DETAILED ACTION
This action is responsive to the amendment filed 03/02/2026 regarding application
number 17/832,645. Claims 1-19 and 21 in the application have been examined and are pending. Claims 1, 10 and 21 are independent claims. Claim 20 has been canceled with claim 21 added. Claims 1, 3-10, 12-19 have been amended.
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 .
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.
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 1-19 and 21 are rejected under 35 U.S.C. 101 because the invention is directed towards abstract idea(s) without significantly more
Regarding claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites executing…one or more auditing techniques… which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompasses using judgement and evaluation to apply a technique and make a recommendation. See 2106.04.(a)(2).III.C.
The claim recites based on the executing, evaluating…one or more performance parameters… which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass making an evaluation after applying a technique and judging performance of a model. See 2106.04.(a)(2).III.C.
The claim recites comparing…the one or more performance parameters to a performance metric which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites revising….at least one setting…based on the comparing which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user judging and choosing a value based on that judgement . See 2106.04.(a)(2).III.C.
The claim recites generating…the one or more recommendations…with the at least one revised setting which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to create a list based on data and requirements. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
at least one processor(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
…of a recommender module that provides one or more recommendations…(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
…based on user factors and content factors for a content library(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
using the recommender module(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
transmitting…the one or more recommendations to a client device for output to a user(recites insignificant extra-solution activity of data transmission(see MPEP 2106.05(g))
where the executing includes the recommender module utilizing a training data set and one or more user parameters(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
of the recommender module…wherein the at least one setting includes a dimension of the recommender module(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
recommender module(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (b) (d) and (h) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (c) (f) and (g) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Further, additional element (e) collecting information and sending information to a device over a network is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
The additional element(s) (a) (b) (c) (d) (e) (f) (g) and (h) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 2:
The rejection of claim 1 with is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the recommender module uses matrix factorization which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))) as matrix factorization is a fundamental mathematical technique in linear algebra
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3:
The rejection of claim 1 with is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining the one or more performance parameter which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to select and decide a performance parameter. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
including at least one or more performance parameters including at least one of user recourse parameter and a content item availability(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 4:
The rejection of claim 3 with is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the evaluating further comprises determining the content item availability parameter based on an aligned-reachable condition with one or more unseen items and an increased value for a number of recommended items which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user evaluating a condition of a model. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5:
The rejection of claim 4 with is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein determining the content item availability comprises computing, for each item of the electronic content whether the aligned-reachable condition is true which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6:
The rejection of claim 5 with is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein determining the content item availability parameter further comprises determining a ratio between a count of items for which the aligned-reachable condition is not true and a count of total items which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7:
The rejection of claim 3 with is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites wherein the evaluating further comprises determining a user recourse at least in part by testing a feasibility for each item which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))) as feasibility of reachability is defined by satisfying a mathematical solution in the specification ([0047] “…By defining the cost with respect to user behavior, the reachability problem encodes both the possibilities of recommendations through its feasibility, as well as the relative likelihood of different outcomes as modeled by the cost” and [0072] “…First, consider the feasibility of Equation (4) with respect to its linear inequality constraints”)
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8:
The rejection of claim 3 with is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the evaluating further comprises determining a lower bound on a user recourse by a portion of one or more unseen items that satisfy an inequality which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
including a product of an item factor and a function of a rating vector(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 9:
The rejection of claim 8 with is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein determining the lower bound comprises computing a cost function for one or more rating changes which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites executing…one or more auditing techniques… which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompasses using judgement and evaluation to apply a technique and make a recommendation. See 2106.04.(a)(2).III.C.
The claim recites based on the executing, evaluating…one or more performance parameters… which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass making an evaluation after applying a technique and judging performance of a model. See 2106.04.(a)(2).III.C.
The claim recites comparing…the one or more performance parameters to a performance metric which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites revising….at least one setting…based on the comparing which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user judging and choosing a value based on that judgement . See 2106.04.(a)(2).III.C.
The claim recites generating…the one or more recommendations…with the at least one revised setting which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to create a list based on data and requirements. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
…at least one processor...(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
…of a recommender module that provides one or more recommendations…(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
…based on user factors and content factors for a content library(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
using the recommender module(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
transmitting…the one or more recommendations to a client device for output to a user(recites insignificant extra-solution activity of data transmission(see MPEP 2106.05(g))
where the executing includes the recommender module utilizing a training data set and one or more user parameters(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
of the recommender module…wherein the at least one setting includes a dimension of the recommender module(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
recommender module(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (b) (d) and (h) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (c) (f) and (g) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Further, additional element (e) collecting information and sending information to a device over a network is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
The additional element(s) (a) (b) (c) (d) (e) (f) (g) and (h) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 11:
The rejection of claim 10 incorporated in claim 11. Claim 11 is rejected under the same rationale as set forth in the rejection of claim 2.
Regarding claim 12:
The rejection of claim 10 incorporated in claim 12. Claim 12 is rejected under the same rationale as set forth in the rejection of claim 3.
Regarding claim 13:
The rejection of claim 12 incorporated in claim 13. Claim 13 is rejected under the same rationale as set forth in the rejection of claim 4.
