DETAILED ACTION
Claims 1-20 are pending in the Instant Application.
Claims 1-20 are rejected (Non-Final Rejection).
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 .
Priority
The Instant Application is a continuation of 18/933,621, filed 31 October 2024. Thus. the earliest effective filing date is 31 October 2024 for what is described therein.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 13 April 2026 was considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12/468,748. Although the claims at issue are not identical, they are not patentably distinct from each other because the only differences between the two is that claims 1 and 11 of U.S. Patent No. 12/468,748 specify two of the plurality of metrics described in the Instant Application. The different claims match-up as follows. If a claim is missing bellow, the claim are identical by number. (For example, if claim 2 in the Instant Application, is the same as claim 2 in the U.S. Patent No. 12/468,748, it will not be listed below).
Instant Application
U.S. Patent No. 12/468,748
1. An apparatus for training a machine learning model to generate a match score using unstructured profile data and unstructured reference data, wherein the apparatus comprises: at least a computing device, wherein the computing device comprises: a memory; and at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to:
receive a plurality of unstructured profile data, wherein each unstructured profile datum of the plurality of unstructured profile data comprise a plurality of metrics;
receive a plurality of unstructured reference data, wherein each unstructured reference datum of the plurality of unstructured reference data is associated with a plurality of criteria; filter, for each unstructured reference datum of the plurality of unstructured reference data, the plurality of unstructured profile data; generate, for each unstructured profile datum of the filtered plurality of unstructured profile data, a match score by calculating, using a matching algorithm, a degree of similarity between the plurality of metrics and the plurality of criteria; and display, using a client device, an output based on the generated match score.
1. An apparatus for training a machine learning model to generate a match score using unstructured profile data and unstructured reference data, wherein the apparatus comprises: at least a computing device, wherein the computing device comprises: a memory; and at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to:
receive a plurality of unstructured profile data, wherein each unstructured profile datum of the plurality of unstructured profile data comprises a plurality of metrics, wherein at least two metrics of the plurality of metrics comprise quantifiable metrics comprising a value a candidate contributed to their last job in terms of generating profits and a weighted value for an educational institution of the candidate;
receive a plurality of unstructured reference data, wherein each unstructured reference datum of the plurality of unstructured reference data is associated with a plurality of criteria; filter, for each unstructured reference datum of the plurality of unstructured reference data, the plurality of unstructured profile data; generate, for each unstructured profile datum of the filtered plurality of unstructured profile data, a match score by calculating, using a matching algorithm, a degree of similarity between the plurality of metrics and the plurality of criteria; and display, using a client device, an output based on the generated match score.
4. The apparatus of claim 1, wherein filtering the plurality of unstructured profile data comprises generating a plurality of filtered profiles, using a filtering algorithm as a function of unstructured reference data and plurality of unstructured profile data.
4. The apparatus of claim 1, wherein filtering the plurality of unstructured profile data comprises generating a plurality of filtered profiles, using a filtering algorithm as a function of the plurality of unstructured reference data and the plurality of unstructured profile data.
11. A method for training a machine learning model to generate a match score using unstructured profile data and unstructured reference data, wherein the method comprises:
receiving a plurality of unstructured profile data, wherein each unstructured profile datum of the plurality of unstructured profile data comprise a plurality of metrics;
receiving a plurality of unstructured reference data, wherein each unstructured reference datum of the plurality of unstructured reference data is associated with a plurality of criteria; filtering, for each unstructured reference datum of the plurality of unstructured reference data, the plurality of unstructured profile data;
generating, for each unstructured profile datum of the filtered plurality of unstructured profile data, a match score by calculating, using a matching algorithm, a degree of similarity between the plurality of metrics and the plurality of criteria; and displaying, using a client device, an output based on the generated match score.
