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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
This Final Office Action is in response Applicant communication filled on 09/10/2026.
Status of Claims
Claims 1,6-9,13-14,16-17 have been amended by Applicant.
Claims 1-20 are currently pending and have been rejected as follows.
Response to Amendments / Arguments
Applicant’s 09/10/2026 amendment necessitated the new grounds of rejection in action.
Response to Applicant’s 101 Rebuttal arguments
Remarks 09/10/2026 p.24 ¶2-p.26 ¶2, again cites Original Specification ¶ [0002] to argue the claims include concrete limitations that improve computing systems that pre-dispatch candidate provider devices utilizing a pre-dispatch model that utilizes forward-looking digital queue filters reflecting forecasted transportation requests and backward-looking provider device dispatch remedial conflict filters, to improve the efficiency, accuracy, and flexibility of computer systems that intelligently transmit pre-dispatch notifications across computer networks to provider computing devices. In addition, Remarks 09/10/2026p.26 ¶2 emphasizes “iteratively re-execute the forward-looking candidate provider digital queue filter model and the backward-looking previously dispatched provider device digital queue filter model to determine an updated forward-looking candidate provider device queue capacity and an updated backward-looking previously dispatched provider device queue capacity at the queued pick-up location that satisfies the provider device queue capacity threshold” of independent Claims 1,9,16.
Remarks 09/10/2026 p.26 ¶3-p27 ¶1, again cites Original Specification ¶ [0022]: “unlike some models that rely predominantly on static constants and current conditions (e.g., current requestor device queues and current provider device queues), the flex forecasting pre-dispatch system can predict projected requestor device queues at estimated times of arrival for intelligently pre-dispatching candidate provider devices to the geographic area”
Remarks 09/10/2026 p.27 ¶2, again cites Original Specification ¶ [0023] to state that: “unlike some models that overcompensate or undercompensate for a requestor device queue at a geographic area, the flex forecasting pre-dispatch system can improve or resolve double-ended queue problem by utilizing a requestor device forecasting model in combination with forward and/or backward-looking queue filters”, then Remarks 09/10/2026 p.27 ¶2, again states that the independent claims recite a dynamic communication with candidate provider devices and improves the double-ended queue problem by determining to transmit a pre-dispatch notification and further determines to not send (e.g., withhold a predispatch notification) a vehicle to the queue (e.g. based on analysis of the forward and backward looking queue filters).
Remarks 09/10/2026 p.27 ¶3-p.28 ¶1, again cites Original Specification ¶ [0024] to argue the system can decrease computational resources of implementing computing devices “by utilizing a requestor device forecasting model with look-ahead model and/or look-behind model, the flex forecasting pre-dispatch system can reduce excess communications to and/or from a transportation matching server for informing requestor devices regarding increased wait times, managing device cancellation requests, and/or re-matching of provider devices and requestor devices in response these cancellations”. Additionally, it is argued the claims execute “a first simulation of a pre-dispatch model” to generate “computational efficiency metrics for bandwidth consumption of the pre-dispatch model”, and executes “a second simulation of an additional pre-dispatch model” to generate “additional computational efficiency metrics for bandwidth consumption of the additional pre-dispatch model”, “compares the computational efficiency metrics”, “and” selects "the pre-dispatch model instead of the additional pre-dispatch model”.
Remarks 09/10/2026 p.28 ¶2-p.29 ¶1, argues that similar to claim 3 of USPTO’s Example 47, the amended independent claims recite that forecasting involves using at least one of a machine-learning model or smoothing model having one or more tuned hyperparameters comprising a look-back window and the machine-learning model is trained with a loss function determined by comparing ground truth data with a predicted projected requestor device queue. In other words, it is argued that the amended independent claims recite trained models which contribute to generating the forward-looking candidate provider device queue capacity. Also, it is argued that similar to the anomalies on Example 47, the amended independent claims also recite additional limitations to highlight how the claimed invention can determine that an additional candidate provider device fails to qualify for pre-dispatch consideration based on an additional forward-looking candidate provider device queue capacity and an additional backward-looking previously dispatched provider device queue capacity. Then, Remarks 09/10/2026 p.29 ¶2 points to the “selecting the pre-dispatch model instead of the additional pre- dispatch model” “based on comparing the first set of simulated performance metrics with the second set of simulated performance metrics” at each of independent Claims 1,9,16, as equivalent to "selected training algorithm" to detect "one or more anomalies in network traffic" at the hypothetical Example 47.
Remarks 09/10/2026 p.29 ¶3 - p.30 ¶3, similar to the previous Remarks 07/17/2025 p.24 ¶1-¶2, argue, like USPTO Example 40, the independent claims recite comparing computational-efficiency metrics for bandwidth consumption generated from simulations of multiple pre-dispatch models and, based on that comparison, selecting the predispatch model instead of the additional pre-dispatch model. Also, it is again argued that similar to Example 40, the current independent claims compare various data types (e.g. projected provider device queue and provider device queue capacity threshold at the queued pick-up location and the plurality of additional projected provider device queues and the provider device queue capacity threshold at the queued pick-up location) to determine the forward looking candidate provider device queue capacity and the backward-looking previously dispatched provider device queue capacity. Further, Remarks 09/10/2026 p.30 ¶3 - p.31 ¶1 again points to “iteratively re-execute the forward-looking candidate provider digital queue filter model and the backward-looking previously dispatched provider device digital queue filter model to determine an updated forward-looking candidate provider device queue capacity and an updated backward-looking previously dispatched provider device queue capacity at the queued pick-up location that satisfies the provider device queue capacity threshold” of newly amended independent Claims 1,9,16. By doing so, it is argued the claim recites a specific improvement of determining whether or not to transmit a pre-dispatch notification to a candidate provider device based on the forward-looking candidate provider device queue capacity and the backward-looking previously dispatched provider device queue capacity.
Remarks 09/10/2026 p.31 ¶3-p.32 ¶2 cites to Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), to argue that similar to Enfish, the independent claims recite improvements to a computing technology by using a more efficient computational model-a pre-dispatch model instead of an additional pre-dispatch model based on comparing the computational efficiency metrics for bandwidth consumption with the additional computational efficiency metrics for bandwidth consumption. Indeed, it is argued that by using the recited simulations, selecting the pre-dispatch model, and using the pre-dispatch model to transmit pre-dispatch notifications to candidate provider devices, the claimed invention improves the overall computational efficiency and accuracy of implementing systems. It is also argued that like Enfish, which was eligible for using a more efficient structure for a computer database and adapting to data as it became available, the claims recite improvements that utilizes a structure that chooses between pre-dispatch model based on computational efficiency metrics, transmits pre-dispatch notifications, and updates the pre-dispatch model based on real-world observations from implementing the pre-dispatch model. Specifically, it is argued that unlike prior systems which suffer from relying predominantly on static constants and current conditions, the claimed invention dynamically selects a computationally superior pre-dispatch model (based on simulation metrics), implements the pre-dispatch model (that operates in a more accurate manner relative to existing systems), and adjusts to real-world observations. While, Remarks 09/10/2026 p.32 ¶3 - p.33 ¶2, admits that the technology in the claims differs from that of Enfish, it is argues that they share, their technology specifically provides technological improvements by employing a more efficient computational structure and adapting to real-world observations, and, as such the 101 rejections are moot.
Examiner fully considered the 101 arguments but respectfully disagrees finding them unpersuasive, reincorporating all the findings and rationales at Non-Final Act 01/25/2024 p2-p6 ¶3, p9 ¶ 5-p17 ¶ 7, Final Act 05/22/2024 p3 ¶2-p6 ¶3, and p9 last ¶-p21 ¶4, and Non-Final Act 04/17/2025 p2-p7 ¶3, p7 ¶6-p18 ¶4, Final Act 08/29/2025 p.3 last ¶-p.7 and p.8 last ¶-p22 ¶2, and Non-Final Act 06/10/2026 p.2-p.26 ¶4.
Examiner submits that even as amended, the argued claims, still recite, describe or set forth the abstract exception. First, Examiner resubmits that both the abstract groupings of “Certain Method of Organizing Human Activities”, at MPEP 2106.04(a)(2) II B. ¶13, and “Mathematical Calculations” at MPEP 2106.04(a)(2) I C, cite the same “In re Maucorps, 609 F.2d 481,482, 203 USPQ 812, 813 (CCPA 1979); to demonstrate that using an algorithm for determining the optimal number of visits by a business representative to a client, still falls within the confines of the abstract exception. Here the claims analogously recite forecasting models to pre-dispatch a candidate service provider to a client in manner not meaningfully different than In re Maucorps.
Thus, the claims similarly set forth or describe the abstract groupings of Certain Method of Organizing Human Activities and/or Mathematical Calculations”.
