DETAILED ACTION
This Final Office Action is in response Applicant communication filed on
1/16/2026. In Applicant’s amendment, claims 1, 2, 4, 6, 10-12, 14, 16, and 20 were amended. Claims 1-20 are currently pending and have been rejected as follows. IDS filed 1/16/2026 and 2/2/2026 have been considered.
Response to Amendments
Rejections under 35 USC 101 are maintained. Rejections under 35 USC 103 are withdrawn.
Response to Arguments
Applicant’s 35 USC 101 rebuttal arguments and amendments have been fully considered but they are not persuasive to overcome the rejection.
Applicant argues on p. 11 that the claims are not directed to an abstract idea under Step 2A, prong 1 because it recites wireless network infrastructure selection with RF and infrastructure specific constraints. Examiner respectfully submits that limiting the field of use to RF and infrastructure specific constraints does not preclude the claim from being directed to an abstract idea.
Applicant argues on p. 11 that the claims are not directed to mathematical concepts because the claim does not recite a mathematical formula or relationship in the abstract. Examiner respectfully disagrees. Claim 1 recites correlations, predictions, scores, churn probabilities, future payments, aggregate profitability metrics, weighting, proximity calculations, threshold comparisons, and model retraining. These are all mathematical operations.
Applicant argues on p. 12 that the claims do not recite a certain method of organizing because the claimed operations involve training machine learning models to correlate device hardware types with churn propensity and implementing an active learning process that compares observed outcomes to predicted outcomes to further train the models. Examiner respectfully disagrees. The claim as a whole is directed to using profitability, churn data, payment predictions for infrastructure deployment selection, which commercial interactions for making a business decision. The high volume data processing and automated feedback loops of active learning does not remove the claim from the abstract realm.
Applicant argues on p. 12-13 that the claims are integrated into a practical application because they are directed to an improvement in computer functionality or another technical field by the specific active learning feedback loop improving the accuracy and performance of the predictive model itself by correcting for prediction divergences. Applicant further states this improvement is tied to the technological field of RAN management. Examiner respectfully disagrees. The claim uses subscriber data, spatial infrastructure data, and signal strength thresholds to generate a candidate location for selection. The claim does not meaningfully apply the abstract idea in a way that improves technology or effects a transformation. The claim merely uses technology specific data to perform an abstract analysis that produces an output of a candidate location for wireless network infrastructure.Improving the accuracy of a wireless network infrastructure planning output is not an improvement to computer functionality. It is an improvement to the quality of the resource planning decision. The claim does not recite a particular improvement to machine learning model operation itself, like Desjardin. The present claim merely recites comparing observed outcomes to predicted outcomes, feeding back those results, and further training the models. This is a generic active learning feedback loop. The alleged improvement of the accuracy and performance of the predictive model is to the desired result/output. This is not a technical improvement under Step 2A, prong 2. Further, the claim does not recite any concrete technical steps for improving RAN management. The claim is directed to planning and selecting a candidate location for possible infrastructure by including signal strength and infrastructure proximity data. Inclusion of that data does not change the characterization of the claim.
Applicant argues on p. 13 that the claims are eligible at Step 2B because the combination of features-including the use of a spatial database of infrastructure, signal strength thresholds, and an active learning feedback loop-is not well-understood, routine, or conventional in the field of network planning; conventional methods do not provide a self-healing model that retrains itself based on observed hardware-specific churn outcomes to optimize infrastructure deployment; and that these features represent an inventive assembly of components that provide a specific technical solution to the problem of network capacity expansion. Examiner respectfully disagrees. The claim recites the use of spatial infrastructure data, signal strength thresholds, and active learning feedback at a high level and does not identify a specific unconventional technical implementation. The inventive concept must be more than the abstract idea itself. Accordingly, the claims remain patent ineligible.
Applicant's prior art arguments have been fully considered and they are persuasive to overcome the rejection. In particular, see p. 13-15 of the filed Remarks.
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 clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method, system). 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) without integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea.
Regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (‘2019 PEG”), Claims 1-10 are directed toward the statutory category of a process (reciting a “method”). Claims 11-20 are directed toward the statutory category of a machine (reciting a “system”).
Regarding Step 2A, prong 1 of the 2019 PEG, Claims 1, 12 and 23 are directed to an abstract idea by reciting obtaining, for a plurality of wireless subscribers, historical data comprising (i) a device hardware type and (ii) a data plan tier associated with each wireless subscriber; training, using the obtained historical data, one or more machine learning models to correlate device hardware types with churn propensity; estimating, using the trained one or more machine learning models a score that is indicative of an expected profitability for each wireless subscriber, wherein the score is calculated by predicting a device-specific future churn probability and at least one future payment; identifying a geolocation by mapping cellular usage patterns of a subset of the plurality of wireless subscribers against a spatial database of existing wireless network infrastructure components, wherein the identified geolocation has at least a threshold data usage level; determining an aggregate profitability metric for the geolocation by weighting the estimated scores of the subset of wireless subscribers based on their proximity to the existing wireless network infrastructure components; and responsive to determining that the aggregate profitability metric satisfies a threshold condition and that existing service coverage at the geolocation exceeds a minimum signal strength threshold, selecting the identified geolocation as a candidate location for wireless network infrastructure, […], the active learning process comprising: comparing observed outcomes associated with the subset of the plurality of wireless subscribers to predicted outcomes generated by the one or more machine learning models, feeding back results of the comparing to the one or more machine learning models to further train the one or more machine learning models, and collecting additional training data from subsequent instances to update the predictions for the device-specific future churn probability. (Example Claim 1).