Regarding claim 14:
The rejection of claim 13 incorporated in claim 14. Claim 14 is rejected under the same rationale as set forth in the rejection of claim 5.
Regarding claim 15:
The rejection of claim 14 incorporated in claim 15. Claim 15 is rejected under the same rationale as set forth in the rejection of claim 6.
Regarding claim 16:
The rejection of claim 12 incorporated in claim 16. Claim 16 is rejected under the same rationale as set forth in the rejection of claim 7.
Regarding claim 17:
The rejection of claim 12 incorporated in claim 17. Claim 17 is rejected under the same rationale as set forth in the rejection of claim 8.
Regarding claim 18:
The rejection of claim 17 incorporated in claim 18. Claim 18 is rejected under the same rationale as set forth in the rejection of claim 9.
Regarding claim 19:
The rejection of claim 10 with is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
a network interface for…(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
…sending the one or more recommendations to the client device(recites insignificant extra-solution activity of data transmission (see MPEP 2106.05(g)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Further, additional element (b) of collecting information and sending information to a device over a network is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 21:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites executing…one or more auditing techniques… which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompasses using judgement and evaluation to apply a technique and make a recommendation. See 2106.04.(a)(2).III.C.
The claim recites based on the executing, evaluating…one or more performance parameters… which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass making an evaluation after applying a technique and judging performance of a model. See 2106.04.(a)(2).III.C.
The claim recites comparing…the one or more performance parameters to a performance metric which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites revising….at least one setting…based on the comparing which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user judging and choosing a value based on that judgement . See 2106.04.(a)(2).III.C.
The claim recites generating…the one or more recommendations…with the at least one revised setting which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to create a list based on data and requirements. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
a memory having processor-readable instructions stored therein; and one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
…of a recommender module that provides one or more recommendations…(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
…based on user factors and content factors for a content library(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
using the recommender module(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
transmitting…the one or more recommendations to a client device for output to a user(recites insignificant extra-solution activity of data transmission(see MPEP 2106.05(g))
where the executing includes the recommender module utilizing a training data set and one or more user parameters(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
of the recommender module…wherein the at least one setting includes a dimension of the recommender module(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
… at least one processor…(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
recommender module(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (b) (d) (h) and (i) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (c) (f) and (g) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Further, additional element (e) collecting information and sending information to a device over a network is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
The additional element(s) (a) (b) (c) (d) (e) (f) (g) (h) and (i) n the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 7-13, 16-19 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liang et. al(“Modeling User Exposure in Recommendation”, henceforth known as Liang) in view of Ding et al(“Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA” henceforth known as Ding) and further in view of Sun et al.(“ Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering”, henceforth known as Sun).
Regarding claim 1:
Liang discloses executing, by at least one processor(Liang, Page 5, Paragraph 2, “On the other hand, the factor updates are still independent across users and items. These updates can therefore easily be parallelized” where parallelization refers to using a processor as it is an activity performed by computer processors), one or more auditing techniques and a recommender module(Liang, Page 1, Col. 2, Paragraph 5, “We develop a probabilistic model for recommendation called Exposure MF (abbreviated as ExpoMF) that separately captures whether a user has been exposed to an item from whether a user has ultimately decided to click on it” where ExpoMF corresponds to an auditing technique and recommender module), where the executing includes the recommender module(Liang, Page 4, Col. 2, Algorithm 1, where algorithm 1 corresponds to executing a recommender module with a data set and user parameters described as user factors θ1) utilizing a training data set(Liang, Page 6, Col. 2, Paragraph 3, “For each dataset we randomly split the observed user-item interactions into training/test/validation sets” where a dataset being split into training set corresponds to utilizing a training data set) and one or more user parameters(Liang , Page 2, Col. 2, Paragraph 2, “where θu and βi represent user u’s latent preferences” where θu and βi corresponds to user parameters)