Next, Examiner stresses that according to MPEP 2106.04(a)(2) II A ¶2, the term fundamental, as in fundamental economic practices or principles, is not used in the sense of necessarily being old, or well-known but rather as a building block of modern economy.
Here, the newly amended “consumption”, stands along with the equally fundamental and abstract concepts of cost or profit as enumerated together at Original Specification ¶ [0105], as one example of such building blocks of modern economy. Examiner applies the aforementioned MPEP 2106.04(a)(2) II A ¶2 test to reason that here, just because such demand or consumption, is exemplified as “bandwidth consumption”, in executing the equally abstract simulat[ed] pre-dispatch models, does not necessarily render the claims less abstract and eligible, first, because its underlining simulat[ed] pre-dispatch models are not meaningfully different than the abstract, Maucorps’ like pre-dispatch models, with the inclusion of demand or “consumption” as a metric within such pre-dispatch models being equally fundamental, economic and abstract; and second because, as tested under MPEP 2106.04(a)(2) II A ¶2, such “bandwidth consumption”, would still be fundamental and abstract, as one of the many building blocks of modern economy, no matter whether or not its consideration, as a performance metric in the simulation of the pre-dispatch models would have been old or well-known. At most, the recitation of “bandwidth” as in “bandwidth consumption”, would represent, a claim drafting attempt to narrow the abstract, judicial exception or law, with limited applications. Yet, MPEP 2106.04 I ¶3 is clear that claims directed to narrow laws that have limited applications were still held patent ineligible1. Alternatively, even if more granularly at the subsequent steps, the term “bandwidth”, as in “bandwidth consumption”, would represent nothing more than a technological environment or field of use upon which the abstract consumption is being considered. Yet, MPEP 2106.05(h) is clear that narrowing an abstract concept to a field of use or technological environment is incapable to save the claims from patent ineligibility. The same rationale would apply to “updating the pre-dispatch model based on real-world observations from implementing the pre-dispatch model”, as an attempt at narrowing to a field of use or technological environment, the combination of collection and analysis of information which according to MPEP 2106.05(h)(vi) does not integrate the abstract idea into a practical application or provide significantly more. In fact, in Flook as also cited by MPEP 2106.05(h), the Supreme Court already ruled that updating a value of repeatedly calculated algorithms of real-world observations of temperature, pressure or flow rate did not amount to an inventive concept.
In absence of any other additional details of how an actual improvement in the computer itself or in actual technology is being performed, the Examiner weighs all the legal findings above against the Applicant’s arguments, and reasons that here, the amended claims still recite describe or set forth the abstract exception. Simply put, the features argued by Applicant above, would at most improve the fundamental practices and principles of pre-allocation or pre-dispatching services of a candidate provider to a customer, rather than being germane to actual improvement in actual technology or an actual improvement in the computer itself. This finding is especially important because, as highlighted by MPEP 2106.05(a) II ¶2, an improvement in the abstract idea itself (e.g fundamental economic concept) is not improvement in technology. Similarly, MPEP 2106.04 I. cites Myriad, 569 U.S. at 591, 106 USPQ2d at 1979: to state that even a “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry”. Following such legal precedents, the Examiner reasons that here too, an attempt at improving the efficiency, accuracy, and flexibility to intelligently transmit pre-dispatch notifications as argued by Applicant at Remarks 09/10/2026 p.24 ¶2-p.27 ¶1, per Original Specification ¶ [0002], ¶ [0022], would be incapable to provide patent eligibility, because it would still represent a mere assertion of a groundbreaking, innovative, or even brilliant discovery in the abstract idea itself. Simply said, the Applicant erroneously reasons that the claims disclose a technological improvement because of an improvement in the abstract process itself, not an actual improvement in underlining technology of the computer itself. see Remarks 09/10/2026 p.27 ¶3 citing Original Specification ¶ [0024]: “by utilizing a requestor device forecasting model with look-ahead model and/or look-behind model, the flex forecasting predispatch system can reduce excess communications to and/or from a transportation matching server for informing requestor devices regarding increased wait times, managing device cancellation requests, and/or re-matching of provider devices and requestor devices in response these cancellations”. Yet, as identified above, MPEP 2106.05(a) II ¶2 is clear that improvement in the abstract idea itself is not improvement in technology. Examiner further investigates “Myriad”, cited by MPEP 2106.04 I. above, and finds that the “Myriad” rationale was also corroborated in “SAP Am, Inc v InvestPic” which, at its turn, was cited by MPEP 2106.04(a)(2) I.C(i). Examiner digs deeper, into the Court’s rationale in “SAP, and equally finds that “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant those features are not enough for eligibility because their innovation is innovation in ineligible subject matter. An advance of that nature is ineligible for patenting”.
Here, as in “SAP” supra, even if one were to submit arguendo that “by utilizing requestor device forecasting model with look-ahead model and/or look-behind model, the flex forecasting pre-dispatch system can reduce excess communications to and/or from a transportation matching server for informing requestor devices regarding increased wait times, managing device cancellation requests, and/or re-matching of provider devices and requestor devices in response these cancellations” (Original Specification ¶ [0024] as cited by Remarks 09/10/2026 p.27 ¶3) and “by utilizing a look-ahead model and/or a look-behind model, the flex forecasting pre-dispatch system can avoid sending too few candidate provider devices or too many candidate provider devices to the geographic area” (Original Specification ¶ [0023] as cited by Remarks 09/10/2026 p.27 ¶1-¶3), the advance itself would still lie in the realm of abstract ideas, namely improvement of organizing pre-dispatching as an example of human activities, through equally abstract, algorithmic “look-ahead model and/or look-behind model” for mitigating excessive or insufficient supply, with no plausibly of the alleged innovation being an non-abstract application realm.
In fact, both the “Certain Method of Organizing Human Activities” at MPEP 2106.04(a)(2) II B. ¶13 and “Mathematical Calculations” at MPEP 2106.04(a)(2) I C cite “In re Maucorps, 609 F.2d 481,482, 203 USPQ 812, 813 (CCPA 1979); to demonstrate that using an algorithm for determining the optimal number of visits by a business representative to a client, still falls within the confines of the abstract idea. It would then follow that here, similar to In re Maucorps, the use of look-ahead and look-behind algorithms to allegedly improve overcompensation or undercompensating of the requester queue at a geographic area, as argued here at Remarks 09/10/2026 p.27 ¶1, would still fall within the abstract confines of Maucorps’ optimization (here “pre-dispatching”) by a business representative (here “provider”) to a client (here “requestor”).
It is thus clear that the claims’ character, as a whole, remains undeniably abstract being closer to the abstract optimization of visits by a business representative to a client, as in In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979), than any of the USPTO Examples 40, 47 as raised by Applicant at Remarks 09/10/2026 p.28 ¶2-p.30 ¶1.
Returning again to “SAP” as cited by MPEP 2106.04(a)(2) I.C(i), the Examiner finds that the challenged patent in “SAP” proposed utilization of resampled statistical methods for analysis of data, which did not assume a normal probability distribution. One such method in “SAP” was a bootstrap method, which estimated distribution of data in a pool (a sample space) by repeated sampling of the data in the pool. Data samples were drawn from the sample space with replacement: samples were drawn from the sample space and then returned to the pool before next sample is drawn. Yet, the Federal Circuit noted: “Dependent method claims 2-7 and 10 add limitations… [that] require the resampling method to be a bootstrap method." SAP, 260 F. Supp. 3d at 715. Likewise, "[c]laims 8 and 9 add limitations that the statistical method is a jackknife method and a cross validation method." Id. at 716. Because bootstrap, jack-knife, and cross-validation methods are all "particular methods of resampling," those features simply provide further narrowing of what are still mathematical operations. They add nothing outside the abstract realm. See Mayo, 566 U.S. at 88-89 (stating that narrow embodiments of ineligible matter, citing mathematical ideas as an example, are still ineligible); buySAFE, 765 F.3d at 1353 (same). Dependent method claims 12-21 are no different”.
Since implementation of sample space, boot-strap, jackknife, cross validation, and resampling in “SAP” modeling did not save its claims from ineligibility, Examiner similarly reasons that the utilization of algorithmic forward-looking and backward-looking digital queue filters, and, in the choice or alternative between a “learning model trained with a loss function determined by comparing ground truth data with a predicted projected requestor device queue” or “smoothing model has one or more tuned hyperparameters comprising a look-back window” as analogous to the boot-strap, jackknife, cross validation techniques of SAP supra, to allegedly improve the pre-dispatching at Remarks 09/10/2026 would also not save the current claims from ineligibility.
