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/Restrictions Applicant’s election without traverse of Group I (claims 1-10) in the reply filed on 02/17/2026 is acknowledged. 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- 1 0 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (a judicial exception without significantly more). Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l , 573 U.S. 208 (2014). Claims 1- 1 0, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 recites a system. Step 2A, prong 1: Claim 1 , taken as representative, recites the abstract idea of providing review-based recommendations . This idea is described by the following steps: accessing review data associated with a first user for a first item in an item listing system; based on the review data, identifying a review-based recommendation guide feature for the first item, wherein the review-based recommendation guide feature is associated with a review-based recommendation guide that identifies user preferences for item features of a corresponding item; mapping the review-based recommendation guide feature of the first item to a review-based recommendation guide feature of a second item, wherein the review-based recommendation guide feature of the second item is associated with a review-based recommendation guide of one or more second users; communicating the second item as a review-based recommended item associated with the review data. The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that they recite item recommendation, i.e., commercial interactions. Additionally , the above recited limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ides, enumerated in MPEP 2106.04(a)(2)(III), in that they recite concepts performed in the human mind. The BRI of these limitations includes a human mentally, or by use of pen and paper, collecting information, mapping features , analyzing information. Step 2A, prong 2: Claim 1 recite s additional elements that fail to integrate the abstract idea into practical application. Claim 1 recite s one or more processors; computer memory storing instructions that are executable by the one or more processors to cause the one or more processors to perform operations. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. These additional computer-related elements merely invoke such additional elements as tools to perform the abstract idea. See MPEP 2106.05(f). Claim 1 additionally recite s using a generative artificial intelligence (AI) model . However, the machine-learned models are recited at a high level of generality and are merely used as tools to perform the process (i.e., generating review-based recommendation guides ) (see MPEP 2106.05(f)). Step 2B: Claim 1 fail s to recite additional elements that amount to an inventive concept. For the reasons identified with respect to Step 2A, prong 2, claim 1 fail s to recite additional elements that amount to an inventive concept. For example, 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 MPEP 2106.05(g)). Even when considered as an ordered combination, the additional elements of claim 1 do es not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claim 1 that transform s the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself. See MPEP 2106.05. Dependent Claims Step 2A : The limitations of the dependent claims merely set forth further refinements of the abstract idea identified at step 2A—Prong One, without changing the analysis already presented. Additionally, for the same reasons as above, the limitations fail to integrate the abstract idea into a practical application because they use the same general technological environment and instructions to implement the abstract idea as the independent claims identified at step 2A—Prong Two. Dependent Claims Step 2B : The dependent claims merely use the same general technological environment and instructions to implement the abstract idea. These do not amount to significantly more for the same reasons they fail to integrate the abstract idea into a practical application. Moreover, the Specification also indicates this is the routine use of known components for the same reasons presented with respect to the elements in the independent claims above. Thus, when considering the combination of elements and the claimed invention as a whole, the claims are not patent eligible. Claim Rejections - 35 USC § 103 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 (i.e., changing from AIA to pre-AIA) 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 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. Claim s 1, 3-10 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng et al. (US 20220237386 ). Claim 1 . Cheng et al. (Cheng) teaches a system for providing aspect-aware sentiment analysis of user reviews, the system comprising: one or more computer processors; and computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations [0079] , the operations comprising: accessing review data associated with a first user for a first item in an item listing system [0020], [0030] ; based on the review data, identifying a review-based recommendation guide feature for the first item, wherein the review-based recommendation guide feature is associated with a review-based recommendation guide that identifies user preferences for item features of a corresponding item, wherein the review-based recommendation guides are generated using review data of users [0073], [0074] ; mapping the review-based recommendation guide feature of the first item to a review-based recommendation guide feature of a second item, wherein the review-based recommendation guide feature of the second item is associated with a review-based recommendation guide of one or more second users [0074] ; communicating the second item as a review-based recommended item associated with the review data [0030] . Cheng teaches multiple different models including a lexicon, neural network extraction, and pointwise mutual information extraction [0048]. However Cheng does not teach using a generative artificial intelligence (AI) model. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cheng to include using a generative artificial intelligence (AI) model , because it would advantageously offer significant advantages, including accelerated content creation, enhanced productivity, and the ability to automate complex, creative, and routine tasks ; as well as allow to analyz e large datasets to identify patterns , thereby enabling personalized customer experiences, improved decision-making, and innovative, human-like, multi-modal content generation (text, images, code) . Claim 3 . Cheng teaches said system wherein identifying the review-based recommendation guide feature for the first item is based on: using the review data, generating review-based recommendation data comprising user preferences for item features for the item; and generating the review-based recommendation guide for the user [0020] . Claim 4 . Cheng teaches said system , wherein mapping the review-based recommendation guide feature is based on review-based recommendation logic that indicates how items should be recommended to users based on user preference attributes and review-based recommendation guide features [0074] . Claim 5 . Cheng teaches said system , wherein mapping the review-based recommendation guide feature of the first item to a review-based recommendation guide feature of a second item