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 .
DETAILED ACTION
Status of Claims
This action is in reply to the communication filed on 04/13/2026.
Claims 1,11, 20 have been amended.
Claims 9,18 have been canceled.
Claims 1-8,10-17,19-20 are currently pending and have been examined.
Response to Applicant’s Arguments
Applicant’s amendments and arguments filed on04/13/2026 have been fully considered and discussed in the next section. Applicant is reminded that the claims must be given its broadest, reasonable interpretation.
With regard to claims 1-8,10-17,19-20 rejection under 35 USC § 101:
Applicant argues that “like the claims in Ex Parte Desjardins, which were found to be directed to a technical improvement since the claims resulted in an improvement to training a machine learning model, Applicant's claim 1 also results in an improved training method of a machine learning model. In this regard, the claims are not directed to commercial interactions, but rather to an improvement in the architecture for model training and inference. In other words, when considered as a whole, the claims are directed to improving the training and operation of the models. For example claim 1 is directed to a method performed on an interconnected neural network architecture in which: " behavior data of a user including the time the user interacts with the plurality of products is input into a recurrent variational autoencoder (VAE) to generate a latent vector; " a first artificial intelligence (AI) model is trained to predict user purchase behavior; " a second Al model, independent of the first Al model, is trained to predict the at least one product combination and the discount rate; " the second Al model includes a shared layer for predicting the at least one product combination and the discount rate; " the VAE is updated based on an output of the shared layer; " the user purchase behavior predicted by the first Al model and the latent vector are provided to a state information layer; and " an output of the state information layer is provided to the shared layer to recursively update the at least one product combination. In Desjardins, Prong 2 was found to be satisfied because the claims reflected an improvement in ML model operation disclosed in the specification. Similarly, claim 1 recites that (i) the first model and the second model operate independently, and (ii) the second model is trained only using data associated with purchase intention. Applicant's specification at paragraphs [0053] and [0106] discloses that the independent operation of the first and second Al models, as configured according to the combined features of claim 1, advantageously increases sampling efficiency and reduces a training time of the Al models. As such, these two features, in combination with the remaining features of claim 1, provide an improvement in Al model training and operation, in that the combination of these features improve sample efficiency and reduce training time. On the basis of at least these features, claim 1 should be characterized not as merely applying an abstract concept, but as being integrated into a practical application that improves the training methodology of the Al model itself. Thus, just like the claims in Ex Parte Desjardins were found to recite patent eligible subject matter because the claims resulted in an improved machine learning model, Applicant's claim 1 similarly results in an improved machine learning model. Additionally, while the Office Action contends that recited "Al model" is described only at a high level of generality (see Office Action at page 6), the combined features of claim 1 do not merely recite the generic use of an Al model. Instead, the combined features of claim 1 result in a specific training and operational framework, namely, a structure that separates two independently operating models and, for the second Al model, a restriction on the training data. Applicant submits that these features provide a specific architecture rather than constituted generic technology. Thus, for these additional reasons, Applicant submits that claim 1 recites patent eligible subject matter. The remarks above for claim 1 apply to claims 11 and 20. For at least the reasons set forth above, Applicant respectfully submits that the claims satisfy 35 U.S.C. § 101 and requests withdrawal of the rejection (page 4/5)”.
Examiner disagrees. Since " behavior data of a user including the time the user interacts with the plurality of products is input into a recurrent variational autoencoder (VAE) to generate a latent vector to predict of a product combination and discount rate, and recursive updating of the product combination and to generate latent vectors, updates state information based on these latent vectors, and recursively infers product combinations and discount rates, are part of the abstract idea itself, they are not capable of transforming the abstract idea into a practical application under Step 2a, Prong 2 and not capable of being considered "significantly more" under Step 2b.
Only technological improvements rooted in the "additional elements" of a claim are capable of transforming an abstract idea into a practical application under Step 2a, Prong 2, and only "additional elements" are capable of being considered "significantly more" under Step 2b. Additional elements are those elements outside of the identified abstract idea itself. In the instant case the only additional elements are ““device, “(first, second AI model) and VAE ” in the claim(s) that would be capable of overcoming the 101 rejection. These additional elements are a general-purpose computer with generic computer components upon which an abstract idea is merely being applied, and also as evidenced by applicant specification [65] which are just general-purpose computers with generic computing components upon which the abstract idea is applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2 or be considered significantly more under Step 2b.
As thus, the AI (first and second) model does no more than claim the application of generic AI model to new data environments without disclosing improvements to the AI models to be applied, are patent ineligible under 35 USC § 101. The claimed device is a general-purpose computer with generic computer components upon which an abstract idea is merely being applied.
