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 .
Election/Restriction
Claims 18-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 03/12/2026.
Status of Claims
This action is in reply to the claims filed on 03/12/2026.
Claims 1-20 are currently pending, and claims 1-17 have been examined.
Information Disclosure Statement
Information Disclosure Statement received 04/04/2024 has been reviewed and considered.
Allowable Subject Matter
Claims 12 and 17 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and 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 1-17 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 1-12 are directed to a system and claims 13- 17 are directed to a method. Claims 1 and 13 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: obtain catalog content and customer content associated with a product type of a product; identify candidate attributes for the product from the catalog content and the consumer content associated with the product type of the product; generate a model prompt to be input into a large language model, the model prompt including at least a portion of the candidate attributes, a product profile associated with the product, and an instruction to generate a recommendation for a new attribute to associate with the product based on the product profile and the at least the portion of the candidate attributes; obtain ... an attribute recommendation that recommends the new attribute to include in the product profile associated with the product; and provide, for display ... the attribute recommendation that recommends the new attribute to include in the product profile associated with the product.
These identified limitations set forth the abstract idea of “obtain and display an attribute recommendation for a product profile” which falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas as it relates to commercial interactions of sales activities or behaviors. Identifying product attributes is a fundamental economic activity and necessary step in sales activities. Accordingly, claims 1 and 13 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 and 13 recite additional elements including: a computing system comprising: a processor; a computer storage memory having computer-readable instructions stored thereon; a large language model; and a user interface. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible (MPEP 2106.05(f)). The computing system, large language model, and user interface are recited at a high-level of generality and used in their ordinary capacity (i.e., processor executes stored code; memory stores computer-readable instructions; large language model accepts input and produces output; and interface displays information). The combination of these additional elements (a computer with access to a large language model) 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 and 13 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 and 13 are not patent eligible.
Dependent claim(s) 2, 4, 5, 7, 8, 10-12 and 15-17 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 2, 4, 5, 7, 8, 10-12 and 15-17 are also ineligible.
Dependent claim 3 recites additional elements including: extracting the candidate attributes using a named entity recognition model, a generative model, or a combination thereof. Similar to the additional elements identified above, these models are described in ordinary terms (i.e. just by name) and merely invoked as tools for “extracting” an item attribute, without describing how the extraction occurs in any relevant technical detail. Accordingly, claim(s) 3 is ineligible because these additional elements amount to no more than mere instruction to apply the abstract idea on a computer.
Dependent claims 6 and 9 recite additional elements including: wherein the large language model is fine-tuned using a dataset; refining the large language model based on customer feedback. These limitations do recite a specific algorithm or technique for fine-tuning/refining the large language model. Reciting only the idea of solution with no description of the mechanism for accomplishing the result is equivalent to “apply it”. See MPEP 2106.05(f)(1). Accordingly, claims 6 and 9 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.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Lin (US 2024/0403947 A1) in view of Yoon et al. (US 11,282,124 B1).
Claim 1 – Lin discloses a computing system comprising:
a processor (¶ [0014]); and
computer storage memory having computer-executable instructions stored thereon which, when executed by the processor (FIG. 1-2), configure the computing system to perform operations comprising:
obtain catalog content and customer content associated with a product type of a product (¶ [0053] “collects order data ... order data may include item data for items that are included in the order ... a customer associated with the order”; ¶ [0085] “the online system 140 obtains an item description associated with an item in an item catalog. In one or more embodiments, the item description is unstructured text. For example, the item description for a cleaning product may include “Kills 99.9% of bacteria (Kills 99.9% of Staphylococcus aureus and Enterobacter aerogenes). Helps eliminate odors in the air with a fresh scent. Great for vinyl, glazed ceramic, sealed marble, laminate, and finished wood floors”);
... generate a model prompt to be input into a large language model (¶ [0086] “ the online system 140 generates a prompt for input to a machine-learned language model”) the model prompt including ... a product profile associated with the product, and an instruction to generate a recommendation for a new attribute to associate with the product based on the product profile (¶ [0086] “the prompt specifying at least the item description and a request to identify one or more attributes of the item”) ... ;
obtain, as output from the large language model, an attribute recommendation that recommends the new attribute to include in the product profile associated with the product (¶ [0088] “At step 340, the online system 140 receives, from the machine-learned language model, an output including a list of attributes and respective values associated with the item based on the item description”); and
provide, for display via a user interface, the attribute recommendation that recommends the new attribute to include in the product profile associated with the product (¶ [0091] “In some embodiments, the online system 140 provides the item, the list of attributes and the respective values associated with the item to a user”).
