DETAILED ACTION
Status of Claims
This action is in reply to the response received on 05 May 2026.
Claims 1, 4, 9, 11-12, 16, and 18-19 have been amended.
Claims 1-20 have been examined and are pending
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 .
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea without significantly more).
Under step 1, it is determined whether the claims are directed to a statutory category of invention (see MPEP 2106.03(II)). In the instant case, claims 1-8 are directed to a method, claims 9-15 are directed to a product of manufacture (recited as a non-transitory computer-readable storage medium), and claims 16-20 are directed to a system.
While the claims fall within statutory categories, under revised Step 2A, Prong 1 of the eligibility analysis (MPEP 2106.04), the claimed invention recites an abstract idea of providing a set of cold start results to a user. Specifically, representative claim 1 recites the abstract idea of:
receiving, by the system, a query from a user of the system;
identifying a candidate set of cold start results to the query, the candidate set of cold start results having been presented to the user less than threshold number of times in a previous time period;
filtering the candidate set of cold start results based on relevance to the query to generate a final set of cold start results;
generating a score for each cold start result of the final set of cold start results using a scoring baseline common to standard results, wherein the score is generated without interaction data and is associated with a probability of conversion, wherein the scoring baseline enables comparison of cold start results without interaction data to the standard results with interaction, wherein without the interaction data to predict the probability of conversion, wherein generating the scores comprises:
generating a query information from the query;
generating, for the final set of cold start results, item information, and determining similarity scores between the query and the item information;
combining the final set of cold start results with a set of standard results to generate a set of combined results, wherein the set of standard results are determined by using interaction data and the scoring baseline;
ranking the final set of cold start results with the set of standard results based on the score for each cold start result using the scoring baseline, wherein ranking the final set of cold start results with the set of standard results comprises comparing the similarity scores to the scoring baseline;
adjusting an order presentation of the set of combined results according to ranking the final set of cold start results with the set of standard results, wherein the order of presentation includes a subset of the final set of cold start results and a subset of the set of standard results; and
in response to accessing and receiving the query, causing rendering the subset of the final set of cold start results to be presented with the subset of the set of standard results for display to the user in respective positions, the respective positions corresponding to ranking the final set of cold start results.
Under revised Step 2A, Prong 1 of the eligibility analysis, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in 2106.04(a) of the MPEP. Even in consideration of the analysis, the claims recite an abstract idea. Representative claim 1 recites the abstract idea of providing a set of cold start results to a user, as noted above. This concept is considered to be a method of organizing human activity. Certain methods of organizing human activity include “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” MPEP 2106.04(a)(2)(II). In this case, the abstract idea recited in representative claim 1 is a certain method of organizing human activity because it relates to sale activities since the claims specifically recite the steps of receiving a query from a user, identifying a candidate set of cold start results based on the query, filtering the candidate set of cold start results based on relevancy to the query, generating scores for each cold start result of the final set to predict a probability of conversion (i.e., purchase), combining the final set of cold start results with a set of standard results, ranking by comparing the information similarity scores to the score baseline, the final set of the cold start results with a set of standard results based on the score for each result, adjusting the presentation of the results to the user, causing the final set of cold start results to be presented to the user in response to the query, thereby making this a sales activity or behavior.
Thus, representative claim 1 recites an abstract idea.
Under Step 2A, Prong 2 of the eligibility analysis, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. MPEP 2106.04(d). The courts have identified limitations that did not integrate a judicial exception into a practical application include limitations merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). MPEP 2106.04(d). In this case, representative claim 1 includes additional elements: a computer system comprising a processor and a computer-readable medium, the computer system, online system, using a machine learning model, the machine learning model is trained, generating a query embedding, item embeddings, computing embedding similarity scores, a scrollable carousel of a graphical user interface (GUI) associated with the online system, the GUI by a user client device, the user client device to display the GUI, and on the scrollable carousel.
Although reciting such additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., providing a set of cold start results to a user) being applied on a general-purpose computer using general purpose computer technology. MPEP 2106.05(f). While the claims recite a machine learning model, the recitations are results based in nature and do not include details as to how the machine learning is actually functioning beyond known functions. Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application.
Under Step 2B of the eligibility analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). MPEP 2106.05. In this case, as noted above, the additional elements of a computer system comprising a processor and a computer-readable medium, the computer system, online system, using a machine learning model, the machine learning model is trained, generating a query embedding, item embeddings, computing embedding similarity scores, a scrollable carousel of a graphical user interface (GUI) associated with the online system, the GUI by a user client device, the user client device to display the GUI, and on the scrollable carousel recited in independent claim 1 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘ad[d] nothing…that is not already present when the steps are considered separately’… [and] [v]iewed as a whole…[the] claims simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, (2014) (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
As such, representative claim 1 is ineligible.