Liang discloses based on the executing, evaluating, by at least one processor, one or more performance parameters of a recommender module that provides one or more recommendations (Liang, Page 956, Col. 2, Paragraph 5, “To evaluate the recommendation performance, we report both Recall@k, a standard information retrieval measure, as well as two ranking-specific metrics: mean average precision (MAP@k) and NDCG@k.” where evaluating the recommendation performance is considered evaluating one or more performance parameters of a recommender providing one or more recommendations(See also Liang, Page 957, Col. 2, Paragraph 3, “In addition, higher values of NDCG@100 and MAP@100 (even when Recall@50 is lower) indicate that the top-ranked items by ExpoMF tend to be more relevant to users' interests”) based on one or more user factors and one or more content factors(Liang, Page 954, Equation 3 and Algorithm 1 shows in lines 1 and 5, and Page 960, Col 2. Paragraph 1,“Finally, we would like to evaluate our proposed model in a more realistic setting, e.g., in an online environment with user interactions” where the use of exposure covariant(xi) as content factors and user specific factors(θ1) as user factors when inferencing with the recommendation model) for a content library(Liang, Page 953, Col. 1, Paragraph 1, “For every combination of users u = 1,…,U and items i = 1,…,I, consider two sets of variables. The first matrix A = {aui} indicates whether user u has been exposed to item i.” and Page 960, Col 2. Paragraph 1,“Finally, we would like to evaluate our proposed model in a more realistic setting, e.g., in an online environment with user interactions” where the evaluation being performed on an online environment with a set of user and items is considered a content library) Liang discloses comparing, by the at least one processor, the one or more performance parameters to a performance metric(Liang, Page 956, Col. 2, Paragraph 3, “We monitor the convergence of the algorithm using the truncated normalized discounted cumulative gain (NDCG@100, see below for details) on the validation set. Hyper-parameters for ExpoMF-based models and baseline models are also selected according to the same criterion” where choosing hyperparameters for baseline models based on convergence of the validation set is considered comparing performance parameters to a performance metric)
Liang discloses revising, by the at least one processor, at least one setting of the recommender module based on the comparing(Liang, Page 954, Col. 2, Algorithm 1, “while performance on validation set increases do” where the user/item factors and the exposure settings are revised contingent on validation performance is considered revising, at least one setting of the recommender based on comparing),
Liang discloses generating, by the at least one processor, one or more recommendations using the recommender module with the at least one revised setting(Liang, Page 957, Col. 1, Paragraph 2, “For the ranking-based measure in all the experiments we set k = 100 which is a reasonable number of items to consider for a user” where recommending 100 is considered generating one or more (in this case 100) recommendations using a recommender module)
Liang does not explicitly disclose the following:
wherein the at least one setting includes a dimension of the recommender module
transmitting, by the at least one processor, the one or more recommendations to a client device for output to a user
Ding discloses wherein the at least one setting includes a dimension of the recommender module(Ding, Page 1, Col. 2, Paragraph 1, “Our focus is to develop an automatic and efficient scheme for choosing the dimensionality parameter k” where the scheme Ding is a recommendation module)
References Liang and Ding are analogous art because they are from the same problem-solving area of using collaborative filters with latent factor modeling for users to recommend items.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Liang and Ding before him or her, to modify Liangs user/item latent factors of Liang to include the fast adaptive PCA of Ding as adjustment of dimensionality k affects both predictive accuracy and computational efficiency. The suggestion/motivation for doing so would have been “Notices that a smaller k causes the inaccuracy of reduced model, while a larger k induces large computational cost and possibly large error due to overfitting.”(Ding, Page 1, Col. 2, Paragraph 1) and “By extending the fixed-precision algorithm…we present a fast adaptive PCA framework which automatically determines the dimensionality parameter k, and is accelerated for processing large sparse matrix” (Ding, Page 1, Col. 2, Paragraph 3)
Liang-Ding does not explicitly disclose the following:
transmitting, by the at least one processor, the one or more recommendations to a client device for output to a user
Sun discloses transmitting, by the at least one processor, the one or more recommendations to a client device for output to a user(Sun, Page 647, Col. 2, Algorithm 1, “for all users u in the system…The system selects one or more items to recommend …based on a specialized recommending strategy…User u picks the selected one or more items and gives rating… for each item i;” where sending one or more items for a user to select/give feedback is considered sending one or more recommendations to a client device for output to a user)
References Liang-Ding and Sun are analogous art because they are from the same problem-solving area of using collaborative recommenders with exposure/seen items for users to recommend items to users.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Liang-Ding and Sun before him or her, to modify the training and feedback method of Liang-Ding to include the interaction of users of Sun as the suggestion/motivation for doing so would have been “Instead, our goal is to simulate the interaction between users and the recommender system and to debias the recommender system during the interaction”(Sun, Page 650, Col. 2, Paragraph 3).
Regarding claim 2:
The rejection of claim 1 with is incorporated and further:
Liang further discloses wherein the recommender module uses matrix factorization(Liang, Page 952, Col. 2, Paragraph 6, “We present exposure matrix factorization (ExpoMF). In Section 3.1, we describe the main model. In Section 3.2 we discuss several ways of incorporating external information into ExpoMF (i.e., topics from text, locations).”)
Regarding claim 3:
The rejection of claim 1 with is incorporated and further:
Liang further discloses wherein the evaluating further comprises determining the one or more performance parameters including at least one of user recourse parameter(Liang, Page 954, Col. 2, Paragraph 1, “In the E-step, we compute expectation of the exposure latent variable E[aui] for all user and item combinations (u,i) for which there are no observed clicks (recall that the presence of clicks yui > 0 means that aui = 1 deterministically” where combinations for observed clicks correspond to a user recourse parameter), and content item availability parameter(Liang , Page 957, Col. 2, Paragraph 2, “For the ranking-based measure in all the experiments we set k = 100 which is a reasonable number of items to consider for a user” where ranking and recommending items is considered determining content items available parameter to evaluate.)
Examiner notes that this claim requires only one of the two recited alternatives, namely user recourse and content item availability. However, as set forth above, Liang teaches both alternatives.
