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 .
DETAILED ACTION
2. This office action is in response to the amendment filed on 11/24/2025. Claim 1-20 are pending and have been considered below
The rejections of Claim 20 under 35 USC 112(a) 112(b) are moot in light of amendments.
The rejections of Claims 1-20 under 35 U.S.C. 101 as directed to non-statutory subject matter are moot pursuant to argument
Claim Rejections - 35 USC § 102
3. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-5, 7-9, 11-13, 15-16 and 18-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Garg et al. (US 10,803,421).
Claim 1. Garg discloses a system comprising: a non-transitory computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to perform operations comprising:
obtaining a first training set of one or more data items having information about whether a first event occurred and information about whether a second event dependent on the first event occurred (col. 8, lines 39-59);
using the first training set as input to a first machine learning algorithm to train a first model to predict whether the second event will occur for a data item passed as input to the first model (col. 12, line 61 – col. 13, lines 1-3);
obtain a second training set of one or more data items having information about whether the first event occurred but not having data about whether the second event occurred (col. 11, lines 4-48);
input the data items in the second training set to the first model to obtain one or more predictions as to whether the second event will occur (col. 11, lines 49-67);
add the predictions as labels to the second training set (col. 8, lines 41-49); and
use the second training set as input to a second machine learning algorithm to train a second model to predict whether the second event will occur for a data item passed as input to the second model, wherein the first event is an earlier event (calibrated job positions) and the second event is a later event in time relative to the first event (prediction and/or selection of candidate(s)) (col. 13, lines 4-15; col. 12, lines 1-14, 25-32).
Claim 2. Garg discloses the system of claim 1, wherein the second event is a confirmed hire for a job and the first event is an application for the job (abstract).
Claim 4. Garg discloses the system of claim 2, wherein the second training set includes data about users who applied for jobs using a graphical user interface of a social networking service (col. 7, lines 64-col. 8, lines1-3).
Claim 5. Garg discloses the system of claim 1, wherein the second training set includes a first portion of one or more data items in which the first event is known to have occurred and a second portion of one or more data items in which the second event is known to have not occurred; and wherein the inputting and adding is only performed for data items in the first portion and not for data items in the second portion (col. 2, line 65 -col. 3, lines 1-65).
Claim 7. Garg discloses the system of claim 1, wherein the first machine learning algorithm is a different machine learning algorithm than the second machine learning algorithm (col. 8, lines 44-49).
Claim 8. Garg discloses the system of claim 7, wherein the first machine learning algorithm is a pointwise deep learning neural network (col. 12, lines 15-32) [Wherein pointwise deep learning considers one item at a time and predicts a relevance score].
Claim 9. Garg discloses the system of claim 8, wherein the second machine learning algorithm is a listwise deep learning neural network (…the candidate in a ranked list including a plurality of candidates at a ranking position determined according to the calculated match score value.) (claim 1) [wherein Listwise deep learning is a subfield of “learning to rank” to optimize ranked list of items].
Claim 11. Garg discloses the system of claim 1, wherein the operations further comprise: obtaining information about a first user and a first item being considered for display to the first user; and passing the information about the first user and first item to the second model, to predict a likelihood of the first event occurring if the first item is displayed to the first user (col. 12, line 61-col. 13, lines 1-16).
Claims 12-13, 15-16 and 18-20 represent the method of claims 1-2, 4-5, and 7-9, respectively and are rejected along the same rationale.
Claim Rejections - 35 USC § 103
4. 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.
5. Claim(s) 3, 6, 10, 14 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garg et al. (US 10,803,421) in view of Mithal et al. (US 2020/0409960).
Claim 3. Garg discloses the system of claim 2, but fails to explicitly disclose wherein the first training set includes data about users that were hired for jobs that they applied to using a graphical user interface of a social networking service.
However, Mithal discloses wherein the first training set includes data about users that were hired for jobs that they applied to using a graphical user interface of a social networking service ([0019]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Garg further in view of Mithal to incorporate the above cited feature. One would have been motivated to do so in order to optimize the jobs/candidates ranking.
Claim 6. Garg discloses the system of claim 5, but fails to explicitly disclose wherein the operations further comprise automatically adding negative labels for data items in the second portion of one or more data items.
However, Mithal discloses (..each instance of a job listing for which a user has undertaken a relevant action is a training example corresponding to a mixture of positive label (e.g., relevant job listing) and negative label (irrelevant job listing) ([0016]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Garg further in view of Mithal to incorporate the above cited feature. One would have been motivated to do so in order to optimize the jobs/candidates ranking.
Claim 10. Garg discloses the system of claim 1, but fails to explicitly disclose wherein the second event is a purchase of a good or service and the first event is the clicking of an advertisement for the purchase of the good or service. However, Mithal discloses the second event is a purchase of a good or service and the first event is the clicking of an advertisement for the purchase of the good or service ([0019]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Garg further in view of Mithal to incorporate the above cited feature. One would have been motivated to do so in order to optimize the jobs/candidates ranking.
Claims 14 and 17 represent the method of claims 3 and 6, respectively and are rejected along the same rationale.
Response to Arguments
6. Applicant’s arguments filed 11/24/2025 have been fully considered but they are moot in light of new ground of rejection(s).
Conclusion
7. 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.
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
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/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146