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
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and the fee set forth in 37 CFR 1.17 (e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on 09/18/2025 has been entered.
Claims 1-2, 11-12, 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 on 09/18/2025 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 “claim 1 as a whole results in an interconnected neural network architecture in which one layer is connected to another layer leading to a prediction of a product combination and discount rate, and recursive updating of the product combination. Applicant submits that the combined features of claim 1 are not directed to methods of organizing human activity or business processes. In this regard, the claimed embodiments utilizes a VAE to generate latent vectors, updates state information based on these latent vectors, and recursively infers product combinations and discount rates, which are not an abstract idea. instead, the combined features of claim 1 as a whole result in a novel arrangement of Al models that increase sampling efficiency and reduce a training time of the neural network, thereby leading to faster processing times. That is, as discussed during the interview, the claimed features result in a neural network architecture that is able to converge faster to a solution with accurate results using a smaller amount of data. See e.g., Specification at paragraphs [0053], [0083], and [0106]. In this regard, the first Al model is used to infer product purchase intent, and the second Al model is used to infer product combinations and discount rates. This approach has the technical benefit of: (i) operating independently and training the product combination and discount rate inference model only on data with purchase intent, thereby increasing sample efficiency and significantly reducing training time. Furthermore, since the product combinations and discount rates are inferred through the same second Al model and the learning and output process is shared through a shared layer, (ii) discount rates can be preferentially inferred for product combinations with high scores. This approach, in turn, provides users with promotions, thereby encouraging purchase. That is, the claimed embodiments has the effect of increasing the efficiency of samples and learning, unlike the existing ones, by inferring the purchase intention from the first Al model and the product combination and discount rate to be provided in the promotion from the second Al model. Furthermore, when inferring the product combination, rather than inferring k product combinations out of n, the claimed embodiments has the technical effect of maintaining the algorithmic complexity of the first Al model to O(n) by inferring x+1 product combinations by deciding whether to add one product to the product combination for a product combination consisting of x items. Therefore, claim 1 as a whole results in a technical improvement (e.g., reduced training time and faster processing time) of a neural network, where without Applicant's claimed advancements, neural networks would require longer training times. Accordingly, Applicant respectfully submits the present claims are eligible at Prong Two of revised Step 2A. Furthermore, Applicant submits that the claims clearly recite "something more" beyond well-known features for the reasons discussed in traversal of the prior art rejection (page 4-5/6)”.
Examiner disagrees. The recitation of 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, is directed to is directed to analyzing data and determining results based on the analysis.
Since analyzing data is part of the abstract idea itself, any improvement obtained by automating the analyzing of the data in an improvement to the abstract idea which is an improvement in ineligible subject matters (see SAP v. Investpic: 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.
As such, the claims as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations, because the merely gather data, analyze the data, determine results based upon the analysis, generate tailored content based on the results, and transmit the tailored content. Accordingly, the claim recites an abstract idea (i.e. MPEP Revised Step 2A Prong One=Yes).
Furthermore, the recitation of: “(first and second AI) and/ or recurrent variational autoencoder (VAE), does not qualify under the other non-limiting non-exclusive examples. In order to overcome a 35 USC 101 rejection, the technical solution to a technical problem must be rooted in the "additional elements". Additional elements are defined as those elements outside the identified abstract idea itself. As thus, the only additional elements of “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.
As evident by Applicant’s specification “artificial intelligence (AI) according to the disclosure is performed by a processor and a memory. The processor may include one or more processors. For example, the one or more processors may include a general-purpose processor such as a central processing unit (CPU), an application processor (AP), or a digital signal processor (DSP), a dedicated graphics processor such as a graphics processing unit (GPU) or a vision processing unit (VPU), or an Al processor such as a neural processing unit (NPU) [31]” and “ the electronic device 10 according to the disclosure may include, but are not limited to, a smart TV, a smartphone, a tablet PC, a laptop computer, an e-book terminal, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), or any other suitable device known to one of ordinary skill in the art [46]”.
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.
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 rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception subject matter, specifically an abstract idea. The analysis for this determination is explained below:
Step 1, determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claim(s) 1-8,10 are directed to a process (i.e. a method); claims 11-18, 19 are directed a machine (i.e. device); claim 20 is directed to a manufacture (i.e. a non transitory computer medium).
The claimed invention is directed to at least one judicial exception (i.e a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 for instance recite(s) the abstract idea of “an electronic device that provides a service to a user by predicting product combination and discount rate, and recursive infer product combinations and discount rates and update the product combination”..
Claim 1 for instance recites the following abstract idea limitations of: “ 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 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”.
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations, because the merely gather data, analyze the data, determine results based upon the analysis, generate tailored content based on the results, and transmit the tailored content. Accordingly, the claim recites an abstract idea (i.e. MPEP 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 “device, recurrent variational autoencoder (VAE) an artificial intelligence (AI) model (s)” (first and second AI Model) and display”. The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. 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 a computer, or merely uses a computer 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. MPEP 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.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using the additional elements of “ an artificial intelligence (AI) model; and display” to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“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 a computer, 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 a general purpose computers communicating over a general purpose network (as evidenced from paragraphs 31, 46); 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.
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.MPEP 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 2-10 and 12-19 appears to merely further limit the abstract idea of Certain methods of organizing Human Activity” as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations), by adding the additional steps of : 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 time that the user interacts with the plurality of products (claims 9 and 18); 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); which is considered part of the abstract idea and therefore only further limit the abstract idea (i.e. MPEP 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. MPEP Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. MPEP Step 2B=No).
Thus, the dependent claims further narrows the abstract idea and/or recite additional elements previously rejected in the independent 1, 11.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102/103
No prior art is found and/ or cited for the limitation of “inputting the behavior data of the user including the time the user interacts with the plurality of products into a recurrent variational autoencoder (VAE) to generate a latent vector; identifying a purchase intention of the user by applying the data to a first artificial intelligence (AI) model 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 A model trained to predict the at least one product combination and the discount rate; and displaying the at least one product combination and the discount rate, wherein the second A 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.
Possible Allowable Subject Matter
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 claim rejection under 35 USC § 101 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Gee et al, US Pub No: 2022/0194400 A1, teaches system and method for enhancing vehicle performance using machine learning.
Veettil, US Pub No: 2021/0295364 A1, teaches method for constructing promotional offers responsive to purchase intent of a consumer.
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 [M- R 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