11. A method for training a machine learning model to generate a match score using unstructured profile data and unstructured reference data, wherein the method comprises:
receiving a plurality of unstructured profile data, wherein each unstructured profile datum of the plurality of unstructured profile data comprises a plurality of metrics, wherein at least two metrics of the plurality of metrics comprise quantifiable metrics comprising a value a candidate contributed to their last job in terms of generating profits and a weighted value for an educational institution of the candidate;
receiving a plurality of unstructured reference data, wherein each unstructured reference datum of the plurality of unstructured reference data is associated with a plurality of criteria; filtering, for each unstructured reference datum of the plurality of unstructured reference data, the plurality of unstructured profile data;
generating, for each unstructured profile datum of the filtered plurality of unstructured profile data, a match score by calculating, using a matching algorithm, a degree of similarity between the plurality of metrics and the plurality of criteria; and displaying, using a client device, an output based on the generated match score.
14. The method of claim 11, wherein filtering the plurality of unstructured profile data comprises generating a plurality of filtered profiles, using a filtering algorithm as a function of unstructured reference data and plurality of unstructured profile data.
14. The method of claim 11, wherein filtering the plurality of unstructured profile data comprises generating a plurality of filtered profiles, using a filtering algorithm as a function of the plurality of unstructured reference data and the plurality of unstructured profile data.
Claim Rejections - 35 USC § 102
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.
Claims 1-5, 7, 8, 11-15, 17 and 18 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by Liu, United States Patent Application Publication No. 2019/0220824.
As per claim 1, Liu discloses an apparatus for training a machine learning model to generate a match score using unstructured profile data and unstructured reference data, wherein the apparatus comprises:
at least a computing device, wherein the computing device comprises: a memory ([0058]); and at least a processor communicatively connected to the memory ([0058]), wherein the memory contains instructions configuring the at least a processor to:
receive a plurality of unstructured profile data ([0029]] wherein a raw resume database is received, wherein raw indicates that the resumes are in their original unstructured formats as noted in [0028]), wherein each unstructured profile datum of the plurality of unstructured profile data comprise a plurality of metrics ([0048] wherein a number of metrics can be obtained from a plurality of resumes such as those listed);
receive a plurality of unstructured reference data, wherein each unstructured reference datum of the plurality of unstructured reference data is associated with a plurality of criteria ([0029] wherein unstructured job criteria from the internet or provided by employers is received);
filter, for each unstructured reference datum of the plurality of unstructured reference data, the plurality of unstructured profile data ([0033] and [0060] wherein for each of the reference datum (recognized as the JOR data in the prior art) the resume data is matched/filtered and matching information between the two is generated);
generate, for each unstructured profile datum of the filtered plurality of unstructured profile data, a match score by calculating, using a matching algorithm, a degree of similarity between the plurality of metrics and the plurality of criteria ([0046] wherein a match score can be calculated based on the degree of similarity (recognized as match in the prior art)); and
display, using a client device, an output based on the generated match score ([0046] wherein the score can be displayed (output in the prior art) to the user on a client device such as user interface 204).
As per claim 2, Liu discloses the apparatus of claim 1, wherein the apparatus comprises a machine learning model, wherein the machine learning model comprises a neural network ([0048] wherein a neural network algorithm is used and trained).
As per claim 3, Liu discloses the apparatus of claim 2, wherein the machine learning model is trained using training data, wherein the training data comprises exemplary metrics of unstructured profile data and exemplary criteria of unstructured reference data correlated to exemplary match scores ([0036] wherein the training occurs using processed resume data and JOR data, both which are metrics from the unstructured resume and job data, described in [0048] and produces matched scores as described in [0059]).
As per claim 4, Liu discloses the apparatus of claim 1, wherein filtering the plurality of unstructured profile data comprises generating a plurality of filtered profiles, using a filtering algorithm as a function of unstructured reference data and plurality of unstructured profile data ([0048] wherein the generation of filtered profiles is performed by a neural network algorithm as a function of the reference and profile data, recognized as the features in the prior art).
As per claim 5, Liu discloses the apparatus of claim 4, wherein the filtering algorithm is configured to: classify each unstructured profile datum of the plurality of unstructured profile data into a labeled cohort as a function of the plurality of metrics and the plurality of criteria ([0033] and [0037] wherein the predictive model generates matching information); and generate the filtered profile based on the classification ([0037] wherein the filtered profile is generated based on the classification).