Regarding claim 4:
The rejection of claim 3 with is incorporated and further:
Liang further discloses wherein the evaluating further comprises determining the content item availability parameter based on an aligned-reachable condition with one or more unseen items and an increased value for number of recommended items(Liang , Page 951, Col. 1, Paragraph 1,“In the recommendation problem, we observe how a set of users interacts with a set of items, and our goal is to show each user a set of previously unseen items that she will like” where recommending with the goal to show previously unseen items that the user will like is considered determining item availability(selected for recommending) based on a condition(relevant content for a user) with no unseen items and an increased value for number of items recommended(an increased ranking in the recommender))
Regarding claim 7:
The rejection of claim 3 with is incorporated and further:
Liang further discloses wherein the evaluating further comprises determining a user recourse at least in part by testing a feasibility for each item(Liang , Page 953, Col. 1, Paragraph 1, “The first matrix A={aui} indicates whether user u has been exposed to item i. The second matrix Y={yui} indicates whether or not user u clicked on item i” where yui evaluating whether or not user u clicked is considered determining user recourse by testing feasibility for each item as indicating whether the user has clicked would determine whether it is feasible for the user to have clicked the item)
Regarding claim 8:
The rejection of claim 3 with is incorporated and further:
Liang further discloses wherein the evaluating further comprises determining a lower bound on a user recourse by a portion of one or more unseen items that satisfy an inequality(Liang, Page 951, Col. 1, Abstract, “In this paper, we propose a new probabilistic approach that directly incorporates user exposure to items into collaborative filtering” where recommending the one or more items to click allows for user recourse (clicking the recommended item) and recommending an item(based on exposure) satisfies the inequality of any item in the one or more have a greater than ranking than the n+1st item is considered determining a lower bound on user recourse by a portion of unseen items satisfies an inequality as recommending an item) including a product of an item factor and a function of a rating vector(Liang, Page 954, Col. 2, Algorithm 1, where Algorithm 1 takes click matrix Y(has the user clicked the item i) as input then computes the expected exposure matrix A(has the user been exposed to item i) in the E-step and uses those expectations to recommend is considered determining a product of an item factor(click matrix Y) and a function of a rating vector(exposure matrix A) to form a lower bound on user recourse(recommending to click))
Regarding claim 9:
The rejection of claim 8 with is incorporated and further:
Liang further discloses wherein determining the lower bound comprises computing a cost function for one or more rating changes(Liang , Page 954, Col. 2, Equation 7, where clicking a recommended item that has not been clicked before changes the exposure prior for the item globally by increasing the sum by 1 is considered computing a cost function for rating changes)
Regarding claim 10:
Liang discloses executing one or more auditing techniques and a recommender module(Liang, Page 1, Col. 2, Paragraph 5, “We develop a probabilistic model for recommendation called Exposure MF (abbreviated as ExpoMF) that separately captures whether a user has been exposed to an item from whether a user has ultimately decided to click on it” where ExpoMF corresponds to an auditing technique and recommender module), where the executing includes the recommender module(Liang, Page 4, Col. 2, Algorithm 1, where algorithm 1 corresponds to executing a recommender module with a data set and user parameters described as user factors θ1) utilizing a training data set(Liang, Page 6, Col. 2, Paragraph 3, “For each dataset we randomly split the observed user-item interactions into training/test/validation sets” where a dataset being split into training set corresponds to utilizing a training data set) and one or more user parameters(Liang , Page 2, Col. 2, Paragraph 2, “where θu and βi represent user u’s latent preferences” where θu and βi corresponds to user parameters)
Liang discloses based on the executing, evaluating one or more performance parameters of a recommender module that provides one or more recommendations (Liang, Page 956, Col. 2, Paragraph 5, “To evaluate the recommendation performance, we report both Recall@k, a standard information retrieval measure, as well as two ranking-specific metrics: mean average precision (MAP@k) and NDCG@k.” where evaluating the recommendation performance is considered evaluating one or more performance parameters of a recommender providing one or more recommendations(See also Liang, Page 957, Col. 2, Paragraph 3, “In addition, higher values of NDCG@100 and MAP@100 (even when Recall@50 is lower) indicate that the top-ranked items by ExpoMF tend to be more relevant to users' interests”) based on one or more user factors and one or more content factors(Liang, Page 954, Equation 3 and Algorithm 1 shows in lines 1 and 5, and Page 960, Col 2. Paragraph 1,“Finally, we would like to evaluate our proposed model in a more realistic setting, e.g., in an online environment with user interactions” where the use of exposure covariant(xi) as content factors and user specific factors(θ1) as user factors when inferencing with the recommendation model) for a content library(Liang, Page 953, Col. 1, Paragraph 1, “For every combination of users u = 1,…,U and items i = 1,…,I, consider two sets of variables. The first matrix A = {aui} indicates whether user u has been exposed to item i.” and Page 960, Col 2. Paragraph 1,“Finally, we would like to evaluate our proposed model in a more realistic setting, e.g., in an online environment with user interactions” where the evaluation being performed on an online environment with a set of user and items is considered a content library)
Liang discloses comparing the one or more performance parameters to a performance metric(Liang, Page 956, Col. 2, Paragraph 3, “We monitor the convergence of the algorithm using the truncated normalized discounted cumulative gain (NDCG@100, see below for details) on the validation set. Hyper-parameters for ExpoMF-based models and baseline models are also selected according to the same criterion” where choosing hyperparameters for baseline models based on convergence of the validation set is considered comparing performance parameters to a performance metric)