Also, the “SAP” findings were corroborated by “Versata Dev Grp, Inc v SAP Am, Inc 115 USPQ2d 1681 Fed Cir 2015” which again underlined the difference between improvement to entrepreneurial goal or objective and actual improvement to actual technology. see MPEP 2106.04. Such improvement in entrepreneurial goal or objective is unpersuasively argued here as improvement in efficiency, accuracy, and flexibility in intelligently transmitting pre-dispatch notifications (Remarks 09/10/2026 p.26 ¶2), drafted through exemplary language such as to improve or resolve overcompensate or undercompensate for a requestor device queue at geographic area (Remarks 09/10/2026 p.27 ¶1), to allegedly reduce excess communications (Remarks 09/10/2026 p.27 ¶3).
Also, a claim is not eligible merely because it applies an abstract idea in a narrow way; that is, “improvement in the judicial exception itself” [akin to Remarks 09/10/2026 p.24 ¶2,p.26 ¶2-p.27 ¶2,p.27 ¶4-p.28 ¶1] “is not an improvement in technology”. MPEP 2106.04(d)(1). Here, utilizing algorithmic forward-looking and backward-looking, argued by Remarks 09/10/2026, does not preclude the claims to recite or at least describe or set forth the abstract exception including fundamental and/or commercial activities per MPEP 2106.04(a)(2) II, practical implemented through computer-aided evaluation and judgment as per MPEP 2106.04(a)(2) III and mathematical relationships expressed in words as broadly defined by MPEP 2106.04(a)(2) I. A.
Also, the fact that the claims utilize algorithmic forward and backward-looking digital queue filters, “learning” or “smoothing model” and a generally recited “server”, represents a mere attempt at applying an algorithm on a computer to perform the aforementioned abstract idea, which according to MPEP 2106.05(f)(2)2 does not integrate the abstract exception into a practical application. Also, the fact that the claims narrow the abstract “provider and requester” “queues” to “provider and requester devices” and narrow the “queue filter models” to “digital queue filter models”, represent mere attempts to narrow the abstract idea to a field of use or technological environment which according to MPEP 2106.05(h) vi3 also does not integrate the abstract exception into a practical application. Step 2A prong two.
As per Applicant’s reliance on Example 47 at Remarks 09/10/2026 at p. 28 ¶2 -p.29 ¶2, and on Example 40 at Remarks 09/10/2026 p.29 ¶3-p.30 ¶2, the Examiner reminds that all 101 examples provided by USPTO, including Examples 40 and 47 are hypothetical and non-precedential. see USPTO “2019 PEG, 101 Examples 37-42 document entitled “Subject Matter Eligibility Examples: Abstract Ideas” p.1, ¶1 2nd sentence. “The examples below are hypothetical and only intended to be illustrative of the claim analysis under the 2019 PEG” corroborating “May 2016 Update: Memorandum - Formulating a Subject Matter Eligibility Rejection and Evaluating the Applicant’s Response to a Subject Matter Eligibility Rejection”, p.5 ¶2 Section C: “USPTO issued examples in conjunction with the Interim Eligibility Guidance, including […] July 2015 Update Appendix I: Examples […]; These examples, many of which are hypothetical, were drafted to show exemplary analyses under the Interim Eligibility Guidance and are intended to be illustrative of the analysis only. While some of the fact patterns draw from U.S. Supreme Court and U.S. Court of Appeals for the Federal Circuit decisions, the examples do not carry the weight of court decisions. Therefore, the examples should not be used as a basis for a subject matter eligibility rejection. Similarly see July 2024 Subject Matter Eligibility Examples, pertaining to Examples 47-49, p.1, ¶1, 2nd sentence: “The examples below are hypothetical and only intended to be illustrative of the claim analysis performed using MPEP 2106, and of the particular issues noted below in the Issue Spotting Chart”.
In any event here, the claims are irreconcilably different than Examples 40 and 47.
At no point do the current claims recite anything remotely analogous to the combination of additional elements of collecting at least one of network delay, packet loss, or jitter relating to the network traffic passing through the network appliance, and collecting additional netflow protocol data relating to the predefined threshold to analyze the cause of the abnormal condition as in hypothetical Claim 1 of the nonprecedential Example 40. Also, at no point do the current claims recite anything remotely analogous to detecting potentially malicious network packets and taking real-time remedial actions, including dropping suspicious packets and blocking traffic from suspicious source addresses as in hypothetical Claim 3 of nonprecedential Example 47.
Rather, as explained by Applicant at Remarks 09/10/2026 p.28 ¶2 3rd sentence, the current independent claims are merely using or “utilizing” , [as an equivalent to the word applying], a “smoothing or machine-learning model, as a computer aid [MPEP 2106.04(a)(2) III C] and/or an additional computer-based element [MPEP 2106.05(f)(2)(i)] to achieve a desired, yet abstract, entrepreneurial goal or objective namely, dropping or “withholding” supply of “a pre-dispatch from the additional candidate provider” as amended at each independent claims and acknowledged by Remarks 09/10/2026. Such “withholding” supply of “a pre-dispatch from the additional candidate provider” appears to address situation of low demand or e lulls in the transportation requests as read in light of the Original Specification ¶ [0021], ¶ [0025], and previously argued by Applicant at Remarks 08/01/2024 p.22 ¶1, and addressed by the Non-Final Act 04/17/2025 p.5-p.6 ¶2.
The fact that such “withholding a pre-dispatch notification from the additional candidate provider device” is “based on the additional forward-looking candidate provider device queue capacity and the additional backward-looking previously dispatched provider device queue capacity” whose amended generati[on] even if argued as groundbreaking, innovative, or even brilliant discovery, as was the case in Myriad, Versata, and SAP supra, would still provide nothing more than an improvement in the abstract, fundamental, and entrepreneurial practice of pre-dispatching candidate providers [or suppliers] to requestors [or customers] based on considerations of supply and demand, reflected here in the queue capacit[ies], be it “forward looking” or “backward-looking” as detailed in each of the amended claims.
As per the “selecting the pre-dispatch model instead of the additional pre- dispatch model” “based on comparing the first set of simulated performance metrics with the second set of simulated performance metrics” as amended at each of independent Claims 1,9,16, as argued by Applicant at Remarks 09/10/2026 p.29 ¶2, and “iteratively re-execute the forward-looking candidate provider digital queue filter model and the backward-looking previously dispatched provider device digital queue filter model to determine an updated forward-looking candidate provider device queue capacity and an updated backward-looking previously dispatched provider device queue capacity at the queued pick-up location that satisfies the provider device queue capacity threshold” of independent Claims 1,9,16, as argued by Applicant at Remarks 09/10/2026 p.26 ¶2, p.27 ¶2, p.30 ¶31, the Examiner finds such limitations not meaningfully different than the abstract approaches of heuristics, iterative refinement or approximation, which set forth the abstract mathematical calculations of MPEP 2106.04(a)(2) (I) C and/or the abstract mathematical relationships expressed in words of MPEP 2106.04(a)(2)(I) (A). Examiner stresses that as ruled by the Federal Circuit in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) and cited by PTAB Appeal 2025-003304: “The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted based on real time changes do not represent a technological improvement” at least because they are “incident to the very nature of machine learning”. Examiner also points again to Brandan Artley, Training a Neural Network by Hand, towardsdatascience webpages, Jun 23, 2022, incorporated herein, corroborating that the training of a neural network by hand to solve a regression problem where the model continually improves its predictions to arrive at a highly accurate model.
It then follows that here, similar to the iteratively training and dynamically adjustment in Recentive Analytics and Brandan Artley, the “selecting the pre-dispatch model instead of the additional pre-dispatch model” “based on comparing the first set of simulated performance metrics with the second set of simulated performance metrics” and “iteratively re-execute the forward-looking candidate provider digital queue filter model and the backward-looking previously dispatched provider device digital queue filter model to determine an updated forward-looking candidate provider device queue capacity and an updated backward-looking previously dispatched provider device queue capacity at the queued pick-up location that satisfies the provider device queue capacity threshold” of independent Claims 1,9,16, as argued by Applicant at Remarks 09/10/2026 would also be incident to the very nature of machine learning, if not abstract right from the onset, as indicative to the mathematical relationships of MPEP 2106.04(a)(2) I A and/or cognitive and evaluation functions of MPEP 2106.04(a)(2) III ¶2, performed in a computer environment of MPEP 2106.04(a)(2) III C #2 or using a computer as a tool of MPEP 2106.04(a)(2) III C #3, or at most a mathematical learning algorithm executed on a computer which, when more granularly tested per MPEP 2106.05(f)(2)(i) would not integrate the abstract exception into a practical application or provide significantly more.
