DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The following FINAL office action is in response to Applicant communication filed on 02/19/2026 regarding application 16/942,237. Claims 1, 9, 16, 26 and 28 have been amended. Claims 1-4, 6-7, 9-12, 14-18 and 24-28 are currently pending have been rejected.
Response to Amendments
2. Applicant’s amendment filed on 02/19/2026 necessitated new grounds of rejection in this office action.
Priority
3. The Examiner has noted the Applicants claiming Priority from Continuation Application 16/517,270 filed on 07/19/2019.
Response to Arguments
4. Applicant’s arguments, see page 12 of 20 filed on 02/19/2026, with respect to Claim Objections to Claims 1, 9, 16, 26 and 28 have been fully considered and are found to be persuasive. Therefore, the Claim Objections to Claims 1, 9, 16, 26 and 28 are withdrawn.
Response to 35 U.S.C. § 101 Arguments
5. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-4, 6-7, 9-12, 14-18 and 24-28 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 12-20, dated 02/19/2026). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-4, 6-7, 9-12, 14-18 and 24-28 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 13-16 of 20, dated 02/19/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that amended Independent Claim 1 is not directed to a method of organizing human activity (see Applicant Remarks, 1st ¶ of Page 14, dated 02/19/2026). Examiner respectfully disagrees.
In response to Applicant’s remarks here for step 2a prong 1 via the 35 U.S.C. 101 analysis, certain/particular claim limitations of Independent Claim 1 indeed recite “Certain Methods of Organizing Human Activities” or “Mental Processes”. For example; the step of “Receiving a request from a client computing device for a product or service” represents receiving data, a classic "method of organizing human activity" or "gathering data" limitation. It is a fundamental, routine activity performed by modern computer systems to facilitate transactions. This is categorized under the “Certain Methods of Organizing Human Activities” category. The second step of “Determining status data generated based on data detected by IoT devices (parking meters, cameras, sensors)” Examiner interprets that while IoT devices are physical, the claim focuses on "determining status data" based on that data. Simply collecting and organizing data from sensors is an abstract idea, especially when the claim does not specify a technological improvement in the sensor hardware itself, but rather the information gleaned from it and therefore is categorized as a “Mental Process”. Examiner points out that “Communicating with a third-party GPS service via API to receive routing data” is noted from API communication to receive external data is a conventional computer function. Analyzing user traffic (routing data) to estimate arrival times is a mental process or a "method of organizing human activity" that can be performed mentally or with minimal manual effort. The step of “Determining current resource utilizations based on routing and status data” involves "evaluating" or "judging" data, which are classic "mental processes". It is a data analysis step used to determine the state of a system based on input, often considered an abstract mathematical concept or a business method for optimizing resource allocation. Moreover, the claim limitation step of “Determining a predicted estimated completion time with a ML model based on data” Examiner points out that the Supreme Court and Federal Circuit have consistently held that applying AI/ML techniques to automate a task, such as forecasting, scheduling, or prediction, without specific technical improvements to the model architecture itself, constitutes an abstract idea. The use of "historical data" to train a model is a generic mathematical modeling technique. This step is hereby interpreted as a “Certain Method of Organizing Human Activity”. Next the claim limitation step of “Generating a ranked list of candidate facilities based on predicted completion time” is interpreted to be directed to an abstract idea under the grouping of "methods of organizing human activity" (managing, scheduling, or organizing) and "mental processes" (evaluating, judging). Ranking, prioritizing, or sorting items based on calculated metrics (like completion time) is a foundational business method that can be performed by a human mind or with pen and paper. Using a "predicted estimated completion time" simply applies a mathematical formula (an abstract concept) to this ranking process, which does not change its fundamental nature. The step of “Determining future staffing requirements using a second machine learning model” is directed to an abstract idea under the grouping of "mental processes". The claim limitation here relies on a "second machine learning model" trained on historical data to predict staffing needs. If the claim does not specify a novel machine learning algorithm or specific architectural improvement to the model itself, it is treated as a generic mathematical algorithm or "black box" applied to data. Determining staffing is a method of organizing human activity that is commonly performed in the human mind or through conventional business analysis, and automating it with a "generic" ML model does not bypass the exception. The step of “Generating output for display to the user via a client computing device” is directed to an abstract idea because it is a limitation that merely implements the results of the previous steps on a conventional computer component. The USPTO guidance notes that outputting information—even if it is the result of a complex calculation—is considered an "insignificant extra-solution activity" or, at best, a limitation on the field of use. Simply displaying a ranked list or staffing requirement on a "client computing device" (a general-purpose computer) does not add a technical, non-abstract element to the claim.
Specifically, Applicant asserts that amended Independent Claim 1 are specific steps geared toward using a machine learning model to determine a ranked list of candidate facilities for completing a request for a good or service within a completion time (see Applicant’s Remarks, last ¶ of Page 15 and 1st ¶ of Page 16, dated 02/19/2026). Examiner respectfully disagrees.
The Applicant argues that using a machine learning (ML) model to rank facilities for completing a service within a time constraint is not a method of organizing human activity. In response, Examiner points out however, the claims are directed to a "fundamental economic practice"—specifically, managing, scheduling, or coordinating business resources (facilities) to fulfill orders. The use of machine learning to automate this process does not change the fact that the underlying idea—matching service providers with requests—is a long-standing commercial activity, which is a recognized abstract idea.
As affirmed in Recentive Analytics (Fed. Cir. April 2025), patents that do no more than claim the application of generic machine learning to new data environments—without disclosing improvements to the machine learning models themselves—are patent-ineligible. The Applicant’s claims use "machine learning" as a black box to perform a business function faster, which is not a technical improvement to the computer or the ML algorithm itself.
