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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/09/2026 has been entered.
Response to Arguments
Applicant's arguments filed 01/09/2026 have been fully considered, but they are not fully persuasive. The updated 35 USC 101 rejection of claims 1, 3-14, and 16-22 are applied in light of Applicant's amendments.
The Applicant argues the claimed subject matter is not abstract and provides an improvement to the technical field (Remarks 01/09/2026).
In response, the Examiner respectfully disagrees. The claimed subject matter, is directed to an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas.
The claimed subject matter is merely claims a method for calculating and analyzing (forecasting) information regarding seller/product data. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the calculating and analyzing (modeling and projecting) data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract idea (organizing human activity). Additionally, the claimed subject matter can also be categorized as a Mental Process as it recites concepts performed in the human mind (observation and evaluation). The steps of calculating data, training/updating models, and generating a model can be performed by a human (mental process/pen and paper). The practice of calculating information and constructing models with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data). The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
The use of Artificial Intelligence (AI), machine learning (ML) models, and/or artificial neural networks (ANN) fall within the realm of abstract ideas. They are, at their core, mathematical algorithms implemented on a computer. As highlighted in Examples 47-49 of the 2024 Patent Subject Matter Eligibility Guidance, the USPTO has consistently viewed claims directed to such models as being drawn to abstract ideas. These examples illustrate claims that, while couched in the language of specific applications, ultimately boil down to mathematical relationships and calculations.
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The claims do not mention to any use of a specialized computer and/or processor. The Applicant is using generic computing components (processors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data specifically for seller information, and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer, or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious, and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding seller/product information, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
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, 3-14, and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1, 3-14, and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry, it is first noted that the method (claims 14-19 and 22), computer program product (claims 20-21), and system (claims 1-13) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas set forth in the 2019 PEG.
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts or the mental processes grouping. Thus, the claim recites a mental process for performing mathematical concepts.
The limitations reciting the abstract idea(s) (Mental process and Mathematical concepts), as set forth in exemplary claim 1, are: obtain… a plurality of pre-trained machine learning models comprising a seller model configured to compute a seller embedding for a seller based on features of the seller and an item model configured to compute an item embedding for an item based on features of the item …trained with seller inventory data, seller catalog data, and labeled affinity scores for item- seller combinations;- receive…a request seeking item recommendations for an existing seller for assortment growth in a marketplace and features of the existing seller;- determine, based on the request, a corresponding arrangement of the plurality of pre-trained machine learning models- based on the arrangement of the plurality of pre-trained machine learninq models: apply the seller model to the features of the existinq seller to qenerate a query seller embeddinq; apply the item model to the query seller embedding to generate a list of candidate item embeddings; and generate, based on at least the query seller embeddinq and the query seller embeddinq, a recommendation list of item-seller combinations, wherein the recommendation list includes a list of recommended items for the existinq seller; and transmit the recommendation list to the computing device.Independent claims 14 and 20 recite the method and CRM for performing the system of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: a non-transitory memory having instructions stored thereon; and at least one processor operatively coupled to the non-transitory memory… from a database… over a network and from a computing device… wherein each of the seller model and the item model comprise one or more neural networks… and transmit, over the network the recommendation list to the computing device…; A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device…; (as recited in claims 1 and 20). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: a non-transitory memory having instructions stored thereon; and at least one processor operatively coupled to the non-transitory memory… from a database… over a network and from a computing device… wherein each of the seller model and the item model comprise one or more neural networks… and transmit, over the network the recommendation list to the computing device…; A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device…; (as recited in claims 1 and 20) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