With respect to Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), as relied upon by Applicant at Remarks 09/10/2026 p.31 ¶3-p.33 ¶1, the Examiner finds that the current claims are irreconcilably different than the eligible subject matter in Enfish supra. This is because the current claims set forth the fundamental and abstract practices or principles of pre-dispatching of services between a candidate provider and a consumer. At no point do the current claims provide anything remotely analogous to the classification of structures for repeated extraction and the importing for mapping with self-referential data structures, as was the case in Enfish, 822 F.3d 1327,1336,118 USPQ2d 1684, 1689 (Fed. Cir. 2016) cited by MPEP 2106.05(a) I
In conclusion, the Examiner submits that the argued limitations still recite, describe or set forth the abstract exception (Step 2A prong one), with the additional, computer-based elements, not integrating it into a practical application (Step 2A prong 2), and for the similar reasons not providing significantly more (Step 2B). Thus, the argued claims are still believed to be ineligible.
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Objection
Claim 9 is independent and objected for informally reciting in the gerund:
- “updating the pre-dispatch model based on real-world observations from implementing the pre-dispatch model”, instead of grammatically correct present tense:
- update the pre-dispatch model based on real-world observations from implementing the pre-dispatch model. Correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1,9, 16 are independent and, have each been amended to recite, among others:
(i) “executing a first simulation of a pre-dispatch model for a set of test data to generate a first set of simulated performance metrics comprising computational efficiency metrics for bandwidth consumption of the pre-dispatch model;” [emphasis added]
(ii) “executing a second simulation of an additional pre-dispatch model for the set of test data to generate a second set of simulated performance metrics comprising additional computational efficiency metrics for bandwidth consumption of the additional pre-dispatch model;” [emphasis added]
(iii) “based on comparing the computational efficiency metrics for bandwidth consumption with the additional computational efficiency metrics for bandwidth consumption,
selecting the pre- dispatch model instead of the additional pre-dispatch model;” [emphasis added]
Claims 1,9,16 are rendered vague and indefinite because it is unclear if bandwidth consumption as firstly recited at limitation (iii), relates back to bandwidth consumption at limitation (i).
Claims 1,9,16 are rendered vague and indefinite because it is unclear if bandwidth consumption as secondly recited at limitation (iii), relates back to bandwidth consumption at limitation (ii).
Claims 1,9, 16 are recommended to be amended, to recite, among others, by example only:
(i) executing a first simulation of a pre-dispatch model for a set of test data to generate a first set of simulated performance metrics comprising computational efficiency metrics for bandwidth consumption of the pre-dispatch model;
(ii) executing a second simulation of an additional pre-dispatch model for the set of test data to generate a second set of simulated performance metrics comprising additional computational efficiency metrics for bandwidth consumption of the additional pre-dispatch model;
(iii) based on comparing the computational efficiency metrics for the bandwidth consumption of the pre-dispatch model with the additional computational efficiency metrics for the bandwidth consumption of the additional pre-dispatch model, selecting the pre-dispatch model instead of the additional pre-dispatch model;
Claims 2-8, 10-15, 17-20 are dependent and rejected based on rejected parent Claims 1,9,16.
Clarification and correction are required.
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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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claim(s) recite(s) set forth or describe the abstract grouping of “Certain Methods of Organizing Human Activities”. More specifically the claims still recite, describe or set forth fundamental economical practices and/or commercial interactions as evidenced by the claimed “pre-dispatch model” “utilize[ed] to” position or “pre-dispatch” “candidate provider” “at geographic area comprising a queued pick-up location for transporting” [a] “requestor” at preamble of each of independent Claims 1,9,16, further elaborated and manipulated at the body of said claims, and culminating at the last limitation with “transmitting” “a pre-dispatch notification” “to pre-dispatch the candidate provider” and “withholding a pre-dispatch notification from the additional candidate provider” “based on the additional forward-looking candidate provider” “queue capacity and the additional backward-looking previously dispatched provider” “queue capacity” at each of each of independent Claims 1,9,16, “pre-dispatch the candidate provider device to the queued pick-up location by dispatching the candidate provider device to the queued pick-up location prior to receiving a transportation request from a requestor device” at dependent Claims 15. It also appears that by ensuring that “the another projected provider” “queue is less than the provider” “queue capacity threshold” (dependent Claims 4,12,19), the claims appear to mitigate “queue capacity” risk, or, at least consider “queue capacity” (independent Claims 1,9,16) as underlining factor considered for fundamental, economic or commercial “pre-dispatching” demonstrated by: “forecasting a projected provider” “queue waiting at the queued pick-up location at the time corresponding to the first ETA based on the projected requestor” “queue”, [and] “the current provider” “queue”, “and the in-transit provider” “queue”; [followed by] “comparing the projected provider” “queue and the provider” “queue capacity threshold at the queued pick-up location to determine the forward-looking candidate provider” “queue capacity at the queued pick-up location” (independent Claims 1,9,16) with the abstract, entrepreneurial or business goal of “withholding a pre-dispatch notification from the additional candidate provider” “based on the additional forward-looking candidate provider” “queue capacity and the additional backward-looking previously dispatched provider” “queue capacity” (independent Claims 1,9,16).
These fundamental, economic or commercial principles are further narrowed as “forecasting the projected provider” “queue at the time corresponding to the first ETA based on the current requestor” “queue, the current provider” “queue, the in-transit provider” “queue, and the projected requestor” “queue” (dependent Claim 2), “forecasting” “additional projected requestor” “queue for the queued pick- up location at a time corresponding to the additional ETA; forecasting another projected provider” “queue at the time corresponding to the additional ETA based on the another projected requestor” “queue; and determining that the another projected provider” “queue is less than the provider” “queue capacity threshold” (dependent Claims 4,12,19), “determining a modified projected provider” “queue for the queued pick-up location at the time corresponding to the first ETA of the candidate provider” “based on pre-dispatching the another candidate provider”; “and comparing the modified projected provider” “queue for the queued pick-up location at the time corresponding to the first ETA of the candidate provider” “with the provider” “queue capacity threshold” (dependent Claims 5,13,20).
Yet, according to MPEP 2106.04(a)(2) II, fundamental practices and mitigating risks of such practices still fall within the abstract “Certain Methods of Organizing Human Activities”.
Examiner points to MPEP 2106.04(a)(2) II ¶6 which states that the [abstract] sub-groupings encompass both activity of a single person (for example, a person following a set of instructions) and activity that involves multiple people (such as a commercial interaction). It also states that certain activity between a person and a computer may still fall within “Certain Methods of Organizing Human Activities” grouping. This finding is especially important here, since the current claims similarly recite the instructions or activities of “pre-dispatching” “candidate provider” and “additional candidate providers”, according to respective “candidate / additional candidate” as part of commercial “provider” activities or interactions between them while also taking into consideration “projected” “requestor/additional projected requestor”. When considered together, these recitations represent examples of multiple parties involved in the interaction or instructions as well as activities relating to such parties and their underlining computers, recited in a manner not meaningfully different than the abstract examples enumerated by MPEP 2106.04(a)(2) II ¶6.
Equally importantly here, akin to the examples enumerated by MPEP 2106.04(a)(2) II ¶6 above, the use of “provider and requestor devices” or computers, underlining the activity or “pre-dispatching” between such persons and a computer, do not necessarily preclude the claims from reciting describing or setting forth the fundamental economic practices and commercial interactions of the abstract Certain Methods of Organizing Human Activities grouping. Also, according to MPEP 2106.04(a)(2) II A ¶2, the term fundamental is not used in the sense of necessarily being old or well-known. This finding is further bolstered by the fact that “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions” as also stated by MPEP 2106.04 I ¶5. For example, MPEP 2106.04(a)(2) II.A ¶2 cited Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 219-20, 110 USPQ2d 1981-82 (2014) to state that the concept of intermediated settlement between multiple parties is a fundamental practice and thus it is abstract. As revealed by MPEP 2106.04(a)(2) ¶6 supra, instructions between multiple entities [akin here “requestor” and “provider”] and requiring that said interactions between such entities be done with respective computer [akin here as “candidate provider” and “projected requestor” “devices”, as well as “a user interface”], do not preclude the claims from reciting, describing or setting forth the abstract idea. Further, MPEP 2106.04(a)(2) II B. ii. cites In re Maucorps, 609 F.2d 481,485,203 USPQ 812,816 CCPA 1979 to state that using an algorithm for determining the optimal number of visits by a business representative to a client, is an example of commercial interaction which falls within “Certain Methods of Organizing Human Activities”. It then follows that the algorithmic “forecasting” limitations (Claims 1,2,4,8-10,12,16,17,19) for determining “pre-dispatch the candidate provider” “to the geographic area based on the forward-looking queue capacity and backward-looking queue capacity” (independent Claims 1,9,16), “pre-dispatching the other candidate provider” “utilizing the forward-looking digital queue filter model and the backward-looking digital queue filter model” (dependent Claims 3,11,18), would similar recite commercial interactions analogous to Maucorps supra. Also, MPEP 2106.04(a)(2) II C cites BSG Tech. LLC v. Buyseasons Inc., 899 F.3d 1281, 1286,127 USPQ2d 1688,1691 to state that considering historical usage information while inputting data is example of managing relationships or interactions which also falls within the Certain Methods of Organizing Human Activity. It then follows that here, considering “historical data” (dependent Claim 9), should similarly recite, describe or set forth the abstract idea.
