DETAILED ACTION
Status of the Application
The following is a Final Office Action. In response to Examiner's communication of October 20, 2025, Applicant, on February 20, 2026, amended claim 16. In view of Applicant’s election of claims 16-20, claims 1-15 are withdrawn from consideration, and claims 16-20 have been examined in this application and rejected below.
The present application is being examined under the pre-AIA first to invent provisions. 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 20, 2026 has been entered.
Response to Amendment
Applicant's amendments are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action. Therefore, these rejections are maintained below.
Applicant's amendments render moot the 35 USC 103 rejections set forth in the previous action. Therefore, new grounds for rejection necessitated by Applicant’s amendments are set forth below.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant argues that claim 16, as amended, recites a specific technological improvement to the functioning of a computer-implemented content-selection interface, in particular, the added limitation-"modifying, based on the score, a content preview gallery rendered via the display and refreshed responsive to score updates ... wherein a refreshed preview gallery defines a set of selectable content items available to the first creative for insertion into a project" - integrates the alleged abstract idea into a practical application, and therefore, Applicant respectfully submits that Claim 16, as a whole, integrates the alleged abstract idea into a practical application of the exception. Examiner respectfully disagrees.
Pursuant to 2019 Revised Patent Subject Matter Eligibility Guidance, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 56.
For the reasons detailed below in Prong 1 of Step 2A, aside from the recitations of “rendered via the display” and “online,” the claim limitations, including those referred to by Applicant, are abstract elements that are mental processes that can be performed mentally and/or with a pen and paper and/or are a certain method of organizing activity that manages human behavior and relationships of creators and marketing and sales activity of a marketplace. Furthermore, under Prong 2 of Step 2A and Step 2B, the recitations of “rendered via the display” and “online content marketplace” are nothing more than generic computer components applying the recited abstract idea, which is not sufficient to integrate an abstract idea into a practical application nor sufficient to amount to significantly more.
The MPEP makes clear “an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology” and that “[m]ere automation of manual processes” is not an improvement in computer technology. See MPEP 2106.05(a). As in the claims at issue in Electric Power Group, the present claims are not focused on a specific improvement in computers or any other technology, but instead on certain independently abstract ideas that simply invokes computers as tools to implement the abstract idea. Electric Power Group, LLC v. Alstom S.A., et al., No. 2015-1778, slip op. at 8 (Fed. Cir. Aug. 1, 2016); MPEP 2106.05(a).
Specifically, prong 1 of Step 2A, claim 16, and similarly claims 17-20, recites “[a] method for training a model to rank creative pairs of subscribers to an … content marketplace, comprising: selecting a first creative and a second creative from a subscriber list to the … content marketplace; forming a first sparse vector from a one or more attributes of the first creative and a second sparse vector from a one or more attributes of the second creative; convolving, by a first model, a one or more coordinates of the first sparse vector into a dense user vector having fewer dimensions than the first sparse vector; convolving, by a second model, a one or more coordinates of the second sparse vector into a dense contributor vector having a same dimension as the dense user vector, wherein the first model and the second model are distinct; finding a first distance between the dense user vector and the dense contributor vector; scoring a user-contributor pair based on the first distance and a distance between the dense user vector and a random dense contributor vector; increasing a score of the user-contributor pair for a content file from the second creative that is selected by the first creative; updating, based on the score, a position of the second creative in a list of creatives presented to the first creative … content marketplace; and modifying, based on the score, a content preview gallery … and refreshed responsive to score updates triggered by the first model and the second model, wherein a refreshed preview gallery defines a set of selectable content items available to the first creative for insertion into a project within the … content marketplace.” Claims 16-20, in view of the claim limitations, recite the abstract idea of selecting first and second creators subscribing to a content marketplace, forming a first and second vector of attributes of the first and second creator, convolving the vectors into dense vectors to reduce the dimensions of the vectors using a first and second model, calculating a distance between the dense vectors of a pair of content creators, scoring the pair of content creator and increasing the score when a first creator selects content of the second creator, and updating and modifying a position to present the second creator and content preview in the marketplace based on the score.
