DETAILED ACTION
Response to Amendment
This Final office action is in response to Applicant’s amendment filed 4/21/2026. Claims 1 and 8 have been amended. Claims 1-14 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The previously pending objection to claims 1 and 8 has been withdrawn.
Applicant's arguments filed 4/21/2026 have been fully considered but they are not fully persuasive.
Regarding the previously pending 35 USC 101 rejection to claims 1-7, the claims as a whole, recite additional elements that integrate the judicial exception into a practical application, under Prong Two of Step 2A of the Alice analysis.
Specifically, independent claim 1 recites, inter alia, “a computing system comprising a processor, a memory, and a network interface; an analytics subsystem comprising a first plurality of programming instructions stored in the memory and operable on the processor which, when operating on the processor, cause the computing system to: …use the training dataset to train a deep learning neural network to predict a probability of a customer behavior or sentiment;…feed the first unified customer record and the predicted behavior or sentiment as input into the trained deep learning neural network to generate a predicted probability of a behavior or sentiment for the customer associated with the first unified customer record; compare the predicted probability of a behavior or sentiment against an actual outcome associated with the customer; and use the comparison to update one or more parameters of the trained deep learning neural network”.
Claim Objections
Claims 4 and 11 are objected to because of the following informalities: The claims fail to define the acronym “JSON”. Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities: The CROSS- REFERENCE TO RELATED APPLICATIONS section must be updated to include updated continuity data, including the status of the Applications (e.g., patented case, abandoned, etc.). Appropriate correction is required.
Terminal Disclaimer
The terminal disclaimer filed on 4/20/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent No. 11985270 has been reviewed and is accepted. The terminal disclaimer has been recorded.
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 8-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Here, under step 1 of the Alice analysis, method claims 8-14 are directed to a series of steps. Thus the claims are directed to a process.
Under step 2A Prong One of the analysis, the claimed invention is directed to an abstract idea without significantly more. The claims recite campaign management and predictive customer engagement, including retrieving, segregating, predicting, receiving, feeding, generating, establishing, obtaining, updating, and using steps.
The limitations of retrieving, segregating, predicting, receiving, feeding, generating, establishing, obtaining, updating, and using, are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Specifically, the claim elements recite retrieving a plurality of customer records from a database; segregating the plurality of customer records into a training dataset and a test dataset; using the training dataset to train to predict a probability of a customer behavior or sentiment; retrieving a first unified customer record from the database; receiving a predicted behavior or sentiment associated with the customer associated with the first unified customer record; feeding the first unified customer record and the predicted behavior or sentiment as input into the trained deep learning neural network to generate a predicted probability of a behavior or sentiment for the customer associated with the first unified customer data profile; comparing the predicted probability of a behavior or sentiment against an actual outcome associated with the customer; using the comparison to update one or more parameters of the trained deep learning neural network; retrieving a unified customer record from the database, wherein the unified customer record indicates the customer has not provided consent to receive a telephone call; establishing a connection with the customer via one or more non-telephonic channels of communication; obtaining customer consent via the one or more non-telephonic channels; updating a consent status in the unified customer record and store the updated unified customer record; and where consent has been obtained, using the predicted probability of a behavior or sentiment for the customer associated with the retrieved unified customer record to generate a call time and making an outbound telephone call to the customer at the generated call time.
That is, other than reciting an analytics subsystem, a centralized campaign manager subsystem, a deep learning neural network, and an automated telephone dialing system, the claim limitations merely cover commercial interactions, including marketing or sales activities or behaviors, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include an analytics subsystem, a centralized campaign manager subsystem, a deep learning neural network, and an automated telephone dialing system. The analytics subsystem, centralized campaign manager subsystem, neural network, and automated telephone dialing system in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As a result, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of an analytics subsystem, a centralized campaign manager subsystem, a neural network, and an automated telephone dialing system amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
None of the dependent claims recite additional limitations that are sufficient to amount to significantly more than the abstract idea. Claim 9 recites additional ingesting, transforming, correlating, and storing steps. Claims 10 and 11 further describe the plurality of sources and the standard data format. Claims 12-14 further describe the database and the predicted behavior or sentiment. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, an analytics subsystem, a centralized campaign manager subsystem, a neural network, and an automated telephone dialing system.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in paragraph 0123 of the specification, “According to specific aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof. In at least some aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments).”
As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed 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. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int’l et al., No. 13-298 (U.S. June 19, 2014).
Response to Amendment
In the Remarks, Applicant argues that he amended claims require an analytics subsystem that segregates customer records into training and test datasets, trains a deep learning neural network on the training dataset to predict a probability of customer behavior or sentiment, feeds a unified customer record together with a separately received predicted behavior or sentiment as combined input into the trained deep learning neural network to generate a predicted probability, compares that predicted probability against an actual outcome associated with the customer, and uses the comparison to update one or more parameters of the trained deep learning neural network. A human sales manager deciding when to call a customer is not segregating records into training and test datasets, training a deep learning neural network, feeding multi-dimensional inputs into a trained deep learning neural network to generate predicted probabilities, or comparing predicted probabilities against actual outcomes to update network parameters. The self-modifying parameter update step is an inherently computational process, the deep learning neural network adjusting its own internal parameters based on measured prediction accuracy, with no analog in human mental activity or methods of organizing human activity. The amended claims further require a centralized campaign manager subsystem that retrieves a unified customer record indicating the customer has not provided consent, establishes a connection via non-telephonic channels, obtains consent through those channels, updates the consent status in the record, and only then uses the predicted probability generated by the analytics subsystem to generate a call time for an automated telephone dialing system. This is a multi-subsystem computational workflow in which digital consent acquisition gates the downstream use of machine learning predictions to control physical telephony infrastructure, a process with no analog in human mental activity or methods of organizing human activity.
