DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Introduction
The following is a final Office Action in response to Applicant’s communications received on December 4, 2025. Claims 1, 4, 7-9, 11, 14 and 17-20 have been amended, claims 2-3, 6, 12-13 and 16 have been canceled.
Currently, claims 1, 4-11, 14-15 and 17-20 are pending with claims 7-10 and 17-20 under consideration and claims 1, 4-5, 11 and 14-15 being withdrawn as being directed to non-elected invention. Claims 7 and 17 are independent.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/04/2025 and 01/16/2026 appear to be in compliance with the previsions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the Examiner.
Election/Restrictions
The Since Applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 1, 4-5, 11 and 14-15 are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
Claims 1, 4-5, 11 and 14-15 drawn to a subcombination for selecting a geolocation as a candidate location for a retail location based on a service coverage provided by the wireless provider at the identified geolocation exceeded a threshold coverage level, classified in G06Q 30/0202 and G06N 20/00.
Claims 7-10 and 17-20 drawn to a subcombination for selecting a geolocation as a candidate location for a retail location, classified in G06Q 30/0205 and G06N 20/00.
The newly amended claims directed to an invention that is independent or distinct from the invention originally claimed for the following reasons:
Inventions I and II are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable. In this case, subcombination I has separate utility such as obtaining historical data comprising (i) historical payments, (ii) historical data usage, (iii) historical cost associated with providing services, and (iv) a type of device associated with each wireless subscriber; training, using the obtained historical data, one or more machine learning models using a supervised learning algorithm or a reinforcement learning algorithm to generate predictions for a corresponding wireless subscriber of the plurality of wireless subscribers; estimating, using the trained one or more machine learning models, a score that is indicative of an expected profitability associated with the corresponding wireless subscriber of the plurality of wireless subscribers; predicting, based on the obtained historical data, (i) at least one future payment from the wireless subscriber, (ii) at least one future cost associated with providing services to the wireless subscriber, and (iii) at least one future churn probability associated with the wireless subscriber, and calculating the score based on the at least one predicted future payment, the at least one predicted future cost, and the at least one predicted future churn probability; identifying a geolocation associated with a subset of the plurality of wireless subscribers based on (i) cellular usage patterns of the subset of the plurality of wireless subscribers and (ii) data regarding locations of wireless network infrastructure components, wherein the identified geolocation has at least a threshold data usage level within a pre-defined radius; and selecting the identified geolocation as a candidate location for a retail location based on a service coverage provided by the wireless provider at the identified geolocation exceeding a threshold coverage level. Subcombination II has separate utility such as training, using historical data and profitability metrics for one or more geolocations, one or more machine learning models using a supervised learning algorithm or a reinforcement learning algorithm to estimate a metric indicative of an expected profitability associated with a geolocation based on demographic data above the geolocation; estimating, using the trained one or more machine learning model, the metric indicative of the expected profitability associated with the geolocation from data usage by one or more wireless subscribers at the geolocation; determining that the metric satisfies a threshold condition, and selecting the identified geolocation as a candidate location for a retail location. See MPEP § 806.05(d).
The examiner has required restriction between subcombinations usable together. Where applicant elects a subcombination and claims thereto are subsequently found allowable, any claim(s) depending from or otherwise requiring all the limitations of the allowable subcombination will be examined for patentability in accordance with 37 CFR 1.104. See MPEP § 821.04(a). Applicant is advised that if any claim presented in a continuation or divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Restriction for examination purposes as indicated is proper because all these inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
(a) the inventions have acquired a separate status in the art in view of their different classification;
(b) the inventions have acquired a separate status in the art due to their recognized divergent subject matter;
(c) the inventions require a different field of search (for example, searching different classes/subclasses or electronic resources, or employing different search queries);
(d) the prior art applicable to one invention would not likely be applicable to another invention;
(e) the inventions are likely to raise different non-prior art issues under 35 U.S.C. 101 and/or 35 U.S.C. 112, first paragraph.
Applicant(s) are reminded that upon the cancellation of claims to a non-elected invention, the inventorship must be amended in compliance with 37 CFR 1.48(b) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. Any amendment of inventorship must be accompanied by a request under 37 CFR 1.48(b) and by the fee required under 37 CFR 1.17(i).
Response to Amendments
Applicant’s amendments necessitated the new ground(s) of rejection in this Office Action.