Liang discloses revising at least one setting of the recommender module based on the comparing(Liang, Page 954, Col. 2, Algorithm 1, “while performance on validation set increases do” where the user/item factors and the exposure settings are revised contingent on validation performance is considered revising, at least one setting of the recommender based on comparing),
Liang discloses generating one or more recommendations using the recommender module with the at least one revised setting(Liang, Page 957, Col. 1, Paragraph 2, “For the ranking-based measure in all the experiments we set k = 100 which is a reasonable number of items to consider for a user” where recommending 100 is considered generating one or more (in this case 100) recommendations using a recommender module)
Liang does not explicitly disclose the following:
wherein the at least one setting includes a dimension of the recommender module
transmitting the one or more recommendations to a client device for output to a user
Ding discloses wherein the at least one setting includes a dimension of the recommender module(Ding, Page 1, Col. 2, Paragraph 1, “Our focus is to develop an automatic and efficient scheme for choosing the dimensionality parameter k” where the scheme Ding is a recommendation module)
References Liang and Ding are analogous art because they are from the same problem-solving area of using collaborative filters with latent factor modeling for users to recommend items.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Liang and Ding before him or her, to modify Liangs user/item latent factors of Liang to include the fast adaptive PCA of Ding as adjustment of dimensionality k affects both predictive accuracy and computational efficiency. The suggestion/motivation for doing so would have been “Notices that a smaller k causes the inaccuracy of reduced model, while a larger k induces large computational cost and possibly large error due to overfitting.”(Ding, Page 1, Col. 2, Paragraph 1) and “By extending the fixed-precision algorithm…we present a fast adaptive PCA framework which automatically determines the dimensionality parameter k, and is accelerated for processing large sparse matrix” (Ding, Page 1, Col. 2, Paragraph 3)
Liang-Ding does not explicitly disclose the following:
transmitting the one or more recommendations to a client device for output to a user
Sun discloses transmitting the one or more recommendations to a client device for output to a user(Sun, Page 647, Col. 2, Algorithm 1, “for all users u in the system…The system selects one or more items to recommend …based on a specialized recommending strategy…User u picks the selected one or more items and gives rating… for each item i;” where sending one or more items for a user to select/give feedback is considered sending one or more recommendations to a client device for output to a user)
References Liang-Ding and Sun are analogous art because they are from the same problem-solving area of using collaborative recommenders with exposure/seen items for users to recommend items to users.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Liang-Ding and Sun before him or her, to modify the training and feedback method of Liang-Ding to include the interaction of users of Sun as the suggestion/motivation for doing so would have been “Instead, our goal is to simulate the interaction between users and the recommender system and to debias the recommender system during the interaction”(Sun, Page 650, Col. 2, Paragraph 3).
Regarding claim 11:
The rejection of claim 10 incorporated in claim 11. Claim 11 is rejected under the same rationale as set forth in the rejection of claim 2.
Regarding claim 12:
The rejection of claim 10 incorporated in claim 12. Claim 12 is rejected under the same rationale as set forth in the rejection of claim 3.
Regarding claim 13:
The rejection of claim 12 incorporated in claim 13. Claim 13 is rejected under the same rationale as set forth in the rejection of claim 4.
Regarding claim 16:
The rejection of claim 12 incorporated in claim 16. Claim 16 is rejected under the same rationale as set forth in the rejection of claim 7.
Regarding claim 17:
The rejection of claim 12 incorporated in claim 17. Claim 17 is rejected under the same rationale as set forth in the rejection of claim 8.
Regarding claim 18:
The rejection of claim 17 incorporated in claim 18. Claim 18 is rejected under the same rationale as set forth in the rejection of claim 9.
Regarding claim 19:
The rejection of claim 10 with is incorporated and further:
Sun discloses a network interface for sending the one or more recommendations to the client device(Sun, Page 647, Col. 2, Algorithm 1, “for all users u in the system…The system selects one or more items to recommend …based on a specialized recommending strategy…User u picks the selected one or more items and gives rating… for each item i;” where sending one or more items for a user to select/give feedback is considered sending one or more recommendations to a client device for output to a user(See Also Sun, Page 645, Col. 1, Abstract, “Recommender Systems (RSs) are widely used to help online users discover products, books, news, music, movies, courses, restaurants, etc.” where sending to online clients requires a network interface))
Regarding claim 21:
Liang discloses a memory having processor-readable instructions stored therein; and one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions(Liang, Page 5, Paragraph 2, “On the other hand, the factor updates are still independent across users and items. These updates can therefore easily be parallelized” where parallelization refers to using a processor as it is an activity performed by computer processors and computer processors have memory built in that is accessible through the processor)
Liang discloses executing, by at least one processor, one or more auditing techniques and a recommender module(Liang, Page 1, Col. 2, Paragraph 5, “We develop a probabilistic model for recommendation called Exposure MF (abbreviated as ExpoMF) that separately captures whether a user has been exposed to an item from whether a user has ultimately decided to click on it” where ExpoMF corresponds to an auditing technique and recommender module), where the executing includes the recommender module(Liang, Page 4, Col. 2, Algorithm 1, where algorithm 1 corresponds to executing a recommender module with a data set and user parameters described as user factors θ1) utilizing a training data set(Liang, Page 6, Col. 2, Paragraph 3, “For each dataset we randomly split the observed user-item interactions into training/test/validation sets” where a dataset being split into training set corresponds to utilizing a training data set) and one or more user parameters(Liang , Page 2, Col. 2, Paragraph 2, “where θu and βi represent user u’s latent preferences” where θu and βi corresponds to user parameters)
