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 Non-Final action is in response to applicant’s amendments of 05/09/2023. 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 Claims 1 and 16 are currently amended and claims 7-15 are withdrawn.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Claims 1-6 and 16-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Group II, there being no allowable generic or linking claim.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 112(b) as set forth in the Office Action have been fully considered and are persuasive. As such, the rejection as previously presented has been withdrawn.
Applicant’s arguments with respect to the rejection under 35 U.S.C. § 103 have been fully considered and are persuasive. As such, the rejection as previously presented has been withdrawn.
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 In response to Applicants' previously filed Step 2A Prong 1 arguments that "the human mind is not equipped with a 'machine learning model' nor is the human mind capable of training such a model using 'an evolutionary algorithm' or 'reinforcement learning"', the Examiner asserts that "the examiner has considered the arguments for step 2A prong 1 and respectfully disagree" without addressing the recited "machine learning model" and related training limitations recited in the claims. Instead, the Examiner ignores these limitations and continues to allege that "other than reciting 'one or more processors'. The claim limitations encompass a person looking at different types of data such as trip characteristics, origin, a destination, time of departure, 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; 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."
Applicants continue to submit that the claims recites "providing the one or more features as an input to a trained machine learning model to predict the ETA of the trip, 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' recite a non-abstract technological implementation of a training a "machine learning model to predict the ETA of the trip" using features not being equipped or capable of being performed practically in the human mind.
The Examiner asserts that the independent claims recite a process that can be practically performed in the human mind. Applicants respectfully disagree and submit that the Examiner's characterization overlooks the technological specificity and computational requirements explicitly recited in the claims.
The claims recite not merely conceptual steps of "discretizing" or "determining features" but also recite:
(1) a trained machine learning model, and
(2) a machine learning model trained by an evolutionary algorithm or a reinforcement learning algorithm to predict the estimated time of arrival (ETA) of a trip; and
(3) the use of one or more pre-computed k-shortest paths.
The use of an evolutionary algorithm or reinforcement learning algorithm is not analogous to a mental process. Both are complex computational techniques that, as explicitly acknowledged in MPEP § 2106.04(a)(2)(III)(A), cannot practically be performed in the human mind.
Evolutionary algorithms require iterative population-level computations and fitness evaluations. Reinforcement learning necessitates environment-agent interactions over potentially thousands of episodes which are all beyond the capability to be practically performed in the human mind.
Furthermore, the pre-computation and retrieval of k-shortest paths for all zone pairs is a combinatorial problem that scales rapidly (e.g., O(k-n2) complexity for graph-based route computation). Such computations inherently require machine execution, as even for moderately sized road networks, this task is infeasible mentally.
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; 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”. The claim limitations encompass a person looking at different types of data such as trip characteristics, origin, a destination, time of departure, 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; 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 by a trained model or a predetermined computation model (pre-computed k-shortest path), they are imitations that could be done in the human mind. The mere nominal recitation of “one or more processors” 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, arguendo, if the Examiner maintains that an abstract idea is recited, Applicants respectfully submit that the claims integrate that idea into a practical application that improves the technical field of travel time prediction.
The claims explicitly recite:
(1) Spatial discretization/aggregation into homogeneous zones based on travel time similarity which is a data-driven approach that generalizes over spatial locations to improve prediction consistency without needed specific routes to be computed.
(2) Training of a machine learning model via evolutionary or reinforcement learning algorithms which are methods expressly aimed at optimizing spatial discretizations that minimize prediction error, thereby achieving technical improvements in the machine learning model and its training process.
(2) Elimination of route computation at prediction time as highlighted in paragraph [0042] of the Specification, which improves computational efficiency by avoiding real-time path computations.
At least these features constitute an improvement to the technological process of ETA prediction by, for instance, replacing prior resource-intensive route-based approaches with a zone- based, pre-computed, and machine-learned features. Claims that offer improvements to a technological process through non-conventional data processing techniques integrate an abstract idea into a practical application.
The Examiner characterizes the receiving, retrieving, and providing steps as "extra- solution activity." However, the training of the model using evolutionary/reinforcement algorithms to optimize spatial aggregations and the elimination of route computation at prediction time is neither data gathering nor mere display. As noted above, the such training constitutes a practical application that enhances system performance and reduces computational burden, satisfying MPEP §2106.05(a).
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 to predict the trip characteristic; and providing the predicted trip characteristic as an output; 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 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. Independent claims 1 and 16 recite discretizing the origin to an origin ETA homogenous zone and the destination to a destination ETA homogenous zone; 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”. The claim limitations encompass a person looking at different types of data such as trip characteristics, origin, a destination, time of departure, 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; 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. The mere nominal recitation of “one or more processors” does not take the claim limitation(s) out of the mental process grouping and merely function to automate the generating steps. Thus, the claims recite 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 to predict the trip characteristic; and providing the predicted trip characteristic as an output; 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 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.
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