Applicant’s amendments to claims 7-9 and 17-20 are NOT sufficient to overcome the 35 U.S.C. § 101 rejection as set forth in the previous Office Action. Therefore, the 35 U.S.C. § 101 rejection to claims 7-10 and 17-20 has been maintained.
Response to Arguments
Applicant’s arguments filed on December 4, 2025 have been fully considered but are not persuasive.
In the Remarks on page 10, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that the amended claims do not recite a judicial exception. Instead, amended claim 1 recites a specific computer-implemented method for profitability-based geolocation selection leveraging predictive modeling and active machine learning techniques.
In response to Applicant’s arguments, the Examiner respectfully disagrees. Claim 1 recites limitations including “obtaining historical data…, predicting a future payment, a future cost associated and a future churn probability based on the obtained historical data associated with a wireless subscriber, identifying a geolocation associated with a subset of the plurality of wireless subscribers…, determining an aggregate profitability metric” that can be performed in the mind, or by a human using a pen and paper, the limitations fall within the mental processes grouping. See CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372-73 (Fed. Cir. 2011) (determining that a claim whose “steps can be performed in the human mind, or by a human using a pen and paper” is directed to an unpatentable mental process). This is true even if the claim recites that a generic computer component performs the acts. See, e.g., Versata Dev. Grp., Inc. v. SAP Am., Inc., 793 F.3d 1306, 1335 (Fed. Cir. 2015) (“Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.”); see also Guidance 84 Fed. Reg. at 52.
In the Remarks on page 10, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that these features recite complex data processing and machine learning techniques that cannot be practically performed in the human mind.
In response to Applicant’s arguments, the Examiner respectfully disagrees. Even if the claim recites a complex data processing method and using the machine learning techniques to perform the steps, the complexity does not render the claim any less abstract. See Accenture Glob. Servs. GmbH v. Guidewire Software, Inc., 728 F.3d 1336, 1345 (Fed. Cir. 2013) (“[T]he complexity of the implementing software or the level of detail in the specification does not transform a claim reciting only an abstract concept into a patent-eligible system or method.”).
With respect to the machine learning techniques, the claims recite “training, using the obtained historical data, one or more machine learning models….to generate predictions for a corresponding wireless subscriber…”, however, the “trained one or more machine learning models” are used to perform the estimation step, for estimating a score that indicative of an expected profitability, which is not an algorithm of the machine learning models. Thus, training a generic machine learning model using actions and collection of data that performed by users in some unspecified way without any technical details of that process fails to reflect any apparent improvement to a computer or other technology. Further, the claims do not recite an improved way to train a model by using machine learning and do not purport to improve machine learning using training data. See Intellectual Ventures, 792 F.3d at 1371.
In the Remarks on page 11, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that even assuming the claims involve “mental processes” as asserted by the Office Action, under Prong Two, the claims integrate the concept into a practical application.
In response to Applicant’s arguments, the Examiner respectfully disagrees. In order for a claim to integrate the exception into a practical application, the additional claimed elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), affect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)). See Revised 2019 Guidance. Here, claim 1 did not even recite an additional element for performing the steps. When given the broadest reasonable interpretation, a machine is not require in the claim to perform the steps. Therefore, there is no additional element in the claim that integrates the abstract idea into a practical application or amount to significantly more than the judicial exception because the claim recites no more than generic models/instructions to apply the abstract idea or merely linking the abstract idea to a particular technological environment. See Revised Guidance, 84 Fed. Reg. at 55.
In the Remarks on page 13, Applicant argues that the cited references, whether taken alone or in proper combination, do not disclose or suggest all features in the independent claims. For example, amended independent claim 1 recites, among other things, “responsive to determining that the aggregate profitability metric associated with the geolocation satisfies the threshold condition, selecting the identified geolocation as a candidate location for a retail location based on a service coverage level.” However, Applicant’s arguments are directed to the newly amended claims, and therefore, the newly amended claims will be fully addressed in this Office Action.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7-10 and 17-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The added subject matter which is not in the original specification is as follows:
Claims 7-10 and 17-19 recite one or more of “retrains/retraining” and “incorporating updated profitability metric and geo-demographic data generated during operation”, which are not disclosed in the Specification and no definition found in the originally filed specification to teach these limitations. Applicant is required to cancel or amend the claims in accordance with written description requirements. See recent Federal Circuit decision in Ariad Pharmaceuticals Inc. v. Eli Lilly & Co., 598 F.3d 1336, 94 U.S.P.Q.2d 1161 (Fed. Cir. 2010).
