DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the claims filed on 02/20/2026.
Claims 1-6, 9-16, and 18-20 are amended.
Claims 7-8 and 17 are cancelled.
Claims 1-6, 9-16, and 19-20 are currently pending and have been examined.
Subject Matter Free from Prior Art
Claims 2-3, 11, and 13-14 are free from prior art and if rewritten to include all of the limitations of the base claim and any intervening claims.
Claim Rejections- 35 U.S.C. § 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 1-6, 9-16, and 18-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the subject matter eligibility (SME) analysis described in MPEP 2106.03, the instant claims fall within the four statutory categories of invention identified by 35 U.S.C. 101. In the instant case, claims 1-6 and 9-11 are directed to a method, claims 12-16 and 18-19 are directed to a manufacture, and claim 20 is directed to a system. Claims 1, 12, and 20 are parallel in nature, therefore, the analysis will use claim 1 as the representative claim.
In Step 2A Prong One, it must be considered whether the claims recite a judicial exception. Claim 1, as exemplary, recites abstract concepts including: clustering, using a similarity score for each pair of items of a plurality of items previously purchased by a user ..., the plurality of items into a plurality of clusters so that each of the plurality of clusters includes a respective subset of the plurality of items that are more similar to each other than to any other item clustered to any of remaining clusters of the plurality of clusters, information about the plurality of items stored ...; ... predict a likelihood of engagement by the user for each item in each of the plurality of clusters; ... applying ... one or more features of each item in each of the plurality of clusters to predict the likelihood of engagement for each item in each of the plurality of clusters; generating, using the predicted likelihood of engagement, a score for each item in each of the plurality of clusters; ranking, using the generated score for each item, each item in each of the plurality of clusters; selecting a highest ranked item in each of the plurality of clusters as a representative item from each of the plurality of clusters for displaying ...; storing ... a set of representative items including information about the representative item from each of the plurality of clusters; causing ... to display ... the representative item from each of the plurality of clusters; and updating, using feedback information from the user in relation to the representative item from each of the plurality of clusters, a set of parameters ... .
These identified limitations recite the abstract idea of “using a cluster analysis to select a set of representative items for displaying”, which falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas as it describes sales activities or behaviors. Accordingly, claims 1, 12, and 20 recite an abstract idea. See MPEP 2106.04.
In Step 2A Prong Two, examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Instant claims 1, 12, and 20 recite additional elements including: a computer system comprising a processor and a computer-readable medium; a database of the computer system; accessing a machine-learning model trained to ... ; applying the machine-learning model to predict ...; storing, at the database, a set of representative items; causing a device associated with the user to display the user interface with ... data retrieved from the database; a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon. The computer system, online system, computer model, device, user interface, and computer program product are each recited at a high-level of generality (i.e., as a generic device performing generic computer functions), without reciting details as to how, for example, the user interface is displayed or how the computer model is applied. In other words, the claims invoke the computer and model as tools to execute the abstract idea, which does not integrate the abstract idea into a practical application, similar to how the recitation of the computer in Alice amounted to mere instruction to apply the abstract idea of intermediated settlement on a generic computer. See MPEP 2106.05(f). The combination of these additional elements is no more than mere instruction to apply an exception with a generic computer. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05. Claims 1, 12, and 20 are thus directed to an abstract idea.
Under Step 2B of the SME 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).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) individually and in combination are merely being used to apply the abstract idea to a general computer components. For the same reason, the elements are not sufficient to provide an inventive concept. As explained in MPEP 2106.05(f), implementing an abstract idea with a generic computer does not add significantly more in Step 2B. Therefore, the additional elements, alone or in ordered combination, there is no inventive concept in the claim, and thus claims 1, 12, and 20 are not patent eligible.
Dependent claims 2-3, 5-6, and 13-16 recite additional elements including: accessing, from a database of the computer system, information; accessing a second machine-learning model of the online system; and applying the second machine-learning model. Similar to the additional elements identified above, the database and second machine-learning model are described in ordinary terms and merely used as a tool in performance of the abstract idea. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B (see MPEP 2106.05(f)). Accordingly, claim(s) 2-3, 5-6, and 13-16, considered both individually and as a combination, are ineligible.
