguments
Applicant’s amendments/arguments with respect to the rejection under 35 USC 101 as being directed to an abstract idea without significantly more have been carefully considered and are not persuasive.
Applicant specifically argues the following:
Step 2A: The claims are not directed to an abstract idea
Applicants The Action asserts that the claims are directed to the abstract idea of a mental process, specifically because even if "performed by a trained model... they are imitations that could be done in the human mind" (Office Action page 5). This is respectfully submitted to be clear legal and factual error, particularly in light of the recent August 4, 2025 Memorandum from Deputy Commissioner Charles Kim to Technology Centers 2100, 2600, and 3600 re: "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" (hereinafter "the Memo").
The Examiner's analysis is the exact "oversimplifying" that the Memo expressly warns examiners to avoid. The Memo at page 2, last paragraph, states: "Examiners are reminded not to expand this [mental process] grouping... Claim limitations that encompass Al in a way that cannot practically be performed in the human mind do not fall within this grouping."10
The claims, particularly as amended, recite limitations that are the epitome of a process that "cannot practically be performed in the human mind." The claims recite "discretizing" an origin into a "homogenous zone" that is explicitly defined as "a result of processing an initial population of candidate spatial aggregations via an evolutionary machine-learning algorithm or a reinforcement machine-learning algorithm."
As detailed in the Specification at paragraphs [0146]-[0171] (e.g., describing Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Deep Reinforcement Learning), these are not simple mental steps. They are complex, iterative, population-based computational techniques used to optimize a "non-linear non-convex black-box optimization problem" (Specification at paragraph [0149]). It is not "practical" for a human mind to perform these steps.
Furthermore, the recent September 12, 2025 PTAB decision in Ex Parte Cajias (Appeal 2024-003198) (attached herein as Attachment I), which originated from this same Technology Center (3600), is highly persuasive even though it is not precedential. In Cajias, the Board reversed a 35 U.S.C. § 101 rejection, finding that claims involving computing digests and using a Bloom filter were not a mental process. The Board at Cajias page 8 held that "the totality of computing digests... encoding such digests to a bloom filter... cannot practically be performed in the human mind even with the use of pen and paper."
The instant claims are all the more not a mental process. The computational complexity of training an ML model via evolutionary or reinforcement learning (see, e.g., Specification at paragraphs [0146]-[0171]) and processing "pre-computed k-shortest paths" (see, e.g., Specification at paragraphs [0043] and [0100]) to generate specific data structures (the ETA homogenous zones) demonstrably exceeds the complexity of the Bloom filter calculations found to be non-mental in Cajias.
Applicants respectfully submit that the claims are not "imitations" of a human process. Instead, they are a new computational process (e.g., "evolutionary machine-learning algorithm" and "reinforcement machine-learning algorithm" as claimed) that has no practical human equivalent. Therefore, the claims do not recite a judicial exception and are eligible under Step 2A, Prong I.
The examiner has considered the arguments for step 2A prong 1 and respectfully disagree. The independent claims 1 and 16 recite discretizing the origin to an origin ETA homogenous zone and the destination to a destination ETA homogenous zone; processing an initial population of candidate spatial aggregations; determining one or more features associated with one or more pre-computed k-shortest paths between an origin-destination (O-D) zone pair comprising the origin ETA homogenous zone and the destination ETA homogenous zone. These limitation(s), as drafted, is (are) a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind.
That is, other than reciting “one or more processors and the processing via an evolutionary machine-learning algorithm or a reinforcement machine-learning algorithm”. The claim limitations encompass a person looking at different types of data such as trip characteristics, origin, a destination, time of departure, spatial aggregations data, and pre-computed data such as shortest paths, could discretize the origin to an origin ETA homogenous zone and the destination to a destination ETA homogenous zone; process initial population of candidate spatial aggregations; and determine one or more features associated with one or more pre-computed k-shortest paths between an origin-destination (O-D) zone pair comprising the origin ETA homogenous zone and the destination ETA homogenous zone. Even if these limitations are being performed/processed via an evolutionary machine-learning model or a reinforcement machine-learning algorithm, they are imitations that could be done in the human mind. The mere nominal recitation of “one or more processors and an evolutionary machine-learning model or a reinforcement machine-learning algorithm” does not take the claim limitation(s) out of the mental process grouping and merely function to automate the generating steps. Thus, the claim recites a mental process. (Step 2A – Prong 1: Judicial Exception Recited: Yes).
Secondly, applicant argues Step 2A, Prong 2 as follows:
Even if, arguendo, the claims were found to recite an abstract idea (which Applicants do not concede), they are patent-eligible because the idea is integrated into a practical application.
Applicants respectfully submit that the Examiner erred in finding the added limitations are "insignificant extra-solution activity" that "merely describe" the zones and the training. As amended, the claims positively limit the "homogenous zones" to those computationally generated by a specific, complex AI process. This is a limitation on the "ETA homogenous zone" used in the claimed "discretizing" step, not a "merely descriptive" label on the model used in the "providing" step.
This limitation, combined with the step of "determining... features associated with one or more pre-computed k-shortest paths" as recited claim 1, recites a specific technical solution to a technical problem.