is performed using review-based recommendation logic that compares the review-based recommendation guide of the first item to a plurality of review-data recommendation guides to match based on the review-based recommendation guide feature [0074] . Claim 6 . Cheng teaches said system , wherein the review-based recommendation guides are associated with a review-based recommendation guide data structure that supports storing review-based recommendation guide features, user preference attributes [0058 ] . Claim 7 . Cheng teaches said system , wherein the second item is associated with review-based insight comprising one or more excerpts of review data corresponding to the one or more second users [0022], [0024] . Claim 8 . Cheng teaches said system , the operations further comprising: accessing review data from a plurality of users for corresponding item associated with an item listing system; using the review data, generating review-based recommendation guide data comprising user preferences for item features associated with each item; generating a plurality of review-based recommendation guides for the users and the corresponding items; and deploying the plurality of review-based recommendation guides to support identifying review-based recommended items for users [0020], [0075] . Claim 9 . Cheng teaches said system , the operations further comprising: communicate review data associated with a user for a first item in an item listing system; based on communicating the review data, access a second item wherein the second item is a review-based recommended item; and cause display of the second item on a graphical user interface associated with the review data [0073], [0074] . Claim 10 . Cheng teaches said system , wherein the review-based insight comprising one or more excerpts of review data corresponding to the one or more second users [0022], [0024] . Claim Rejections - 35 USC § 103 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 (i.e., changing from AIA to pre-AIA) 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 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. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng et al. (US 20220237386) in view of Vashishtha et al. (US 20240257214 ) . Claim 2 . Cheng teaches all the limitations of claim 2 except providing near real-time review-based recommended items based on review data that is received via the review interface. Vashishtha et al. (Vashishtha) teaches a system for providing a review-based recommendations. As shown in FIG. 5 , based on the anchor item selected by the user, the website can provide a second carousel including personalized item recommendations to the user. T he second carousel includes additional bedsheet items that are similar to the selected bedsheet item, in terms of a feature aspect “soft.” For example, the system may have determined that the user is interested in soft bedsheets, based on: e.g. historical interactions with the website by the user , user reviews of the anchor item . As “soft” has been determined to reflect an intent of the user when the anchor item is selected, all the recommended items in the second carousel are soft bedsheets based on their product descriptions and/or user reviews [0086]. The tasks are performed in real-time [0047] , [0074] . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include providing near real-time review-based recommended items based on review data that is received via the review interface as taught by Vashishtha in the system of Cheng , since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. KSR, 127 S.Ct. at 1740, 82 USPQ2d at 1396. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20230196437 to Kumar et al. discloses a system and method for generating product recommendations. The system includes a data module configured to extract one or more information for one or more products, a first attribute extraction module configured to extract one or more product attributes of interest to the customer and a corresponding sentiment, a product identification module configured to identify one or more products similar to a product of interest, a second attribute extraction module configured to extract one or more similar product attributes and a corresponding sentiment, an attribute comparison module configured to compare the one or more product attributes of interest and the corresponding sentiment with the one or more similar product attributes and the corresponding sentiment, and identify a product for recommendation, and a product recommender configured to recommend to the customer the identified product . US 20220172229 to Chen discloses product opinion evaluation system. Referring to FIG. 4, a special feature point of a product can be defined as a product feature that may be considered by certain users to be positive or advantageous, and at the same time also be considered by certain other users as negative or disadvantageous. For example, a first user may, based on his or her inner opinion, leave on the product review website a positive review on a particular product that encompasses a first group of features P of the product, and a second user may, also based on his or her inner opinion, leave on the product review website a negative review on the particular product that encompasses a second group of features N of the product. In certain embodiments, the product review website recommends to a user and places advertisement on the page(s) thereon for at least one product in the user interest list through a keyword advertisement algorithm executed by the remote computing device . US 9607325 to Sriram discloses a behavior-based item review system. These systems and processes can dynamically collect data on a user's interest in specific item attributes from the user's interaction with an electronic catalog and can store this data in association with the user. This data may be used to provide users with a personalized set of reviews which will provide the user with information relevant to interesting item attributes. Further, this data may be used to display a set of dynamically generated statements and/or questions that prompt users to write an item review on specific item attributes. Accordingly, user attribute interest data can potentially both present and elicit more thorough and useful feedback than existing review systems in some embodiments. US 20180067935 to Kumar discloses system s configured to analy ze large number of reviews a nd create metadata - attributes which define particular media content. The system further uses attributes to recommend media items based on users view history. US 11023953 to Furlan et al. discloses a system and methods for implementing a recommendation engine. The recommendation engine can at least generate a segmentation identifying a customer group for a product, receive a customer review from a storage location, generate a customer review profile based on the customer review, match the customer review profile to the customer group based on comparing purchase factors, preference levels, or a combination thereof associated with the segmentation and purchase factors, preference levels, or a combination thereof associated with the customer review profile, and recommend the product, one or more features of the product, or a combination thereof to a further customer based on the matched customer review profile. 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