Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/533,045, filed on December 7, 2023. In response to Examiner’s Non-Final Office Action of July 22, 2025, Applicant, on October 22, 2025, amended 1, 10 and 19 and added claim 21-24 ; and cancelled claims 6-7 and 15-16. Claims 1-5, 8-14, 17-24 are pending in this application and have been rejected below. Information Disclosure Statement (IDS) filed 10/22/2025 and 1/16/2026 is acknowledged.
Acknowledgement is hereby made of receipt of Information Disclosure Statements filed by Applicant on 22 October 2025. 1449' s are attached. Examiner notes that due to the excessively voluminous Information Disclosure Statement submitted by Applicant, the Examiner has given only a cursory review of the listed references. In accordance with MPEP 609.04(a), Applicant is encouraged to provide a concise explanation of why the information is being submitted and how it is understood to be relevant. Concise explanations (especially those which point out the relevant pages and lines) are helpful to the Office, particularly where documents are lengthy and complex and Applicant is aware of a section that is highly relevant to patentability or where a large number of documents are submitted and applicant is aware that one or more are highly relevant to patentability. See MPEP.2004 Aids to Compliance With Duty of Disclosure
"13. It is desirable to avoid the submission of long lists of documents if it can be avoided. Eliminate clearly irrelevant and marginally pertinent cumulative information. If a long list is submitted, highlight those documents which have been specifically brought to applicant’s attention and/or are known to be of most significance. See Penn Yan Boats, Inc. v. Sea Lark Boats, Inc., 359 F. Supp. 948, 175 USPQ 260 (S.D. Fla. 1972), aff ’d, 479 F.2d 1338, 178 USPQ 577 (5th Cir. 1973), cert. denied, 414 U.S. 874 (1974). But cf. Molins PLC v. Textron Inc., 48 F.3d 1172, 33 USPQ2d 1823 (Fed. Cir. 1995)."
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amended claims have been considered
and are insufficient to overcome the rejection. Please refer to the 35 U.S.C. § 101 rejection for further explanation and rationale.
The 35 U.S.C. § 103 rejections are withdrawn.
Response to Arguments
Applicant’s arguments filed October 22, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed October 22, 2025.
On Pgs. 10-11, regarding the 35 U.S.C. § 101 rejection, Applicant states a human mind would not be able to create the two models; nor a human mind would be able to determine a CATE score based on the outcomes of the two models. Even if the claims were directed to an abstract idea, the claims integrate any such abstract idea to the practical application of allocating additional data usage quota to a wireless subscriber based on a study of other wireless subscribers that share at least some of the features identified for the wireless subscriber. In response, the claims primarily recite the additional element of using computer components to perform each step. The “computing system”, “memory”, “processor”, “machine-readable storage device”, and “processing device” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
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 1-5, 8-14, 17-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 are directed to assignment of data usage quota to wireless customers using machine learning.
Claim 1 recites a method for assignment of data usage quota to wireless customers using machine learning, Claim 10 recites a system for assignment of data usage quota to wireless customers using machine learning and Claim 19 recites an article of manufacture for assignment of data usage quota to wireless customers using machine learning, which include identifying a set of one or more features corresponding to a wireless subscriber; determining that the wireless subscriber is likely to exceed a quota of data usage allotted to the wireless subscriber; determining a score that is indicative of an expected profitability associated with the wireless subscriber; determining that the score satisfies a threshold condition; determining whether allocating an additional data usage quota to the wireless subscriber will likely provide a satisfactory result by determining a conditional average treatment effect (CATE) score for the wireless subscriber, wherein the CATE score represents an expected difference in outcomes for (i) treated subscribers in a first model and (ii) untreated subscribers in a second model, wherein the treated and the untreated subscribers share at least some of the one or more features with the wireless subscriber, wherein the first model and the second model are created based on an A/B testing, wherein the first model is created for subscribers that received respective additional data usage quota and the second model is created for subscribers that did not receive any additional data usage quota, determining whether the CATE score is associated with receiving the satisfactory result, and in response to determining that the CATE score is associated with receiving the satisfactory result, allocating the[[an]] additional data usage quota to the wireless subscriber.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “computing system”, “memory”, “processor”, “machine-readable storage device”, and “processing device”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “computing system”, “memory”, “processor”, “machine-readable storage device”, and “processing device” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 10 and claim 19 recite using one or more machine learning analysis techniques. The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning processing is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses 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 claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in data analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computing system”, “memory”, “processor”, “machine-readable storage device”, and “processing device” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- 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) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Regarding Step 2B and the “machine learning”- it is a tool to perform the abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-9, 11-18, and 20-24 recite the set of one or more features comprises historical payments made by the wireless subscriber, historical data usage, historical costs associated with providing services to the wireless subscriber, a type of device of the wireless subscriber, a type of data plan associated with the wireless subscriber, a longevity of a business relationship with the wireless subscriber, and/or demographic features of the wireless subscriber; the quota of data usage allotted to the wireless subscriber corresponds to a quota of data usage beyond which additional data usage is throttled; determining the score that is indicative of the expected profitability associated with the wireless subscriber comprises: predicting, based on the second portion of the set of one or more features and using the one or more second machine learning models: (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 determining 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; classifying the wireless subscriber into one of a plurality of cohorts based on the set of one or more features corresponding to the wireless subscriber, and selecting the one or more second machine learning models for use based on the classification of the wireless