In addition, Applicant’s Specification (paragraph [0047]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., 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).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-13, 15-19, and 21-22) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 2-7 and 21-22 “the plurality of machine learning models comprises a seller model and an item model; the seller model is configured to compute a seller embedding for a seller based on features of the seller; and the item model is configured to compute an item embedding for an item based on features of the item; the request is seeking item recommendation for an existing seller; the corresponding arrangement includes the seller model followed by the item model; and the recommendation list includes a list of recommended items for the existing seller; obtaining features of the existing seller; applying the seller model to the features of the existing seller to compute a query seller embedding; and determining, for each division of a plurality of divisions in the marketplace, a list of candidate item embeddings having highest affinity scores to the query seller embedding based on the item model, wherein the affinity scores are computed based on a pre-trained retrieval model; combining all candidate item embeddings from all divisions to generate a combined list of candidate item embeddings; ranking the combined list of candidate item embeddings to generate a ranked list based on a pre-trained ranking model; and generating the list of recommended items based on the ranked list; the request is seeking seller recommendation for a cold-start item; the corresponding arrangement includes the item model followed by the seller model; and the recommendation list includes a list of recommended sellers for the cold-start item; obtaining features of the cold-start item; applying the item model to the features of the cold-start item to compute a cold-start item embedding; determining, for each division of a plurality of divisions in the marketplace, a list of candidate seller embeddings having highest affinity scores to the cold-start item embedding based on the seller model, wherein the affinity scores are computed based on a pre-trained retrieval model; combining all candidate seller embeddings from all divisions to generate a combined list of candidate seller embeddings; ranking the combined list of candidate seller embeddings to generate a ranked list based on a pre-trained ranking model; and generating the list of recommended sellers based on the ranked list; the request is seeking item recommendation for a cold-start seller selling an existing item; the corresponding arrangement includes the item model followed by the item model; and the recommendation list includes a list of recommended items for the cold-start seller; obtaining features of the existing item; applying the item model to the features of the existing item to compute a query item embedding; determining, for each division of a plurality of divisions in the marketplace, a list of candidate item embeddings having highest affinity scores to the query item embedding based on the item model, wherein the affinity scores are computed based on a pre-trained retrieval model; combining all candidate item embeddings from all divisions to generate a combined list of candidate item embeddings; ranking the combined list of candidate item embeddings to generate a ranked list based on a pre-trained ranking model; and generating the list of recommended items based on the ranked list; train a multi-tower retrieval model including the seller model, the item model and a combined model based on neural networks using a same set of training data, wherein the combined model is trained to compute an affinity score for each pair of seller embedding and item embedding computed by the seller model and the item model, respectively; and the multi-tower retrieval model is trained to determine hyperparameters and weights in the seller model, the item model and the combined model for optimizing an objective function based on labelled affinity scores in the training data; generate, for each respective seller, a list of item indices indicating closest item neighbors to the respective seller based on affinity scores computed based on the trained multi-tower retrieval model; generate, for each respective item, a list of seller indices indicating closest seller neighbors to the respective item based on affinity scores computed based on the trained multi-tower retrieval model; generate, for each respective seller, a list of seller indices indicating closest seller neighbors to the respective seller based on affinity scores computed based on the trained multi-tower retrieval model; and generate, for each respective item, a list of item indices indicating closest item neighbors to the respective item based on affinity scores computed based on the trained multi-tower retrieval model; the seller model, the item model and the combined model are agnostic to geography and language; filtering, based on at least one filtering model, a list of recommended item-seller combinations to remove: items already in a catalog of a query seller, and items already recommended to a query seller; train a multi-tower retrieval model including the seller model, the item model and a combined model based on the neural networks using a same set of training data, wherein :the combined model is trained to compute an affinity score for each pair of seller embedding and item embedding computed by the seller model and the item model, respectively; and the multi-tower retrieval model is trained to determine hyperparameters and weights in the seller model, the item model and the combined model for optimizing an objective function based on labelled affinity scores in the training data; obtaining features of the cold-start item; applying the item model to the features of the cold-start item to compute a cold- start item embedding; determining, for each division of a plurality of divisions in the marketplace, a list of candidate seller embeddings having highest affinity scores to the cold-start item embedding based on the seller model, wherein the affinity scores are computed based on a pre-trained retrieval model; combining all candidate seller embeddings from all divisions to generate a combined list of candidate seller embeddings; ranking the combined list of candidate seller embeddings to generate a ranked list based on a pre-trained ranking model; and generating the list of recommended sellers based on the ranked list ”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (15-19) recite the method for performing the system of claims 2-13. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:Brovman; Yuri Michael. OPTIMIZING SIMILAR ITEM RECOMMENDATIONS IN A SEMI-STRUCTURED ENVIRONMENT, .U.S. PGPub 20170293695 This disclosure generally addresses technical problems associated with optimizing similar item recommendations in a semi-structured environment. In one example, these problems are addressed more specifically in formulating product recommendations in a large semi-structured networked marketplace. Technical problems such as data limitations imposed by a variable inventory and lack of structured information about product listings renders traditional collaborative filtering algorithms difficult to use.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/Primary Examiner, Art Unit 3625