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited selecting first and second creators subscribing to a content marketplace, forming a first and second vector of attributes of the first and second creator, convolving the vectors into dense vectors to reduce the dimensions of the vectors using a first and second model, calculating a distance between the dense vectors of a pair of content creators, scoring the pair of content creator and increasing the score when a first creator selects content of the second creator, and updating and modifying a position to present the second creator and content preview in the marketplace based on the score could all be reasonably interpreted as a human observing and making selections of information regarding a first and second content creator, a human using judgment to select attributes regarding the creators to generate a first and second vector regarding the vectors, a human using judgment and performing an evaluation using models to convolve the attributes into dense vectors to reduce the dimensions of the vectors, a human performing an evaluation of the vectors to calculate a distance between vectors, score and increase the score of the pair of creators, and a human deciding and modifying a position to present the second creator and content preview based on the score manually and/or with a pen and paper; therefore, the claims, including the features referred to by Applicant, recite a mental processes. In addition, each of these limitations manage the relationships and human behavior and sales and marketing activity of content creators subscribing to a marketplace based on the human behavior represented by the attributes of content creators and what they create to market content to subscribers to the marketplace, including by presenting content creators and their content to the subscribers, and thus, the claims, including the features referred to by Applicant, recite a certain method of organizing human activity. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 17-20, recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the generic computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper. Accordingly, since the claims recite mental processes and a certain method of organizing human activity, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea “online,“ “via a display associated with the online content marketplace,” and “gallery rendered via the display” in claim 16, and similarly in claims 17-20; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components, which is not sufficient to integrate an abstract idea into a practical application. Further, these elements generally link the abstract idea to a field of use. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 17-20 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s specification at [0093]-[0096] (describing the invention can be implemented by one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or to control the operation of, the computer system 1300, which includes a general-purpose microprocessor or any other suitable entity that can perform calculations or other manipulations of information, according to any method well-known to those of skill in the art). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, electronic record keeping, storing and retrieving information in memory, and presenting offers, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 17-20 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea.
Response to Arguments - Prior Art
Applicant’s arguments with respect to the prior art rejections have been fully considered, but they are now moot in view of new grounds for rejection necessitated by Applicant’s amendments.
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 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under prong 1 of Step 2A, claim 16, and similarly claims 17-20, recites “[a] method for training a model to rank creative pairs of subscribers to an … content marketplace, comprising: selecting a first creative and a second creative from a subscriber list to the … content marketplace; forming a first sparse vector from a one or more attributes of the first creative and a second sparse vector from a one or more attributes of the second creative; convolving, by a first model, a one or more coordinates of the first sparse vector into a dense user vector having fewer dimensions than the first sparse vector; convolving, by a second model, a one or more coordinates of the second sparse vector into a dense contributor vector having a same dimension as the dense user vector, wherein the first model and the second model are distinct; finding a first distance between the dense user vector and the dense contributor vector; scoring a user-contributor pair based on the first distance and a distance between the dense user vector and a random dense contributor vector; increasing a score of the user-contributor pair for a content file from the second creative that is selected by the first creative; updating, based on the score, a position of the second creative in a list of creatives presented to the first creative … content marketplace; and modifying, based on the score, a content preview gallery … and refreshed responsive to score updates triggered by the first model and the second model, wherein a refreshed preview gallery defines a set of selectable content items available to the first creative for insertion into a project within the … content marketplace.” Claims 16-20, in view of the claim limitations, recite the abstract idea of selecting first and second creators subscribing to a content marketplace, forming a first and second vector of attributes of the first and second creator, convolving the vectors into dense vectors to reduce the dimensions of the vectors using a first and second model, calculating a distance between the dense vectors of a pair of content creators, scoring the pair of content creator and increasing the score when a first creator selects content of the second creator, and updating and modifying a position to present the second creator and content preview in the marketplace based on the score.
