Status of Claims
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Accordingly, Applicant's filed response has been entered.
This is a Non-Final office action in response to communication received on January 07, 2026. Claim 7 is canceled. Claims 1-6 and 8-20 are pending and examined herein.
Double Patenting - Withdrawn
2. Note the approved terminal disclaimer of record 09/23/2025.
Claim Rejections - 35 USC § 101
3. 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 1-6 and 8-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Next using the 2019 Revised Patent Subject Matter Eligibility Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, Claims 1-5 are a method; claims 7-15 are a system; and claim 16-20 are a method. Thus, each claim 1-6 and 8-20, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-6 and 8-20 [i.e. recitation with the exception of additional elements, which are first considered under step 2A prong two when claim(s) is/are reconsidered as a whole and exclusively under step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of generating one or more recommendation campaign(s) by evaluating short-term and long-term user behavior which is certain methods of organizing human activity (but for its implementation in network based environment - which is considered further under prong two and step 2B analysis as set forth below).
The phrase "Certain methods of organizing human activity" applies to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Further, see MPEP 2106.04(a)(2) II. A-C.
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-6 and 8-20 at least are computer-implemented method, machine learning system trained to generate models (claim 1), computing system comprising one or more processors, a memory storing program instructions the, when executed by the one or more processors, cause the one or more processors to generate using a machine learning system recommendation campaign (claim 6), online service statistics such as access frequency, time duration, object identifier, or a purchase frequency during short-term and long-term behaviors (claims 13-14), a machine learning trained on user data, training outputs of the machine learning system include a plurality of user models, processing, using the machine learning system (claim 16). Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are generic computer components. The additional elements such as one or more processors and machine learning models are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)). The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1, 7, and their associated disclosure. Furthermore user behavioral data is obtained and/or recommendations communicated to one or more devices merely sending/receiving data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Gathered data is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes gathered data to be able to make recommendations based on short-term and long-term behavior. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is a tailored content such as recommendation based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent). The abstract idea is intended to be merely carried out in a technical environment such as collecting data via a network and analyzing data via a generic processor to provide personalized user behavioral based recommendations, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, 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 as explained above.
Thus, the abstract idea of generating one or more recommendation campaign(s) by evaluating short-term and long-term user behavior which is certain methods of organizing human activity (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-6 and 8-20, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of generating one or more recommendation campaign(s) by evaluating short-term and long-term user behavior which is certain methods of organizing human activity which is certain methods of organizing human activity - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as data gathering or post solution activity such as displaying on interface, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) [similarly here user's behavioral data appears to received online]; and
(ii) Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)." [similarly here as a post solution recommendation is generated/displayed].
Next, in view of compact prosecution only further analysis per the Berkheimer Memo dated April 19, 2018 is being conducted as the following additional elements would be readily apparent as generic to a person having ordinary skill in the art (hereinafter PHOSITA), in other words analysis is similar to Berkheimer claim 1 and not claims 4-7 where there was "a genuine issue of material fact in light of the specification," nevertheless the Examiner finds the additional elements when considered both individually and as a combination to be well-understood, routine or conventional and expressly supports in writing as follows:
1. The Examiner provides citation to one or more publications as noting the well-understood, routine, conventional nature of machine learning as follows:
i) Chandramouli, Patent: US 8,442,683 note para. [0005]-[0007] and [0029]-[0033]; (ii) Lee, Pub. No.: US 2002/0107926 note para. [0020]; (iii) Kwok, Pub. No.: US 2002/0150295 note para. [0015]; (iv) Teller, Pub. No.: US 2004/0133081 [0236]-[0238]; (v) Agrawal and Srikant Patent No.: US 6546389 note "As recognized herein, the primary task of data mining is the development of models about aggregated data. Accordingly, the present invention understands that it is possible to develop accurate models without access to precise information in individual data records."; (vi) Deshpande et al., Pub. No.: US 2015/0134413 [0046] Using the target and input features, in step F3 of FIG. 1, a plurality of forecasting models are built for a product or a product category, a location, and a time window. A plurality of forecasting models can be built using existing machine learning based methods and/or time-series forecasting methods, and using the standard training-testing-validation methods. In an exemplary embodiment, only the highest quality models with high quality (high accuracy, precision, recall, etc.) are retained.; [0078] The processing system forecasting engine 202 can also include a forecasting model building engine 224 and a forecast calculation engine 226. In the model building stage, target and input features based on a customer or a customer segment's past data are used to train, test, and validate different types of forecasting models using machine learning and/or time series forecasting based approaches. Individual models are retained depending on the performance. The output of plurality of these retained models can then be fused into a single model 228. The fusion can be based on a rule-based approach or by assigning weights to individual model and combining those using ranking or combination techniques." (vii) Wei et al., Pub. No.: US 2015/0235260 [0080] Then, analysis module 532 may determine one or more predefined model(s) 546 based on event data 538 and the one or more targeting criteria. For example, analysis module 532 may use training and testing subsets of this information to generate one or more machine-learning models. The one or more predefined model(s) 546 may allow estimates of the number of future events to be determined for terms 544 in the one or more targeting criteria 542.; (viii) Beatty, Pub. No.: US 2012/0166267 see [0177] note "the prediction of conversion rate is performed by a machine-learning system that is trained using historical purchase data available to the ad system. The training set contains instances of purchase/no purchase decisions and many data points about the (user, context, offer). For example, the training examples might contain the following data points about the offer that was made to a user: price of offer, % discount of offer, popularity of merchant, time of day, gender of user, income of user, interests of user, websites visited by user, categories of websites visited by user, search queries by user, category of business, number of friends that had purchased the offer, "closeness" of friends that had purchased the offer, physical distance between the user's home and the business, physical distance between the user's workplace and the business, the "cluster id" of the user (generated by a clustering algorithm that placed, and users into clusters based on similar attributes of preferences)."
