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 .
1. Claims 1-20 filed 02/25/2025 are pending for examination.
2. Continuity: This application filed 02/25/2025 is a Continuation of 18159357 , filed 01/25/2023 ,now U.S. Patent # 12266006.
Double Patenting
3. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12266006, hereinafter Patent ‘006. Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations of claims 1-20 of the instant Application are anticipated by the claims 1-20 of the Patent’ 006. For example, a comparison of claim 1of the Patent’006 and claim 1 of the instant application is given below, wherein the all the highlighted limitations of claim 1 of the instant application are disclosed and covered by the underlined limitations in claim 1 of the Patent’996:
Claim 1 of the instant application:
1. A method comprising, at a computer system comprising a processor and a computer-readable medium:
receiving a query from a user device corresponding to a user of an online system, wherein the query includes free text;
generating a query embedding for the query by applying a query embedding model to the free text of the query, wherein the query embedding model is a machine- learning model that is trained to generate query embeddings based on free text from queries;
accessing a set of candidate items;
computing a personalization score for each candidate item of the set of candidate items, wherein computing the personalization score for a candidate item comprises:
accessing a user embedding associated with the user, where the user embedding is stored by the online system, and wherein the user embedding is generated by a user embedding model that is trained to generate user embeddings based on user data;
accessing an item embedding for the candidate item stored by the online system, wherein the item embedding is generated by a first item embedding model that is trained to generate item embeddings based on item data; and
computing the personalization score for the candidate item based on the user embedding and the item embedding;
computing a query specificity score for the query, wherein the query specificity score is computed by computing an entropy score for the query, wherein the entropy score represents an uncertainty in outcomes for the query;
adjusting the personalization score for each candidate item of the set of candidate items based on the query specificity score; computing a ranking score for each candidate item of the set of candidate items based on the adjusted personalization score for each candidate item;
ranking the candidate items based on the ranking scores; and transmitting the set of candidate items for display on the user device based on the ranking.
Claim 1 of the Patent’006:
1. A method comprising, at a computer system comprising a processor and a computer-readable medium:
receiving a query from a user device corresponding to a user of an online concierge system, wherein the query includes free text;
generating a query embedding for the query by applying a query embedding model to the free text of the query, wherein the query embedding model is a machine-learning model that is trained to generate query embeddings based on free text from queries;
accessing a set of candidate items;
computing a relevance score for each candidate item of the set of candidate items, wherein computing a relevance score for a candidate item comprises:
accessing an item embedding for the candidate item stored by the online concierge system, wherein the item embedding is generated by a first item embedding model that is trained to generate item embeddings based on item data; and
computing the relevance score for the candidate item based on the query embedding and the item embedding;
computing a personalization score for each candidate item of the set of candidate items, wherein computing the personalization score for a candidate item comprises:
accessing a user embedding associated with the user, where the user embedding is stored by the online concierge system, and wherein the user embedding is generated by a user embedding model that is trained to generate user embeddings based on user data;
accessing an item embedding for the candidate item stored by the online concierge system, wherein the item embedding is generated by a second item embedding model that is trained to generate item embeddings based on item data; and
computing the personalization score for the candidate item based on the user embedding and the item embedding;
computing a query specificity score for the query, wherein the query specificity score is computed based on an entropy score for the query, wherein the entropy score describes historical user interactions with items in search results and represents, for a set of possible interaction outcomes, an uncertainty in which interaction outcome may result from the query, wherein an interaction outcome represents a user interaction with an item presented in search results for the query;
adjusting the personalization score for each candidate item of the set of candidate items based on the query specificity score;
computing a ranking score for each candidate item of the set of candidate items, wherein the ranking score for a candidate item is computed based on the relevance score for each candidate item and the adjusted personalization score for each candidate item;
ranking the candidate items based on the ranking scores; and
transmitting the set of candidate items for display on the user device based on the ranking.
Examiner has reviewed and compared the dependent claims 2-10 of claim1 with the dependent claims 2-10 of the Patent’006. Limitations of the dependent claims 2,3,4,5,6,7,8,9,and 10 of the instant application are taught and covered in the limitations of the claims 1, 8, 1, 1, 3, 6, 9, 2, and 10 of the Patent’0006 respectively.