As per claim 7, Liu discloses the apparatus of claim 1, wherein the matching algorithm is configured to: assign a weight value to each metric of the plurality of metrics ([0042] wherein weights can be assigned to each feature); compare the plurality of metrics to the plurality of criteria ; calculate the degree of similarity as a function of the comparison ([0046] wherein metrics are compare, weighted and scored); and generate the match score as a function of the degree of similarity ([0046] wherein a matching score is determined that indicates a degree of similarity).
As per claim 8, Liu discloses the apparatus of claim 1, wherein the matching algorithm is further configured to rank the filtered plurality of unstructured profile data based on the match scores ([0037] wherein the unstructured profile data (resume data) is ranked based on the match scores and returned to the client).
As per claim 11, claim 11 is the method performed by the apparatus of claim 1 and is rejected for the same rationale and reasoning.
As per claim 12, claim 12 is the method performed by the apparatus of claim 2 and is rejected for the same rationale and reasoning.
As per claim 13, claim 13 is the method performed by the apparatus of claim 3 and is rejected for the same rationale and reasoning.
As per claim 14, claim 14 is the method performed by the apparatus of claim 4 and is rejected for the same rationale and reasoning.
As per claim 15, claim 15 is the method performed by the apparatus of claim 5 and is rejected for the same rationale and reasoning.
As per claim 17, claim 17 is the method performed by the apparatus of claim 7 and is rejected for the same rationale and reasoning.
As per claim 18, claim 18 is the method performed by the apparatus of claim 8 and is rejected for the same rationale and reasoning.
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.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of PARTHASARATHY, (“Parthasarathy”), United States Patent Application Publication No. 2020/0057864.
As per claim 6, Liu discloses the apparatus of claim 1, but does not disclose wherein the matching algorithm comprises a gross validation function. However, Parthasarathy wherein the matching algorithm comprises a gross validation function ([0016] wherein the result of pattern matching is checked with a validation function.)
Both Liu and Parthasarathy describe matching. One could use the validation function from Parthasarathy with the matching model in Liu to teach the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the method of matching unstructured profile and reference data in Liu with the validation function in Parthasarathy in order to be able to check matches that are not an exact match.
As per claim 16, claim 16 is the method performed by the apparatus of claim 6 and is rejected for the same rationale and reasoning.
Claims 9, 10, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Hardtke et al. (“Hardtke”), United States Patent Application Publication No. 2014/0122355.
As per claim 9, Liu discloses the apparatus of claim 1, wherein the matching algorithm is further configured to pair at least an unstructured profile datum of the filtered plurality of unstructured profile data to each unstructured reference datum of the plurality of unstructured reference data based on the match score ([0060] wherein a number of unstructured reference data in the prior art (recognized as unstructured reference data) are matched with a number of profiles (recognized as resumes in the prior art)), but does not disclose wherein the match score is within a predefined threshold. However, Hardtke teaches herein the match score is within a predefined threshold ([0069] wherein the data is paired if a matching threshold is met).
Both Liu and Hardtke describe matching resume data and determining a match score. One could use the threshold in Hardtke with the match score in Liu to teach the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the method of using machine learning to match unstructured data in Liu using a match score in Liu with the match score representing a pair when it is above a threshold in Hardtke in order to limit the matches to only the better matches.
As per claim 10, note the rejection of claim 9 where Liu and Hardtke are combined. The combination teaches the apparatus of claim 9, Liu further discloses wherein the output comprises an indication of whether the at least an unstructured profile datum of the filtered plurality of unstructured profile data is paired to the unstructured reference datum ([0060]-[0061] wherein the matches are outputted with annotations and scores indicating a match).
As per claim 19, claim 19 is the method performed by the apparatus of claim 9 and is rejected for the same rationale and reasoning.
As per claim 20 claim 20 is the method performed by the apparatus of claim 10 and is rejected for the same rationale and reasoning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KANNAN SHANMUGASUNDARAM whose telephone number is (571)270-7763. The examiner can normally be reached M-F 9:00 AM -6:00 PM.
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/KANNAN SHANMUGASUNDARAM/Primary Examiner, Art Unit 2168
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