Independent claims 9 and 16 are similar in nature to representative claim 1 and Step 2A, Prong 1 analysis is the same as above for representative claim 1. It is noted that in independent claim 9 includes the additional elements of a non-transitory computer-readable storage medium storing instructions executable by one or more processors for performing and independent claim 16 includes the additional elements of one or more processors and a non-transitory computer-readable storage medium storing instructions executable by the one or more processors for performing. The Applicant’s specification does not provide any discussion or description of the claimed additional elements in claims 9 and 16, as being anything other than generic elements. Thus, the claimed additional elements of claims 9 and 16 are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. As such, the additional elements of claims 9 and 16 do not integrate the judicial exception into a practical application of the abstract idea. Additionally, the additional elements of claims 9 and 16, considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer.
As such, claims 9 and 16 are ineligible.
Dependent claims 2-8, 10-15, and 17-20, depending from claims 1, 9, and 16 respectively, not aid in the eligibility of the independent claims and representative independent claim 1. The claims of 2-8, 10-15, and 17-20 merely act to provide further limitations of the abstract idea and are ineligible subject matter.
It is noted that dependent claims include the additional elements of applying the machine learning model to the final set of cold start results trained to (claims 3, 11, & 18), and training the machine learning model (claims 4, 12, & 19). Applicant’s specification does not provide any discussion or description of the claimed additional elements as being anything other than a generic element. The claimed additional elements, individually and in combination do not integrate into a practical application and do not provide an inventive concept because they are merely being used to apply the abstract idea using a generic computer (see MPEP 2106.05(f)). Although the claims recite applying the machine learning model and training the machine learning model, the recitations are results based in nature and do not include details as to how the machine learning model is actually functioning beyond known functions. Accordingly, claims 3, 4, 11, 12, 18, and 19 are directed towards an abstract idea. Additionally, the additional elements of claims 3, 4, 11, 12, 18, and 19, considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer. It is further noted that the remaining dependent claims 2, 5-8, 10, 13-15, 17, and 20 do not recite any further additional elements to consider in the analysis, and therefore would not provide additional elements that would integrate the abstract idea into a practical application and would not provide an inventive concept.
As such, the dependent claims 2-8, 10-15, and 17-20 are ineligible.
Reasons for Allowable Subject Matter
Prior Art Considerations:
Upon review of the evidence at hand, it is concluded that the totality of evidence in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
Regarding the independent claims, the features are as follows:
ranking the final set of cold start results with the set of standard results based on the score for each cold start result using the scoring baseline, wherein ranking the final set of cold start results with the set of standard results comprises comparing the embedding similarity scores to the scoring baseline; and
adjusting an order of presentation of the set of combined results in a scrollable carousel of a graphical user interface (GUI) associated with the online system according to ranking the final set of cold start results with the set of standard results, wherein the order of presentation includes a subset of the final set of cold start results and a subset of the set of standard results;
The most apposite prior art of record includes Bhardwaj, A., et al. (PGP No. US 2024/0256578 A1), Wu, J., et al. (Patent No US 9,959,563 B1), and Inamdar, S., et al. (PGP No. US 2021/0097471 A1), to teach a method for providing recommendations of cold-start results.
The reference of Bhardwaj discloses a system for providing results in response to a search request from a user interface of a user device, where the search results provided are generated cold-start candidate products in response to a user that searches for a specific anchor product (Bhardwaj, see: paragraph [0058]-[0059]). Bhardwaj further describes that in response to the request from the user for an anchor product, the candidate products are combined that also include the cold-start products that do not have any or little interaction information (Bhardwaj, see: paragraphs [0058]-[0059]). The initial set of recommendations is selected from the first candidate products, and then ranked in accordance with user query (Bhardwaj paragraph [0059]). The cold-start recommendations are also compared with products with more interaction history data, which is then used in a machine learning model to train the model for ranking the candidate products (Bhardwaj, paragraphs [0046], [0055], [0059], and [0079]). The set of recommendations is then displayed to a user device via an interface engine, where the recommendations also include all or just a subset of the products (Bhardwaj, see: paragraph [0079]-[0080]). It is also noted that Bhardwaj does discuss a machine learning model that is a graph based model and is configured to provide the set of cold-start candidates, where a semantic encoder model generates items embeddings for each product feature (Bhardwaj, see: paragraphs [0061]-[0063] and FIG. 6). Although Bhardwaj describes these features, Bhardwaj does not disclose the allowable features indicated above. Further, although Bhardwaj describes using the item embeddings for providing cold-start recommendations, Bhardwaj does not disclose or discuss the specific features regarding a query embedding or computing an embedding similarity score.