Since the limitations of claims 11-19, and 20 of the instant application are similar to the limitations of the claims 1-9 and 1 respectively of the instant application, they are analyzed on the same basis as claims 1-9 and rejected on the ground of nonstatutory double patenting.
Claim Rejections - 35 USC § 101
4. 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 an abstract idea without significantly more, when analyzed as per MPEP 2106.
Step 1 analysis:
Claims 1-10 are to a process comprising a series of steps, clams 11-19 to manufacture, and claim 20 to system /apparatus, which are statutory (Step 1: Yes).
Step 2A Analysis:
Claim 1 recites:
1. A method comprising, at a computer system comprising a processor and a computer-readable medium:
(i) receiving a query from a user device corresponding to a user of an online system, wherein the query includes free text;
(ii) generating a query embedding for the query by applying a query embedding model to the free text of the query, wherein the query embedding model is a machine- learning model that is trained to generate query embeddings based on free text from queries;
(iii) accessing a set of candidate items;
(iv) computing a personalization score for each candidate item of the set of candidate items, wherein computing the personalization score for a candidate item comprises:
(v) accessing a user embedding associated with the user, where the user embedding is stored by the online system, and wherein the user embedding is generated by a user embedding model that is trained to generate user embeddings based on user data;
(vi) accessing an item embedding for the candidate item stored by the online system, wherein the item embedding is generated by a first item embedding model that is trained to generate item embeddings based on item data; and
(vii) computing the personalization score for the candidate item based on the user embedding and the item embedding;
(viii) computing a query specificity score for the query, wherein the query specificity score is computed by computing an entropy score for the query, wherein the entropy score represents an uncertainty in outcomes for the query;
(ix) adjusting the personalization score for each candidate item of the set of candidate items based on the query specificity score;
(x) computing a ranking score for each candidate item of the set of candidate items based on the adjusted personalization score for each candidate item;
(xi) ranking the candidate items based on the ranking scores; and
(xii) transmitting the set of candidate items for display on the user device based on the ranking.
Step 2A Prong 1 analysis: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claims 1-20 recite abstract idea.
The limitations in italics of claim 1 comprising, “ a method comprising, generating a query embedding for the query by applying a query embedding model to the free text of the query, computing a personalization score for each candidate item of the set of candidate items, wherein computing the personalization score for a candidate item comprises: wherein the user embedding is generated by a user embedding model that is trained to generate user embeddings based on user data; wherein the item embedding is generated by a first item embedding model that is trained to generate item embeddings based on item data; and computing the personalization score for the candidate item based on the user embedding and the item embedding; computing a query specificity score for the query, wherein the query specificity score is computed by computing an entropy score for the query, wherein the entropy score represents an uncertainty in outcomes for the query; adjusting the personalization score for each candidate item of the set of candidate items based on the query specificity score; computing a ranking score for each candidate item of the set of candidate items based on the adjusted personalization score for each candidate item; and ranking the candidate items based on the ranking scores”, under their broadest reasonable interpretation, relate to mathematical concepts and Mental Processes. The steps of generating embeddings, training the embedding model , using the embedding for computing personalization scores , computing query specific scores by computing entropy score to measure uncertainty, adjusting the personalization score using the calculated query specific scores, calculating ranking scores based on adjusted personalization scores and ranking the items based on the ranking scores comprise using mathematical and statistical concepts and performing calculations manually. That is, other than reciting “by a generic computer” and applying “machine learning” nothing in the claim elements precludes the step from practically being performed manually using a pen and paper using mathematical concepts and formulas. The mere nominal recitation of by a computer and applying machine learning model does not take the claim limitations out of the Mathematical Concepts and Mental Processes groupings. Thus, the claim 1 and its dependent claims 2-10 recites Mathematical concepts and mental process.
If a claim that includes two or more abstract ideas groupings per Step 2A, Prong One , as per MPEP 2106.04, subsection IIB, under such circumstances, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Since the limitations of claim 1 fall within the mathematical concepts and mental process groupings of abstract ideas, they are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES)
Since the other two independent claims 11-19 and 20 recite similar limitations as claims 1-9, they are analyzed on the same basis as claim 1 reciting Mathematical Concepts and Mental Processes groupings.