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited selecting first and second creators subscribing to a content marketplace, forming a first and second vector of attributes of the first and second creator, convolving the vectors into dense vectors to reduce the dimensions of the vectors using a first and second model, calculating a distance between the dense vectors of a pair of content creators, scoring the pair of content creator and increasing the score when a first creator selects content of the second creator, and updating and modifying a position to present the second creator and content preview in the marketplace based on the score could all be reasonably interpreted as a human observing and making selections of information regarding a first and second content creator, a human using judgment to select attributes regarding the creators to generate a first and second vector regarding the vectors, a human using judgment and performing an evaluation using models to convolve the attributes into dense vectors to reduce the dimensions of the vectors, a human performing an evaluation of the vectors to calculate a distance between vectors, score and increase the score of the pair of creators, and a human deciding and modifying a position to present the second creator and content preview based on the score manually and/or with a pen and paper; therefore, the claims recite a mental processes. In addition, each of these limitations manage the relationships and human behavior and sales and marketing activity of content creators subscribing to a marketplace based on the human behavior represented by the attributes of content creators and what they create to market content to subscribers to the marketplace, including by presenting content creators and their content to the subscribers, and thus, the claims recite a certain method of organizing human activity. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 17-20, recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the generic computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper. Accordingly, since the claims recite mental processes and a certain method of organizing human activity, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “online,“ “via a display associated with the online content marketplace,” and “gallery rendered via the display” in claim 16, and similarly in claims 17-20; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Further, these elements generally link the abstract idea to a field of use. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 17-20 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s specification at [0093]-[0096] (describing the invention can be implemented by one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or to control the operation of, the computer system 1300, which includes a general-purpose microprocessor or any other suitable entity that can perform calculations or other manipulations of information, according to any method well-known to those of skill in the art). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, electronic record keeping, storing and retrieving information in memory, and presenting offers, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 17-20 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 16-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 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.
Claims 16, 19, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gao, et al. (US 20170017886 A1), hereinafter Gao, in view of Liu, et al. (US 20230394084 A1), hereinafter Liu, in further view of Dodge (US 20140143333 A1), hereinafter Dodge.
Regarding claim 16, Gao discloses a method for training a model to rank creative pairs of subscribers to an online content marketplace, comprising (Abstract):
selecting a first creative and a second creative from a subscriber list to the online content marketplace ([0042]-[0043], the model training system 400 can start learning a model by taking in training data 510 of the first and second objects being analyzed at the processing stage 410 by the loader engine 420, wherein the training data can be associated with multiple first objects and multiple second objects, and an individual data set 510i may be associated with a different user than an individual data set 510 i+1 or an individual dataset 510 i+2, [0020], examples of objects include a user and a page of the social networking system, [0079], a user may form or join groups, become a fan of page within the social networking system, and a user may create, download, view, upload, link to, tag, edit, or play a social networking system object);
forming a first sparse vector from a one or more attributes of the first creative and a second sparse vector from a one or more attributes of the second creative ([0042], the training data 510 is prepared in a predefined format where the first three columns correspond to the sparse attribute vectors 500, 502 of the first and second objects being analyzed (e.g., {right arrow over (u)} and {right arrow over (v)}) and a label 504 associated with the two objects (e.g., label y));
convolving, by a … model, a one or more coordinates of the first sparse vector into a dense user vector having fewer dimensions than the first sparse vector;
convolving, by a … model, a one or more coordinates of the second sparse vector into a dense contributor vector ([0045], [0047]-[0048], at 412, the parser engine 430 parses the raw string into an appropriate data structure that represents the training data 510, and then at 416, the learning engine 450 performs operations on the individual data sets 510 i-N to update the parameter of the model, wherein the operations (performed by the learning engine 450) include executing a stochastic gradient descent (SGD) process that computes a gradient for the individual data sets 510 i-N by performing a dimensionality reduction on the individual data sets 510 i-N to generate a set of latent vectors 520 i-N for attributes derived from the individual data sets 510 i-N, [0018], the output of the multi-threaded pipeline architecture is a feature vector, which is a short array of real numbers and “dense representation” of the raw attributes of the first object, e.g., user, or the second object, wherein the raw attributes are transformed into the dense representation (feature vector) by the pipeline architecture, and in particular, the multi-threaded pipeline architecture can produce two sets of dense representations: one set for the first object's attributes and one set for the second object's attributes) having a same dimension as the dense user vector ([0048], performing dimension reduction on the individual data sets 510 i-N generates a set of latent vectors 520 i-N, and Examiner notes in fig. 5 depicted below, the dense/latent vectors 520-1 and 520-N for each object have the same number of attributes,