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Reason(s) for Non-Applicability of Prior Art
4. As per claims 1-5 and 16-20, the following are the closest prior art references:
Besides the closest prior art references of record per parent application prosecution, the Examiner found prior art as noted in the Non-Final Rejection of record 05/06/2025 to be the closest.
However, the above noted closest prior art references of record do not appear to teach or suggest per the recitation of independent claims: “computer-implemented method, comprising: accessing a plurality of user models generated by a trained machine learning system, wherein: the trained machine learning system is trained based at least in part using a plurality of training user profiles, a plurality of training recommendation campaigns, a plurality of actual training long-term behavior, and a plurality of actual training short- term behavior; and each of the plurality of user models is associated with a respective user profile information and includes a respective predicted short-term behavior for the respective user profile information and a respective predicted long-term behavior for the respective user profile information; determining a user profile, a desired short-term behavior, and a desired long-term behavior, wherein the desired short-term behavior corresponds to a short-term time period that ends prior to a long-term time period that corresponds to the desired long-term behavior; processing the user profile, the desired short-term behavior, and the desired long-term behavior using the trained machine learning system to: identify a user model from the plurality of user models; and generate, based at least in part on the respective predicted short-term behavior and the respective predicted long-term behavior associated with the user model, a recommendation campaign configured to encourage the desired short-term [[user]] behavior to occur during the short-term time period and the desired long-term [[user]] behavior to occur during the long-term time period; and initiating the recommendation campaign”;
The above noted closest prior art references fail to, singularly and/or in-combination, anticipate or make prima facie obvious the inventions as claimed.
Therefore the claims are allowable over prior art.
Remarks
5. Regarding 101, the Applicant argues that the claim as a whole somehow integrates the abstract idea into practical application by noting “Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential).” However, the Examiner respectfully disagrees.
Under prong two, the claim as a whole is evaluated, including the additional elements. The Examiner notes that the claim must be given their broadest reasonable interpretation in light of the as-filed specification under 2019 PEG based 101 analysis. Contrary to the Applicant’s assertion(s) the claims here are not analogous to Ex Parte Desjardins as the unique facts of instant application do not align with the invention in Ex Parte Desjardins which presented a technical improvement, note “training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of "catastrophic forgetting" encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task" reflected the improvement disclosed in the specification” . As explained under prong two analysis, the additional elements are recited at a high level of generality. Further evaluation of level of specificity is inherent in the 2019 PEG based 101 analysis which is incorporated in the MPEP. Thus, the Applicant’s argument in view of specificity and technology as supported by as-filed specification paragraphs [0012]-[0013] are unpersuasive as explained under prong two, note “claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-6 and 8-20 at least are computer-implemented method, machine learning system (claim 1), computing system comprising one or more processors, a memory storing program instructions the, when executed by the one or more processors, cause the one or more processors to execute programmed instructions, an online service (claim 6), online service statistics such as access frequency, time duration, object identifier, or a purchase frequency during short-term and long-term behaviors (claims 13-14), a machine learning system trained on user behaviors of a user of an online service (claim 16). Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are generic computer components. The additional elements such as one or more processors and machine learning models are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)). The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1, 7, and their associated disclosure. Furthermore user behavioral data is obtained and/or recommendations communicated to one or more devices merely sending/receiving data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Gathered data is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes gathered data to be able to make recommendations based on short-term and long-term behavior. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is a tailored content such as recommendation based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent). The abstract idea is intended to be merely carried out in a technical environment such as collecting data via a network and analyzing data via a generic processor to provide personalized user behavioral based recommendations, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, 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 as explained above.
Thus, the abstract idea of generating one or more recommendation campaign(s) by evaluating short-term and long-term user behavior which is certain methods of organizing human activity (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.”
Accordingly, there is a clear distinction between merely using machine learning as a tool to evaluate user data to output recommendations as part of a recommendation campaign versus presenting a technical improvement to machine learning as set forth in Ex Parte Desjardins.
Therefore, the Examiner respectfully finds the Applicant’s arguments unpersuasive and maintains the rejection.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted by the Applicant as they can be subsequently used during prosecution.
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621