As such, any purported improvement in what the applicant calls a technical field is an improvement in ineligible subject matter. In order for an improvement to a technology or technological filed to overcome a 35 USC 101 rejection, the purported improvement must be rooted in the "additional elements" which in this case they are not. The claimed additional elements are merely a general purpose computer and generic AI model upon which an abstract idea is merely being applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2.
Thus, any purported technological improvement obtained by practicing the claimed invention is rooted solely in the abstract idea itself which is merely applied using the general-purpose computer, and not rooting in the additional elements upon which the abstract idea is applied. Improvements of this nature are improvement to an abstract idea which are improvements in ineligible subject matter (SAP v. Investpic decision: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.).
Furthermore, the instant claims bear no similarity to the Ex Parte Desjardins Holdings decision, because the instant claim merely and only using data associated with purchase intention to train the machine learning model to increases sampling efficiency and reduces a training time of the Al models, and / or training and operational framework, namely, a structure that separates two independently operating models and, for the second Al model, a restriction on the training data, whereas Ex Parte Desjardins (claims to a method of training a machine learning model were directed to improvements in the machine learning technology itself and additionally included data structure elements reciting adjustments in values to plurality of performance parameters while preserving prior values). Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about 2 previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
As such Applicant's claimed solution is NOT technological and does not addresses a technological problem. Hence, Examiner maintains that the claims do not define substantially more than an abstract idea. Therefore, the claim rejection of claims 1-8,10-17,19-20 under USC § 101 is maintained.
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 1-8,10-17,19-20 are directed to a system and a method which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes). However, claims 1-8,10-17,19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the following abstract idea:
receiving (e.g. obtaining) data related to at least one of the user, a plurality of products, or one or more marketing activities, wherein the data comprises behavior data of the user and time information corresponding to the behavior data of the user including a time that the user interacts with the plurality of products;
inputting the behavior data of the user including the time the user interacts with the plurality of products into an algorithm to generate a latent vector;
identifying a purchase intention of the user by applying the data to a first algorithm trained to predict user purchase behavior; updating state information by the purchase intention and the latest vector;
identifying at least one product combination comprising at least two products from among the plurality of products and a discount rate of the at least one product combination by applying the purchase intention of the user and the data to a second algorithm trained to predict the at least one product combination and the discount rate; and
transmitting (e.g. displaying) the at least one product combination and the discount rate, wherein the second algorithm model comprises a shared layer for predicting the at least one product combination and the discount rate, wherein the latent vector is generated by adding the time information to the data related to the plurality of products, and, wherein the state information is provided to the shared layer to recursively update the at least one product combination and the discount rate of the at least one product combination, wherein the applying the data to the first algorithm to identify the purchase intention of the user is performed independently of the applying the purchase intention of the user and the data to the second Algorithm to identify the at least one product combination, and wherein the second algorithm is trained only using data associated with the purchase intention;
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising, marketing and sales activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes). This judicial exception is not integrated into a practical application because the claim only recites the additional elements of a computer “electronic device, autoencoder, an artificial intelligence (AI) model (s)” (first and second AI Model) and display”, (e.g. a general purpose computer with generic computer components).
The following limitations, if removed from the abstract idea and considered additional elements, merely perform generic computer function of processing, storing, communicating (e.g., transmitting and receiving), and displaying data and, as such, are insignificant extra-solution activities (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving (e.g. obtaining) data related to at least one of the user, a plurality of products, or one or more marketing activities, wherein the data comprises behavior data of the user and time information corresponding to the behavior data of the user including a time that the user interacts with the plurality of products;
transmitting (e.g. displaying) the at least one product combination and the discount rate, wherein the second algorithm model comprises a shared layer for predicting the at least one product combination and the discount rate, wherein the latent vector is generated by adding the time information to the data related to the plurality of products, and, wherein the state information is provided to the shared layer to recursively update the at least one product combination and the discount rate of the at least one product combination, wherein the applying the data to the first algorithm to identify the purchase intention of the user is performed independently of the applying the purchase intention of the user and the data to the second Algorithm to identify the at least one product combination, and wherein the second algorithm is trained only using data associated with the purchase intention;
More The additional technical elements above are recited at a high-level of generality (i.e., as a generic processor and generic computer components performing a generic computers function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and generic computer components. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using 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 MPEP 2106.05(e) and Vanda memo). Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on one or more computers, or merely uses computers as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes). When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
Specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer “electronic device, autoencoder, an artificial intelligence (AI) model (s)” (first and second AI Model) and display”, (e.g. a general purpose computer with generic computer components).