Applicant respectfully submits that the amended claims, when considered as a whole per MPEP §2106.04(d), define an integrated technical architecture in which the individual limitations interact with and depend upon one another. The analytics subsystem trains a deep learning neural network on segregated customer data and generates a predicted probability of customer behavior or sentiment for a specific customer. The centralized campaign manager subsystem retrieves a customer record, determines that consent has not been provided, obtains consent through non- telephonic digital channels, updates the record, and only upon successful consent acquisition uses the predicted probability from the analytics subsystem to generate a call time and direct an automated telephone dialing system to place an outbound call. The analytics subsystem then compares its predicted probability against the actual outcome associated with the customer and uses that comparison to update the parameters of the deep learning neural network. These subsystems form an interdependent technical architecture with a closed feedback loop: the analytics subsystem generates predictions that the centralized campaign manager subsystem consumes to drive telephony operations, and the actual outcomes of those operations flow back to the analytics subsystem to update the deep learning neural network's parameters, which in turn affects future predictions. The centralized campaign manager subsystem cannot generate a call time without the predicted probability produced by the analytics subsystem, the analytics subsystem's predictions have no operational effect on telephony infrastructure without the centralized campaign manager subsystem's consent acquisition and ATDS integration, and the parameter update step has no basis without actual outcomes produced by the system's operation. Further, the consent determination acts as a technical gate, the claim explicitly requires that the system first identify that consent is absent, obtain it through non-telephonic channels, and update the database before any prediction-driven dialing occurs.
The analytics subsystem replaces static or rule-based call scheduling with a pipeline that trains a deep learning neural network on segregated customer data and combines a unified customer record with a separately predicted behavior or sentiment as joint input to generate a predicted probability. This produces dynamically generated call times that static call lists and conventional autodialers cannot produce, it is a fundamentally different technical approach to how the automated dialing system determines when to place outbound calls. Moreover, the amended claims require the system to compare its predictions against actual outcomes and use those comparisons to update the deep learning neural network's parameters. This is the system improving its own operational accuracy over time based on measured results -not merely applying a static model to a business problem, but continuously refining the computational model that drives the system's behavior.
The centralized campaign manager subsystem addresses a further technical limitation of conventional autodialers by integrating digital consent acquisition into the dialing workflow. Rather than dialing all numbers on a list regardless of consent status, the system retrieves a customer record, identifies the absence of consent, obtains consent through non-telephonic channels, updates the database, and only then permits prediction-driven dialing. This is the system conditioning its own operational behavior on a digitally verified consent status an improvement to how automated telephone dialing systems function. The Federal Circuit has recognized that claims reciting specific improvements to existing technological processes are patent-eligible. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016) (claims improving how a database itself functioned); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15 (Fed. Cir. 2016) (claims using specific rules to improve an existing technological process). The self-modifying parameter update is directly analogous to the self-referential table in Enfish, a mechanism by which the system generates and refines its own operational parameters from data rather than relying on externally supplied rules. In Enfish, the self-referential table improved database functionality by enabling the database to define its own structure; here, the feedback loop improves dialing system functionality by enabling the deep learning neural network to refine its own predictive parameters based on actual engagement outcomes. The consent-gated dialing workflow is analogous to McRO, replacing manual consent tracking and list management with a computational process that automates consent acquisition, record updating, and prediction-driven call scheduling as an integrated pipeline.
Applicant respectfully submits that the amended claims do not broadly claim the concept of predicting customer behavior and making telephone calls. They require a specific pipeline: segregating customer records into training and test datasets; training a deep learning neural network on the training dataset; retrieving a unified customer record; receiving a separately predicted behavior or sentiment; feeding both the record and the predicted behavior as combined input into the trained deep learning neural network to generate a predicted probability; comparing the predicted probability against an actual outcome; using the comparison to update one or more parameters of the deep learning neural network; retrieving a second unified customer record indicating the absence of consent; establishing a connection via non-telephonic channels; obtaining consent; updating the consent status; and only then using the predicted probability to generate a call time and making an outbound call using an automated telephone dialing system. Under MPEP §2106.05(f), claiming a particular solution to a problem may integrate a judicial exception into a practical application. The Examiner respectfully disagrees.
Importantly, as an initial point, independent claim 1 recites “a computing system comprising a processor, a memory, and a network interface; an analytics subsystem comprising a first plurality of programming instructions stored in the memory and operable on the processor which, when operating on the processor, cause the computing system to…and a centralized campaign manager subsystem comprising a second plurality of programming instructions stored in the memory and operable on the processor which, when operating on the processor, cause the computing system to…and where consent has been obtained, use the predicted probability of a behavior or sentiment for the customer associated with the first unified customer record to generate a call time”.
Contrarily, method claims 8-14 fail to recite “a computing system comprising a processor” implementing each of claimed method step elements. As a result, while independent method claim 8 includes an analytics subsystem, a centralized campaign manager subsystem, a deep learning neural network, and an automated telephone dialing system, these elements in the steps are recited at a high-level of generality, such that they amount to no more than mere instructions to apply the exception using a generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application.
Conclusion
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 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 ANDRE D BOYCE whose telephone number is (571)272-6726. The examiner can normally be reached M-F 10a-6:30p.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623 June 7, 2026
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