Lin does not disclose limitations associated with identifying candidate attributes. However Yoon – which is also directed to recommending item attributes – teaches:
obtain customer content (Yoon: Col 7, ll. 40-45 “For example, the attribute determination engine 110 may access browsing sessions of users of the electronic catalog 112”);
identify candidate attributes for the product from the catalog content and the consumer content associated with the product type of the product (Yoon: Col 7, ll. 15-20 “The attribute determination engine 110 may access the item descriptions associated with a particular category. The engine 110 may then identify which of the candidate keywords are to be associated with each item associated with the particular category.”; Col 7, ll. 30-50 “For example, the attribute determination engine 110 may measure a frequency with which any two items mapped to different candidate keywords have been interacted with by a same set of users.”);
the model prompt including at least a portion of the candidate attributes (Yoon: Col 15, ll. 25-35 “ In some embodiments, the system may use machine learning techniques. ... For example, a k-nearest neighbor (KNN) technique may be used to cluster attributes into respective classes of attributes. Thus, the system may cluster sizes of t-shirts, as identified based on user browsing sessions, into a same class of attribute.”)
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 candidate attributes as taught by Yoon in the system of Lin in order to advantageously determine which attribute is a key attribute of interest to the user (Yoon: Col 3, ll.45-50).
Claim 2 – The combination of Lin in view of Yoon teaches the computing system of claim 1.
Lin further discloses, wherein the product type is identified via the product profile for the product (¶ [0090] “The online system 140 may identify a relevant item category for the item and each item in the list of relevant items and stores the relevant item category in association with the item and each item in the list of relevant items in the item catalog.”).
Claim 3 – The combination of Lin in view of Yoon teaches the computing system of claim 1.
Lin does not disclose limitation associated with identifying candidate attributes, however Yoon further teaches wherein the candidate attributes are identified by extracting the candidate attributes using a named entity recognition model, a generative model, or a combination thereof (Yoon: Col 5, ll. 25-30 “hidden Markov model’).
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 generative model as taught by Yoon in the system of Lin in order to advantageously determine which attribute is a key attribute of interest to the user (Yoon: Col 3, ll.45-50).
Claim 4 – The combination of Lin in view of Yoon teaches the computing system of claim 1.
Lin does not disclose relevance scores associated with candidate attributes, however Yoon further teaches: further comprising selecting the at least the portion of the candidate attributes, from among the candidate attributes, based on relevance scores associated with the candidate attributes (Yoon: Col 10, ll. 45-50).
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 candidate attributes as taught by Yoon in the system of Lin in order to advantageously determine which attribute is a key attribute of interest to the user (Yoon: Col 3, ll.45-50).
Claim 5 – The combination of Lin in view of Yoon teaches the computing system of claim 1.
Lin further discloses, wherein the model prompt further includes relevance scores associated with the at least the portion of the ... attributes (¶ [0074]). Lin does not disclose candidate attributes, however Yoon teaches candidate attributes as described in the rejections of claim 1 and 4.
Claim 6 – The combination of Lin in view of Yoon teaches the computing system of claim 1.
Lin further discloses, wherein the large language model is fine-tuned using a dataset including product type and attribute pairs (Lin ¶ [0074] “In one or more implementations, the item attribute module 225 may finetune the machine learned model using a plurality of training examples. Each training example may include a training prompt including at least instructions (as described above) and an item description of an item with known attributes”).
Claim 7 – The combination of Lin in view of Yoon teaches the computing system of claim 1.
Lin further discloses, wherein the product profile includes a set of attributes and one or more of a product title, a product description, and the product type (¶ [0085]).
Claim 8 – The combination of Lin in view of Yoon teaches the computing system of claim 1.
Lin further discloses, receiving a selection to include the new attribute in the product profile (¶ [0091] “ receives, from the user, modifications to the list of attributes and the respective values associated with the item”); and
based on the selection, updating the product profile to include the new attribute (¶ [0091] “the online system 140 stores the modified list of attributes and values”).
Claim 9 – The combination of Lin in view of Yoon teaches the computing system of claim 1.