The Applicant argues that the "specific steps" of the algorithm provide an inventive concept. However, the claims merely recite conventional ML steps: (i) collecting data (request, facility status), (ii) processing (machine learning model), and (iii) outputting a ranked list. Merely automating this logistical, administrative process using conventional computing, even with AI, is not sufficient to "transform" the abstract idea into a patent-eligible application.
The claimed invention aims to optimize a business goal (ranking facilities for efficiency), not a technical problem (e.g., improving how data is stored, how the CPU processes information, or how the ML model itself is trained). The steps described are essentially a manual, mental, or human activity that has simply been accelerated by software.
Following the guidance in Recentive Analytics, claims that apply generic AI techniques to optimize logistics (i.e., ranking facilities) without improving the underlying technology are ineligible, regardless of whether the steps are technically structured, because they are directed to the abstract idea of organizing human commercial activity.
In conclusion, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 still recite an abstract idea under 35 U.S.C. § 101 step 2a prong one under the “Certain Methods of Organizing Human Activities” or “Mental Processes” Groupings and thus are maintained.
Argument #2:
(B). Applicant argues that Claims 1-4, 6-7, 9-12, 14-18 and 24-28 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, 1st ¶ on Page 17 of 20, dated 02/19/2026). Examiner respectfully disagrees.
Applicant argues that using "less persistent data" to update a machine learning (ML) model constitutes a technical improvement in predictive modeling in amended Independent Claim 1. However, the Examiner respectfully disagrees. Independent Claim 1 for example as amended is directed to the abstract idea of analyzing data to update a model. The use of sensor data or satellite feeds is merely an ingestion of data sources, which is a common, conventional activity in data modeling. The claimed steps of training a model using this data do not improve the functioning of the computer itself, but rather describe a mathematical process (training an ML model) applied in a specific, known field. No Transformation: The claimed invention does not improve the "technical field" in a way that goes beyond mere automation. Reducing "data staleness" by feeding data to a model is a standard data processing technique, not a technological improvement to computer functionality. Result-Oriented: Independent Claim 1 is directed to the result of having an updated model, not a specific, technological solution that improves the underlying computer system
Therefore, the claimed invention does not amount to an improvement in the functioning of a computer or another technology under 35 U.S.C. 101, Step 2A, Prong 2. The claim remains directed to an abstract idea without incorporating an inventive concept that integrates it into a practical application. In conclusion, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 are not integrated into a practical application.
Argument #3:
(C). Applicant argues that for amended Independent Claim 1 "by learning from continuously updated information (e.g., the satellite and sensor feed) rather than only static historical datasets, the model adapts more effectively to temporal changes, environmental variability, and emerging patterns that traditional models that are only batch-trained cannot capture until their next retraining cycle. This specialized training enables the model to handle real-world noise and anomalies, strengthening its resilience and generalization capabilities in ways that static datasets cannot. This enables lower latency, more accurate predictions in dynamic contexts, and greater robustness against concept drift. Using transient data also reduces the need for long-term storage and large-scale data retrieval operations, lowering memory and I/O overhead and allowing the training pipeline to run efficiently on edge or resource-constrained systems corroborates reciting additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 16-18 of 20, dated 02/19/2026). Examiner respectfully disagrees.
Applicant contends that the invention is eligible because it "adapts more effectively," "handles real-world noise," and "lowers memory and I/O overhead." These arguments are unpersuasive for the following reasons: Result-Oriented Functionality vs. Technical Improvement: The claims describe what the model achieves (improved adaptation, better handling of noise) rather than how the computer is specifically improved. Simply stating that a model is "more accurate" or "resilient" does not constitute a technical improvement to the functioning of the computer itself, but rather describes a result of better, or more, data. Conventional Use of Computer Components: The mention of "edge or resource-constrained systems" and reducing "long-term storage" does not provide a specific, technical solution to a computer technology problem. The processes of training a model in real-time, reducing data storage, and optimizing I/O operations are standard, conventional, and well-understood practices in computer science, particularly in machine learning, and are thus not "significantly more" than the abstract idea. Generic Application of Machine Learning: The claimed "specialized training" on "transient data" is merely an application of standard, conventional machine learning techniques to a new context (dynamic sensor data). As confirmed by recent Federal Circuit guidance, claims that do no more than apply conventional machine learning to new data environments without disclosing specific, inventive improvements to the model architecture itself are ineligible. Failure to Address "Concept Drift" as a Technical Limitation: The claim to "handle real-world noise... and greater robustness against concept drift" is a functional goal, not a specific technical implementation. The claim lacks specific, novel algorithmic steps that define how the network architecture is structured to achieve this, relying instead on the broad concept of "using transient data".
Therefore, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 are directed to the abstract idea of analyzing and updating data models based on incoming information. Even considering the "transient data" and "reduced storage" limitations, the claims fail to provide an improvement in the functioning of the computer itself. They are, at best, a conventional, albeit efficient, application of a mathematical model, which does not constitute a specific improvement to a technological process. The claims are thus ineligible under 35 U.S.C. § 101.
The applicant asserts that real-time data exposure strengthens model resilience and generalization, transforming a "static batch-learning framework" into a "responsive, adaptive computational architecture," thereby providing a material improvement in performance, efficiency, and reliability (see Applicant Remarks, 1st ¶ of Page 18, dated 02/19/2026).
Examiner respectfully disagrees. The claims are not directed to a "specific improvement in the functioning of a computer or other technology" (Step 2A, Prong 2). Instead, the claims are directed to an abstract idea—specifically, mathematical concepts, mental processes, and/or data analysis—applied to conventional computer components.