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. 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. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on one or more computers, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires one or more general-purpose computer and generic computer components (as evidenced from paragraphs 31, 46 of the Applicant’s specification) therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
Finally, the following limitations, if removed from the abstract idea and considered additional elements, would be considered insignificant extra solution activity as they are directed to merely receiving, displaying, storing, and/or transmitting data (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving (e.g. obtaining) data related to at least one of the user, a plurality of products, or one or more marketing activities, wherein the data comprises behavior data of the user and time information corresponding to the behavior data of the user including a time that the user interacts with the plurality of products;
transmitting (e.g. displaying) the at least one product combination and the discount rate, wherein the second algorithm model comprises a shared layer for predicting the at least one product combination and the discount rate, wherein the latent vector is generated by adding the time information to the data related to the plurality of products, and, wherein the state information is provided to the shared layer to recursively update the at least one product combination and the discount rate of the at least one product combination, wherein the applying the data to the first algorithm to identify the purchase intention of the user is performed independently of the applying the purchase intention of the user and the data to the second Algorithm to identify the at least one product combination, and wherein the second algorithm is trained only using data associated with the purchase intention;
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e., “PEG” Step 2B=No). For the same reason these elements are not sufficient to provide an inventive concept. For these reasons, there is no inventive concept in the claim, and thus the claim is not patent eligible. Same Judicial analysis is applied here to independent claims 11 and 20.
The dependent claims 2-8,10,12-17 and19 appear to merely further limit the abstract idea wherein the data comprises at least one of behavior data of the user, data related to a web page, data on a viewed product, data on the marketing, or data on a time corresponding to the behavior data (claims 2 and 12); to infer the at least one product combination and the discount rate for the plurality of products (claims 3 and 13); wherein the identifying of the at least one product combination and the discount rate comprises: receiving, based on a reward function, a reward value according to feedback of the user on the at least one product combination and the discount rate; and adjusting the discount rate based on the reward value (claims 4 and 14); wherein the identifying of the at least one product combination and the discount rate comprises identifying, based on data comprising at least one of a preference for a product group, a preference for a product group for each path in a web page through which the user enters to view the product group, a preference for a product group for each digital marketing provided to the user, or a preference according to the discount rate( claim 5); identifying the discount rate based on a determination that a suitability of the user for the at least one product combination is equal to or greater than a preset value (claims 6 and 15); identifying a priority of the user for the at least one product combination and the discount rate; determining an arrangement order of the at least one product combination and the discount rate based on the priority; and displaying the at least one product combination and the discount rate based on the arrangement order (claims 7 and 16);wherein the identifying of the at least one product combination and the discount rate comprises identifying the at least one product combination by applying the data and a reward value according to feedback of the user (claims 8 and 17); wherein the data comprises a first time at which the at least one product combination is identified or a second time at which the discount rate is identified, and wherein a weight value is set for each of the data based on the first time or the second time(claims 10 and 19); and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No).
Thus, based on the detailed analysis above, claims 1-8,10-17,19-20 are not patent eligible.
Possible Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter: Independent claims recite combination of features of which Examiner is unable to find a prior art that discloses the claimed features as cited.
The most relevant prior the examiner has found is:
Gee et al, US Pub No: 2022/0194400 A1, teaches A machine learning algorithm, for example, a neural network, is trained to offer predictions, recommendations, and/or insights regarding vehicle components, products or services that are customized to a particular driver. The trained machine learning algorithm is subsequently deployed.
Veettil, US Pub No: 2021/0295364 A1, teaches One variation of a method for generating reward packages includes: in response to receiving selection of a primary product, in a set of products offered by a merchant, at a computing device associated with a user, populating a basket with the primary product; estimating a first intent score for the user for purchase of a first secondary product, in the set of products, based on the primary product and historical purchases of products at the merchant; calculating a first reward value predicted to increase the first intent score toward a target intent score; and, in response to the first minimum reward value falling below a maximum reward value, selecting a first reward for pairing with purchase of the first secondary product based on the first reward value and serving a first reward package, including the first secondary product paired with the first reward, to the user at the computing device.
As thus, claims 1-8,10-17,19-20 recite subject matter that would be allowable over the prior art if the Applicant were to be able to overcome the 35 USC § 101 rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Jeong , US Pub No: 20220318836 A1 teaches Provided is an information providing method of an electronic apparatus including acquiring information on or regarding an item and information on a user, confirming one or more promotional events related to the item, confirming whether each of the one or more promotional events is valid based on the information on the user and price information on the item, and providing discount price information on the item to which discount benefit information included in at least one of the one or more promotional events is applied based on whether each of the one or more promotional events is valid. Other example embodiments are possible.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. 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 files within TWO MONTHS from 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Affaf Ahmed whose telephone number is 571-270-1835. The examiner can normally be reached on [ Mon-Thursday 8-6 pm ].
If attempts to reach the examiner by telephone are unsuccessful, the examiner' s supervisor, Ilana 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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/AFAF OSMAN BILAL AHMED/
Primary Examiner, Art Unit 3622
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