Liang discloses based on the executing, evaluating, by at least one processor, one or more performance parameters of a recommender module that provides one or more recommendations (Liang, Page 956, Col. 2, Paragraph 5, “To evaluate the recommendation performance, we report both Recall@k, a standard information retrieval measure, as well as two ranking-specific metrics: mean average precision (MAP@k) and NDCG@k.” where evaluating the recommendation performance is considered evaluating one or more performance parameters of a recommender providing one or more recommendations(See also Liang, Page 957, Col. 2, Paragraph 3, “In addition, higher values of NDCG@100 and MAP@100 (even when Recall@50 is lower) indicate that the top-ranked items by ExpoMF tend to be more relevant to users' interests”) based on one or more user factors and one or more content factors(Liang, Page 954, Equation 3 and Algorithm 1 shows in lines 1 and 5, and Page 960, Col 2. Paragraph 1,“Finally, we would like to evaluate our proposed model in a more realistic setting, e.g., in an online environment with user interactions” where the use of exposure covariant(xi) as content factors and user specific factors(θ1) as user factors when inferencing with the recommendation model) for a content library(Liang, Page 953, Col. 1, Paragraph 1, “For every combination of users u = 1,…,U and items i = 1,…,I, consider two sets of variables. The first matrix A = {aui} indicates whether user u has been exposed to item i.” and Page 960, Col 2. Paragraph 1,“Finally, we would like to evaluate our proposed model in a more realistic setting, e.g., in an online environment with user interactions” where the evaluation being performed on an online environment with a set of user and items is considered a content library)
Liang discloses comparing, by the at least one processor, the one or more performance parameters to a performance metric(Liang, Page 956, Col. 2, Paragraph 3, “We monitor the convergence of the algorithm using the truncated normalized discounted cumulative gain (NDCG@100, see below for details) on the validation set. Hyper-parameters for ExpoMF-based models and baseline models are also selected according to the same criterion” where choosing hyperparameters for baseline models based on convergence of the validation set is considered comparing performance parameters to a performance metric)
Liang discloses revising, by the at least one processor, at least one setting of the recommender module based on the comparing(Liang, Page 954, Col. 2, Algorithm 1, “while performance on validation set increases do” where the user/item factors and the exposure settings are revised contingent on validation performance is considered revising, at least one setting of the recommender based on comparing)
Liang discloses generating, by the at least one processor, one or more recommendations using the recommender module with the at least one revised setting(Liang, Page 957, Col. 1, Paragraph 2, “For the ranking-based measure in all the experiments we set k = 100 which is a reasonable number of items to consider for a user” where recommending 100 is considered generating one or more (in this case 100) recommendations using a recommender module)
Liang does not explicitly disclose the following:
wherein the at least one setting includes a dimension of the recommender module
transmitting, by the at least one processor, the one or more recommendations to a client device for output to a user
Ding discloses wherein the at least one setting includes a dimension of the recommender module(Ding, Page 1, Col. 2, Paragraph 1, “Our focus is to develop an automatic and efficient scheme for choosing the dimensionality parameter k” where the scheme Ding is a recommendation module)
References Liang and Ding are analogous art because they are from the same problem-solving area of using collaborative filters with latent factor modeling for users to recommend items.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Liang and Ding before him or her, to modify Liangs user/item latent factors of Liang to include the fast adaptive PCA of Ding as adjustment of dimensionality k affects both predictive accuracy and computational efficiency. The suggestion/motivation for doing so would have been “Notices that a smaller k causes the inaccuracy of reduced model, while a larger k induces large computational cost and possibly large error due to overfitting.”(Ding, Page 1, Col. 2, Paragraph 1) and “By extending the fixed-precision algorithm…we present a fast adaptive PCA framework which automatically determines the dimensionality parameter k, and is accelerated for processing large sparse matrix” (Ding, Page 1, Col. 2, Paragraph 3)
Liang-Ding does not explicitly disclose the following:
transmitting, by the at least one processor, the one or more recommendations to a client device for output to a user
Sun discloses transmitting, by the at least one processor, the one or more recommendations to a client device for output to a user(Sun, Page 647, Col. 2, Algorithm 1, “for all users u in the system…The system selects one or more items to recommend …based on a specialized recommending strategy…User u picks the selected one or more items and gives rating… for each item i;” where sending one or more items for a user to select/give feedback is considered sending one or more recommendations to a client device for output to a user)
References Liang-Ding and Sun are analogous art because they are from the same problem-solving area of using collaborative recommenders with exposure/seen items for users to recommend items to users.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Liang-Ding and Sun before him or her, to modify the training and feedback method of Liang-Ding to include the interaction of users of Sun as the suggestion/motivation for doing so would have been “Instead, our goal is to simulate the interaction between users and the recommender system and to debias the recommender system during the interaction”(Sun, Page 650, Col. 2, Paragraph 3).