Lin further discloses, implementing the updated product profile in an e-commerce system (¶ [0072] “for a given item, based on its corresponding category, a list of default attributes may be used for attribute extraction, and the item attribute module 225 may update the list of attributes as the item attribute module 225 receives the usage pattern and new item information”);
obtaining consumer feedback associated with the updated product profile or the product (Lin ¶ [0074] “For example, positive feedback may be obtained when a user is presented with a list of relevant items that were filtered based on extracted attributes, and for which the conversion rate was above a threshold”); and
refining the large language model based on the consumer feedback (Lin ¶ [0074] “For example, the item attribute module 225 may obtain prompts that identify attributes correctly as positive feedback to finetune parameters of the machine learned model. For example, positive feedback may be obtained when a user is presented with a list of relevant items that were filtered based on extracted attributes, and for which the conversion rate was above a threshold”).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Yoon and further in view of Siddiqui et al. (US 2018/0276726 A1).
Claim 10 – The combination of Lin in view of Yoon teaches the computing system of claim 1.
The combination of Lin in view of Yoon does not teach limitations associated with generating a quality indicator associated with a product profile. However, Siddiqui – which like Lin is directed to updating product information in an electronic catalog – teaches further comprising using the attribute recommendation to generate a quality indicator associated with the product profile (Siddiqui ¶ [0056] “Method 400 also can comprise an activity 415 of determining if the accuracy score for the one or more product attributes of the existing product information of each product of the plurality of products exceeds a predetermined accuracy threshold or is below the predetermined accuracy threshold”).
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 quality indicator as taught by Siddiqui in the system of Lin and Yoon in order to help customers/users of a website of an online retailer see better quality product information (Siddiqui ¶ [0060]).
Claim 11 – The combination of Lin in view of Yoon, and further in view of Siddiqui teaches the computing system of claim 10.
The combination of Lin in view of Yoon does not teach limitations associated with generating a quality indicator associated with a product profile. However, Siddiqui further teaches: wherein the quality indicator is generated using a rule-based analysis (Siddiqui ¶ [0056] “More particularly, activity, 415 can comprise marking a product attribute of the existing product information as correct for scores a<X<1, marking a product attribute of the existing product information for further review for scores b<X<a”).
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 quality indicator as taught by Siddiqui in the system of Lin and Yoon in order to help customers/users of a website of an online retailer see better quality product information (Siddiqui ¶ [0060]).
Claims 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Yoon, and further in view of Kopru et al. (US 2022/0327550 A1).
Claim 13 – Lin discloses a computer-implemented method comprising:
generating, via a prompt generator, a model prompt to be input into a large language model (¶ [0086] “ the online system 140 generates a prompt for input to a machine-learned language model”), the prompt generator including a product profile having a set of attributes associated with a product (¶ [0086] “the prompt specifying at least the item description and a request to identify one or more attributes of the item”)...;
obtaining, as output from the large language model, an attribute recommendation that recommends a new attribute, that is different from the set of attributes, to include in the product profile (¶ [0088] “At step 340, the online system 140 receives, from the machine-learned language model, an output including a list of attributes and respective values associated with the item based on the item description”);
... causing display, via a profile recommendation provider, of the attribute recommendation that recommends the new attribute (¶ [0091] “In some embodiments, the online system 140 provides the item, the list of attributes and the respective values associated with the item to a user”) ... .
Lin does not disclose limitations associated with identifying candidate attributes. However Yoon – which is also directed to recommending item attributes – teaches:
the prompt generator including ... a set of candidate attributes identified using catalog content and consumer content (Yoon: Col 7, ll. 15-20 “The attribute determination engine 110 may access the item descriptions associated with a particular category. The engine 110 may then identify which of the candidate keywords are to be associated with each item associated with the particular category.”; Col 7, ll. 40-45 “For example, the attribute determination engine 110 may access browsing sessions of users of the electronic catalog 112”; Col 15, ll. 25-35 “ In some embodiments, the system may use machine learning techniques. ... For example, a k-nearest neighbor (KNN) technique may be used to cluster attributes into respective classes of attributes. Thus, the system may cluster sizes of t-shirts, as identified based on user browsing sessions, into a same class of attribute.”);
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 candidate attributes as taught by Yoon in the system of Lin in order to advantageously determine which attribute is a key attribute of interest to the user (Yoon: Col 3, ll.45-50).