Examiner also reminds the Applicant that according to MPEP 2106.04(a)(2) II A ¶2, the term fundamental, as in fundamental economic practices or principles, is not used in the sense of necessarily being old, or well-known but rather as a building block of modern economy. Here, the newly amended “consumption”, stands along with the equally fundamental and abstract concepts of cost or profit as enumerated together at Original Specification ¶ [0105], as an example of such building blocks of modern economy. Examiner applies such MPEP 2106.04(a)(2) II A ¶2 test to reason here, that simply because such demand or consumption, happens to be narrowed to “bandwidth consumption”, in executing the equally abstract simulat[ed] pre-dispatch models, does not render the claims less abstract and eligible. First, such simulat[ed] pre-dispatch models are not meaningfully different than the abstract, Maucorps’ like pre-dispatch models, with the inclusion of demand or “consumption” as one metric within such pre-dispatch models being equally fundamental, economic and abstract. As tested under MPEP 2106.04(a)(2) II A ¶2, such “bandwidth consumption”, would still be fundamental and abstract, as a building block of modern economy, no matter whether or not its consideration in executing the simulation of the re-dispatch models would have been old or well-known.
Examiner remains within the confines of Step 2A prong one, and further points to MPEP 2106.04(a): instructing that “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible…”. Examiner abides by such guidance and submits that here, give the broad recitation of “forecasting”, “determining” and “comparing” in the above fundamental economic practices or commercial interactions, could also be viewed as describing or setting forth equally abstract mathematical relationships expressed in words of the abstract “Mathematical Concepts” grouping [MPEP 2106.04(a)(2) I] and/or evaluation, judgement and observation as part of the equally abstract “Mental Processes” grouping [MPEP 2106.04(a)(III)] as: 1. Performed on a generic computer 2. Performed in a computer environment and/or 3. Performed using the computer as a tool [MPEP 2106.04(a)(III) C]. Examiner follows the same MPEP 2106.04 guidelines to reason that, given their broad recitation, and no matter of their computerized implementation, the claims’ character as a whole remains undeniably abstract. [Step 2A prong one]. For example, as stated by MPEP 2106/04(a)(2) III A ¶8 a claim directed to the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis4, remains directed to the abstract mental processes. Here, given the broad recitation of “executing” “a first simulation” and “a second simulation” at Claims 1,8,9,16, and similarly “executing” “a third simulation” and “a fourth simulation” at Claims 7,14 for “selecting the pre-dispatch model” “based on comparing the computational efficiency metrics for bandwidth consumption with the additional computational efficiency metrics for bandwidth consumption” at independent Claims 1,9,16, “selecting the pre-dispatch model” “based on comparing the third set of simulated performance metrics with the fourth set of simulated performance metrics” at dependent Claims 7,14, nothing would have precluded one of ordinary skills in the art to perform such simulations on generic computer or to use a computer as a tool for “executing” such “simulations”. Same rationale applies to “comparing projected provider” “queue” “and” “provider” “queue capacity threshold at the queued pick-up location to determine a backward-looking” “previously dispatched provider” “queue capacity” “at the queued pick-up location” (independent Claims 1,9,16) and “utilizing the forward-looking candidate provider” “queue filter model” “to determine a forward-looking candidate provider” “queue capacity for the queued pick-up location”, “by determining” “for a candidate provider” “a first estimated time of arrival (ETA) for the candidate provider” “at the queued pick-up location” and similarly “utilizing the backward-looking” “queue filter model to determine a backward-looking provider” “queue capacity for the queued pick-up location” “by determining”, “a plurality of estimated times of arrival (ETAs) for the previously pre-dispatched provider[s]” (at independent Claims 1,9,16), and similarly “utilizing forward”-looking candidate provider” “queue filter model” and “backward looking previously dispatched provider” “filter model” “for pre-dispatching the another candidate provider” (dependent Claims 3-5,18,20), “utilizing a double exponential smoothing model comprising the additional look-back window” (dependent Claim 11), “determining a relationship between a scaled interval of time and a time interval for the candidate provider device”; “and” “generating the scaled projected requestor device queue by combining the relationship with the projected requestor device queue” at dependent Claim 12. For example, given the broad recitation of such features, nothing would have precluded one of ordinary skills in the art to cognitively construct and observe models [MPEP 2106.04(a)(2) III ¶2] to evaluate using the aforementioned mathematical relationships [MPEP 2106.04(a)(2) III ¶2] the underlining “queues” and respective estimated time of arrival ETA to come up with judgment [MPEP 2106.04(a)(2) III] regarding economic practices or commercial interactions of “pre-dispatching the another candidate provider” [MPEP 2106.04(a)(2) II]. Indeed, as demonstrated above, MPEP 2106.04(a)(2) III cited Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); to state that a claim directed to the combination of collecting information, analyzing it, and displaying certain results of collection and analysis, falls within the abstract exception. Here too, the “forecasting”, “utilizing a smoothing model” “a projected requestor” “queue for the queued pick-up location at a time corresponding to the first ETA of the candidate provider” and “forecasting, a projected provider” “queue for the queued pick-up location at the time corresponding to the first ETA based on the projected requestor” “queue, the current provider” “queue, and the in-transit provider” “queue” followed by “comparing the projected provider” “queue and a provider” “queue capacity threshold at the queued pick-up location to determine the forward-looking provider” “queue capacity at the queued pick-up location”, “determining” “for previously pre-dispatched provider”, “additional projected provider” “queues at the plurality of ETAs for the previously pre-dispatched provider[s]” “based on modeling the previously pre-dispatched provider[s]” “arriving at the queued pick-up location at the plurality of ETAs and the candidate provider” “arriving at the queued pick-up location at the first ETA” and “comparing the plurality of additional projected provider” “queues and the provider queue capacity threshold at the queued pick-up location to determine the backward-looking previously dispatched provider” “queue capacity for the queued pick-up location” of independent Claims 1,9,16; set forth the abstract analysis identified above, while “transmitting” “a pre-dispatch notification to pre-dispatch the candidate provider” “to the geographic area based on the updated forward-looking candidate provider” “queue capacity and the updated backward-looking previously dispatched provider” “queue capacity that satisfies the provider device queue capacity threshold” of independent Claims 1,9,16 set forth the abstract displaying certain results of collection and analysis above.
Thus here, there is a preponderance of legal evidence showing that the claims still recite, describe or set forth the abstract exception. Step 2A prong one.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, the individual or combination of the additional, computer-based elements are/is found, to merely narrow the abstract character of the claims to a field of use or technological environment [MPEP 2106.05(h)] and/or at most apply the above abstract idea [MPEP 2106.05(f)]. Here, the additional elements are “instructions” “by at least one processor” “execut[ing]” the abstract exception above at Claims 9-15 as well as “memory device(s)” and “computing device(s)” executing the abstract exception at Claims 16-20, and possibly the “user interface” of independent Claims 1,9,16 and the preponderantly recited “devices” of the respective “candidate provider / additional candidate provider” and “projected requestor / additional projected requestor” as a computer environment upon which the abstract exception is executed, while the generally and alternatively recited of the broader “learning model” or the narrower “smoothing model” which “has one or more tuned hyperparameters comprising a look-back window and the machine-learning model is trained with a loss function determined by comparing ground truth data with a predicted projected requestor device queue” of independent Claims 1,9,16, the tuned “hyperparameters” of
Claims 11, and the broad “neural network” of dependent Claim 17 represent the computerized algorithms upon which the abstract forecast[ing] is implemented. For example, when tested per MPEP 2106.05(f)(2) said additional elements merely apply the abstract idea, such as merely applying business method and mathematical algorithm on computer5. This is reflected straight from independent Claims 1,9,16 as “forward-looking digital queue filter model and a backward-looking digital queue filter model to transmit pre-dispatch notifications to candidate provider devices at geographic area corresponding to a combined pick-up location for requestor devices” further detailed throughout Claims 1-20 body, including “utilizing” [akin to applying] the choice or alternative of “a machine-learning model trained with a loss function determined by comparing ground truth data with a predicted projected requestor device queue” or “smoothing model has one or more tuned hyperparameters comprising a look-back window” disclosed in the alternative at independent Claims 1,9,16, and the amended “iteratively re-execute the forward-looking candidate provider digital queue filter model and the backward-looking previously dispatched provider device digital queue filter model to determine an updated forward-looking candidate provider device queue capacity and an updated backward-looking previously dispatched provider device queue capacity at the queued pick-up location that satisfies the provider device queue capacity threshold” and “updating the pre-dispatch model based on real-world observations from implementing the pre-dispatch model” at independent Claims 1,9,16, and “utilizing a neural network trained on observed queue data to generate projected provider device queue” at dependent Claim 17 and similarly “applying the exponential smoothing model to the historical data pairing rates to generate a forecast of the projected requestor device queue” at dependent Claim 10, and similarly “generating, utilizing the forward-looking candidate provider digital queue filter model, an additional forward-looking candidate provider device queue capacity for the queued pick-up location based on an additional ETA of an additional candidate provider device”; “generating, utilizing the backward-looking previously dispatched provider device digital queue filter model, an additional backward-looking previously dispatched provider device queue capacity for the queued pick-up location based on a plurality of additional ETAs for a plurality of previously pre-dispatched provider devices” at independent Claims 1,9,16, “generating the first set of simulated performance metrics that includes average requestor wait times, a size of simulated requestor device queue, and simulated conversion metrics; and generating the second set of simulated performance metrics that includes additional average requestor wait times, an additional size of simulated requestor device queue, and additional simulated conversion metrics”.