The assertion that "real-time data exposes the model to the full spectrum of real-world noise" describes a data selection process, not a technological improvement to the computer itself. The Federal Circuit has established that simply applying conventional machine learning (ML) techniques to new or different data (even in real-time) does not make an otherwise ineligible abstract idea patent-eligible. The "improvement" claimed is a better mathematical model, not a better computer system. "Adaptive" and "Responsive" are Generic Functional Results. Describing a system as "adaptive" or "responsive" in contrast to "batch-learning" is merely a description of the result of the improved algorithm, not a specific, tangible modification to the underlying hardware architecture. The claims fail to specify how the computer is modified to be faster or more efficient, but rather what data it processes. Generalization and Reliability are Non-Technical Goals. "Increased reliability and generalization capabilities" are desirable outcomes of any analytical model, but under 35 U.S.C. 101, these represent an improvement to the quality of the output, not the functionality of the technological tool. The claims are directed to a result (better model performance) rather than a specific technical solution to a computer bottleneck, such as memory management or processor speed. Failure to Add "Significantly More" (Step 2A, Prong 2 Analysis). Even if the claims are interpreted as having a practical application, they lack an "inventive concept" (Step 2B) because using real-time data to train or update models is a well-understood, routine, and conventional activity in the field of artificial intelligence. The claimed "transformation" from batch to real-time is an iterative, conventional process in AI, which has been found ineligible.
Therefore, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 are directed to the abstract idea of using real-time data for modeling, and the additional elements, when considered individually or in combination, do not improve the functioning of the computer itself, but merely use a computer to execute a superior mathematical algorithm.
Applicant argues that the claims do not attempt to monopolize the general concept of facility management. Instead, they are restricted to a responsive, adaptive computational architecture that transforms the system from a static framework into a materially improved technical tool. Because the claims focus on the technological solution of improving the accuracy and efficiency of predictions in dynamic environments (see Applicant Remarks, 2nd ¶ of Page 18 of 20, dated 02/19/2026).
Examiner notes that these remarks emphasize that the "responsive, adaptive computational architecture" is merely a functional description executed on generic hardware, and the "materially improved technical tool" is merely a result-oriented claim. The Claimed "Improvements" are Merely Desired Results (Result-Oriented Claims). Applicant asserts that the claims improve the "accuracy and efficiency of predictions in dynamic environments." However, the claims fail to detail the specific technical method—such as a specific, novel algorithm, or a unique physical sensor network configuration—required to achieve these results. The claims merely recite the goal of prediction in a dynamic environment, which is an abstract idea. Simply using a computer to perform a "responsive, adaptive" calculation faster or more efficiently than a human does not render an abstract idea patent-eligible. The Architecture is Described at a High Level of Generality. The "responsive, adaptive computational architecture" described by the Applicant is not tied to a specific, non-conventional hardware configuration. Rather, the claims use functional language to describe generic computer components (e.g., "memory," "processor," "network interface") that are simply programmed to perform a task. Without a specific, non-routine technical implementation—such as a specific, novel hardware configuration that is not commonly used for facility management—the "architecture" is a generic tool, not a special-purpose machine. The Claims are Directed to a Fundamental Business/Management Practice. "Facility management" is a long-standing business practice (organizing human activity). Using a computer to perform "dynamic" or "adaptive" management does not move the claim away from an abstract idea. The claims, as drafted, do not improve the functioning of the computer itself, nor do they improve any other technology. Instead, they apply conventional, well-understood computational techniques to the abstract concept of monitoring and controlling building environments, which is merely "applying" an abstract idea on a computer. Failure to Limit the Claim to a "Specific" Application. To qualify as a "practical application," the claim must do more than just mention "facility management." The claims do not focus on a particular technical solution to a specific technological problem. Instead, they attempt to monopolize the entire concept of using computers to provide "adaptive" facility management. Such broad claims, lacking a specific, tangible, and non-conventional improvement to a technical tool, are in essence directed to a method of organization, which is not patent-eligible.
In summary, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 recite an abstract idea (Step 2A, Prong 1) of managing facility systems. The "additional elements" (e.g., adaptive modeling, data gathering) are either routine, well-understood, or described at such a high level of abstraction (e.g., "adaptive architecture") that they do not transform the claim into a specific practical application (Step 2A, Prong 2). The claimed "improvements" are simply the intended results of a computer-implemented method, not a concrete improvement to technology.
Argument #4:
(D). Applicant argues that Claims 1-4, 6-7, 9-12, 14-18 and 24-28 recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B of the 35 U.S.C. § 101 analysis (see Applicant Remarks, 3rd ¶ thru 4th ¶ on Page 17 of 20, dated 02/19/2026). Examiner respectfully disagrees.
Applicant argues that the Examiner improperly evaluated the recited elements (machine learning models, GPS, sensors) in isolation rather than as an ordered combination that produces a technical synergy. Applicant contends this combination constitutes a non-conventional, inventive step under Step 2B of the Alice/Mayo framework. First, Examiner refers Applicant to BSG Tech LLC v. Buyseasons Inc. decision (Aug. 15, 2018) court case noting that: “But the relevant inquiry is not whether the claimed invention as a whole is unconventional or non-routine. At Step two, we “search for an ‘inventive concept’… that is sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.” Alice, 134 S. Ct. at 2355 (internal quotation marks omitted) (quoting Mayo, 566 U.S. at 72-73). But this simply restates what we have already determined is an abstract idea. At Alice step two, it is irrelevant whether considering historical usage information while inputting data may have been non-routine or unconventional as a factual matter. As a matter of law, narrowing or reformulating an abstract idea does not add “significantly more” to it. See SAP Am., Inc. v. InvestPic, LLC. No. 2017-2081, slip op. at 14 (Fed. Cir. 2018). Applicant’s suggestion that specific limitations (or the claimed invention as a whole) must be shown to be well-understood, routine, and conventional to support the conclusion of subject matter ineligibility is not persuasive.
Secondly, the Examiner’s rejection is proper, and the claims remain ineligible because the combination, when analyzed as a whole, represents WURC activity that fails to provide "significantly more" than an abstract idea. The Applicant’s specification generally describes the use of GPS, sensors, and machine learning (ML) models in a generic sense. The USPTO 2024 Guidance updates and case law (e.g., Recentive Analytics, Inc. v. Fox Corp., 2025) affirm that simply applying machine learning, GPS tracking, and sensors—even to a new dataset or field—is a well-known technique. The components themselves are not the invention.