Thus, claims 1-20 recite an abstract idea.
Step 2A Prong 2 analysis: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claims 1-20: The judicial exception is not integrated into a practical application.
Claim 1 recites the additional limitations of using generic computer components comprising a generic computer/processor performing the following steps:
(i) receiving a query from a user device corresponding to a user of an online system, wherein the query includes free text;
(ii) generating a query embedding for the query by applying a query embedding model to the free text of the query, wherein the query embedding model is a machine- learning model that is trained to generate query embeddings based on free text from queries;
(iii) accessing a set of candidate items;
(iv) computing a personalization score for each candidate item of the set of candidate items, wherein computing the personalization score for a candidate item comprises:
(v) accessing a user embedding associated with the user, where the user embedding is stored by the online system, and wherein the user embedding is generated by a user embedding model that is trained to generate user embeddings based on user data;
(vi) accessing an item embedding for the candidate item stored by the online system, wherein the item embedding is generated by a first item embedding model that is trained to generate item embeddings based on item data; and
(vii) computing the personalization score for the candidate item based on the user embedding and the item embedding;
(viii) computing a query specificity score for the query, wherein the query specificity score is computed by computing an entropy score for the query, wherein the entropy score represents an uncertainty in outcomes for the query;
(ix) adjusting the personalization score for each candidate item of the set of candidate items based on the query specificity score;
(x) computing a ranking score for each candidate item of the set of candidate items based on the adjusted personalization score for each candidate item;
(xi) ranking the candidate items based on the ranking scores; and
(xii) transmitting the set of candidate items for display on the user device based on the ranking.
The limitations in steps (i), (iii), (v, (vi), and (xii) “(i) receiving a query from a user device corresponding to a user of an online system, wherein the query includes free text; (iii) accessing a set of candidate items; (v) accessing a user embedding associated with the user, where the user embedding is stored by the online system, (vi) accessing an item embedding for the candidate item stored by the online system, (xii) transmitting the set of candidate items for display on the user device based on the ranking.”, are mere data gathering, data collecting [accessing] and outputting/transmitting data recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering, accessing and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering/accessing and outputting/transmitting. See MPEP 2106.05. Further, these limitations are recited as being performed by a computer. The computer is recited at a high level of generality and is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f).
The limitations in steps (ii), (iv), (v), (vi), (vii), (viii), (ix), (x), and (xi), “ (ii) generating a query embedding for the query by applying a query embedding model to the free text of the query, wherein the query embedding model is a machine- learning model that is trained to generate query embeddings based on free text from queries; (iv) computing a personalization score for each candidate item of the set of candidate items, wherein computing the personalization score for a candidate item comprises: (v wherein the user embedding is generated by a user embedding model that is trained to generate user embeddings based on user data; (vi) wherein the item embedding is generated by a first item embedding model that is trained to generate item embeddings based on item data; and (vii) computing the personalization score for the candidate item based on the user embedding and the item embedding; (viii) computing a query specificity score for the query, wherein the query specificity score is computed by computing an entropy score for the query, wherein the entropy score represents an uncertainty in outcomes for the query; (ix) adjusting the personalization score for each candidate item of the set of candidate items based on the query specificity score; (x) computing a ranking score for each candidate item of the set of candidate items based on the adjusted personalization score for each candidate item; (xi) ranking the candidate items based on the ranking scores; “ , the computer is used to perform an abstract idea, and as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
In the limitations in step “(ii) generating a query embedding for the query by applying a query embedding model to the free text of the query, wherein the query embedding model is a machine- learning model that is trained to generate query embeddings based on free text from queries the language, “ using a trained machine learning model” to provide to generate query embeddings based on free text from queries provide nothing more than mere instructions to implement an abstract idea of implementing a mathematical concept of generating query embedding from the free text of a query on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process, which, here it is a mathematical process ; and (3) the particularity or generality of the application of the judicial exception.