. The Technical Problem: As detailed in the Specification at paragraph [0041],
conventional ETA systems are "resource-intensive" and suffer from "expensive path computation at the time of prediction."
. The Technical Solution: As stated in the Specification at paragraph [0042],
the claimed subject matter solves this problem by creating a new, computationally-efficient method that "does not need to compute any route at the time of prediction." As claimed, it achieves this by using the specific, computationally generated "homogenous zones" and "pre-computed k-shortest paths" as inputs to an ML model. According to the Specification at paragraph [0046], this "reduce[s] the computational cost at the time of prediction and lead[s] to faster query responses." This is a specific technological improvement in the field of computational travel time prediction.
The August 2025 Memo at page 4, third paragraph, is again instructive, stating: "The claim itself does not need to explicitly recite the improvement described in the specification." The claim only needs to "reflect" the improvement. The instant claims (e.g., by reciting the tools of the improvement such as the computationally generated zones and the pre-computed paths) clearly "reflect" the disclosed technical improvement.
Finally, the Board in Ex Parte Cajias found that solving the "technical problems of.. bandwidth issues and map version issues" constituted a practical application (see, e.g., Cajias at page 10, last paragraph, to page 11, first paragraph). The instant claims solve an analogous technical problem of computational resource constraints and system latency in a networked client- server ETA system (see, e.g., Specification at paragraphs [0040]-[0042]). This, too, is a practical application.
The examiner has considered the arguments for step 2A prong 2 and respectfully disagree. The independent claims 1 and 16 recite the additional limitations/elements of receiving a request for the ETA of the trip, wherein the request specifies an origin, a destination, and a time of departure; retrieving one or more pre-computed k-shortest paths for an origin-destination (O-D) zone pair comprising the origin homogenous zone and the destination homogenous zone; providing the one or more features as an input to a trained machine learning model to predict the ETA of the trip, an evolutionary machine-learning algorithm or a reinforcement machine-learning algorithm, a non-transitory computer-readable medium, and one or more processors.
The receiving and retrieving steps are recited at a high level of generality (i.e., receiving/collecting various data (trip characteristics, origin, a destination, time of departure, and pre-computed data such as shortest paths, etc.) and amount to mere data gathering, which is a form of insignificant extra-solution activity. The providing steps/elements are recited at a high level of generality (i.e., as a general action or change being taken based on the results of the generating step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Furthermore, the limitations of wherein the origin ETA homogenous zone is a first geographic region determined so that one or more first trips starting from the first geographic region have first travel times within a threshold similarity regardless of an exact start location within the first geographic region, and wherein the destination ETA homogenous zone is a second geographic region determined so that one or more second trips ending in the second geographic region have second travel times within the threshold similarity regardless of an exact destination location within the second geographic region; wherein the trained machine learning model was trained based on an evolutionary algorithm or a reinforcement learning algorithm to process an initial population of candidate spatial aggregations into the origin ETA homogenous zone, the destination ETA homogenous zone, or a combination thereof; merely describe what homogeneous zones (received date) for origin and destination location and how the trained machine learning model was trained. The additional limitation(s) of an evolutionary machine-learning algorithm or a reinforcement machine-learning algorithm, a non-transitory computer-readable medium, and one or more processors is/are recited at a high level of generality and merely function to automate the generating steps. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim(s) is/are directed to the abstract idea (Step 2A—Prong 2: Practical Application?: No).
Thirdly, applicant argues Step 2B:
The Examiner asserts that the claimed elements are "well-understood, routine, and conventional." Applicants respectfully disagree and note that under Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018), the question of whether elements are conventional is a factual inquiry. The specification (e.g., [0041]-[0045]) details how spatial aggregation-based ETA prediction using machine-learned homogeneous zones trained via evolutionary or reinforcement learning was neither conventional nor routine at the time of filing.
For example, the combination of ETA homogenous zones to discretize origins and destinations and machine learning-based ETA prediction using evolutionary and reinforcement training are non-conventional. Conventional methods computed routes at prediction time (resource-intensive) or did not account for heterogeneous route choices of travelers.
The claimed subject matter provides technical improvements by reducing compute time at inference and improves accuracy by leveraging ETA homogenous zones optimized during training.
The examiner has considered the arguments for step 2B and respectfully disagree. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than insignificant extra-solution activity.
Under the 2019 PEG, a conclusion that an additional element/limitation is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving, retrieving, and providing steps/additional elements were considered to be extra-solution activities in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The specification does not provide any indication that these steps are performed by anything other than conventional components performing the conventional activity (steps) of the claim. 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 collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer. The claim is ineligible (Step 2B: Inventive Concept?: No). As such, the rejection under USC 101 is maintained herein.
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 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is not directed to patent eligible subject matter.
101 Analysis
Based upon consideration of all of the relevant factors with respect to the claim as a whole, the claim is determined to be directed to an abstract idea. The rationale for this determination is explained below:
When considering subject matter eligibility under 35 U.S.C. § 101 under the 2019 Revised Patent Subject Matter Eligibility Guidance, the Office is charged with determining whether the scope of the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1).