Dependent claims 10 and 20 are also rejected as each depends on the rejected claims.
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 7-10 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 7-10 are directed to a method for selecting a geolocation as a candidate location for a retail location without tied to a particular machine for performing the steps, which fall outside of the four statutory categories. However, claims 7-10 will be included in Step 2 Analysis for the purpose of compact prosecution. Claims 17-20 are directed to systems comprising a memory and one or more processors, which fall within the statutory category of a machine.
With respect to claim 7, the claim is directed to non-statutory subject matter because the claim is directed to a method without tied to a particular machine in the body of the claims for performing the steps. One factor to consider when determining whether a claim recites a §101 patent eligible process is to determine if the claimed process (1) is tied to a particular machine or; (2) transforms a particular article to a different state or thing. See In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008) (en banc) aff’d, Bilski v. Kappos, 561 U.S. ___, 130 S. Ct. 3218, 95 USPQ2d 1001 (U.S. 2010). (Machine-or-Transformation Test).
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking the method of claims 7-10 as representative, the claims recite limitations of “training one or more machine learning models [] to estimate a metric indicative of an expect profitability associated with a geolocation based on demographic data about the geolocation, estimating the metric indicative of the expected profitability associated with a geolocation, determining that the metric satisfies a threshold condition, selecting the identified geolocation as a candidate location for a retail location, aggregating scores for a plurality of individuals using data at each of the other geolocation, training one or more additional machine learning models using a supervised learning algorithm or a reinforcement learning algorithm, predicting (i) at one future payment, (ii) at least one future cost, (iii) at least one future churn probability, determining the score for each individual based on the at least one predicted future payment, at least one predicted future cost, and the at least one predicted future churn probability”. Other than “one or more machine learning models”, nothing in the claim limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to processes, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of “using one or more machine learning models”. Nothing in the claim elements precludes the steps from practically being performed in the mind. For example, the claim encompasses a person can manually estimating the metric indicative of the expected profitability associated with the geolocation from data usage, and selecting the geolocation as a candidate location for a retail location in the mind (including an observation, evaluation, judgment, opinion), or by a human using a pen and paper, which fall within the “mental processes” grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Further, “using one or more machine learning models” is merely adding the words “apply it” or using “a particular machine” with an abstract idea, or merely linking the abstract idea to a particular technological environment. The Supreme Court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. See Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). As to learning per se, such an argument overlooks the entire education system. Reciting machine learning is placing such learning in a computer context, offering no technological implementation details beyond the conceptual idea to use a machine for learning. Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, claim 7 recites no additional element for performing the steps, when given the broadest reasonable interpretation, a machine is not required in the claim. Even if claim 7 recites the additional elements of “a memory” and “one or more processors” as recited in claim 17 for performing the steps. The specification describes these additional elements at a high level of generality and merely invoked as tools to perform the generic computer functions including receive interaction data over a network. For example, the specification discloses “The computer system includes a memory configured to store instructions and one or more processors configured to execute the instructions to perform operations.” (See ¶ 12). The additional elements are no more than generic computer components for performing generic computer functions including receiving, storing, manipulating and transmitting information over a network. The courts have held that merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014); see also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims”). Further, “training a machine learning model using the collected demographic data and data usage to predict aggregate customer lifetime value” does not reflect any improvement to the functioning of a computer or other technology. Claim 5 does not recite an improved way to train a model by using machine learning and does not purport to improve machine learning using training data. See Intellectual Ventures, 792 F.3d at 1371. However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, claim 7 recites no additional element for performing the steps, when given the broadest reasonable interpretation, a machine is not required in the claim. Even if claim 7 recites the additional elements of “a memory” and “one or more processors” as recited in claim 17 for performing the steps. The specification describes these additional elements at a high level of generality and merely invoked as tools to perform the generic computer functions including receive interaction data over a network. For example, the specification discloses “The computer system includes a memory configured to store instructions and one or more processors configured to execute the instructions to perform operations.” (See ¶ 12). Taking the claim elements separately and as an ordered combination, the one or more processors, at best, may perform the generic computer functions including receiving, storing, manipulating, and transmitting information over a network. However, generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); 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); Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351-52, 119 USPQ2d 1739, 1740 (Fed. Cir. 2016)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 7-10 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other claims 17-20 parallel claims 7-10—similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Blueowl, (WO 2022/010791 A1), and in view of Livne et al., (US 2019/0043068, hereinafter: Livne), and further in view of Walden (WO 2016/183302), and Cella et al., (US 2023/0419277, hereinafter: Cella).