The claims are considered abstract because these steps recite mathematical concepts like mathematical calculations characterized by the scoring and aggregating; certain methods of organizing human activity like commercial interactions characterized by the analysis to decide where to deploy infrastructure based on profitability. Applicant’s disclosure explains the claimed steps aim to provide accurate predictions of payments, costs, and churn probability for users, improve the user experience for high-value customers, and enable efficient allocation of a network provider's resources to establish new wireless retail locations and to install new wireless infrastructure equipment (Applicant’s Specification, [0007]). By this evidence, the claims recite a type of mathematical concept like mathematical calculations and certain methods of organizing human activity like commercial interactions common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea (e.g., system and method for machine learning-based selection of locations for wireless network infrastructure).
Regarding Step 2A, prong 2 of the 2019 PEG, the judicial exception is not integrated into a practical application because the claims (the judicial exception and the additional elements such as a memory configured to store instructions; and one or more processors configured to execute the instructions; wherein the one or more machine learning models are configured to implement an active learning process to continually improve performance of the one or more machine learning models) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception (see MPEP §§ 2106.05(a-c, e)).
Dependent claims 2-5, 7-10, 12-15 and 17-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP 2106.05(f).
Regarding Step 2B of the 2019 PEG, the additional elements have been considered above in Step 2A Prong 2. The claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP
2106.05(f).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, “Performing repetitive calculations,” Flook, and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing steps of “obtaining” historical data, “training” one or more ML models, “estimating” an expected profitability, “identifying” a geolocation, “determining” an aggregate profitability metric, “selecting” the identified geolocation, “implement” an active learning process, “comparing” outcomes, “feeding” back results, and “collecting” additional training data (Example Claim 1).
By the above, the claimed computing “call[s] for performance of the claimed information collection, analysis, and display functions ‘on a set of generic computer components' and display devices” [Elec. Power Group, 830 F.3d at 1355] operating in a “normal, expected manner” [DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1245, 1258 (Fed. Cir. 2014)].
Conclusively, Applicant's invention is patent-ineligible. When viewed both individually and as a whole, Claims 1-20 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 8019352 B2: A system and method for estimating the position of wireless devices within a wireless communication network combines measured RF channel characteristics for the wireless device with one or more predicted performance lookup tables, each of which correlates an RF channel characteristic to some higher order network performance metric and/or a position within an environmental model. Measured RF channel characteristics for wireless devices are compared against the performance lookup tables to determine the set of lookup tables that most closely match the measured RF channel characteristics. The positions within the environmental model corresponding to the selected set of matching lookup tables are identified as possible locations for the wireless device. The performance lookup tables are uniquely constructed by site-specific location, technology, wireless standard, and equipment types, and/or the current operating state of the communications network.
WO 2024/074189 A1: A method of managing a wireless communication network includes obtaining network configuration and network performance data for the wireless communication network and urban infrastructure data for a geographic area. A plurality of structure topologies of structures in the geographic area are extracted from the urban infrastructure data. Respective ones of the structure topologies are associated with one or more cells of the wireless communication network. The performance of the wireless communication network at the plurality of structures is assessed based on the network configuration and performance data, and, for each of the structures, a benefit metric is generated that indicates a potential benefit associated with upgrading or deploying wireless communication network equipment. Related systems and computer program products are disclosed.
Salau et al., Machine Learning Analysis of Multi-Radio Access Technology Selection in 5G NSA Network, 2023: The exponential growth of traffic across the mobile networks called for exploitation of new spectrum bands of 5G networks; whose deployment still rely on support from underlying 4G long term evolution (LTE) networks in both stand-alone (SA) and non-stand-alone (NSA) architectures. This scenario poses challenges on the choice of Radio Access Technology (RAT) selection between 4G LTE and 5G new radio (NR) networks to these ever increasing mobile users with respect to their geographical location, mobility and network coverage. Hence, this study investigates joint user requirements and network constraints for appropriate RAT selection between 4G LTE and 5G NR by recording live radio measurements over a distance of 300 meters between a pedestrian user and 5G NSA base-station. The problem (RAT selection) was formulated as a classification process, hence implemented with classification machine learning (ML) algorithms: Decision Tree (DT), Extra Tree (XTREE), Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost) to select an appropriate RAT. Evaluation of results with standard classification metrics, show measure of accuracy of algorithms: DT at 91.82%, RF at 87.64%, XTREE at 86.75%, GB at 91.86%, and XGBoost at 93.86%, where XGBoost showed highest performance value, therefore proposed as ML model for RAT selection to achieve effective and efficient resource allocation..
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED EL-BATHY whose telephone number is (571)270-5847. The examiner can normally be reached on M-F 8AM-4:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA MUNSON can be reached on (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 the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624
Prosecution Insights