Dependent claim(s) 4, 10-11, and 19 do not aid in the eligibility of the independent claims. These claims merely further define the abstract idea without reciting any further additional elements. Thus dependent claims 4, 10-11 and 19 are also ineligible.
Dependent claims 9 and 18 recite additional elements including: accessing, from a database of the computer system; applying the machine-learning model; and training the machine-learning model. These elements are each described at high level of generality, without reciting any relevant technical detail as to how the computer accesses the database, applies the computer model, or trains the computer model. Accessing the database is considered an insignificant data-gathering step in prong two, and well-understood computer activity in step 2B (see Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 in MPEP 2106.05(d). The application and training of the model also fail to transform the nature of the claim into a patent-eligible application. Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) (finding claims drawn to training a machine learning model and applying it to a new data environment are not patent eligible where the claims did not specify a specific method for improving the mathematical algorithm or making machine learning better). Accordingly, claims 9 and 18 are ineligible.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4-6, 9-10, 12, 15-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al. (US 2012/0109778 A1) in view of Iyer et al. (US 2022/0245701 A1), further in view of Dornadula et al. (US 2022/0284499 A1).
Claim 1 – Chan discloses a method, performed at a computer system comprising a processor and a computer-readable medium (Fig. 9), comprising:
clustering, using a similarity score for each pair of items of a plurality of items previously purchased by a user of the computer system, the plurality of items into a plurality of clusters so that each of the plurality of clusters includes a respective subset of the plurality of items that are more similar to each other than to any other item clustered to any of remaining clusters of the plurality of clusters (¶ [0050] “Assuming the collection is sufficiently large, an appropriate clustering algorithm is used to subdivide the collection into multiple clusters (block 36). As part of this step, distances between the items may be computed using any appropriate metric”), information about the plurality of items stored at a database of the computer system (¶ [0034] “representation of that item in a computer (e.g., a product or web site identifier or description stored in a database)”;
accessing a machine-learning model trained to predict a likelihood of engagement by the user for each item in each of the plurality of clusters (¶ [0052] “a score may be assigned to each item in the collection”);
applying the machine-learning model to one or more features of each item in each of the plurality of clusters to predict the likelihood of engagement for each item in each of the plurality of clusters (¶ [0053] “The recommendation engine then uses this list to generate and return a ranked list of recommended items”);
ranking, using the generated score for each item, each item in each of the plurality of clusters (¶ [0052] “a score may be assigned to each item in the collection” ... “If all the items are scored, some pre-selected number (e.g., 64) of the most highly scored items may be selected for use as the sources”);
selecting a highest ranked item in each of the plurality of clusters as a representative item from each of the plurality of clusters (¶ [0052] “If all the items are scored, some pre-selected number (e.g., 64) of the most highly scored items may be selected for use as the sources”) ...
causing the device associated with the user to display the user interface with the representative item from each of the plurality of clusters retrieved from the database (¶ [0034] “stored in a database”; ¶ [0075] “In step 76, the recommended items, as arranged by cluster/category, are output to the user together with the associated category names selected in step 74.”;); ... .
Chan does not disclose generating, using on the predicted likelihood of engagement, a score for each item in each of the plurality of clusters or selecting a highest ranked item ... for displaying at a user interface of a device associated with the user.. However, Iyer – which like Chan uses a cluster analysis to determine items to recommend consumers – teaches:
generating, using the predicted likelihood of engagement, a score for each item in each of the plurality of clusters (Iyer ¶ [0049] “rank the categories of items that are available on the ecommerce marketplace in order of the likelihood that a customer will choose an item in the category to further explore and possibly purchase”; ¶ [0051] “determine an item ranking for each item in each category”);
selecting a highest ranked item in each of the plurality of clusters as a representative item from each of the plurality of clusters for displaying at a user interface of a device associated with the user (Iyer ¶ [0076] “Within each category, the items with the highest item recommendation rankings (from the item recommendation engine) can be displayed to the customer”);
storing, at the database, a set of representative items including information about the representative item from each of the plurality of clusters (Iyer ¶ [0053] “The recommender computing device 102 can also store the final recommendations or the featured categories in the database 108”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the score and selection of a highest ranked item, as taught by Iyer, in the method of Chan in order to present recommendations that may encourage customers to explore new items and new categories of items on the ecommerce marketplace (Iyer ¶ [0028]).
