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 Objections
Claim 21 is objected to because of the following informalities:
It seems that the sentence “executing a plurality iterations…” is repeated. For the purpose of examination, examiner interprets the two sentences are repeated. Appropriate correction is required.
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Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 21-25, 27-32, and 34-39 are rejected under 35 U.S.C. 103 as being unpatentable over Kenthapadi (U.S. Pub 2020/0372435 A1), in view of FA*IR: A Fair Top-k Ranking Algorithm, Meike Zehlike, 2 July 2018
Claim 21
Kenthapadi discloses a computer-implemented method, comprising:
receiving, from an external ranking system (fig. 1, machine learning models 114), a ranking of a plurality of items, the ranking comprising an amount of bias ([0046], “… monitoring system 112 includes functionality to detect, quantify, and/or mitigate bias in machine learning models 114…” [0042], “… Machine learning models 114 may then output scores representing the strengths of qualified candidates 132… One or more rankings 116 of recommended candidates may then be generated by ordering qualified candidates 132 by descending score…” <examiner note: the ML 114 which is external ranking system generates rankings for candidates 132. The monitoring system detects and mitigate bias in rankings generated by ML 114>);
executing a plurality of iterations modifying the ranking, wherein an iteration of modifying the ranking comprises ([0066], “… monitoring system 112 generates one or more reranking 126 to achieve the target proportions…”)
identifying a value of a multi-valued protected feature of the plurality of items subject to bias in exceeding the modification criterion (70% male in ranking 116 exceed 60% male in qualified candidates 132) ([0063], “… For example, qualified candidates 132 may have a gender distribution that is 60% male and 40% female. As a result, the ranking may have an underrepresented female group if the gender distribution in the ranking is 70% male and 40% female…”)
elevating a rank of an item of the plurality of items having the value of the multi-valued protected feature ([0073], “… When the minimum number of candidates can only be met by including a candidate with the underrepresented attribute value in the position, the highest-scoring candidate from the attribute-specific ranking containing the underrepresented attribute value is moved to the position…” <examiner note: to meet the target proportion, female who are underrepresented group is elevated/included in the re-rankings. Each position in the re-ranking is verified to meet the target proportion>)
outputting the modified ranking of the plurality of items ([0030], “… Finally, at least a portion of the reranking is outputted in a response to the request. For example, a certain portion of the original ranking (e.g., the top “n” candidates) may be replaced with the reranking to improve the representation of one or more attribute values in top-ranked candidates shown to a user making the request…”)
However, Kenthapadi does not explicitly disclose wherein an iteration of modifying the ranking comprises: responsive to determining that a likelihood of bias for the ranking exceeds a modification criterion: identifying a value of a multi-valued protected feature of the plurality of items subject to bias in exceeding the modification criterion; and elevating a rank of an item of the plurality of items having the value of the multi-valued protected feature; outputting the modified ranking of the plurality of items
Zehlike discloses in pg. 1, “… On a ranking, the desired good for an individual is to appear in the result and to be ranked amongst the top- k positions. The outcome is unfair if members of a protected group are systematically ranked lower than those of a privileged group. The ranking algorithm discriminates unfairly if this ranking decision is based fully or partially on the protected feature… We propose a post-processing method to remove the systematic bias by means of a ranked group fairness criterion…” <examiner note: the initial ranking is biased, Zehlike propose a post-processing method to the bias in the ranking. Let assume that the initial ranking is M, M, M, M, M, M, F, F, F, F, F, F. The F = female is protected group, and M = male is non-protected group >)
wherein an iteration of modifying the ranking comprises (pg. 7, algorithm 2, line 9, while tp + tn <k <examiner note: let k is 10, tp=protected item, e.g., Female or F, and tn=non-protected item, e.g., Male or M, p=0.5): responsive to determining that a likelihood of bias for the ranking exceeds a modification criterion; identifying a value of a multi-valued protected feature of the plurality of items subject to bias in exceeding the modification criterion; and elevating a rank of an item of the plurality of items having the value of the multi-valued protected feature; outputting the modified ranking of the plurality of items (line 10, if tp < m [ tp + tn + 1 ] <examiner note: it compares current count (tp) against the m table 2 in pg. 4. At p=.5, the first 3 position is filled by non-protected Male. At the position 4, if another Male is filled in, there will be a bias the ranking list. At the position 4, a Female in protected group is filled
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<examiner note: by the end the modified ranking list M, M, M, F, M, M, F, M, F, M, M, F>)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a fair top-k ranking algorithm as disclosed by Zehlike into Kenthapadi because this algorithm is a post-processing method to remove the systematic bias by means of a ranked group fairness criterion.
