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 .
Status of Claims
This Office Action is in response to the communication filed on 02/02/2026 .
Claims 1, 10 and 20 have been amended.
4. Claims 1-20 are currently pending and are considered below.
Information Disclosure Statement
5. The Applicant is respectfully reminded that each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability as defined in 37 CFR 1.56.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 1, recites a computer-implemented method comprising executing on a processor one or more steps comprising:
obtaining a dataset corresponding to a prediction task;
performing, via a randomizer, a random selection of whether to apply an artificial neural network comprising a Siamese neural network model to the dataset or whether to perform a random prediction for the prediction task, wherein a plurality of prediction scenarios are implemented, at least one prediction scenario where the Siamese neural network model is applied to data in the dataset, and at least one prediction scenario where the random prediction is applied to data in the dataset;
determining subsequent to the random selection via the randomizer, a gathered
outcome for each prediction scenario based on applying or not applying the model to 10 the dataset including a difference between the gathered outcome for each prediction scenario and expected and/or actual outcomes; and
feeding back the gathered outcome and the difference to retrain the Siamese
neural network model for use in performing the prediction task.
The steps of,
obtaining a dataset corresponding to a prediction task;
performing, via a randomizer, a random selection of whether to apply an artificial neural network comprising a Siamese neural network model to the dataset or whether to perform a random prediction for the prediction task, wherein a plurality of prediction scenarios are implemented, at least one prediction scenario where the Siamese neural network model is applied to data in the dataset, and at least one prediction scenario where the random prediction is applied to data in the dataset;
determining subsequent to the random selection via the randomizer, a gathered
outcome for each prediction scenario based on applying or not applying the model to 10 the dataset including a difference between the gathered outcome for each prediction scenario and expected and/or actual outcomes; and
feeding back the gathered outcome and the difference to retrain the Siamese
neural network model for use in performing the prediction task,
as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity. Given the broadest reasonable interpretation, the claim recites a method for providing a twin neural model for uplift. The above identified method steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to improving timeline of events for product location pairs.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as commercial interaction, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a processor, memory, a Siamese Neural Network Model, a randomizer and a computing device. The computing device is recited at a high level of generality (i.e., as a generic processor performing a generic computer functions of obtaining a dataset corresponding to a prediction task; performing a random selection of whether to apply an artificial neural network; determining subsequent to the random selection of the randomizer, a gathered outcome for each prediction scenario;
And feeding back the gathered outcome and the difference to retrain the Siamese
neural network model) such that they amount to no more than mere instructions to apply the exception using generic computer components. As for the limitation apply an artificial neural network comprising a Siamese neural network model to perform a random prediction for the prediction task, this feature is considered math, and therefore is a part of the abstract idea. Because the Siamese neural network model in this claim is used as a tool for improving the abstract idea, rather than improving any technical feature or function, it is not sufficient to integrate the judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements of a processor, memory, a Siamese Neural Network Model, a randomizer and a computing device amount to no more than mere instructions to apply the exception using generic computer components. The additional elements are similar to the additional elements found by courts to be mere instructions to apply an exception because they do no more than merely invoke computers or machinery to perform an existing process such as: a common business method or mathematical algorithm being applied on a general purpose computer (Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 US 208, 223; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334); generating a second menu from a first menu and sending the menu to the second location as performed by a generic computer components (Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considered as an ordered combination, the additional elements add nothing that is already present when the steps are considered separately. That is, a processor, memory, a Siamese Neural Network Model, a randomizer and a computing device, performing commercial interactions including: obtaining a dataset corresponding to a prediction task; performing a random selection of whether to apply an artificial neural network; determining subsequent to the random selection of the randomizer, a gathered outcome for each prediction scenario;
And feeding back the gathered outcome and the difference to retrain the Siamese
neural network model, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, independent claims 1, 10 and 20 are not eligible.
As for dependent claims 2-7 and 11-16, these claims recite “wherein feeding back the gathered outcome further comprises performing a comparison of whether the gathered outcome aligns with a predefined expected outcome for applying or not applying the model as previously used to train the Siamese neural network model.” These claims recite limitations that further define the same abstract idea in claims 1 and 10, to performing a random prediction for the prediction task. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claims 8-9 and 17-18, these claims recite limitations that further define the same abstract idea in claims 1 and 10. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
8. Applicant’s arguments, filed 02/02/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. 103(a) have been fully considered and are persuasive. The rejection of claims 1-20 has been withdrawn.
10. Applicant's arguments filed 02/02/2026 with respect to the rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
11. Applicant argued that” Step 2A - Prong 1
As required by MPEP 2106.04(a): "Some claims are not directed to an abstract idea because they do not recite an abstract idea, although it may be apparent that at some level they are based on or involve an abstract idea. Because these claims do not recite an abstract idea (or other judicial exception), they are eligible at Step 2A Prong One (Pathway B)". That is, although a claim may involve an abstract idea, this does not necessarily mean that the claims recite an abstract idea…” Remarks pages 4-6
12. Examiner notes that for abstract idea directed to "Certain Methods of organizing
Human Activity" and specifically abstract idea that fall within the subgrouping of
commercial and legal activities namely advertising, marketing and sales activities, the
courts have determined that steps directed to gathering data, analyzing data,
determining results generating tailored content, and transmitting the tailored content are
all part of the abstract idea itself. In the instant case, the argued limitation are all
directed to gathering data, analyzing data and determining and transmitting data results in the process of performing advertising, marketing of sales activities. As such, the argued limitations are clearly part of the identified abstract idea and fall squarely within the "Certain Method of Organizing Human Activity." Thus, the rejection has been maintained."