No Improved Functioning of Computing Device: The combination of GPS, sensors, and ML models does not improve the underlying technology of the sensors or the processing speed of the computer, but rather uses them for their intended purpose. Resulting in Conventional Output: The integration of these components—e.g., collecting data via sensors/GPS, analyzing it via ML, and providing an output—is routine in modern "smart" systems, such as autonomous vehicles or IoT devices, as shown in Recentive. The Combination Amounts to Conventional Activity (WURC). The Examiner properly identified that the claimed combination—using sensors to collect data, GPS to determine location, and ML to predict or categorize—is widely understood in the art as a routine approach to automation. The Applicant has not identified specific, non-generic improvements to the machine learning algorithms themselves or a specific, non-generic technical synergy that transforms this from a collection of conventional parts into an inventive concept.
Because the combination of machine learning, GPS, and sensors is, in this context, WURC, and because the claims fail to offer a specific, technical improvement to the functioning of the computer or the technology itself, the rejection under 35 U.S.C. § 101 is proper.
Applicant argues that the specific combination of real-time data addresses "data staleness and concept drift," constituting a technical solution to a computational problem rather than a mental process. The Examiner disagrees because: Abstract Idea Remains: The core of the claim is to avoid data staleness. This is a business or practical goal, not a technical improvement to the computer's operation itself. The Federal Circuit has established that "iteratively training" or "dynamically updating" based on new inputs is a conventional, known use of AI and does not automatically translate into a technical improvement. Lack of "Significant More" (Step 2B): Even if the claim were considered to recite an abstract idea, the additional elements (using sensors, satellite feeds) are conventional computing steps. The claims do not include an "inventive concept" (e.g., a specific, novel algorithm modification) that would transform the abstract idea into a patent-eligible application. They do not explain how the computing functionality itself is improved, only that it is applied faster to new data.
The Applicant is applying ML techniques to a new, faster data environment. Without specific, technical limitations explaining how the algorithm itself is redesigned to handle data staleness, the claims remain directed to an abstract idea, and thus, are ineligible under 35 U.S.C. § 101.
Argument #5:
(E). Applicant argues that for Independent Claim 1 for example a human could not mentally process or manually calculate predicted completion times based on the simultaneous real-time ingestion of “routing data” from a plurality of GPS users combined with “less persistent data” from multiple sensors and satellite feeds across an entire set of candidate facilities and therefore recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B of the 35 U.S.C. § 101 analysis (see Applicant Remarks, 1st ¶ on Page 18 of 20, dated 02/19/2026). Examiner respectfully disagrees.
The Supreme Court and MPEP § 2106 define "mental processes" broadly to include concepts that can be performed in the human mind, or by a human using a pen and paper. While a human cannot process millions of GPS points simultaneously, the method of collecting data, applying a formula, and outputting a prediction is a classic example of an abstract idea—specifically, a mathematical concept or method of organizing human activity. The claim simply automates a, albeit complex, manual, "well-known, routine, or conventional activity". The Examiner's rejection properly identifies that using a computer to perform calculations faster than a human does not transform an abstract idea into a patentable invention. The claimed "simultaneous real-time ingestion" and "calculation" of routing data are merely the use of a computer as a tool—like a calculator—to perform data processing, which does not add an "inventive concept". The claim is essentially "applying" an abstract mathematical concept to a "technical environment" (GPS/sensors) without inventing a new way to take the measurements or compute the result.
Under Step 2B, the question is whether the additional elements (the sensors, GPS, satellite feeds) amount to "significantly more" than the abstract idea itself. The use of conventional GPS units, satellites, and sensors is well-understood and conventional. Result-Oriented: The claim is directed to a desired result (predicting completion times) rather than a specific, unconventional method of achieving that result. No Technical Solution: The claim does not improve the functioning of the computer itself, or the sensors themselves; it merely uses them to collect data for a calculation. The claimed invention is, at its core, a method of organizing business or logistical activity by calculating data. Even if the calculation is too fast for a human, the type of activity is inherently abstract, and the claim does not provide an unconventional, technical solution that transforms it into a patent-eligible application.
Even if a human cannot perform these calculations simultaneously, the claim is considered to "recite" an abstract idea if it covers the underlying mental process or algorithm. The use of GPS and sensors does not change the fact that the core, recited invention is the calculation itself. Applicant argues that the sheer volume of real-time data makes this impossible for a human. However, under MPEP § 2106.04(d), simply gathering large amounts of data (GPS, sensors) and running it through a computer does not transform an abstract idea into a patent-eligible application. The Computer as a Tool: The computer is acting merely as a tool to speed up a mental calculation (e.g., "fast calculator"), rather than improving the computer's own functionality. No Technical Solution: The claim does not improve how GPS measurements are taken or how the server functions; it merely uses them to perform a method of planning.
Combining "real-time ingestion" of "GPS data" with "sensor data" constitutes "well-understood, routine, and conventional" activity in the logistics field. Automation of a Manual Process: Merely automating a manual process (routing logistics) using standard computational devices does not add an inventive concept. While recent 2025 USPTO guidance reminds examiners to be careful not to stretch the "mental process" exception to things that are genuinely impossible for a human to do, it also emphasizes that just because a computer is faster does not mean the process is not abstract. If the claim does not provide a specific, technical improvement in how the data is processed by the machine, it remains an abstraction.
The Examiner's rejection is proper because the claim is directed to the abstract idea of predictive routing, and the recited "real-time ingestion" of data is conventional in the field, representing an "apply it" approach rather than a technological improvement to the computer itself.