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[0018], wherein a simple aggregation of the attributes (e.g., average) for the first object (e.g., user) can further be computed to obtain the dense representation of the first object; similarly, the aggregation of attributes for the second object can be computed to obtain the dense representation of the second object) …;
finding a first distance between the dense user vector and the dense contributor vector ([0048], the gradient can be computed by generating a set of latent vectors 520 i-N for attributes, and the learning engine 450 then utilizes the gradient to update the model parameters stored in the same data structure, a weight matrix 530 of FIG. 5 (i.e., matrix w), wherein updating the model parameters involves computing a compatibility score, where such computation involves a vector product between the two new transformed latent vectors corresponding to the pair of objects 500 and 502, [0066], the strength of each relationship between a pair of objects u.sub.1 and v.sub.1 can be determined based on the distance between those objects, where that distance is represented by a weight value associated with that relationship);
scoring a user-contributor pair based on the first distance and a distance between the dense user vector and a random dense contributor vector ([0069], the training data in the table format is shuffled randomly, [0043], the individual data sets 510 i-N are shuffled before being loaded into the system 400, e.g., upon a shuffle, the first training instance being analyzed can be the individual data set 510-3 corresponding to the third object, while the second training instance being analyzed can be the individual data set 510-2 corresponding to the first object, [0047]-[0048], the learning engine 450 can perform the operations on the individual data sets 510 i-N in parallel to update the parameters, processors of the learning engine 450 can process the individual data sets 510 i-N at the same time to update the model parameters, and the learning engine 450 then utilizes the gradient to update the model parameters stored in the same data structure, a weight matrix 530 of FIG. 5 (i.e., matrix w) involving computing a compatibility score that involves a vector product between the two new transformed latent vectors corresponding to the pair of objects 500 and 502, [0071], after model parameters corresponding to a weight associated with a pairing of features are updated based on the data sets in block 1010, at block 1012, a score is generated for each pairing of objects based on the individual data sets of the training data);
increasing a score of the user-contributor pair for a content file from the second creative that is selected by the first creative ([0071], after model parameters corresponding to a weight associated with a pairing of features are updated based on the data sets in block 1010, wherein the more matches (e.g., based on the label) are found for the individual data sets, the greater the weight value, at block 1012, a score is generated for each pairing of objects based on the individual data sets of the training data, [0020], the learning process can learn a latent vector, of an age attribute “25” (i.e., age 25) of user u to include a target ID of a website “funny.com” and a target keyword “sports cars”; a prediction can be made that the user is likely interested in sports cars and interested in websites related to the funny.com website, e.g., a latent vector of a liked page “climbing” can include “outdoors” target word and “hiking” target word; that is, a prediction can be made that a user who has liked a “climbing” page is likely interested in ads related to climbing and the outdoors, [0028], wherein the interest-based attributes of the user u can include liked pages 212, e.g., pages of a social networking system that the user u has liked in the past based on historical data, [0030], the model training system analyzes historical data about interactions between groups of attributes to learn a pairwise similarity between attributes from both sides (e.g., of objects 200 and 202) to map correlations 230 between the objects, [0061]-[0062], in compatibility prediction system 700 evaluate compatibilities for various groupings of objects, e.g., a “user A” accessing a service application implemented by a social networking system, responsive to user A visiting the service application, the production engine 720 computes score(s) 704 for the user A and a particular ad of different ads based on attributes of the user and the respective ads, e.g., five scores for user A with five different ads, which are potential objects that can be potentially compatible with user A, and then final score for that user and ad pair is then generated);
updating, based on the score, a position of the second creative in a list of creatives presented to the first creative via a display associated with the online content marketplace ([0062]-[0063] e.g., for five different ad objects, the production engine 720 computes five scores for user A with five different potential objects that can be potentially compatible with user A, the final score for that user and ad object pair is then generated, the production engine 720 ranks the potential objects (e.g., ads) for user A based on the computed (final) scores 704, and an object 734 having the highest score (e.g., ad) is presented to the user A on the application service of the social networking system); and
modifying, based on the score, a content preview gallery rendered via the display and … responsive to score updates triggered by the first model and the second model ([0062]-[0063], the production engine 720 ranks the potential objects (e.g., ads) for user A based on the computed (final) scores 704, and an object 734 having the highest score (e.g., ad) is presented to the user A on the application service of the social networking system), wherein a … preview gallery defines a set of selectable content items available to the first creative for insertion into a project within the online content marketplace ([0079], a social networking system may provide means to interact with nonperson objects within the social networking system, e.g., a user may download, view, link to, tag, edit, or play a social networking system object).