Regarding claim 7, Blueowl discloses the method comprising:
training, using historical data and profitability metrics for one or more geolocations, one or more machine learning models using a supervised learning algorithm or a reinforcement learning algorithm to estimate a metric indicative of an expected profitability associated with a geolocation based on demographic data about the geolocation (see ¶ 112-113, ¶ 128, ¶ 186-187);
estimating, using the trained one or more machine learning models (see ¶ 101, ¶ 187), the metric indicative of an expected profitability associated with the geolocation from data usage by one or more wireless subscribers at the geolocation (see ¶ 29, ¶ 87, ¶ 90, ¶ 95, ¶ 98, ¶ 103, ¶ 112, ¶ 195);
determining that the metric satisfies a threshold condition (see ¶ 110, ¶ 112-114, ¶ 123, ¶ 127).
Blueowl discloses a plurality of subscribers (see ¶ 82), and a plurality of marketplace participants (e.g., subscribers) (see ¶ 84). Blueowl further disclose the accumulative profitability milestone exceeding a threshold and the predicting a profitability metric factoring profitability during a plurality of time period (see ¶ 127-128).
Blueowl does not explicitly disclose the following limitations; however, Livne in an analogous art for predicting a rating score of a cellular service for a plurality of cellular subscribers discloses
a plurality of cellular subscribers (see ¶ 8, ¶ 12-14, ¶ 16-18, ¶ 26, ¶ 62, ¶ 137);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Blueowl to include teaching of Livne in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution with a more specific name. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Blueowl discloses a method for providing a telematics action marketplace to select a plurality of winning bids related to a vehicle operator by determining a number of winning bids satisfying a bid threshold (see ¶ 123).
Blueowl and Livne do not explicitly disclose the following limitations; however, Walden in an analogous art for transmitting content to customers discloses
responsive to determining that the metric satisfies the threshold condition, selecting the identified geolocation as a candidate location for a retail location (see ¶ 30-31, ¶ 41-45, ¶ 50-51). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Blueowl and in view of Livne to include teaching of Walden in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operation efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Blueowl discloses the method may determine a set of program-evaluation metrics (e.g., predicted probability of acquisition, actual acquisition data, acquisition cost, and operator desirability scores and trends); the method may further compare the first set of program-evaluation metrics against the second set of program-evaluation metrics and generate a comparison report for the marketplace participants to evaluate the effectiveness of its user-retention programs (see ¶ 121).
Blueowl, Livne and Walden do not explicitly disclose the following limitations; however, Cella in an analogous art of training dataset management discloses
wherein the one or more machine learning models are further configured to implement an active learning approach that compares observed outcomes to predicted outcomes and retrains the one or more machine learning model using generated training data comprising subsequent observed profitability metrics and geo-demographic data (see ¶ 679, ¶ 819, ¶ 877, ¶ 968, ¶ 3909).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Blueowl and in view of Livne and Walden to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of improving training based on set of outcomes, in turn of operation efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8, Blueowl discloses the method of claim 7, wherein the one or more machine learning models are trained using (i) demographic data and/or data usage patterns for other geolocations and (ii) one or more profitability metrics for the other geolocations (see ¶ 103, ¶ 108, ¶ 112, ¶ 148, ¶ 157, ¶ 186),
wherein retraining comprises incorporating updated profitability metric and geo-demographic data generated during operation (see ¶ 50, ¶ 98, ¶ 138).
Regarding claim 17, Blueowl discloses a computing system comprising:
a memory configured to store instructions (see ¶ 17); and
one or more processors configured to execute the instructions (see ¶ 17) to perform operations comprising:
training, using historical data and profitability metrics for one or more geolocations, one or more machine learning models using a supervised learning algorithm or a reinforcement learning algorithm to estimate a metric indicative of an expected profitability associated with a geolocation based on demographic data about the geolocation (see ¶ 113, ¶ 128, ¶ 186-187);
estimating, using the trained one or more machine learning models, the metric indicative of the expected profitability associated with a geolocation from data usage by one or more wireless subscribers at the geolocation (see ¶ 38, 43, ¶ 50, ¶ 63, ¶ 95);
determining that the metric satisfies a threshold condition (see ¶ 110, ¶ 112-114, ¶ 123, ¶ 127).