The examiner’s rejection under 35 U.S.C. § 101 is proper because, despite the complexity, the claims are directed to the abstract idea of analyzing data and classifying information, which does not become patent-eligible simply because it is performed faster by a computer than by a human mind. While the applicant argues that the "specialized predictive framework" and calculations are impossible for a human to perform, the Supreme Court and Federal Circuit have clarified that a process that can be mentally performed—or performed with pen and paper—is an abstract idea. The mere fact that the calculations are complex, high-velocity, or require environmental awareness does not change their nature as mathematical algorithms or data analyses (i.e., "mental processes"). The key is whether the methodology is fundamentally a mental calculation, regardless of the speed at which it is performed.
Under Alice Step 2B, the claim must include an "inventive concept" that is "significantly more" than the abstract idea itself. The claim, as described, uses high-velocity sensor data as input and produces a ranked list as output. The examiner correctly identifies that gathering, analyzing, and ranking data—even if done using complex algorithms—is a routine and conventional activity. The "specialized predictive framework" is simply an instruction to apply a mathematical model to raw data, which is a classic abstract idea. For AI and predictive models, the USPTO requires that the invention improve the functioning of the computer itself, rather than just using a computer as a tool to speed up a mathematical analysis. The applicant argues that the data is complex, but has not shown that the computer system is modified or improved to operate in a novel way. The claim describes "what" is calculated (the result) rather than "how" the system is technically improved. Even if the claim cannot be performed by a human, the 2024 updated guidance reminds examiners to look at whether the claim is integrated into a practical application. The claim is directed to an abstract idea that is not integrated into a practical application because it does not result in a specific, tangible improvement to a technology, but rather a "ranked list" which is often considered a mere information-based result.
The Examiner properly identified that the claim recites an abstract idea (data manipulation) and that the additional limitations (sensor data, predictive framework) simply instructions to use a computer to perform the mental process faster, which does not constitute an inventive concept under 35 U.S.C. § 101 step 2B.
Argument #6:
(F). Applicant argues that for Independent Claim 1 for example “"the ordered combination of features provides a meaningful limitation. Even if individual elements were conventional, their ordered combination provides a meaningful limitation by restricting the claim to a specific, adaptive computational architecture. By requiring the model to be trained on "less persistent data" to automatically update "resource utilizations", the claims do not "preempt" the abstract idea of facility management. Instead, they provide a specific, technologically-grounded improvement in how completion times are predicted in high-noise, dynamic environments” recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B of the 35 U.S.C. § 101 analysis (see Applicant Remarks, 2nd ¶ on Page 18 of 20, dated 02/19/2026). Examiner respectfully disagrees.
While the Applicant argues the invention provides a "technologically-grounded improvement," the core of the invention—predicting completion times and updating resource utilization based on data—is a method of organizing human activity and a mental process. The use of "less persistent data" is a data management technique, which has been found to be an abstract idea, not a technological improvement to the functioning of the computer itself. Even if the data is "less persistent," the underlying action is still merely collecting data and using it for optimization, which is a fundamental economic or business practice.
Even assuming the claim elements (training a model, using less persistent data, updating resources) are not individually conventional, their ordered combination does not amount to "significantly more" than the abstract idea. Merely Ordering Conventional Steps: The steps are arranged in a logical, conventional order of data processing—collect data, train model, update resource. Stringing together conventional steps, even if they are in a specific order, does not provide an inventive concept if they merely execute an abstract concept. No Technical Improvement: The claims do not describe how to improve the underlying machine learning model or the computer hardware itself. Instead, they apply generic machine learning techniques to a new environment (facility management), which is not a patentable improvement. "High-Noise" Context: Simply adding a, "high-noise, dynamic environment" does not make a claim to an abstract idea patent-eligible; it merely restricts the field of use, which is insufficient for eligibility. The Applicant argues that the combination provides a "specific, adaptive computational architecture." However, if these specific, non-conventional details are not explicitly recited in the claims, they cannot be used to save the claims. The claims, as written, are too broad and functional. The Examiner correctly identified that the claims are directed to an abstract idea (Step 2A). Furthermore, the Examiner properly determined that the limitations, individually or as an ordered combination, do not provide an inventive concept (Step 2B), as they do not improve the computer's functioning but rather use conventional computer technology to implement an abstract concept in a specific field.
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1-4, 6-7, 9-12, 14-18 and 24-28 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-4, 6-7, 9-12, 14-18 and 24-28 are each focused to a statutory category namely, a “system” or an “apparatus” (Claims 1-4, 6-7 and 24-25), a “non-transitory machine-readable medium” or an “article of manufacture” (Claims 9-12, 14-15 and 26-27) and a “method” or a “process” (Claims 16-18 and 28).