Claim(s) 5-6 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liang et. al(“Modeling User Exposure in Recommendation”, henceforth known as Liang) in view of Ding et al(“Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA” henceforth known as Ding) and in further view of Sun et al.(“ Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering”, henceforth known as Sun) with Herlocker et al.(“Evaluating Collaborative Filtering Recommender Systems”, henceforth known as Herlocker)
Regarding claim 5:
The rejection of claim 4 with is incorporated and further:
The Liang-Sun combination does not explicitly disclose, however Herlocker does disclose wherein determining the content item availability parameter comprises computing, for each item of the electronic content, whether the aligned-reachable condition is true(Herlocker, Page 24, Paragraph 2, “We have also seen precision measured…with relevant items being selected from a small pool of rated items and predicted items being selected from a much larger set of items”
References Liang-Sun and Herlocker are analogous art because they are from the same problem-solving area of using collaborative recommenders with exposure/seen items for users to recommend items to users.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Liang-Sun and Herlocker before him or her to modify the performance parameters of Liang-Sun to include the classification framework of Herlocker as the suggestion/motivation for doing so would have been “Precision and recall are the most popular metrics for evaluating information retrieval systems…Precision and recall are computed from a 2 × 2 table, such as the one shown in Table I.”(Herlocker, Page 22, Paragraph 6).
Regarding claim 6:
The rejection of claim 5 with is incorporated and further:
The Liang- Sun combination does not explicitly disclose, however Herlocker does disclose wherein determining the content item availability parameter further comprises determining a ratio between a count of items for which the aligned-reachable condition is not true and a count of total items(Herlocker, Page 22, Table 1, where tracking all select/unselected and total of all items is considered determining a ratio between count of items for which the aligned-reachable condition(relevant content for a user) is not true and a count of total items)
References Liang-Sun and Herlocker are analogous art because they are from the same problem-solving area of using collaborative recommenders with exposure/seen items for users to recommend items to users.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Liang-Sun and Herlocker before him or her to modify the performance parameters of Liang-Sun to include the count of relevant, non-relevant and total items of Herlocker as the suggestion/motivation for doing so would have been “If we assume that the distribution of relevant items and nonrelevant items within the user’s test set is the same as the true distribution for the user across all items, then the precision and recall will be much closer approximations of the true precision and recall.”(Herlocker, Page 24, Paragraph 3).
Regarding claim 14:
The rejection of claim 13 incorporated in claim 14. Claim 14 is rejected under the same rationale as set forth in the rejection of claim 5.
Regarding claim 15:
The rejection of claim 14 incorporated in claim 15. Claim 15 is rejected under the same rationale as set forth in the rejection of claim 6.
Response to arguments:
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive. A breakdown of arguments can be found below:
101:
Applicant appears to argue on pages 9-11 that claimed invention does not recite a judicial exception and therefor does not recite and abstract idea. Applicant appears to specifically argue the following goes beyond simple mental processes
executing, by at least one processor, one or more auditing techniques and a recommender module, where the executing includes the recommender module utilizing a training data set and one or more user parameters
based on the executing, evaluating, by the at least one processor, one or more performance parameters of the recommender module that provides one or more recommendations based on one or more user factors and one or more content factors for a content library
revising, by the at least one processor, at least one setting of the recommender module based on the comparing, wherein the at least one setting includes a dimension of the recommender module
generating, by the at least one processor, the one or more recommendations using the recommender module with the at least one revised setting
a:
Applicant respectfully disagrees as executing, by at least one processor, one or more auditing techniques and a recommender module, where the executing includes the recommender module utilizing a training data set and one or more user parameters
Examiner respectfully disagrees as a processor and a recommender module merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) and the executing…one or more auditing techniques of an auditing technique and recommendation module is a mental task that uses judgment and observation to perform an audit and outputting a set of recommended items. Additionally, where the executing includes the recommender module utilizing a training data set and one or more user parameters merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
b:
Applicant respectfully disagrees as based on the executing, evaluating, by the at least one processor, one or more performance parameters of the recommender module that provides one or more recommendations based on one or more user factors and one or more content factors for a content library as the processor merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) and the based on the executing, evaluating…one or more performance parameters describes the mental process of making an evaluation after applying a technique and judging performance of a model and the recommender module that provides one or more recommendations merely recites a generic computer on which to perform the abstract idea of providing recommendations, e.g. "apply it on a computer" (see MPEP 2106.05(f)) and based on one or more user factors and one or more content factors for a content library merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
c:
Applicant respectfully disagrees as the processor merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) and revising…at least one setting…based on the comparing describes the mental process of using choosing a value based on a judgement . (See 2106.04.(a)(2).III.C) and the of the recommender module…wherein the at least one setting includes a dimension of the recommender module merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
d:
Applicant respectfully disagrees as the processor and using the recommender module merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) and generating…the one or more recommendations…with the at least one revised setting describes the mental process of using judgement to create a list based on data and requirements. See 2106.04.(a)(2).III.C.
Further 101 arguments:
Applicant appears to argue on pages 11-14 that claimed invention integrates into a practical application and cites to paragraph 18 from the specification stating additional element are related to a specific and particular manner for “enable[ing] specialization for concerns most relevant for information retrieval systems... This insight yields a novel perspective on user cold-start problems, where a user with no rating history is introduced to a system. In addition, a computationally efficient model is proposed for auditing/evaluating recommender systems”.