While Gao discloses all of the above, including convolving, by a … model, a one or more coordinates of the first sparse vector into a dense user vector having fewer dimensions than the first sparse vector;
convolving, by a … model, a one or more coordinates of the second sparse vector into a dense contributor vector having a same dimension as the dense user vector … (as above), Gao does not expressly disclose the remaining element of the following limitations, which however, are taught by further teachings in Liu.
Liu teaches convolving, by a first model, a one or more coordinates of the first sparse vector into a dense user vector having fewer dimensions than the first sparse vector;
convolving, by a second model, a one or more coordinates of the second sparse vector into a dense contributor vector having a same dimension as the dense user vector, wherein the first model and the second model are distinct ([0020], the hierarchical deep neural network model includes a two-tower model comprising two encoder towers: a user tower and a page tower, wherein each encoder tower is its own deep neural network, the user tower and the page tower embed users and pages (respectively) into a low-dimensional space, [0033]-[0036], [0046], ingestion platform 200 obtains information from profile 118, social graph 120, user activity and behavior 122, and feed objects 124 databases, at prediction time, to determine which feed objects to present to a user in what order, the ingestion platform 200 sends information of the particular user and items being examined to deep learning model 201, this information is transmitted in the form of feature vectors, and the deep learning model 201 is a hierarchical deep neural network model with embeddings generated by a user side 400 and a page side 402 of a two tower model, [0048]-[0049], Claim 3, the U2P score could be generated by performing of a dot product of output from the first tower with output of the second tower and passing the result to a sigmoid function for each request; Examiner notes, to determine a dot product of two vectors, the two vectors must have the same dimension); … and
updating, based on the score, a position of the second creative in a list of creatives presented to the first creative via a display ([0023], the prediction may be called a “user-to-page-score” (U2PS), which may then be used to determine content to present to a corresponding user, in the context of a feed, [0033], [0035], creating the feed means ranking the items, including job, user postings, and sponsored posts, creating the user feed presents the items in sequential order, and at prediction time, such as when a social networking service needs to determine which feed objects to present to a particular user and in what order, the ingestion platform 200 sends information corresponding to the particular user and one or more items being examined to deep learning model 201, which is a hierarchical deep neural network model with embeddings generated by a user side 400 and a page side 402 of a two tower model, [0048]-[0049], Claim 3, the U2P score could be generated by performing of a dot product of output from the first tower with output of the second tower and passing the result to a sigmoid function for each request).
Gao and Liu are analogous fields of invention because both address the problem of presenting content to users by reducing the dimensional space of vectors representing content and users. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Gao the ability to convolve coordinates of a first sparse vector into a dense user vector and coordinates of the second sparse vector into a dense contributor vector having a same dimension as the dense user vector by a first model and second model that are distinct, as taught by Liu, 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 combination would produce the predictable results of convolving coordinates of a first sparse vector into a dense user vector and coordinates of the second sparse vector into a dense contributor vector having a same dimension as the dense user vector by a first model and second model that are distinct, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Gao with the aforementioned teachings of Liu in order to produce the added benefit of improving the user and customer/page experience and promoting a better ecosystem. [0075].
Further, while Gao discloses all of the above, including modifying, based on the score, a content preview gallery rendered via the display and … responsive to score updates triggered by the first model and the second model, wherein a … preview gallery defines a set of selectable content items available to the first creative for insertion into a project within the online content marketplace (as above), Gao does not necessarily disclose that the preview galley rendered via the display is refreshed in the following remaining elements of the aforementioned limitation, which however is taught by further teachings in Dodge.