The judicial exception of “generating query embedding from the free text of a query is performed “using a trained machine learning model.” The trained machine learning model is used to generally apply the abstract idea of using a mathematical concept without placing any limits on how the trained machine learning model functions. Rather, these limitations only recite the outcome of “generating query embeddings ” and do not include any details about how the “detecting” and “analyzing” are accomplished. See MPEP 2106.05(f). The recitation of “using a trained machine learning model” in step (ii) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained machine learning model” limits the identified judicial exceptions “generating query embeddings”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly, even when considered individually and in combination, these additional elements of claim 1 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim 1 is directed to an abstract idea. Since the limitations of the other two independent claims 11 and 20 recite similar limitations as claim 1, they are analyzed on the same basis as directed to an abstract idea.
Dependent claims 2-10 recite limitations merely extending the scope of the limitations of claim 1 directed to non-functional descriptive data qualifying the limitations recited in claim 1 reciting mathematical concepts , mental processes or non-significant extra-solution activity. Claim 2 merely explains computing the entropy score, claim 3 recites computing probability, claims 4-8 recite computing relevance score using item embeddings , the user historical data on interactions, normalizing the relevance score, and computing a linear combination of the relevance score , which all relate to perfuming mathematical calculations and the same can be done by humans using mathematical calculations on a pen and paper. The limitations of claim 9 recites filtering set of items based on item availability, which is a mental process. Claim 10 recites a non-significant extra-solution activity of transmitting data related to higher ranked items. Thus, all the limitations of dependent claims do not add any meaningful limits on practicing the abstract idea and are directed to an abstract idea similar to the limitations of claim 1. Since the limitations of claims 12-19 recite limitations similar to the limitations of claims 2-9, they are analyzed on the same basis as directed to an abstract idea.
Even when viewed individually and in combination, the additional elements in claims 1-20 do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Step 2A=Yes. Claims 1-20 are directed to abstract ideas.
Step 2B analysis: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
Since claims are as per Step 2A are directed to an abstract idea, they have to be analyzed per Step 2B, if they recite an inventive step, i.e., the claim recite additional elements or a combination of elements that amount to “Significantly More” than the judicial exception in the claim. As discussed above with respect to Step 2A Prong Two, the additional elements in the claims 1-20 amount to no more than mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B, i.e., mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use using a generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements comprising receiving data, transmitting data and accessing/gathering data were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering/transmitting/outputting/displaying/presenting/storing data . However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). ). The background of the example does not provide any indication that the computer components recited in the claims 1-20 are anything other than a generic, off the shelf computer component and the Symantec, TLI, OIP Techs, Versata court decisions cited in MPEP 2106.05(d) (ii) indicate that mere data gathering/ transmitting/ outputting/displaying/presenting/ data steps using a generic computer are well-understood, routine, conventional function when they are claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the receiving data, acquiring/accessing data, and transmitting/displaying data are well-understood, routine conventional activities are supported under Berkheimer Option 2. See MPEP 2106.05 (f) 2: Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. 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).
Even when considered individually and in combination, the additional elements in claims 1-20 represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Thus, claims 1-20, as recited, are not patent eligible.
5. Prior art discussion:
Reference independent claims 1, 11, and 20, the prior art record, alone or combined, neither teaches nor renders obvious at least the limitations, as a whole, comprising a processor receiving a query including a free text from a user device corresponding to a user of an online system, generating a query embedding for the query by applying a query embedding model to the free text of the query, wherein the query embedding model is a machine-learning model that is trained to generate query embeddings based on free text from queries; accessing a set of candidate items; computing a personalization score for each candidate item of the set of candidate items by accessing a user embedding associated with the user, where the user embedding is stored by the online system, and wherein the user embedding is generated by a user embedding model that is trained to generate user embeddings based on user data, accessing an item embedding for the candidate item stored by the online system, wherein the item embedding is generated by a first item embedding model that is trained to generate item embeddings based on item data, and computing the personalization score for the candidate item based on the user embedding and the item embedding, computing a query specificity score for the query by computing an entropy score based on an entropy score for the query, wherein the entropy score represents an uncertainty in outcomes for the query, adjusting the personalization score for each candidate item of the set of candidate items based on the query specificity score, computing a ranking score for each candidate item of the set of candidate items based on the relevance score for each candidate item and the adjusted personalization score for each candidate item, ranking the candidate items based on the ranking scores, and transmitting the set of candidate items for display on the user device based on the ranking Claims 2-10 depend from clam 1, claims 12-19 depend from claim 11.