Step 2A Prong One: Independent Claims 1, 9 and 16 recite limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“” (see Independent Claim 1);
“” (see Independent Claim 1);
“receive a request for a product or service to be provided to a user at a facility of a set of candidate facilities” (see Independent Claim 1);
“determine status data generated based on data detected by associated with each candidate facility of the set of candidate facilities, ” (see Independent Claim 1);
“communicate with a third-party service, , to receive routing data of users of the third-party service, the routing data identifying a plurality of users of the third-party service that are en route to one of the set of candidate facilities” (see Independent Claim 1);
“determine a set of current resource utilizations for each candidate facility in the set of candidate facilities based on the routing data and the status data, wherein the current resource utilization is automatically updated with the data detected , wherein the set of current resource utilizations for a respective candidate facility comprises an availability indication of at least one of two or more requisite resources to provide the product or service requested by the user” (see Independent Claim 1);
“determine a predicted estimated completion time to fulfill the request for the product or service for each candidate facility in the set of candidate facilities with a first model and based on the set of current resource utilizations that are automatically updated with the data detected wherein the first model has been trained using historical resource utilization data associated with completion times of requests for the product or service for each candidate facility of the set of candidate facilities to generate output of the predicted estimated completion time to fulfill the request for the product or service for each candidate facility of the set of candidate facilities, has been further trained using less persistent data, ” (see Independent Claim 1);
“generate a ranked list of the set of candidate facilities based, at least in part, on the predicted estimated completion time for each candidate facility of the set of candidate facilities, the ranked list being in order of estimated completion time” (see Independent Claim 1);
“determine one or more future staffing requirements for each candidate facility of the set of candidate facilities, the one or more future staffing requirements comprising a staffing addition, and wherein the future staffing requirements are determined by providing the set of current resource utilizations to a second model trained on historical resource utilizations” (see Independent Claim 1);
“generate output, for display to the user , comprising the ranked list of the set of candidate facilities” (see Independent Claim 1);
“determine two or more requisite resources to provide a product based on a request for the product from a user, the two or more requisite resources comprising at least one device and at least one skill” (see Independent Claim 9);
“identify a location associated with the user based on the request for the product from the user” (see Independent Claim 9);
“determine a set of candidate facilities to fulfill the request for the product based on the two or more requisite resources to provide the product requested by the user and the location associated with the user, wherein each candidate facility in the set of candidate facilities to fulfill the request for the product comprises each of the two or more requisite resources to provide the product requested by the user and satisfy a proximity threshold to the location associated with the user” (see Independent Claim 9);
“communicate with a third-party service, to receive routing data of users of the third-party service, the routing data identifying a plurality of users of the third-party service that are en route to one of the set of candidate facilities” (see Independent Claim 9);
“compute a set of current resource utilizations for each candidate facility in the set of candidate facilities, wherein the set of current resource utilizations for a respective candidate facility are based on the routing data and includes an availability indication of at least one of the two or more requisite resources to provide the product requested by the user, wherein the set of current resource utilizations are automatically updated with data detected associated with each candidate facility of the set of candidate facilities, ” (see Independent Claim 9);
“determine a predicted estimated completion time to fulfill the request for the product or service for each candidate facility in the set of candidate facilities with a first model and based on the set of current resource utilizations that are automatically updated with data detected wherein the first model has been trained using historical resource utilization data associated with completion times of requests for the product or service for each candidate facility of the set of candidate facilities to generate output of the predicted estimated completion time to fulfill the request for the product or service for each candidate facility of the set of candidate facilities, has been further trained using less persistent data, ” (see Independent Claim 9);
“generate a ranked list of the set of candidate facilities based, at least in part, on the predicted estimated completion time for each candidate facility of the set of candidate facilities, the ranked list being in order of estimated completion time” (see Independent Claim 9);
“determine one or more future staffing requirements for each candidate facility of the set of candidate facilities, the one or more future staffing requirements comprising a staffing addition, and wherein the future staffing requirements are determined by providing the set of current resource utilizations to a second model trained on historical resource utilizations” (see Independent Claim 9);
“generate output, for display to the user , comprising the ranked list of the set of candidate facilities” (see Independent Claim 9);
“identifying a request for a product from a user” (see Independent Claim 16);
“determining two or more requisite resources to provide the product requested by the user, the two or more requisite resources comprising at least one device and at least one skill” (see Independent Claim 16);
“identifying a location associated with the user based on the request for the product from the user” (see Independent Claim 16);
“determining a set of candidate facilities to fulfill the request for the product based on the two or more requisite resources to provide the product requested by the user and the location associated with the user, wherein each candidate facility in the set of candidate facilities to fulfill the request for the product comprises each of the two or more requisite resources to provide the product requested by the user and satisfy a proximity threshold to the location associated with the user” (see Independent Claim 16);
“communicating with a third-party service, , to receive routing data of users of the third-party service, the routing data identifying a plurality of users of the third-party service that are en route to one of the set of candidate facilities” (see Independent Claim 16);
“computing a set of current resource utilizations for each candidate facility in the set of candidate facilities, wherein the set of current resource utilizations for a respective candidate facility is based on the routing data and an availability indication of at least one of the two or more requisite resources to provide the product requested by the user, wherein the set of current resource utilizations are automatically updated with data detected associated with each candidate facility of the set of candidate facilities, ” (see Independent Claim 16);
“determining a predicted estimated completion time to fulfill the request for the product or service for each candidate facility in the set of candidate facilities with a first model and based on the set of current resource utilizations that are automatically updated with data detected wherein the first model has been trained using historical resource utilization data associated with completion times of requests for the product or service for each candidate facility of the set of candidate facilities to generate output of the predicted estimated completion time of the product or service for each candidate facility of the set of candidate facilities, has been further trained using less persistent data, ” (see Independent Claim 16);
“generating a ranked list of the set of candidate facilities based, at least in part, on the predicted estimated completion time for each candidate facility of the set of candidate facilities, the ranked list being in order of estimated completion time” (see Independent Claim 16);
“determining one or more future staffing requirements for each candidate facility of the set of candidate facilities, the one or more future staffing requirements comprising a staffing addition, and wherein the future staffing requirements are determined by providing the set of current resource utilizations to a second model trained on historical resource utilizations” (see Independent Claim 16);
“generate output, for display to the user , comprising the ranked list of the set of candidate facilities” (see Independent Claim 16).
The claimed process as a whole for Independent Claims 1, 9 and 16 is "directed to" an abstract idea—specifically, the use of machine learning to analyze real-time data for resource scheduling and prediction for banking branch facilities. These abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) commercial interactions (including marketing or sales activities or behaviors or business relations).
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid, in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C. Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mathematical Concepts” which pertains to (5) mathematical calculations.
That is, other than reciting (e.g., “a client computing device” & “one or more sensors” & “one or more cameras” & “satellite feed” & “one or more devices” & “parking meter” & “API” & “GPS” & “a processor” & “a memory”, etc…) nothing in the claim elements precludes the steps from being performed as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) commercial interactions (including marketing or sales activities or behaviors or business relations) and additionally or alternatively as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid.
