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 rce filed on 05/08/2026. Claim 1-20 are pending and have been considered below
Claim Rejections - 35 USC § 103
3. 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) 1-2, 4-5, 7-9, 11-13, 15-16 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garg et al. (US 10,803,421) in view of Ghosh et al. (US 2021/0042667).
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);
Garg does not explicitly disclose add the predictions from the first model as labels to the second training set; 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.
However, Ghosh discloses add the predictions from the first model as labels to the second training set; 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 mode ([0007]). 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 Ghosh to incorporate the above cited feature. One would have been motivated to do so in order to predictably enhance the second model’s ability to identify relationships between the first event and the second event; thereby achieving model efficiency.
Garg further discloses 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 and Ghosh disclose the system of claim 1, Garg further discloses 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 and Ghosh disclose the system of claim 2, Garg further discloses 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 and Ghosh disclose the system of claim 1, Garg further discloses 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 and Ghosh disclose the system of claim 1, Garg further discloses 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 and Ghosh disclose the system of claim 7, Garg further discloses 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 and Ghosh disclose the system of claim 8, Garg further discloses 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 and Ghosh disclose the system of claim 1, Garg further discloses 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.
4. 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 Ghosh et al. (US 2021/0042667) and further in view of Mithal et al. (US 2020/0409960).
Claim 3. Garg and Ghosh disclose the system of claim 2, but fail 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 and Ghosh disclose the system of claim 5, but fail 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 and Ghosh disclose the system of claim 1, but fail 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
5. Applicant’s arguments filed 05/08/2025 have been fully considered but they are moot in light of new ground of rejection(s).
Conclusion
6. 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.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146
Prosecution Insights