The combination of Chan in view of Iyer does not disclose limitations associated with updating parameters of the machine-learning model using feedback information. However, Dornadula – which is also directed to generating item recommendations using machine-learning technology – further teaches:
updating, using feedback information from the user in relation to the representative item from each of the plurality of clusters, a set of parameters of the machine-learning model (Dornadula ¶ [0040] “In addition, one or more of a ranking and a weight associated with the plurality of different recommendation methods may be updated based on implicit feedback derived from one or more user actions with respect to at least one of the one or more recommendations (e.g., whether a given recommendation was adopted, implemented, saved or ignored)”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the parameter updating, as taught by Dornadula in the method of Chan in order to improve the performance of the content item (Dornadula ¶ [0040]).
Claim 4 – The combination of Chan, Iyer, and Dornadula teaches the method of claim 1. Chan further discloses, further comprising:
prior to accessing the computer model, filtering out one or more items from the plurality of clusters, each of the one or more items associated with at least one of a last purchase date or a repurchase frequency that do not satisfy one or more minimum threshold requirements (¶ [0043]; ¶ [0050]).
Claim 5 – The combination of Chan in view of Iyer in further in view of Dornadula teaches the method of claim 1. Chan further discloses, wherein applying the machine-learning model further comprises:
applying the machine-learning model further to information about at least one of a repurchase frequency or a last purchase date for each item in each of the plurality of clusters to predict the likelihood of engagement for each item in each of the plurality of clusters (¶ [0043]; ¶ [0047] “including the application of the clustering algorithm”).
Claim 6 – The combination of Chan in view of Iyer in further in view of Dornadula teaches the method of claim 1. Chan further discloses, wherein generating the score for each item in each of the plurality of clusters comprises:
applying the machine-learning model to the one or more features of each item in each of the plurality of clusters to generate at least a component of the score that accounts for a level of relevancy to the user of each item in each of the plurality of clusters (¶ [0045]; ¶ [0047]).
Claim 9 – The combination of Chan in view of Iyer in further in view of Dornadula teaches the method of claim 1. Chan further discloses, further comprising:
accessing, from a database of the computer system, information about engagement rates for a set of items (¶ [0141]).
The combination of Chan in view of Iyer in further in view of Dornadula does not disclose limitations associated with a training dataset, or training the computer model. However, Iyer further teaches:
applying the machine-learning model to the information about the engagement rates for the set of items to generate a training dataset with information about a likelihood of engagement by the user for each item in the set of items (Iyer ¶ [0049] “ The discovery category engine 304 can determine discovery category rankings that can rank the categories of items that are available on the ecommerce marketplace in order of the likelihood that a customer will choose an item in the category to further explore and possibly purchase.”; ¶ [0063] “The discovery category model 414 can attempt to predict accurate scores to fully populate the User-Category Matrix 502. One method of doing so is using an Alternating Least Square (ALS) training routine”); and
training using the training dataset the machine-learning model to identify a level of relevancy to the user for each item in the set of items (Iyer ¶ [0049]; ¶ [0064] “The gradient descent can be run in parallel across multiple partitions of the underlying training data of the customer data to train the discovery category model 414”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the machine-learning model, as taught by Iyer, in the method of Chan because there is a need for improved recommender systems that have improved functionality to promote the exploration of new categories and new items on the ecommerce marketplace (Iyer ¶ [0003]).