The combination of Lin in view of Yoon does not teach limitations associated with determining a quality indicator of the set of attributes associated with the product profile. However, Kopru – which is also directed to analyzing item listings – further teaches:
generating, via a quality manager, a quality indicator indicating quality of the set of attributes associated with the product profile (Kopru ¶ [0033] “ The plurality of inconsistent attributes of an item listing can be compared to inconsistent attributes of a fraudulent item listing motif. A determination (e.g., a machine learning engine prediction) can be made that an item listing is a potential fraudulent item listing based on a threshold similarity (e.g., a similarity score) between the inconsistent attributes of the item and the set of inconsistent attributes of a fraudulent item listing motif”); and
causing display of the quality indicator indicating quality of the set of attributes associated with the product profile (Kopru ¶ [0033] “As such, an indication of a potential fraudulent item listing is generated for the listing”).
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 quality indicator as taught by Kopru in the method of Lin in view of Yoon because conventional system are limited in their capacity to support a framework for detecting inconsistencies between item features that are provided and actual item features of items (Kopru ¶ [0002]).
Claim 14 – The combination of Lin in view of Yoon, and further in view of Kopru teaches the computer-implemented method of claim 13.
The combination of Lin in view of Yoon does not teach limitations associated with a wherein the quality indicator is generated using a model-based text evaluation for entropy that compares the new attribute with the set of attributes existing in the product profile (Kopru ¶ [0038]).
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 quality indicator as taught by Kopru in the method of Lin in view of Yoon because conventional system are limited in their capacity to support a framework for detecting inconsistencies between item features that are provided and actual item features of items (Kopru ¶ [0002]).
Claim 15 – The combination of Lin in view of Yoon, and further in view of Kopru teaches the computer-implemented method of claim 13.
Lin further discloses, wherein the model prompt further includes at least one of:
an instruction to generate the new attribute given the product profile (¶ [0086] “the prompt specifying at least the item description and a request to identify one or more attributes of the item”).
Lin does not disclose limitations associated with a set of candidate attributes. However, Yoon further teaches:
the model prompt including the set of candidate attributes (Yoon: Col 15, ll. 25-35 “ In some embodiments, the system may use machine learning techniques. ... For example, a k-nearest neighbor (KNN) technique may be used to cluster attributes into respective classes of attributes. Thus, the system may cluster sizes of t-shirts, as identified based on user browsing sessions, into a same class of attribute.”); and
a set of relevance cores associated with the set of candidate attributes (Yoon: Col 10, ll. 45-50).
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 candidate attributes as taught by Yoon in the method of Lin in order to advantageously determine which attribute is a key attribute of interest to the user (Yoon: Col 3, ll.45-50).
Claim 16 – The combination of Lin in view of Yoon, and further in view of Kopru teaches the computer-implemented method of claim 13. Lin does not disclose limitations associated with identifying a set of candidate attributes, however Yoon further teaches wherein the set of candidate attributes included in the model prompt are identified by:
extracting candidate attributes from the catalog content and the consumer content (Yoon: Col 7, ll. 15-20 “The attribute determination engine 110 may access the item descriptions associated with a particular category. The engine 110 may then identify which of the candidate keywords are to be associated with each item associated with the particular category.”; Col 7, ll. 30-50 “For example, the attribute determination engine 110 may measure a frequency with which any two items mapped to different candidate keywords have been interacted with by a same set of users.”);
generating relevance scores for each of the candidate attributes (Yoon: Col 10, ll. 45-50); and
selecting the set of candidate attributes, from the extracted candidate attributes, based on the relevance scores (Yoon: Col 10, ll. 60-65 “A threshold number of the attributes with highest attribute relevance scores may be assigned as key attributes.”).
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 candidate attributes as taught by Yoon in the method of Lin in order to advantageously determine which attribute is a key attribute of interest to the user (Yoon: Col 3, ll.45-50).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Brown et al. (US 2024/0037497 A1) is related to identifying and remediating changes in item listing data in online retail environments to improve system efficiency and optimize consumer-centric data.
Carpenter et al. (US 2023/0368271 A1) generally relates to identifying and presenting an indication of low-quality signals of an electronic listing to a user via a graphical user interface.
Thirumalai et al. (US 7,881,974 B1) relates to the management of descriptions of items based in part on a rating of cumulative quality of the descriptions from each submitting merchant.
Nagar et al. (US 2024/0070724 A1) relates to a method and system for adding attributes to an item being offered.
Weimann, Kuba, and Tim OF Conrad (NPL Reference U) presents a solution for the cold-start problem when new items are added to the catalog using a large embedding network.
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
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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