Examiner respectfully disagrees as Applicant does not highlight any specific additional element that positively recites or enables the proposed practical application.
Applicant appears to argue on pages 12-14 that the claimed invention integrates into a practical application and cites to paragraph 18 from the specification stating additional element are related to a specific and particular manner for “enable[ing] specialization for concerns most relevant for information retrieval systems... This insight yields a novel perspective on user cold-start problems, where a user with no rating history is introduced to a system. In addition, a computationally efficient model is proposed for auditing/evaluating recommender systems.”
Further, applicant asserts the particular manner for modifying a setting of a recommender module based on executing and evaluating auditing techniques and the recommender module results in increasing the efficiency and accuracy of the recommender module to integrate the idea practically while citing to Alice and Berkheimer.
Examiner respectfully disagrees as, although Applicant cites to the conclusion of Alice and Berkeheimer, Examiner does not see the parallels between current claims and the court case cited. Examiner asks Applicant to highlight the similarities for deeper consideration.
Further, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant does not highlight any specific additional element that positively recites or enables the proposed practical application of increasing efficiency and accuracy.
103:
Applicant appears to argue on pages 15-16 that the follow claimed limitations are not taught in prior art:
executing, by at least one processor, one or more auditing techniques and a recommender module, where the executing includes the recommender module utilizing a training data set and one or more user parameters
based on the executing, evaluating, by the at least one processor, one or more performance parameters of the recommender module that provides one or more recommendations based on one or more user factors and one or more content factors for a content library
revising, by the at least one processor, at least one setting of the recommender module based on the comparing, wherein the at least one setting includes a dimension of the recommender module
generating, by the at least one processor, the one or more recommendations using the recommender module with the at least one revised setting
a:
Applicant respectfully disagrees that prior art does not disclose executing, by at least one processor, one or more auditing techniques and a recommender module where the executing includes the recommender module as Liang discloses on Page 1, Col. 2, Paragraph 5 “We develop a probabilistic model for recommendation called Exposure MF (abbreviated as ExpoMF) that separately captures whether a user has been exposed to an item from whether a user has ultimately decided to click on it” as capturing whether a user has been exposed to an item from whether a user has ultimately decided to click on it is an auditing technique and ExpoMF is a recommender module that uses Liang, Page 4, Col. 2, Algorithm 1 and utilizes a training data set and one or more user parameters as Liang discloses on Page 2, Col. 2, Paragraph 2, “where θu and βi represent user u’s latent preferences” where the θu and βi corresponds to user parameters and Liang, Page 6, Col. 2, Paragraph 3, “For each dataset we randomly split the observed user-item interactions into training/test/validation sets” shows the dataset is split into a training dataset.
b:
Applicant respectfully disagrees that prior art does not disclose based on the executing, evaluating, by the at least one processor, one or more performance parameters of the recommender module that provides one or more recommendations as Liang evaluates the recommendation performance(i.e. the recommendations provided by the recommender) as disclosed in Liang, Page 956, Col. 2, Paragraph 5, “To evaluate the recommendation performance, we report both Recall@k, a standard information retrieval measure, as well as two ranking-specific metrics: mean average precision (MAP@k) and NDCG@k” and Liang, Page 957, Col. 2, Paragraph 3, “In addition, higher values of NDCG@100 and MAP@100 (even when Recall@50 is lower) indicate that the top-ranked items by ExpoMF tend to be more relevant to users' interests” as well as the evaluation being based on one or more user factors and one or more content factors as Liang, Page 954, Equation 3 and Algorithm 1 shows in lines 1 and 5 the use of exposure covariant(xi) as content factors and user specific factors(θ1) as user factors when inferencing with the recommendation model and the content coming from a content library is disclosed in Liang, Page 953, Col. 1, Paragraph 1, “For every combination of users u = 1,…,U and items i = 1,…,I, consider two sets of variables. The first matrix A = {aui} indicates whether user u has been exposed to item i.” and Page 960, Col 2. Paragraph 1,“Finally, we would like to evaluate our proposed model in a more realistic setting, e.g., in an online environment with user interactions” where the evaluation being performed on an online environment with a set of user and items is considered a content library
c:
Applicant respectfully disagrees that prior art does not disclose revising, by the at least one processor, at least one setting of the recommender module based on the comparing as Liang, Page 954, Col. 2, Algorithm 1, Lines 3-8, shows revising a setting of the user/item factors and the exposure settings are revised contingent on validation performance(Line 3, while performance on validation set increases do) and wherein the at least one setting includes a dimension of the recommender module as Applicant’s arguments have been considered but are moot because the new ground of rejection for the amended limitation does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument and has been covered with new prior art Ding.
d:
Applicant respectfully disagrees that prior art does not disclose generating, by the at least one processor, the one or more recommendations using the recommender module with the at least one revised setting as Liang discloses generating a set of recommendations using the ExpoMF recommender with revised settings as Liang, Page 4, Col. 2, Algorithm 1 shows recommender module with a data set and user parameters described as user factors that are revised.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.J.J./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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