Claim 10 – The combination of Chan in view of Iyer in further in view of Dornadula teaches the method of claim 1. Chan does not disclose limitations associated with updating parameters of the model, however Iyer further teaches, wherein updating the set of parameters of the machine-learning model comprises: collecting information about engagement by the user for the set representative items (Iyer ¶ [0057]-[0058]). The combination of Chen in view of Iyer does not teach updating parameters of the model, however Dornadula further teaches:
updating, using the collected information, the set of parameters of the machine-learning model (Dornadula ¶ [0040] “ In addition, one or more of a ranking and a weight associated with the plurality of different recommendation methods may be updated based on implicit feedback derived from one or more user actions with respect to at least one of the one or more recommendations (e.g., whether a given recommendation was adopted, implemented, saved or ignored).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the parameter updating, as taught by Dornadula in the method of Chan in order to improve the performance of the content item (Dornadula ¶ [0040]).
Claims 12, 15-16, and 18-19 which are directed to a manufacture, recite limitations that are parallel in nature as those addressed above for method claims 1, 5-6, and 9-10. Claim(s) 12, 15-16, and 18-19 are therefore rejected for the same reasons as set forth above for claims 1, 5-6, and 9-10 respectively.
Claim 20 which is directed to a system, recite limitations that are parallel in nature as those addressed above for method claim 1. Claim(s) 20 is therefore rejected for the same reasons as set forth above for claim 1.
Response to Arguments
Applicant's arguments filed 02/20/2026 have been fully considered but they are not persuasive. With respect to the 35 U.S.C. § 101 rejection of claims 1-20, Applicant argues amended claim 1 provides “a practical application of a computer system that performs specific operations to reduce the size of data stored in database, where the stored data of reduced size is retrieved for displaying at a user interface” (Remarks, pg. 14). Specifically, Applicant argues retrieving information of a smaller size from the database for displaying allows for “reduced memory access bandwidth, faster memory access, faster data transfer, and reduced latency” (Remarks, pg. 14).
The Examiner respectfully disagrees. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). Examples that the courts have indicated may not be sufficient to show an improvement to technology include: A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Applicant argues the practical application is provided by filtering data so that a smaller size of data is retrieved from a database. The Courts have established that “filtering content” is an abstract idea (BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016). The recited additional elements include a general purpose computer which stores information at a database of the computer system and displays a user interface with items retrieved from the database. Storing and retrieving information is memory is the ordinary capacity of a computer. If less data is stored/retrieved due to business logic (i.e., predicting a set of representative items) and not a technical improvement (e.g., Particular structure of a server that stores organized digital images; Improved, particular method of digital data compression) the computer system is still performing the same ordinary operations. The listed improvements such as “reduced memory access bandwidth” on page 14 of the Remarks, result from an abstract recommendation process rather than an improvement to the computer technology itself. For at least these reasons, the Examiner is maintaining the 101 rejections of the pending claims.
Applicant’s arguments with respect to the 35 U.S.C. § 103 rejections have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The Examiner is relying on the combination of Chan, in view of Iyer, and further in view of Dornadula to reject independent claims 1, 12, and 20.
Examiner notes that the independent claims were amended to include the subject matter of previous claim 8, which was indicated as containing allowable subject matter in the rejection on 12/09/2025. However, upon further search and consideration, a reference was found to teach these recited limitations. Accordingly, the Examiner is the maintaining the § 103 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Graham et al. (US 8,548,878 B1) relates to aggregating product information for electronic product catalogs and describes selecting a most representative image for each product cluster using a highest similarity score.
D. Wang and M. Ogihara (NPL Reference U) explores a problem of finding product trends through the posts on Pinterest, a rising social media for sharing interests using uploaded photographs and text comments.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNEDY A GIBSON-WYNN whose telephone number is (571)272-8305. The examiner can normally be reached M-F 8:30-5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.G.W./Examiner, Art Unit 3688
/KELLY S. CAMPEN/Primary Examiner, Art Unit 3691
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