Claim 22
Claim 21 is included, Kenthapadi discloses wherein the likelihood of bias of the plurality of items for the ranking of the plurality of items is determined with respect to the multi-valued protected feature ([0046], “… A given machine learning model may be biased when the machine learning model systematically ranks members of a “disadvantaged” group with a certain attribute (e.g., gender, age range, ethnicity, location, etc.) below that of other groups…”)
Claim 23
Claim 21 is included, Kenthapadi discloses wherein the modification criterion comprises a threshold of demographic parity with respect to the multi-valued protected feature, and wherein demographic parity with respect to the multi-valued protected feature comprises ranking items of the plurality of items with a particular value of the feature proportional to a rate of occurrence of the particular value of the multi-valued protected feature relative to all values of the multi-valued protected feature ([0085], Male (target proportion 0.3): 0.6, 0.5, 0.35, 0.15, 0.05; [0086] Female (target proportion 0.4): 0.7, 0.4, 0.3, 0.25, 0.23, [0087] Unknown (target proportion 0.2): 0.5, 0.45, 0.2, 0.1, 0.02 [0088] The top seven positions in the reranking include the top three “Male” candidates, the top two “Female” candidate, and the top two “Unknown” candidates. At the eighth position, the number of “Female” candidates in the ranking drops below the minimum number of “Female” candidates (floor(8*0.4), or 3) required to maintain the target proportion of 0.4. As a result, the highest-scoring “Female” candidate that is not already in the reranking (i.e., the candidate with the score of 0.3 in the “Female” ranking) may be selected for the eighth position…”)
Claim 24
Claim 21 is included, Kenthapadi discloses wherein the external ranking system is a ranking classifier trained using machine learning (fig. 1, machine learning model 114)
Claim 25
Claim 21 is included, Kenthapadi discloses wherein an iteration of modifying the ranking further determining that the likelihood of bias for the ranking exceeds the modification criterion comprises applying a Bayes factor to the ranking of the plurality of items to determine the likelihood of bias for the ranking of the items. ([0062], line 1-7, “... monitoring system 112 uses metrics 122 to detect and mitigate bias in machine learning models 114. For example, monitoring system 112 may identify bias in a machine learning model when the skew metric and/or divergence metric for a ranking outputted by the machine learning model exceeds a threshold...” [0063], line 1-5, “... Monitoring system 112 may also, or instead, assess bias in the machine learning model by directly comparing the distribution of attributes 118 in the ranking with the distribution of attributes 118 in the corresponding set of qualified candidates 132...” <examiner note: metrics 22 are applied to outputs (i.e., ranked results) of machine learning models to detect and mitigate bias of ranked results>);
Claim 27
Claim 21 is included, Kenthapadi discloses wherein an iteration of modifying the ranking further comprises applying respective Bayes factors to individual ones of the plurality of items to determine the likelihood of bias for the ranking of the items ([0062], line 1-7, “... monitoring system 112 uses metrics 122 to detect and mitigate bias in machine learning models 114. For example, monitoring system 112 may identify bias in a machine learning model when the skew metric and/or divergence metric for a ranking outputted by the machine learning model exceeds a threshold...” [0063], line 1-5, “... Monitoring system 112 may also, or instead, assess bias in the machine learning model by directly comparing the distribution of attributes 118 in the ranking with the distribution of attributes 118 in the corresponding set of qualified candidates 132...”)
Claims 28-32 and 34-39 are rejected because the claims are similar to claims 21-25 and 27.
Allowable Subject Matter
Claim 26, 33, and 40 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
THIS ACTION IS MADE FINAL. 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.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/ Primary Examiner, Art Unit 2154
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