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 .
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.
Claim 1, 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al Bias and Debias in Recommender System: A Survey and Future Directions. ACM Trans. Inf. Syst. 41, 3, Article 67 (February 2023) “Chen” (IDS), in view of Do offline metrics predict online performance in recommender systems? Krauth et al. arXiv preprint arXiv:2011.07931, 2020, “Krauth”
Regarding claim 1, CHEN discloses a computer-implemented method (CHEN, pg. 4, sec. 2.2, …system learn model from dataset… make recommendation in the serving stage) comprising: identifying one or more offline evaluation (CHEN, pg. 13, section 4.1.1, see also Pg. 5, sect. ) metrics (CHEN, abstract) that indicate, for a given feedback loop (CHEN, pg. 4, sec. 2.1 feedback loop) in a recommendation system, one or more feedback loop characteristics that are detrimental (CHEN, pg. 5, bias) to the feedback loop; generating a predictive (as cited above, i.e. model predict user preference) machine learning (ML) model (CHEN, pg. 4, sec. 2.2, learning a parametric model)
It is noted that CHEN is silent about model that correlates the identified offline evaluation metrics with one or more indications of the feedback loop characteristics that are detrimental to the feedback loop; instantiating the predictive ML model to predict, using the correlated offline evaluation metrics and the detrimental feedback loop characteristics, how the feedback loop will be negatively affected over time; and providing, to at least one entity, an indication of how the feedback loop will be negatively affected over time due to the detrimental feedback loop characteristics as claimed
However KRAUTH discloses recommendation model that correlates the identified offline evaluation metrics (KRAUTH, pg. 9, section. 5.1) with one or more indications of the feedback loop characteristics that are detrimental (see CHEN, citation above) to the feedback loop; instantiating the predictive ML model to predict, using the correlated (KRAUTH, pg. 8, section 5.1) offline evaluation metrics and the detrimental feedback loop characteristics, how the feedback loop will be negatively affected over time (see KRAUTH, introduction); and providing, to at least one entity, an indication of how the feedback loop will be negatively affected (KRAUTH, section. 7.12 diminishing return) over time due to the detrimental feedback loop characteristics (KRAUTH, pg. 12, sect. 7.1).
Both CHEN and KRAUTH teach systems with recommendation system, and those systems are comparable to that of the instant application. Because the two cited references are analogous to the instant application, it 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, to include in the CHEN disclosure, offline model, as taught by KRAUTH. Such inclusion would have increased the usefulness of the system by designing better recommendation systems, and would have been consistent with the rationale of combining prior art elements according to known methods to yield predictable results to show a prima facie case of obviousness (MPEP 2143(I)(A)) under KSR International Co. v. Teleflex Inc., 127 S. Ct. 1727, 82 USPQ2d 1385, 1395-97 (2007).
Regarding claim 14, CHEN/KRAUTH, for the same motivation of combination, further discloses a system comprising: at least one physical processor; an electronic display; and physical memory comprising computer-executable instructions that, when executed by the physical processor, cause the physical processor to: identify one or more offline evaluation metrics that indicate, for a given feedback loop in a recommendation system, one or more feedback loop characteristics that are detrimental to the feedback loop; generate a predictive machine learning (ML) model that correlates the identified offline evaluation metrics with one or more indications of the feedback loop characteristics that are detrimental to the feedback loop; instantiate the predictive ML model to predict, using the correlated offline evaluation metrics and the detrimental feedback loop characteristics, how the feedback loop will be negatively affected over time; and provide, to at least one entity, an indication of how the feedback loop will be negatively affected over time due to the detrimental feedback loop characteristics (This claim recited similar limitation as claim 1 and is rejected under the same ground).
Regarding claim 20, CHEN/KRAUTH, for the same motivation of combination, further discloses a non-transitory computer-readable medium comprising one or more computer-executable instructions that, when executed by at least one processor of a computing device, cause the computing device to: identify one or more offline evaluation metrics that indicate, for a given feedback loop in a recommendation system, one or more feedback loop characteristics that are detrimental to the feedback loop; generate a predictive machine learning (ML) model that correlates the identified offline evaluation metrics with one or more indications of the feedback loop characteristics that are detrimental to the feedback loop; instantiate the predictive ML model to predict, using the correlated offline evaluation metrics and the detrimental feedback loop characteristics, how the feedback loop will be negatively affected over time; and provide, to at least one entity, an indication of how the feedback loop will be negatively affected over time due to the detrimental feedback loop characteristics (This claim recited similar limitation as claim 1 and is rejected under the same ground).
Allowable Subject Matter
Claims 2-13, 15-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20140136438 A1 SYSTEMS AND METHODS OF OBTAINING CANDIDATE QUALIFICATIONS USING SELF-RANKING TESTING TOOLS
US 20140067597 A1 HYBRID RECOMMENDATION SYSTEM
US 20110184806 A1 PROBABILISTIC RECOMMENDATION OF AN ITEM
US 20110035379 A1 PROBABILISTIC CLUSTERING OF AN ITEM
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/FRANK F HUANG/Primary Examiner, Art Unit 2485