Therefore, at step 2a prong 1, Yes, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1, 9 and 16 recite additional elements directed to: (e.g., “a client computing device” & “one or more devices” & “a processor” & “a memory”, etc…). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h). Furthermore, even if the steps of “receiving data” and “transmitting data / generating output” are evaluated as additional elements, these activities at most amount to insignificant extra-solution activities, which are not indicative of a practical application, as noted in MPEP § 2106.05 (g).
Independent Claims 1, 9 and 16: With respect to reliance on (e.g., “a third-party global positioning system (GPS) service” & “application programming interface (API)” & “one or more sensors” & “parking meter” & “one or more cameras” & “one or more satellite feed”) as additional elements shown in Independent Claims 1, 9 and 16 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application due to: (1) limiting a particular technological environment or field of use by monitoring and analyzing a set of current resource utilizations for each facility, determining one or more recommended facilities coupled with an estimated completion time to fulfill a task and determining one or more future staffing requirements for an available facility in the set of available facilities in an enterprise service monitoring environment (see MPEP § 2106.05 (h)).
Secondly, with respect to reliance on (e.g., “first machine learning model” & “second machine learning model”) as additional elements shown in Independent Claims 1, 9 and 16 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, these additional elements do not integrate the abstract idea into a practical application under step 2a prong 2 due to the following: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) limiting a particular technological environment or field of use by monitoring and analyzing a set of current resource utilizations for each facility, determining one or more recommended facilities coupled with an estimated completion time to fulfill a task and determining one or more future staffing requirements for an available facility in the set of available facilities in an enterprise service monitoring environment (see MPEP § 2106.05 (h)). The claims require the use of software to tailor information and provide the results to the user on a computer.
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claims 1, 9 and 16 recite additional elements directed to: (e.g., “a client computing device” & “one or more devices” & “a processor” & “a memory”, etc…). These additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using computing elements (computer hardware, interface) or instructions/software (engine) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification (see, e.g., ¶ [0027] denoting a general-purpose computer & ¶ [0115] denoting a conventional communication network) suggests that virtually any computing device(s) under the sun may be used to implement the invention.
Furthermore, even if the steps of mere “receiving data” and mere “transmitting/generating output data” are evaluated as additional elements, these activities at most amount to insignificant extra-solution activities, which has been recognized as Well-Understood, Routine, and Conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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) (computer receives and sends information over a network). See MPEP § 2106.05(d) ii – Electronic Recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log).
The additional element of “a third-party global positioning system (GPS) service” or “GPS” in Independent Claims 1, 9 and 16 does not amount to significantly more than the judicial exceptions under step 2B due being expressly recognized as WURC (well-understood, routine and conventional) in the art.
Examiner points to the following references shown below corroborating this aspect.
-> US Patent Application (US 2017/0132540 A1) – “System for Identifying Events and Preemptively Navigating Drivers to Transport Passengers from the Events”, hereinafter Haparnas.
See Haparnas at ¶ [0017]: “The application may establish a pick-up location automatically or based on user input (e.g., locations may include the current location of the mobile device 104 as determined by a global positioning system (GPS) of the mobile device or a different user-specified location). In certain embodiments, the user may specify a destination location as well. The locations may be specified in any suitable format, such as GPS coordinates, street address, establishment name (e.g., LaGuardia Airport, Central Park, etc.), or other suitable format. See Haparnas at ¶ [0035]: “GPS units 210 and 212 may include any suitable hardware and/or software for detecting a location of their respective mobile devices 104 and 108. For example, a GPS unit may comprise a system that receives information from GPS satellites, wireless or cellular base stations, and/or other suitable source and calculates a location based on this information (or receives a calculated position from a remote source). In one embodiment, the GPS unit is embodied in a GPS chip.”
US Patent Application (US 2016/0019641 A1) – “Multiple Party Branch Recommendation”, hereinafter Barnett. See Barnett at ¶ [0030]: “Location information relating to the user can be collected using any suitable location information gathering technique; for example, gathering GPS information (e.g., GPS information from the user's cellular telephone), geofencing information (e.g., geofences established around branches and/or ATMs that are triggered when the user's phone, for example, enters a respective geofence), shopping information (e.g., information about where the user shops based on financial activity data), traffic camera information, cellular telephone tower triangulation, banking activity (e.g., information about which ATMs, branches, etc., the user visits, and the types of activities the user performs at those locations), gym membership and activity information, calendar information (e.g., time, place and/or subject matter of the user's meetings extracted from a calendar on the user's cellular telephone), etc. Using such information, the computing system can recommend a physical location in proximity to a grocery store at which the user completes their weekly grocery shopping, or a physical location in proximity to a gym at which the user exercises daily, for example.”
Additionally, with respect to the “GPS” or “a third-party global positioning system (GPS) service”, this has been expressly recognized as WURC (Well-Understood, Routine and Conventional) in the art based on the limitations recited in Independent Claims 1, 9 and 16 of: “receive routing data of users of a third-party global positioning system (GPS) service via an API with the GPS service, the routing data identifying a plurality of users of the GPS service that are en route to one of the set of candidate facilities” & “receive a request from a client computing device for a product or a service to be provided to a user of the client computing device at a facility of a set of candidate facilities”. Examiner supports this by citing MPEP 2106.05 (d) ii: - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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) (computer receives and sends information over a network).
Both the references cited noting the conventionality and the MPEP § 2106.05 (d) ii court denote the “GPS” saying the location data can be performed in any suitable format supports/corroborates that this additional element is WURC in the art.