Blueowl discloses a plurality of subscribers (see ¶ 82), and a plurality of marketplace participants (e.g., subscribers) (see ¶ 84). Blueowl further disclose the accumulative profitability milestone exceeding a threshold and the predicting a profitability metric factoring profitability during a plurality of time period (see ¶ 127-128).
Blueowl does not explicitly disclose the following limitations; however, Livne in an analogous art for predicting a rating score of a cellular service for a plurality of cellular subscribers discloses
a plurality of cellular subscribers (see ¶ 8, ¶ 12-14, ¶ 16-18, ¶ 26, ¶ 62, ¶ 137);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Blueowl to include teaching of Livne in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution with a more specific name. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Blueowl discloses a method for providing a telematics action marketplace to select a plurality of winning bids related to a vehicle operator by determining a number of winning bids satisfying a bid threshold (see ¶ 123).
Blueowl and Livne do not explicitly disclose the following limitations; however, Walden in an analogous art for transmitting content to customers discloses
responsive to determining that the metric satisfies the threshold condition, selecting the identified geolocation as a candidate location for a retail location (see ¶ 30-31, ¶ 41-45, ¶ 50-51).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Blueowl and in view of Livne to include teaching of Walden in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operation efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Blueowl discloses the method may determine a set of program-evaluation metrics (e.g., predicted probability of acquisition, actual acquisition data, acquisition cost, and operator desirability scores and trends); the method may further compare the first set of program-evaluation metrics against the second set of program-evaluation metrics and generate a comparison report for the marketplace participants to evaluate the effectiveness of its user-retention programs (see ¶ 121).
Blueowl, Livne and Walden do not explicitly disclose the following limitations; however, Cella discloses
wherein the one or more machine learning models are further configured to implement an active learning approach that compares observed outcomes to predicted outcomes and retrains the one or more machine learning model using generated training data comprising subsequent observed profitability metrics and geo-demographic data (see ¶ 679, ¶ 819, ¶ 877, ¶ 968, ¶ 3909).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Blueowl and in view of Livne and Walden to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of improving training based on set of outcomes, in turn of operation efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 18, Blueowl discloses the computing system of claim 17, wherein the one or more machine learning models are trained using (i) demographic data and/or data usage patterns for other geolocations and (ii) one or more profitability metrics for the other geolocations (see ¶ 103, ¶ 108, ¶ 112, ¶ 148, ¶ 157, ¶ 186), and
wherein retraining comprises incorporating updated profitability metric and geo-demographic data generated during operation (see ¶ 50, ¶ 98, ¶ 138).
Claims 9-10 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Blueowl and in view of Livne and Walden as applied to claims 7-8 and 17-18 above, and further in view of Krishnan et al. (US 2025/0061350, hereinafter: Krishnan).
Regarding claim 9, Blueowl discloses the method of claim 8, wherein the one or more profitability metrics for the other geolocations are estimated by aggregating scores for a plurality of individuals using data at each of the other geolocations, wherein the score for each individual is indicative of an expected profitability associated with the individual, and wherein determining the score for each individual of the plurality of individuals comprises:
training, using historical data for the individual, one or more additional machine learning models using a supervised learning algorithm or a reinforcement learning algorithm (see ¶ 113, ¶ 128, ¶ 186-187),
predicting, based on one or more features corresponding to the individual and using the trained one or more additional machine learning models:
(i) at least one future payment from the individual (see ¶ 126-128, ¶ 175),
(ii) at least one future cost associated with providing services to the individual (see ¶ 105-106, ¶ 116).
Blueowl discloses determine one or more user-management metrics include actual costs of retention, probability of retention, and predicted costs of retention.
Blueowl, Livne and Walden do not explicitly disclose the following limitations; however, Krishnan in an analogous art for churn prediction discloses
(iii) at least one future churn probability associated with the individual (see ¶ 7, ¶ 14-15, ¶ 60, ¶ 79, ¶ 86 ), and
determining the score for each individual based on the at least one predicted future payment, the at least one predicted future cost, and the at least one predicted future churn probability (see ¶ 12, ¶ 50, ¶ 60, ¶ 78-79).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Blueowl and in view of Livne and Walden to include teaching of Krishnan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of analysis, resulting in more focused solution. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Blueowl discloses the method may determine a set of program-evaluation metrics (e.g., predicted probability of acquisition, actual acquisition data, acquisition cost, and operator desirability scores and trends); the method may further compare the first set of program-evaluation metrics against the second set of program-evaluation metrics and generate a comparison report for the marketplace participants to evaluate the effectiveness of its user-retention programs (see ¶ 121).