Discussion of the most relevant prior art:
The following references have been identified as the most relevant prior art to the claimed invention. Though some of the references do teach some of the elements individually, but none of these references alone or combined with one or more references teach the limitations of claims 1, 11 and 20 as a whole, described above.
NPL reference:
(i) Hafed Zarzour, Ziad Al-Sharif, Y. Jararweh; “RecDNNing: a recommender system using deep neural network with user and item embeddings”; Published in International Conference on 11 June 2019, Computer Science; retrieved from IP.COM on 06/17/2026 describes [See Abstract] a novel approach called RecDNNing with a combination of embedded users and items combined with deep neural network comprising creating a dens numeric representation for each user and item including user embedding and item embedding, respectively, wherein the items and users embedding are averaged and then concatenated before being fed into the deep neural network. The concatenated users and items embedding are input in a model of the deep neural network to predict the scores of rating by applying forward propagation algorithm. The article states that the experimental results show that the proposed RecDNNing outperforms state-of-the-art algorithms.
(ii) Hsieh et al. [US 20250021792 A1, see Abstract] describes system and method for an item such as a content-centric personalized recommendation engine that includes inputting user personal data as input to a user neural network model and yielding a user embedding, processing the user embedding through a trained matchmaking neural model to map user embeddings and item/content embeddings to a shared dimensional space, and yielding a user shared-item embedding, and selecting at least one content item associated with a content shared-item embedding within the matchmaking neural network.
(iii) Mosenia et al. [ US 12468740 B2; see col. 5, Lines 40-63, describes a category recommendation system along with as search engine having access to user embeddings , item embeddings and query embeddings. In order to enable the category recommendation system 130, the search engine 120 has access to stored user embeddings 132, item embeddings 134, and query embeddings 136, wherein the user embeddings 132 are generated using a supervised learning engine, the item embeddings 134 generated from item data/item interaction data (item descriptors and/or identifiers, history of interaction by a user or users using a supervised learning engine and each of the query embeddings 136 generated from respective query data (representation of text, image, audio, video, tactile, etc. information provided as a query or in response to a query provided to a search engine).
(iv) Pendse et al. [US 12169512 B2; see claim 1] describes a computer device obtaining search results for items from a server, the computer device determining a relevance value for each of the set of search results items based on information associated with user behavior, and then generating a personalization score for each item of the set of search results items based on a corresponding relevance value for the particular search results item and modifying the set of search results items to generate a personalized set of search results items based on the personalization score for the first search results item; for presentation to the user computing device.
(v) Gunaselara et al. [US 2021/0365500 A1 cited in the IDS file 02/04/2026 and in the parent application 18159357, now US Patent# 12266006;; see para 0104] describes processing user data through the UNN [user neural network] and at least the matchmaking neural network (e.g., the CML-Collaborative Metric Learning Model model) in generating a user shared-item embedding, calculating a personalization score between the user and context keywords based on a spatial analysis of the user shared-item embedding and other shared-item and content may be mapped and then context keywords identified in content segments may be used to calculated personalization scores to different context keywords for a user. Gunaselara reference may teach one or two steps recited in claims 1, 11, and 20 but it alone or combined does not teach or render obvious all the elements in combination, as a whole, as discussed above.
Foreign reference:
(vi) CN 106663114 B describes calculating an improving factor for a preferred content item based on the other relevance score for the ranking based on the proposed content item, wherein the relevance score of the preferred content item is corresponding to the location of the preferred content item in the ranking, and the other relevance score of the proposed content item corresponds to the location of the proposed content item in the ranking.
6. Allowability Note: If the independent claims 1, 11, and 20 are amended to overcome 35 USC 101 rejection, and a proper Terminal Disclaimer is filed to overcome the ground of nonstatutory double patenting, the claims can be placed in condition for allowance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOGESH C GARG whose telephone number is (571)272-6756. The examiner can normally be reached Max-Flex.
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/YOGESH C GARG/Primary Examiner, Art Unit 3688
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