at dependent Claims 6,13, and “execute a third simulation of the pre-dispatch model that is updated based on the real-world observations for an additional set of test data to generate a third set of simulated performance metrics” at dependent Claims 7,14, “updating the pre-dispatch model based on additional real-world observations from implementing the pre-dispatch model to transmit the additional pre-dispatch notifications” at dependent Claims 8,14
Examiner stresses that as ruled by Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) and cited by PTAB Appeal 2025-003304: “The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted based on real time changes do not represent a technological improvement” at least because they are “incident to the very nature of machine learning”.
It then follows that here, similar to the iteratively training and dynamically adjustment in Recentive Analytics, the amended “selecting the pre-dispatch model instead of the additional pre-dispatch model” “based on comparing the computational efficiency metrics for bandwidth consumption with the additional computational efficiency metrics for bandwidth consumption” and “iteratively re-execute the forward-looking candidate provider digital queue filter model and the backward-looking previously dispatched provider device digital queue filter model to determine an updated forward-looking candidate provider device queue capacity and an updated backward-looking previously dispatched provider device queue capacity at the queued pick-up location that satisfies the provider device queue capacity threshold” of independent Claims 1,9,16, “generating the first set of simulated performance metrics that includes average requestor wait times, a size of simulated requestor device queue, and simulated conversion metrics; and generating the second set of simulated performance metrics that includes additional average requestor wait times, an additional size of simulated requestor device queue, and additional simulated conversion metrics” at dependent Claim 6,13, “execute a third simulation of the pre-dispatch model that is updated based on the real-world observations for an additional set of test data to generate a third set of simulated performance metrics” at dependent Claims 7,14 etc. would also constitute, along with, “utilizing” [akin to applying] the choice or alternative of “a machine-learning model trained with a loss function determined by comparing ground truth data with a predicted projected requestor device queue” or “smoothing model has one or more tuned hyperparameters comprising a look-back window” disclosed in the alternative at independent Claims 1,9,16, and “generate, utilizing a double exponential smoothing model comprising the additional look- back window and the additional smoothing factor, the forecast of the projected requestor device queue elements integral to the very nature of machine learning” at dependent Claim 11.
Also, when tested per MPEP 2106.05(f)(2) mere use of “devices” of “candidate provider / additional candidate provider” and “projected requestor / additional / another projected requestor” represent mere use of computerized telecommunication or location devices in their ordinary capacity to perform the aforementioned economic tasks [here identified above] and other tasks to transmit and receive data6. Such data is recited here with respect to the underlining “requestor” and “provider” “queues” and underlining “estimated time of arrival (ETA)” as recited throughout claims 1-5, 9-10, 12, 16-20 and recitations of “to transmit additional pre-dispatch notifications to additional candidate provider devices” “utilizing the pre-dispatch model comprising the forward-looking candidate provider digital queue filter model and the backward-looking previously dispatched provider device digital queue filter model” at dependent Claims 8,14.
Moreover, according to MPEP 2106.05(f)(2) the capabilities of computer to monitor audit log data7 and tailor information to be provided to the user on a generic computer8 are also examples of applying the abstract idea which do not integrate it into practical application. Here, the general computer is set forth by the “one or more servers”, “memory” and “processor” as well as “user interface” and by various “devices” of the “candidate provider / additional candidate provider” and “projected requestor” / “additional projected requestor” and the monitoring audit log data is set forth here by the “queues” and “ETA”. Similarly, requiring computer use to tailor information and provide it to user on generic computer as in MPEP 2106.05(f)(2) supra, is recited here as “digital filter(s)” and by “via user interface” “pre-dispatch notification for display of the candidate provider device to pre-dispatch the candidate provider device to the geographic area based on the forward-looking candidate provider device queue capacity and backward-looking previously dispatched provider device queue capacity” at independent Claims 1,9,16, and “based on” “GPS” “determining” “location” at independent Claims 1,9,16.
It then follows that here, the capabilities of “devices”, “one or more servers based on GPS location” to perform or aid such functions represent mere attempt at applying the abstract exception, which do not integrate it into a practical application. This is corroborated by MPEP 2106.05(f)(1) iii stating that wireless delivery of out-of-region broadcasting content to a cellular telephone9 represent mere instructions to apply the abstract exception which again does not integrate it into a practical application. In fact, according to MPEP 2106.05(f)(2) even invocation of the combination of telephone unit and a server, as tools to execute the abstract idea10 does not integrate the abstract idea into a practical application. Such recitations do not integrate the abstract idea into a practical application when tested per MPEP 2106.05(h). Step 2A Prong two.
More to the point, such computerized functions could be also argued as examples of narrowing the abstract idea to a field of use or technological environment. For example, MPEP 2106.05(h) cites Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016) where the claim recited a broadcast system in which a cellular telephone (1) requested and receives network-based content from broadcaster via a streaming signal, (2) was configured to wirelessly download an application for performing those functions and (3) contained a display that allows the user to select particular content. 838 F.3d at 1255-56, 120 USPQ2d at 1202. The court identified the claimed concept of providing access to regional broadcast content as an abstract idea, and then noted that the additional elements limited the wireless delivery of regional broadcast content. 838 F.3d at 1258-59, 120 USPQ2d at 1204. Although the additional elements did limit the use of abstract idea, the court explained that this type of limitation merely confines use of abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. 838 F.3d at 1259, 120 USPQ2d at 1204. Following such example, the Examiner reasons that here a similar limiting of “pre-dispatching” to “forecasted” “candidate provider device queue” and “projected requestor device queue” at most confines use of abstract idea to particular “devices” without integrating it into a practical application.
MPEP 2106.05(h) also cited buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014) to state that requiring that the abstract creating of a contractual relationship (a) be performed using a computer that receives and sends information over a network, or (b) be limited to guaranteeing online transactions, is another example indicating a field of use or technological environment in which to apply the abstract idea. Here, Examiner finds that similar to Affinity Labs and buySAFE supra, the current combination of “devices” aid in performing the analogous functions to allow to “pre-dispatch the candidate provider device”. These however, when tested per MPEP 2106.05(h) do not integrate the abstract idea into a practical application. Step 2A prong two. In fact, MPEP 2106.05(h)11 goes as far to state that narrowing the combination of collecting information, analyzing, and displaying certain results of the collection and analysis to data related to the particular technological environment does not integrate the abstract idea into a practical application.