If the claim falls within one of the statutory categories (Step 1), the Office must then determine the two-prong inquiry for Step 2A whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea), and if so, whether the claim is integrated into a practical application of the exception.
Claims 1-6 and 16-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1: Statutory Category
Independent claims 1-6 and 16-20 are rejected under 35 USC §101 because the claimed invention is directed to a process and machine respectively, which are statutory categories of invention (Step 1: Yes).
101 Analysis – Step 2A Prong 1: Judicial Exception Recited
The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). The abstract idea falls under “Mental Processes” Grouping. The independent claims recite discretizing the origin to an origin ETA homogenous zone and the destination to a destination ETA homogenous zone; processing an initial population of candidate spatial aggregations; determining one or more features associated with one or more pre-computed k-shortest paths between an origin-destination (O-D) zone pair comprising the origin ETA homogenous zone and the destination ETA homogenous zone. These limitation(s), as drafted, is (are) a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind.
That is, other than reciting “one or more processors and the processing via an evolutionary machine-learning algorithm or a reinforcement machine-learning algorithm”. The claim limitations encompass a person looking at different types of data such as trip characteristics, origin, a destination, time of departure, spatial aggregations data, and pre-computed data such as shortest paths, could discretize the origin to an origin ETA homogenous zone and the destination to a destination ETA homogenous zone; process initial population of candidate spatial aggregations; and determine one or more features associated with one or more pre-computed k-shortest paths between an origin-destination (O-D) zone pair comprising the origin ETA homogenous zone and the destination ETA homogenous zone. Even if these limitations are being performed/processed via an evolutionary machine-learning model or a reinforcement machine-learning algorithm, they are imitations that could be done in the human mind. The mere nominal recitation of “one or more processors and an evolutionary machine-learning model or a reinforcement machine-learning algorithm” does not take the claim limitation(s) out of the mental process grouping and merely function to automate the generating steps. Thus, the claim recites a mental process. (Step 2A – Prong 1: Judicial Exception Recited: Yes).
101 Analysis – Step 2A Prong 2: Practical Application
The independent claims 1 and 16 recite the additional limitations/elements of receiving a request for the ETA of the trip, wherein the request specifies an origin, a destination, and a time of departure; retrieving one or more pre-computed k-shortest paths for an origin-destination (O-D) zone pair comprising the origin homogenous zone and the destination homogenous zone; providing the one or more features as an input to a trained machine learning model to predict the ETA of the trip, an evolutionary machine-learning algorithm or a reinforcement machine-learning algorithm, a non-transitory computer-readable medium, and one or more processors.
The receiving and retrieving steps are recited at a high level of generality (i.e., receiving/collecting various data (trip characteristics, origin, a destination, time of departure, and pre-computed data such as shortest paths, etc.) and amount to mere data gathering, which is a form of insignificant extra-solution activity. The providing steps/elements are recited at a high level of generality (i.e., as a general action or change being taken based on the results of the generating step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Furthermore, the limitations of wherein the origin ETA homogenous zone is a first geographic region determined so that one or more first trips starting from the first geographic region have first travel times within a threshold similarity regardless of an exact start location within the first geographic region, and wherein the destination ETA homogenous zone is a second geographic region determined so that one or more second trips ending in the second geographic region have second travel times within the threshold similarity regardless of an exact destination location within the second geographic region; wherein the trained machine learning model was trained based on an evolutionary algorithm or a reinforcement learning algorithm to process an initial population of candidate spatial aggregations into the origin ETA homogenous zone, the destination ETA homogenous zone, or a combination thereof; merely describe what homogeneous zones (received date) for origin and destination location and how the trained machine learning model was trained. The additional limitation(s) of an evolutionary machine-learning algorithm or a reinforcement machine-learning algorithm, a non-transitory computer-readable medium, and one or more processors is/are recited at a high level of generality and merely function to automate the generating steps. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim(s) is/are directed to the abstract idea (Step 2A—Prong 2: Practical Application?: No).
101 Analysis – Step 2B: Inventive Concept
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than insignificant extra-solution activity.
Under the 2019 PEG, a conclusion that an additional element/limitation is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving, retrieving, and providing steps/additional elements were considered to be extra-solution activities in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The specification does not provide any indication that these steps are performed by anything other than conventional components performing the conventional activity (steps) of the claim. 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 collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer. The claim is ineligible (Step 2B: Inventive Concept?: No).
Dependent claims 2-6 and 17-20 do not include any other additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the Claims 1-6 and 16-20 are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Allowable Subject Matter
Claims 1-6 and 16-20 be allowable if rewritten to overcome the rejections under 35 U.S.C 101 set forth in this office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant’s amendment necessitated the new ground of rejection presented in the office action. Accordingly, THIS ACTION IS MADE FINAL. 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 extension fee 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.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDALLA A KHALED whose telephone number is (571)272-9174. The examiner can normally be reached on Monday-Thursday 8:00 Am-5:00, every other Friday 8:00A-5:00AM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faris Almatrahi can be reached on (313) 446-4821. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDALLA A KHALED/Examiner, Art Unit 3667
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