subscriber; and allocating the additional data usage quota to the wireless subscriber and one or more other wireless subscribers from the group of wireless subscribers based upon an aggregate of the profitability metrics corresponding to the wireless subscriber and the one or more other wireless subscribers from the group of wireless subscribers; determining a magnitude of the additional data usage quota based upon an amount of data available for allocation; estimating respective CATE scores for subscribers in a test set of wireless subscribers; dividing the test set of wireless subscribers such that subscribers with estimated CATE scores greater than a specific threshold are assigned to a first group, and the rest of subscribers in the test set of subscribers are assigned to a second group; determining a first percentage of subscribers in the first group and a second percentage of subscribers in the second group that achieved the specific goal; and determining a difference between the first percentage and the second percentage, wherein a higher difference indicates a more likelihood that the satisfactory result is received; wherein the treated and the untreated subscribers used for creating the first and the second model are selected by using stratification to represent the test set; wherein the CATE score is determined as being associated with receiving the satisfactory result when the difference between the first percentage and the second percentage is greater than a specific threshold; wherein the satisfactory result includes a churn prevention; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 10 and 19. Regarding claims 4-5, 13-14 and 20 and the additional element of machine learning model - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1-5, 8-14, 17-23 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 10, claim 19), the prior art does not teach or fairly suggest:
“… determining whether allocating an additional data usage quota to the wireless subscriber will likely provide a satisfactory result by determining a conditional average treatment effect (CATE) score for the wireless subscriber,wherein the CATE score represents an expected difference in outcomes for (i) treated subscribers in a first model and (ii) untreated subscribers in a second model, wherein the treated and the untreated subscribers share at least some of the one or more features with the wireless subscriber, wherein the first model and the second model are created based on an A/B testing, wherein the first model is created for subscribers that received respective additional data usage quota and the second model is created for subscribers that did not receive any additional data usage quota,determining whether the CATE score is associated with receiving the satisfactory result, andin response to determining that the CATE score is associated with receiving the satisfactory result, allocating the[[an]] additional data usage quota to the wireless subscriber”.
Examiner finds that Pius et al., US Publication No. 20210211900A1 teaches systems, methods, devices, and other techniques by which a client wireless communication device determines characteristics of one or more access points in one or more wireless networks used by the client device. In addition to, learning the characteristics of the access point, the client device can also learn its own hardware characteristics, its usage profile, its environmental operation condition, its user preferences, and its computing context. The computing context can include communication routines used by the client device as well as activity sessions that involve transmitting and receiving data signals at a given wireless location. The client devices use the learned characteristics to realize computing efficiencies based on dynamic configuration of wireless network settings at the client device (see par. 0003). In particular, Pius discloses determining, based on the predicted characteristics of the data model, a first measure of stability of a data connection between the access point of the wireless network and the client device, the first ea sure of stability indicating a duration of the data connection; and adjusting, based on the configuration setting for the client device, a parameter of a first association process between the client device and the access point to increase the first measure of stability of the data connection. (see par. 0014-0015).
Siebel et al., US Publication No. 20220405775A1 teaches systems and methods for obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective (see par. 0003). In particular, Siebel discloses model orchestrator function 242 may be responsible for identifying at least one machine learning model template to be used for each CRM use case (such as templates for opportunity scoring, precision revenue forecasting, next best offer/product/action identification, and churn prediction). A machine learning model template that is selected by the model orchestrator function 242 may include one or more features, one or more algorithms, one or more scoring metrics, or one or more other characteristics (see par. 0116).
Munoz Sanchez et al. (U.S. PG Publication 20190297487 ) A device may receive, from a network device, a credit control request to allocate a quota to a subscriber device. The quota may relate to access, by the subscriber device, to network resources provided by a network provider. The device may determine a subscriber value based on historical usage of network resources by the subscriber device, identify a group value based on historical usage of network resources by a group of subscriber devices with which the subscriber device is associated, and determine a particular quota to allocate to the subscriber device based on a baseline quota, the subscriber value, and a group value. The device may perform one or more actions to cause the particular quota to be allocated to the subscriber device based on determining the particular quota.) teaches a device for communicating orders for transportation, to a vehicle-based communication device, to a system for communicating transportation orders and/or to an associated switching method (see Abstract).
Although Pius, Siebel and Munoz Sanchez teaches the model analysis elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the analysis and allocation modelling.
The dependent claims 2-5, 8-9, 11-14, 17-18, 20-23 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1 (claim 10 and claim 19) that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure US Publication No. 20230141593A1 to Ramer- Abstract-“ Disclosed herein are methods and systems for enabling a host provider to provide a consumer homeowner with improved maintenance and repair services for items in the home, including under a subscription model that provides the consumer with predictable cost while assuring reliable services. Also disclosed herein are methods and systems for covering the cost of long-term repair and maintenance services for consumer and commercial subscribers through a host company's platform that may make use of information technology.”
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 Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
Another resource that is available to applicants is the Patent Application Information Retrieval (PAIR). Information regarding the status of an application can be obtained from the (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAX. 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, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624