Dodge teaches modifying, based on the score, a content preview gallery rendered via the display and refreshed responsive to score updates triggered by the first model and the second model ([0097], Fig., 2I, in a panel of user generated content in the form of photos, the social discovery platform may display level of likeness scores or ranked listing between the user and other users, which may be dynamically updated or changed responsive to the user's interactions with the photos), wherein a refreshed preview gallery defines a set of selectable content items available to the first creative for insertion into a project within the online content marketplace ([0097], Fig., 2I illustrates an embodiment of a panel of user generated content in the form of photos, a user may share the photo).
Gao and Dodge are analogous fields of invention because both address the problem of presenting content to users that are most relevant to them. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Gao the ability to modify, based on the score, a content preview gallery rendered via the display and refreshed responsive to score updates triggered by the first model and the second model, as taught by Dodge, 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 combination would produce the predictable results of modifying, based on the score, a content preview gallery rendered via the display and refreshed responsive to score updates triggered by the first model and the second model, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Gao with the aforementioned teachings of Dodge in order to produce the added benefit of helping users discover photos from other users from the enumerated list who are like the users. [0112].
Regarding claim 19, the combined teachings of Gao, Liu, and Dodge teach the method of claim 16 (as above). Further, Gao discloses wherein increasing the score of the user-contributor pair comprises adjusting a convolution parameter in the model to reduce the first distance between the dense user vector and the dense contributor vector ([0047]-[0048], at the processing stage 416, the learning engine 450 can be configured to perform one or more operations to update parameters of the model being learned by the model training system 400, wherein the operations include executing steps of a stochastic gradient descent (SGD) process including computing a gradient for the individual data sets 510 i-N by performing a dimensionality reduction on the individual data sets 510 i-N to generate a set of latent vectors 520 i-N and the learning engine 450 then utilizes the gradient to update the model parameters stored in the same data structure, a weight matrix 530 of FIG. 5 (i.e., matrix w), which involves computing a compatibility score, where such computation involves a vector product between the two new transformed latent vectors corresponding to the pair of objects 500 and 502, [0064]-[0066], wherein a graphical depiction of correlations between groupings of objects based on weight parameters of a model generated by a model training system the distance between the set of objects 800 and the set of objects 802 can be graphically illustrated based on the set of weights 804).
Regarding claim 20, the combined teachings of Gao, Liu, and Dodge teach the method of claim 16 (as above). Further, Gao discloses further comprising providing, to the first creative, a second display with contributor recommendations based on a score of a pairing between the first creative and a contributor having an embedded vector separated from the dense user vector by less than a pre-selected threshold ([0016], the compatibility score can be used for ranking unknown data (e.g., a new pair of user and ad), [0062]-[0063], Responsive to user A visiting the service application, the production interface 730 sends a request to the production engine 720 including information about the user A (i.e., object 832, male, age 23, likes Techcrunch™), the production engine 720 computes score(s) 704 for the user A with e.g., five different ads based on attributes of the user and the respective ads, ranks the potential objects (e.g., ads) for user A based on the computed (final) scores 704, and an object 734 having the highest score (e.g., ad) can be returned to the production interface 730 (i.e. less than threshold), e.g., the object 734 that corresponds to the object 832, based on its highest score, is presented to the user A on the application service of the social networking system, [0020], while the example discussed above refers to a user and an ad pair for illustrative purpose, the disclosed embodiments may be implemented to determine correlations, between other types of objects, including a user of a social networking system and a page of the social networking system, [0078], [0088], the social networking system may utilize a web-based interface or a mobile interface comprising a series of inter-connected pages displaying and enabling users to interact with social networking system objects and information, and content items from the content store 1212 may be displayed when a user profile is viewed or when other content associated with the user profile is viewed, e.g., displayed content items may show images or video associated with a user profile or show text describing a user's status, invite new users to the system or to increase interaction with the social networking system by displaying content related to users, suggested connections or suggestions to perform other actions, media provided to (e.g., pictures or videos), status messages or links posted by users to the social networking system).
Claims 17 & 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gao, et al. (US 20170017886 A1), hereinafter Gao, in view of Liu, et al. (US 20230394084 A1), hereinafter Liu, in further view of Dodge (US 20140143333 A1), hereinafter Dodge, and Titus, et al. (US 20090132435 A1), hereinafter Titus.