In this instant case, such analyzing is preponderantly recited as various forms of “forecasting” recited throughout Claims 1,2,4,8,9,10,12,16,17,19. Also here, an analogues notification of certain results of collection and analysis could relate to “pre-dispatching” as recited throughout Claims 1, 3-9, 13-16, 18-20. Such recitations, however, do not integrate the abstract idea into a practical application when tested per MPEP 2106.05(h). Step 2A Prong two. Thus, there is a preponderance of legal evidence showing the additional computer-based elements be it structural, functional or both, not integrating the abstract idea into practical application.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the additional computer-based elements merely apply the already recited abstract idea and link the use of abstract idea to a field of use or technological environment. MPEP 2106.05(f), (h). For these reasons, said computer-based additional elements similarly do not provide significantly more than the abstract idea itself as sufficient option for evidence. Even assuming arguendo, that further evidence would be required to demonstrate conventionality of the additional, computer-based elements, Examiner would point to MPEP 2106.05(d) to demonstrate conventionality of: recording customer order12 and performing repetitive calculations13. Recording customer order is reflected here by capabilities of computerized “instructions” at dependent Claim 10 and similarly by the capabilities of “computing devices” at dependent Claim 17 for “identifying a current requestor device queue at the queued pick-up location” and “current provider device queue” (dependent Claims 10,17). Also performing repetitive calculations is reflected here by the capabilities of computerized “instructions” at Claim 9 to “utilize a pre-dispatch model comprising a forward-looking candidate provider digital queue filter model and a backward- looking previously dispatched provider device digital queue filter model to transmit pre-dispatch notifications”;
“updating the pre-dispatch model based on real-world observations from implementing the pre-dispatch model” and by recitations of “comparing the projected provider device queue and the provider device queue capacity threshold at the queued pick-up location to determine the forward-looking candidate provider device queue capacity at the queued pick-up location” and “comparing the plurality of additional projected provider device queues and the provider device queue capacity threshold at the queued pick-up location to determine the backward-looking previously dispatched provider device queue capacity for the queued pick-up location” at claims 9,16, and similarly by capabilities of “computing devices”, “servers” at independent Claim 16,19 to “forecast a projected requestor device queue for the queued pick-up location at a time corresponding to the first ETA of the candidate provider device”; at independent Claims 9,16 “utilizing” [generally recited] “smoothing model or machine learning model” at independent Claim 1,9,16 and similarly “forecast a projected provider device queue waiting at the queued pick-up location at the time corresponding to the first ETA based on the projected requestor device queue, the current provider device queue, and the in-transit provider device queue” at independent Claims 9,16;
“forecasting another projected requestor device queue for the queued pick-up location at a time corresponding to the another ETA of the another candidate provider device; forecasting another projected provider device queue at the time corresponding to the another ETA based on the another projected requestor device queue; and determining that the another projected provider device queue is less than the provider device queue capacity threshold” at dependent Claim 19.
If necessary, the Examiner would also follow MPEP 2106.05(d) I.2.(a), and point as evidence for conventionality of the additional elements at:
Original Spec. ¶ [0064] 2nd sentence, reciting at high level: “the requestor device forecasting model302 can perform acts and algorithms according to a double exponential smoothing model, a Poisson regression model, an autoregressive integrated moving average (ARIMA) model, a seasonal autoregressive integrated moving average (SARIMA) model, or a non-linear model such as LightGBM”
Original Spec. ¶ [0065] 2nd sentence, reciting at high level: “For example, the requestor device forecasting model302 can include a variety of neural networks, such as a recurrent neural network (RNN) (e.g., a long short-term memory (LSTM) neural network)”.
Original Spec. ¶ [0123] reciting at high level of generality: “Embodiments of the present disclosure may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory, as discussed in greater detail below. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. In particular, one or more of the processes described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., memory), and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein”.
Original Spec. ¶ [0124] reciting: “Computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that store computer-executable instructions are non-transitory computer-readable storage media (devices). Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the disclosure can comprise at least two distinctly different kinds of computer-readable media: non-transitory computer-readable storage media (devices) and transmission media”.
Original Spec. ¶ [0125]: “Non-transitory computer-readable storage media (devices) includes RAM, ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM), Flash memory, phase-change memory (“PCM”), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer”.
Original Spec. ¶ [0129] “Computer-executable instructions comprise, for example, instructions and data which, when executed by a processor, cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. In some embodiments, computer-executable instructions are executed by a general-purpose computer to turn the general-purpose computer into a special purpose computer implementing elements of the disclosure. The computer-executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims”.
Original Spec. ¶ [0130]: “Those skilled in the art will appreciate that the disclosure may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices”.
As per, “utilizat[ion]” of “look-ahead” and “look-behind” “models” at Claims 1,3-5,9,16,18-20, assuming arguendo that applying of mathematical algorithm at MPEP 2106.05(f)(2)(i)14 would still require additional conventionality evidence, then, the Examiner would point as evidence to the conventionality of “utilizing” of “forward-looking” and “backward-looking” “models” as follows:
US 20070113223 A1 Dynamic Instruction Sequence Selection During Scheduling reciting at ¶ [0047] 3rd sentence: The operation of the reverse look ahead scheduler 270 can be similar to the operation of a conventional scheduler when scheduling instructions for which no alternative instruction pattern exists.
US 5963642 A column 13 lines 64-67: “Many other algorithms are available that are well known to one skilled in the art which enable forward and reverse lookups to be performed efficiently on pairs of associated items”.
US 20060015381 A1 Business Lifecycle Management System and US 20060015380 A1 teaching at ¶ [0075] The general approach of the decision analysis framework 1300 is to look or forecast ahead, and then work backwards.
The same principles apply to “utilizing at least one of a smoothing model or a machine-learning model” at independent Claims 1,9,16 and “neural network: at dependent Claim 17, Examiner points to
* US 20190362494 A1 ¶ [0006] 3rd sentence: Using the FFR prediction as an example, a conventional FFR prediction system based on machine learning and learning network is usually composed of multiple modules, including a feature extraction module, an FFR prediction module, and an FFR smooth post-processing module
* US 20070005341 A1 ¶ [0024] 1st sentence: In the conventional graphical approaches to semi-supervised learning, an underlying intuition is that a function should vary smoothly across the graph, so that closely clustered points tend to be assigned similar function values.
* US 20200391884 A1 ¶ [0046] 1st sentence: For the sake of brevity, conventional techniques related to component monitoring, maintenance, and inspections, clustering, smoothing, machine learning, artificial intelligence, data analysis and other aspects of the systems (and the individual operating components of the systems) may not be described in detail herein.
* US 20230367850 A1 ¶ [0023] The synthesis may be performed using conventional methods like smoothing or using a deep learning based method in which a neural network may be trained
* US 20220120617 A1 ¶ [0023] 1st sentence: Finally, in the post-processing stage, the algorithm processes the approximate estimations outputted from the machine learning model through a generic temporal stability and smoothing phase to generate smoothed estimations for the number of occupants
With respect to the amended “hyperparameter”, as now recited in the alternative at independent Claims 1,9,11,16, the Examiner follows MPEP 2106.05(d) I.2.(c) practice, and points to evidence of conventionality “hyperparameter”, as articulated in at least the following:
* US 20240346291 A1 ¶ [0057] 2nd sentence: parameter tuning and/or hyperparameter tuning of conventional machine learning training processes
* US 20200410342 A1 ¶ [0032] A “hyperparameter” is understood in the present instance to mean a parameter that does not describe the artificial neural network to be trained per se, but instead is used, for example, to control the training process of the network to be trained. Typical hyperparameters are the “learning rate,” which represents an indicator of the adaptation of the network for each learning pass. A further conventional hyperparameter is the number of training epochs, an “epoch” referring to a complete pass through the training data.
* US 20240104431 A ¶ [0127] Conventionally, hyperparameters are experimentally set to various values to train the artificial neural network, and are set to optimal values that provide stable training speed and accuracy as a result of training.
* US 20240045926 A1¶ [0066] 2nd sentence conventional hyperparameter optimization for hierarchical time series forecaster training involves choosing a held-out validation time period from the data where the trained model's performance is evaluated and by optimizing that validation performance the hyperparameters of the model are selected
* US 20240208533 A1 [0026] Conventional hyperparameter tuning optimizes a ML model using training/validation losses.
With respect to the conventionality of “GPS” use, as now recited at independent Claims 1,9,16, the Examiner follows MPEP 2106.05(d) I.2.(c) practice, and points to evidence of “GPS” conventionality, as articulated in at least the following publications:
* US 20140171116 A1 entitled Location-aware mobile application management reciting at ¶ [0001] 4th sentence: “Conventionally location determination is typically accomplished by using Global Positioning Systems (GPS), some form of triangulation or interpolation of multiple radio signals, internet protocol (IP) geo-location, or some combination thereof”.
* US 20190028484 A1 reciting at ¶ [0004] 2nd sentence: “Location information is conventionally obtained from GPS coordinates or from Internet IP address”.
* US 20150215735 A1 entitled “Identifying mobile device location and corresponding support center locations to provide support services over a network” [0002] 1st-2nd sentences: “Conventionally, a mobile device may be tracked via its IP address, base station service center, GPS coordinates, etc. The user operating the device may, in turn, receive information related to the device's current location”.