The reference of Wu is further relied upon to teach features of results of recommendations that include cold start recommendations, and identifying the products that have no interaction history or less than a threshold amount of interaction history (Wu, Col. 19, ln. 64-65). Wu also describes scores that are determined to associate the recommendation with a specific product category (Wu, Col. 16, ln. 33-37). Although Wu describes these scores utilized for the recommendations, Wu does not specifically describe the amended limitations of the allowable subject matter indicated above. It is further noted that Wu does describe features of predictions of recommendations for cold-start products, but the recommendations are related to those that the user would only be interested in (Wu, see Col. 20, ln. 39-43), and not providing a prediction of a conversion.
The reference of Inamdar describes a method for presenting cold start recommendations, utilizing a search query of the user to parse the text to identify candidate items (Inamdar, paragraph [0035]). Further, Inamdar describes ranking sets of candidate recommendations based on weighted scores (Inamdar, paragraph [0036]), however the reference does not specifically discuss any type of comparison or similarity scores that are generated representing the score between the query embedding and the item embeddings, as now recited in the claims. Inamdar does not cure the deficiencies of the prior art and does not teach the allowable subject matter as indicated above.
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. Moreover, the combination of features of independent claims, would not have been obvious to one of ordinary skill in the art because any combination of evidence at hand to reach the combination of features as claimed would require substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias and resulting in an inappropriate combination.
It is hereby asserted by the Examiner, that in light of the above and in further deliberation over all of the evidence at hand, that the claims recite allowable subject matter, as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Examiner’s Comment
The Examiner notes that the non-patent literature (NPL) document, titled Improving complementary-product recommendations, published in Amazon Science (2020), documented on PTO-892 form as reference U, and hereinafter referred to as ‘Improving’, describes a modeling technique to increase accuracy for recommendations for customers. The improvement comes from the utilizing of better training data selection, using embedding data to determine relationships between items. Although the reference ‘Improving’ describes such features, the reference does not disclose or teach the allowable features that are stated above, and does not remedy the deficiencies of the noted prior art.
Response to Arguments
With respect to the rejections made under 35 USC § 101, the Applicant’s arguments filed on 05 May 2026, have been fully considered but are not considered persuasive.
In response to the Applicant’s arguments found on pages 14-15 of the remarks stating that in regards to the rejection, the “Applicant respectfully traverses” and “These activities are not human activities, and the claims therefore no longer cover Organizing Human Activity (or other categories of 101 rejections) as noted in the Office Action,” and Under at least a Prong 2 analysis, the amendment is patent-eligible” and further “Like Ex Parte Desjardins, the amendments describe how re-training provides an improvement to a machine learning model,” the Examiner respectfully disagrees. First, under Step 2A, Prong One of the eligibility analysis, the amended claims, are still directed to the abstract idea. Since the claims recite specific steps for providing a user the subset of final set of cold start results, the abstract idea falls into the enumerated sub-grouping of a certain method of organizing human activity, where the activities recited in the claims are related to sales activities or behaviors. Next, under Step 2A, Prong Two of the analysis, when considering the amendments, and even though the claims now recite other additional elements that are considered beyond the abstract idea, such as the a computer system comprising a processor and a computer-readable medium, the computer system, online system, using a machine learning model, the machine learning model is trained, generating a query embedding, item embeddings, computing embedding similarity scores, a scrollable carousel of a graphical user interface (GUI) associated with the online system, the GUI by a user client device, the user client device to display the GUI, and on the scrollable carousel, the additional elements are still recited at high-level and are used to apply the abstract idea with generically recited computing components and a generic computer. The additional elements are not sufficient to integrate the abstract idea into a practical application. Further the claims do not recite features similar to those recited in the Ex Parte Desjardins decision. In that decision, it was determined that the claims and specification did in fact support the disclosed improvement. In the decision, it was determined that the specification supported the improvement to “effectively learn new tasks in succession whilst protecting knowledge about previous tasks” and also provided support that “the claimed improvement allows artificial intelligence (AI) systems to ‘us[e] less of their storage capacity’ and enables ‘reduced system complexity’” such that when evaluating the claim language, the independent claim 1 reflected that improvement (see Ex Parte Desjardins et al Rehearing Decision). In this case, the additional elements, are recited in a generic manner. The improvements in the instant case are directed to providing improvements to the abstract idea of providing a set of cold start results to a user, which is a commercial task. Therefore the claims do not integrate the abstract idea into a practical application, and do not reflect improvements to the technology and thus, the Examiner maintains the 101 rejection.
With respect to the rejections made under 35 USC § 103, the Applicant’s arguments filed on 05 May 2026, have been fully considered. In light of the Applicant’s amendments to the claims, the claims now recite allowable subject matter, as indicated above, and thus, the 103 rejection is withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/ASHLEY D PRESTON/Primary Examiner, Art Unit 3688
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