Regarding claim 17, the combined teachings of Gao, Liu, and Dodge teach the method of claim 16 (as above). Further, while Gao discloses wherein selecting the first creative comprises selecting a user that has … to the online content marketplace ([0042]-[0043], the model training system 400 can start learning a model by taking in training data 510 of the first and second objects being analyzed at the processing stage 410 by the loader engine 420, wherein the training data can be associated with multiple first objects and multiple second objects, and an individual data set 510i may be associated with a different user than an individual data set 510 i+1 or an individual dataset 510 i+2, [0020], examples of objects include a user and a page of the social networking system), Gao does not expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in Titus.
Titus teaches has licensed more than a pre-selected number of content files from one or more contributors to the online content marketplace ([0044], the routine 600b begins at operation 612 where the popularity module 132 determines the value of the content creator popularity measure, a higher content creator popularity measure indicates a higher popularity of the participant 202 who has created the participant-generated content item 126, and, if the value of the content creator popularity measure is higher than the threshold value, then the routine 600b continues to operation 618 where the price of the participant-generated content item 126 is increased by a given amount, [0036], the popularity of the participant-generated content item 126 may be determined based on any suitable factors including, but not limited to, the number of participants purchasing the participant-generated content item 126, wherein the content creator popularity measure refers to the popularity of the participant 202 who created the participant-generated content item 126, [0026], when the participant attempts to access the content item 126, the participant is directed to the DRM module 106 where the participant can purchase or otherwise obtain the license 124 from the license database 114).
Gao and Titus are analogous fields of invention because both address the problem of providing access to user generated content. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Gao the ability to select the first creative comprises selecting a user that has licensed more than a pre-selected number of content files from one or more contributors to the online content marketplace, as taught by Titus, 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 combination would produce the predictable results of selecting the first creative comprises selecting a user that has licensed more than a pre-selected number of content files from one or more contributors to the online content marketplace, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Gao with the aforementioned teachings of Titus in order to produce the added benefit of helping content creators distribute their own content. [0003].
Regarding claim 18, the combined teachings of Gao, Liu, and Dodge teach the method of claim 16 (as above). Further, while Gao discloses wherein selecting the second creative comprises selecting a contributor that has … more than a pre-selected number of … the online content marketplace ([0063], the production engine 720 ranks the potential objects (e.g., ads) for user A based on the computed (final) scores 704, and an object 734 having the highest score (e.g., ad) can be returned to the production interface 730 (i.e. less than threshold), e.g., the object 734 that corresponds to the object 832, based on its highest score, is presented to the user A on the application service of the social networking system, [0078], the social networking system may utilize a web-based interface or a mobile interface comprising a series of inter-connected pages displaying and enabling users to interact with social networking system objects and information), Gao does not expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in Titus.
Titus teaches wherein selecting the second creative comprises selecting a contributor that has licensed more than a pre-selected number of content files to one or more users of the online content marketplace ([0044], the routine 600b begins at operation 612 where the popularity module 132 determines the value of the content creator popularity measure, a higher content creator popularity measure indicates a higher popularity of the participant 202 who has created the participant-generated content item 126, and, if the value of the content creator popularity measure is higher than the threshold value, then the routine 600b continues to operation 618 where the price of the participant-generated content item 126 is increased by a given amount, [0036], the popularity of the participant-generated content item 126 may be determined based on any suitable factors including, but not limited to, the number of participants purchasing the participant-generated content item 126, wherein the content creator popularity measure refers to the popularity of the participant 202 who created the participant-generated content item 126, [0026], when the participant attempts to access the content item 126, the participant is directed to the DRM module 106 where the participant can purchase or otherwise obtain the license 124 from the license database 114).
Gao and Titus are analogous fields of invention because both address the problem of providing access to user generated content. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Gao the ability to select the second creative comprises selecting a contributor that has licensed more than a pre-selected number of content files to one or more users of the online content marketplace, as taught by Titus, 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 combination would produce the predictable results of selecting the second creative comprises selecting a contributor that has licensed more than a pre-selected number of content files to one or more users of the online content marketplace, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Gao with the aforementioned teachings of Titus in order to produce the added benefit of helping content creators distribute their own content. [0003].
Conclusion
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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623
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