* US 20150019338 A1 ¶ [0004] 4th sentence: “Location information of mobile devices is conventionally captured directly from the device, for example, from the GPS tracking applications that user the mobile device's connections to cellular towers, GPS satellite tracking sites, or reverse internet protocol (IP) address look-up services”.
* US 20200082442 A1 ¶ [0004] 4th sentence: “Location information of mobile devices is conventionally captured directly from the device, for example, from the GPS tracking applications the mobile device's managed connections to cellular towers, GPS satellite tracking sites, or reverse internet protocol (IP) address look-up services”.
* US 20170195339 A1 reciting at ¶ [0042] 1st sentence “Conventional systems used to determine a user's location, such as IP-based location lookups, or the self-reported GPS location of a mobile device”
* US 20140094187 A1 reciting at ¶ [0001] 4th sentence “Conventionally, location determination is typically accomplished by using Global Positioning Systems (GPS), some form of triangulation or interpolation of multiple radio signals, internet protocol (IP) geo-location, or some combination thereof”.
* US 8856115 B1 entitled Framework for suggesting search terms reciting at column 3 lines 3-35: “For example, the location identification engine may use conventional techniques to determine a location from the IP address or GPS coordinates”.
* US 20140187272 A1 reciting at ¶ [0001] 3rd sentence: Conventionally location determination is typically accomplished by using Global Positioning Systems (GPS), some form of triangulation or interpolation of multiple radio signals, internet protocol (IP) geo-location, or some combination thereof.
* US 20160180476 A1 entitled System and method for discovering restaurants, ordering food and reserving tables reciting at mid-¶ [0084] “The processor 34 may use any conventional device(s) and/or technique(s) for determining the GRU, examples of which include, but are not limited to, processing the user's internet protocol (IP) address to determine the city and state in which the user's computing device is located, processing GPS information captured by a GPS unit on-board the user's computing device, and the like”.
* US 20150234868 A1 entitled “Creating and Using Access Zones for Delivering Content” Reciting at ¶ [0003] 1st sentence “Conventional methods for obtaining relevant content from the Internet typically involves typing in search terms in a search engine provided on a website, optionally providing the location of the user via the user's IP address or GPS location, and retrieving a list of search results matching the search terms and optionally matching the location of the user”.
* US 20090204597 A1 entitled System and method for preferred services in nomadic environments reciting at ¶ [0053] 2nd sentence: conventional location detection devices such as global positioning systems (GPS) for, e.g., wireless devices, latitude and longitude for mobile telephones, and/or IP address geo-location, amongst other conventional location detection devices.
* US 20140180576 A1 ¶ [0001] 2nd sentence: “the mobile device is capable of determining its location in the real world. Conventionally, location determination is typically accomplished by using Global Positioning Systems (GPS), some form of telemetry based upon multiple radio signals (e.g., cellular), internet protocol (IP) geo-location, or some combination thereof”.
* US 20140171117 A1 reciting at ¶ [0001] 3rd sentence: “Conventionally location determination is typically accomplished by using Global Positioning Systems (GPS), some form of triangulation or interpolation of multiple radio signals, internet protocol (IP) geo-location, or some combination thereof”.
* US 20100083142 A1 reciting at ¶ [0035] 4th sentence: “The physical location can be determined using conventional GPS or IP address locating techniques”.
* US 20090195447 A1 ¶ [0007] 1st sentence: “In conventional A-GPS systems, mobile devices, such as mobile telephones, communicate with networked location information servers pursuant to established communications and Internet protocols collectively referred to as the Internet protocol suite (also referred to as TCP/IP)”.
* US 9552430 B1 entitled Identifying resource locations reciting at column 6 lines 9-13: “The expression location may be an actual location identified using conventional means, for example an IP Address, GPS coordinates, or a location provided by a cellular provider”.
* US 20080288523 A1 entitled Event-based digital content record organization reciting at ¶ [0043] last sentence the following combination: “Such metadata may be developed or associated with the digital content records using any technique known in the art, such as time stamping a time-date of creation for the time-date of capture metadata, GPS-provided location information for the location of capture metadata, network device MAC address, IP address, or other network address information for the communicatively connected or capable-of-being-communicatively-connected network device that may provide network access to the capture device, manual input or biometric acquisition of information identifying an acquirer of a digital content record for the acquirer metadata, or conventional voice or subject recognition processing techniques for the subject metadata”.
* US 20100220673 A1 ¶ [0016]: “conventional techniques that utilize an extended real-time polling service”,
* “Burnett v. Panasonic Corp., 741 Fed. Appx. 777 (Fed. Cir. 2018), Court Opinion” finding, at the second step of the abstract idea analysis, the combination of geospatial media recorder and allowing user to geospatially reference entities or objects based on the identified geospatial positional location and point identification, insufficient to save the claims from patent ineligibility. Examiner asserts that as a question of law, Burnett’s geospatial media recorder is comparable to the currently amended “GPS location data” and “GPS data for previously pre-dispatched provider devices” at independent Claims 1,9,16. Examiner further points to: “Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362”, “TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”; “OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)”; “buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)” to submit that receiving or transmitting data over a network, including utilizing an intermediary computer to forward information [here “location data”] remains well-understood, routine and conventional.
Thus, Claims 1-20 although directed to statutory categories (“method” or process at Claims 1-8, “non-transitory medium” or computer product at Claims 9-15, “system” or machine at Claims 16-20) they still recite or set forth the abstract idea (Step 2A prong one), with their additional, computer-based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B). Thus, the claims are believed to be ineligible.
- Allowable subject matter over the prior art -
Claims 1-20 now overcome the prior art with the following being the Examiner rationale similar to the one at Final Act 05/22/2024 p.6 ¶5 -p.7. The closest prior art on record remains:
- Lei Zhao et al US 20180121847 A1 by Applicant Uber hereinafter Uber, in view of
- Sawahashi US 20210004929 A1 hereinafter Sawahashi, and in further view of
- Miller; H. Roy US 20070214033 A1 hereinafter Miller.
However, none of Uber, Sawahashi, Miller nor any other prior art on record, teaches either alone or, in together with adequate rationales, the combination of:
- i. “forecasting, by the one or more servers, utilizing at least one of a smoothing model or a machine-learning model, a projected requestor device queue for the queued pick-up location at a time corresponding to the first ETA of the candidate provider device”;
- ii. “forecasting a projected provider device queue waiting at the queued pick-up location
at the time corresponding to the ETA based on the projected requestor device queue, the current
provider device queue, and the in-transit provider device queue”;
- iii. “comparing the projected provider device queue and the provider device queue capacity at the queued pick-up location to determine the forward-looking candidate provider device queue capacity at the queued pick-up location to determine the forward-looking provider device queue capacity at the queued pick-up location”;
- iv. “utilizing the backward-looking previously dispatched provider device digital queue filter model to determine a backward-looking previously dispatched provider device queue capacity for the queued pick-up location by:”
= “determining, by the one or more servers and GPS data for previously pre-dispatched provider devices, a plurality of estimated times of arrival (ETAs) for the previously pre-dispatched provider devices”;
= “determining, by the one or more servers for the previously pre-dispatched provider devices, a plurality of additional projected provider device queues at the plurality of ETAs for the previously pre-dispatched provider devices based on modeling the previously pre-dispatched provider devices arriving at the queued pick-up location at the plurality of ETAs and the candidate provider device arriving at the queued pick-up location at the first ETA”
= “comparing the plurality of additional projected provider device queues and the provider device queue capacity threshold at the queued pick-up location to determine the backward-looking previously dispatched provider device queue capacity for the queued pick-up location” as recited in each of independent Claims 1,9, 16.
Claims 2-8 are dependent upon Claim 1 and overcomes the prior art by dependency to claim 1.
Claims 10-15 are dependent upon Claim 9 and overcomes prior art by dependency to claim 9.
Claims 17-20 is dependent upon Claim 16 and overcomes prior art by dependency to claim 16.
To be clear, novelty (35 USC 102) and non-obviousness (35 USC 103) still pertain to features that are abstract, or incapable to integrate the abstract idea or provide significantly more, which do not render the claims patent eligible (35 USC 101). Simply said, the novel and non-obviousness rationale above do not necessarily render the claims patent eligible. MPEP 2106.04 I ¶5, 3rd sentence citing Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 "the novelty of the mathematical algorithm is not a determining factor at all”.
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Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
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Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Octavian Rotaru/
Primary Examiner, Art Unit 3624 A
September 26th, 2026
1 Mayo, 566 U.S. at 79-80, 86-87, 101 USPQ2d at 1968-69, 1971
2 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
3 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to a particular technological environment,
4 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
5 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
6 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
7 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
8 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)
9 Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)
10 TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1747, 1748 (Fed. Cir. 2016)
11 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)
12 Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016)
13 Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values);
Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)
14 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