The additional element of “API” in Independent Claims 1, 9 and 16 does not amount to significantly more than the judicial exceptions under step 2B due being expressly being recognized as WURC (Well-Understood, Routine and Conventional) in the art, based on the limitations recited in Independent Claims 1, 9 and 16 of: “receive routing data of users of a third-party global positioning system (GPS) service via an API with the GPS service, the routing data identifying a plurality of users of the GPS service that are en route to one of the set of candidate facilities”. Examiner supports this by citing MPEP § 2106.05 (d) ii: - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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) (computer receives and sends information over a network).
Additionally, for the API as an additional element for Independent Claims 1, 9 and 16, Examiner cites US PG Pub (US 2013/0246207 A1) – Novak at ¶ [0022] for expressly being recognized as that an API is (Well-Understood, Routine and Conventional) in the art: “For example, the applications can include or use an application programming interface (API), such as an externally facing API, to communicate data with the device interface 110. The externally facing API can provide access to system 100 via secure access channels over the network through any number of methods, such as web-based forms, programmatic access via restful APIs, Simple Object Access Protocol (SOAP), remote procedure call (RPC), scripting access, etc., while also providing secure access methods including key-based access to ensure system 100 remains secure and only authorized users, service providers, and/or third parties can gain access to system 100.”
Furthermore, the additional element of “one or more sensors” for Independent Claims 1, 9 and 16, this additional element does not amount to significantly more than the judicial exceptions under step 2B due being expressly being recognized as WURC (well-understood, routine and conventional) in the art. For example, Examiner cites US PG Pub (US 2020/0410623 A1) to Vahabzadeh at ¶ [0036]: “For example, conventional metering by a municipality involves selecting locations such as a first parking stall 232 and providing a parking meter 228 adjacent to the first parking stall 232. In the event that this first parking stall 232 were empty at the time of pick-up, this stall 232 would be used for the curbside pick-up of the participant 203 without receiving any income by way of receiving funds at the parking meter 228” for being recognized as that the “one or more sensors” is (well-understood, routine and conventional) in the art:
From Applicant’s Original Specification at ¶ [0033]: “Further examples of data associated with an enterprise facility include utility statuses of the facility, such as operating electricity, internet, and water capabilities and rates, and the number of vehicles parked in a parking lot for the facility, as detected by a parking meter, a security camera or sensor, or satellite feed.” Therefore, Vahabzadeh reference supports that “one or more sensors”, which a parking meter is considered a type of sensor, for monitoring vehicles in a parking lot is WURC in the art.
Independent Claims 1, 9 and 16: With respect to reliance on (e.g., “a third-party global positioning system (GPS) service” & “application programming interface (API)” & “one or more sensors” & “parking meter” & “one or more cameras” & “one or more satellite feed”) as additional elements shown in Independent Claims 1, 9 and 16 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to: (1) limiting a particular technological environment or field of use by monitoring and analyzing a set of current resource utilizations for each facility, determining one or more recommended facilities coupled with an estimated completion time to fulfill a task and determining one or more future staffing requirements for an available facility in the set of available facilities in an enterprise service monitoring environment (see MPEP § 2106.05 (h)).
Secondly, with respect to reliance on (e.g., “first machine learning model” & “second machine learning model”) as additional elements shown in Independent Claims 1, 9 and 16 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) limiting a particular technological environment or field of use by monitoring and analyzing a set of current resource utilizations for each facility, determining one or more recommended facilities coupled with an estimated completion time to fulfill a task and determining one or more future staffing requirements for an available facility in the set of available facilities in an enterprise service monitoring environment (see MPEP § 2106.05 (h)). The claims require the use of software to tailor information and provide the results to the user on a computer.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-4, 6-7, 10-12, 14-15, 17-18 and 24-28 recite similar abstract ideas as Independent Claim 1, 9 and 16 along with further steps/details that could also be concepts performed in the human mind as “Mental Processes” (1) concepts performed in the human mind (including evaluations or judgements or observations) or (2) using pen to paper as a “physical aid” and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (4) commercial interactions (including marketing or sales activities or behaviors or business relations) and additionally or alternatively as “Mathematical Concepts” which pertains to (5) mathematical calculations.
Dependent Claims 6, 14, 24-25 and 27 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and Step 2B for Independent Claims 1, 9 and 16. Dependent Claims 2-4, 7, 10-12, 15, 17-18, 26 and 28: With respect to the additional elements of “a number of navigation systems” (see Dependent Claims 2, 10 and 17) & “a user interface” (see Dependent Claims 3-4, 11-12 and 18) & “secure tunnel” (see Dependent Claims 7, 15 and 28) & “one or more sensors” (see Dependent Claims 26 and 28)) when considered individually and in combination (as a whole) with these recited claim limitations, these additional elements do not integrate the abstract idea into a practical application under step 2a prong 2 and also secondly do not amount to significantly more than the judicial exceptions under step 2B due to: reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or limiting a particular technological environment or field of use by monitoring and analyzing a set of current resource utilizations for each facility, determining one or more recommended facilities coupled with an estimated completion time to fulfill a task and determining one or more future staffing requirements for an available facility in the set of available facilities in an enterprise service monitoring environment (see MPEP § 2106.05 (h)).
Next, when the “machine learning models and/or machine learning” is evaluated as an additional element, this feature is recited at a high level and has not been shown to improve the computer or any technology, or otherwise integrate the claim into a practical application. See the following references shown below: US Patent Application (US 2016/0019641 A1) – “Multiple Party Branch Recommendation”, hereinafter Barnett. See Barnett at ¶ [0032]: “In some examples, the computing system's physical location recommendation can be at least partially based on machine learning of the user's activities and behaviors, including the user's past behaviors when completing activities at a physical location. For example, a machine learning algorithm can learn the physical locations at which the user most often completes activities, and can give those locations more weight in the recommendation determination. This machine learning can be any suitable machine learning implementation, such as a rule-based machine learning algorithm and/or a neural network-based machine learning algorithm.” The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the Dependent Claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-4, 6-7, 9-12, 14-18 and 24-28 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625