13. Applicant argued that “Step 2A - Prong 2
According to MPEP 2106.04(d) in Prong 2 of Step 2A, Examiners are meant to evaluate whether the claim as a whole integrates the exception into a practical application of that exception. Moreover, as set forth in MPEP 2106.04(d)(II), the Examiner needs to consider the limitations of the claims in combination as well as individually.
In the alternative, even if, for the sake of argument, the claims were found to recite an abstract idea, they are eligible under Step 2A, Prong 2, because they are integrated into a specific practical application.
As discussed above, the present application clearly addresses a technical solution to a computer-native problem, namely that most prior models in the field of predicting optimization are either likelihood-based (modification of regression), or else tree-based (capturing non-linear patterns). While both of these models have unique advantages, neither are properly suited to the task and therefore do not give useful results. Existing models are also prone to overfitting issues in that they do not perform well on unseen data and therefore inaccurate. Other prior computerized methods are difficult to train and require manual manipulations. Applicant directs the Examiner to, for example, paras. [0036], [0077], [0095], and [0097] of the application as filed.
Moreover, analogous to Enfish, LLC V. Microsoft Corp. (Enfish), Applicant submits that the claims relate to an architecture that provides a specific technical improvement to the functionality of the computer system itself. In Enfish, the patents describe a table-based model as "self-referential" because all data entities are maintained in a single table, and the attributes in a particular column may be defined by reference to a record in another row in the same table. The patents purport to claim an improvement over conventional relational databases, which maintain different data entities in separate tables. The claimed improvement was found to provide for "faster searching of data than with the relational model," "more efficient storage" of certain types of data, and "more flexibility in configuring the database."..” Remarks pages 6-8
14. Examiner notes that aside from the " a processor, memory, a Siamese Neural Network Model, a randomizer and a computing device" which are "additional elements', the remainder of the claims have been identified as part of the abstract idea itself which is merely applied using a general-purpose computer (i.e., processing device coupled to a data storage device executing software). In order to overcome a 35 USC 101 rejection under Step 2a, Prong 2 the purported improvement must be rooted in the "additional elements'. Additional elements are defined as those elements outside of the identified abstract idea itself. Thus, the "additional elements" as a whole are just a processing device coupled to a data storage device executing software upon which an abstract idea is merely being applied which is insufficient to transform the abstract idea into a practical application. Any purported improvement obtained by practicing the claimed invention is an improvement to the abstract idea which is an improvement in ineligible subject matter. Thus, the rejection has been maintained. Finally, Enfish invention "is a specific type of data structure designed to improve the way a computer stores and retrieves data in memory. Enfish at 18. The present invention does not improve a computer in any way rather utilizes them to perform well-understood functions, such as in Des Jardins.
15. Applicant argued that “Step 2B
As set forth in MPEP 2106.05, the second part of the Alice/Mayo test is often referred to as a search for an inventive concept. Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. Consideration of the elements in combination is particularly important, because even if an additional element does not amount to significantly more on its own, it can still amount to significantly more when considered in combination with the other elements of the claim. The additional elements must also be considered in light of specification…” Remarks pages 8-9
16. Examiner notes that in order to overcome a 35 USC 101 rejection under Step 2b
it is the "additional elements" that must be considered "significantly more". Additional
elements are defined as those elements outside of the identified abstract idea itself. In
the instant case, the only "additional elements" found in the claim are a processor, memory, a Siamese Neural Network Model, a randomizer and a computing device, which is merely a general-purpose computer upon which the abstract idea is being applied. Thus, the additional elements cannot be considered significantly more than the abstract idea. The purported improvement of the technology of "improving the use of an AI-related system, overfitting of the training data and improving uplift predictions " in the manner claimed is part of the abstract idea itself and, as such, cannot be considered "significantly more" than the abstract idea under Step 2b. Thus, the rejection has been maintained.
Conclusion
17. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
18. Kini et al. (U.S. Patent No. 11,403,668) discloses multitask learning is applied to predict a customer's propensity to purchase an item within a particular category of items. Then, the network is tuned using transfer learning for a specific promotional campaign. Retail revenue and promotional revenue are jointly optimized, conditioned on customer trust. Accordingly, a particular promotional program may be selected that is specific to the user (see at least the Abstract).
19. Updated prior art search found:
20. Beddo et al. (U.S. Pub. No. 2020/0090195) discloses an electronic neural network system for dynamically producing predictive data using varying data. In some embodiments, a system is provided that includes (a) forecasting apparatus, which stores product information and a neural network; and (b) a computing system that access the forecasting apparatus via a web portal and transmits some or all of the product information to the forecasting apparatus, which typically comprises varying or changing data. In some embodiments, the forecasting apparatus is configured to determine an initial forecast using at least a portion of the data, via the neural network, modify the initial forecast to generate a final forecast, and present the final forecast to the computing system. In some embodiments, an input vector associated with the neural network is too large to be inputted into the neural network without modification (see at least the Abstract).
21. The references alone or in combination fail to teach or suggest the following limitations of the amended claims 1, 10 and 20 “performing, via a randomizer, a random selection of whether to apply an artificial neural network comprising a Siamese neural network model to the dataset or whether to perform a random prediction for the prediction task, wherein a plurality of prediction scenarios are implemented, at least one prediction scenario where the Siamese neural network model is applied to data in the dataset, and at least one prediction scenario where the random prediction is applied to data in the dataset; and determining, subsequent to the random selection [[of]] via the randomizer, a gathered outcome for each prediction scenario based on applying or not applying the model to the dataset including a difference between the gathered outcome for each prediction scenario and expected and/or actual outcomes”.
22. 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.
23. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARILYN G MACASIANO whose telephone number is (571)270-5205. The examiner can normally be reached Monday-Friday 12:00-9:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, llana Spar can be reached at 571)270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 06/27/2026
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