Blueowl, Livne and Walden do not explicitly disclose the following limitations; however, Cella discloses
wherein the one or more additional machine learning models are further configured to implement an active learning approach that retrains based on observed outcomes compared to predicted outcomes (see ¶ 679, ¶ 819, ¶ 877, ¶ 968).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Blueowl and in view of Livne and Walden to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of improving training based on set of outcomes, in turn of operation efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 10, Blueowl discloses the method of claim 9, wherein the one or more features corresponding to the individual comprises historical payments made by the individual, historical data usage, historical costs associated with providing services to the individual, a type of device of the individual, a type of data plan associated with the individual, a longevity of a business relationship with the individual, and/or demographic features of the individual (see ¶ 43, ¶ 89, ¶ 98, ¶ 101-103, ¶ 199).
Regarding claim 19, Blueowl discloses the computing system of claim 18, wherein the one or more profitability metrics for the other geolocations are estimated by aggregating scores for a plurality of individuals using data at each of the other geolocations, wherein the score for each individual is indicative of an expected profitability associated with the individual, and wherein determining the score for each individual of the plurality of individuals comprises:
training, using historical data for the individual, one or more additional machine learning models using a supervised learning algorithm or a reinforcement learning algorithm (see ¶ 113, ¶ 128, ¶ 186-187),
predicting, based on one or more features corresponding to the individual and using one or more additional machine learning models:
(i) at least one future payment from the individual (see ¶ 126-128, ¶ 175),
(ii) at least one future cost associated with providing services to the individual (see ¶ 105-106, ¶ 116).
Blueowl discloses determine one or more user-management metrics include actual costs of retention, probability of retention, and predicted costs of retention.
Blueowl, Livne and Walden do not explicitly disclose the following limitations; however, Krishnan in an analogous art for churn prediction discloses
(iii) at least one future churn probability associated with the individual (see ¶ 7, ¶ 14-15, ¶ 60, ¶ 79, ¶ 86 ), and
determining the score for each individual based on the at least one predicted future payment, the at least one predicted future cost, and the at least one predicted future churn probability (see ¶ 12, ¶ 50, ¶ 60, ¶ 78-79).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Blueowl and in view of Livne and Walden to include teaching of Krishnan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of analysis, resulting in more focused solution. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Blueowl discloses the method may determine a set of program-evaluation metrics (e.g., predicted probability of acquisition, actual acquisition data, acquisition cost, and operator desirability scores and trends); the method may further compare the first set of program-evaluation metrics against the second set of program-evaluation metrics and generate a comparison report for the marketplace participants to evaluate the effectiveness of its user-retention programs (see ¶ 121).
Blueowl, Livne and Walden do not explicitly disclose the following limitations; however, Cella discloses
wherein the one or more additional machine learning models are further configured to implement an active learning approach that retrains based on observed outcomes compared to predicted outcomes (see ¶ 679, ¶ 819, ¶ 877, ¶ 968).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Blueowl and in view of Livne and Walden to include teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit of improving training based on set of outcomes, in turn of operation efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 20, Blueowl discloses the computing system of claim 19, wherein the one or more features corresponding to the individual comprises historical payments made by the individual, historical data usage, historical costs associated with providing services to the individual, a type of device of the individual, a type of data plan associated with the individual, a longevity of a business relationship with the individual, and/or demographic features of the individual (see ¶ 43, ¶ 89, ¶ 98, ¶ 101-103, ¶ 199).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sharma et al., (WO 2020/136512 A1) discloses method for prioritizing subscribers in a cellular communications network based on revenue generation and churn probability.
Harris et al., (US 8645187 B1) discloses a method for retaining customers for a wireless service provider by identifying influences among a group of wireless subscribers.
Meier-Hellstern et al., (US 2021/0014698) discloses a method for creating importance layers for potential build location for equipment of the one or more communication networks and identifying desirable build locations among the potential build locations.
Raleigh et al., (US 2017/0201850) discloses a method for wireless device activation to subscribers account of a master wireless device associated with a subscriber.
Latvaitis, “Churn Prediction in Telecommunication industry using Machine Learning”, Vilnius University, Faculty of Mathematics and Informatics, Modelling and Data Analysis Master’s Study Program. 2022.
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.
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/PAN G CHOY/Primary Examiner, Art Unit 3624