DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
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 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.
Status of the Claims
This communication is in response to communications received on 1/30/26. Claims 60-61, 64, 69-72, 78, 84, and 86-87 were amended, no claims were cancelled, and no new claims were added. Applicant does not provide any information on where support for the amendments can be found in the instant specification. Therefore, Claims 60-87 are pending and have been addressed below.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 1/30/26 was/were considered by the examiner.
Response to Arguments
Applicant’s arguments, see applicant’s remarks, filed 1/30/26, with respect to rejections under 35 USC 112 for claims 60-87 have been fully considered and are persuasive. The Examiner respectfully withdraws rejections under 35 USC 112 for claims 60-87.
Applicant’s arguments, see applicant’s remarks, filed 1/30/26, with respect to rejections under 35 USC 101 for claims 60-87 have been fully considered but they are not persuasive as far as they apply to the amended 101 rejection(s) below.
Applicant’s arguments, see applicant’s remarks, filed 1/30/26, with respect to rejections under 35 USC 103 for claims 60-87 have been fully considered but they are not persuasive as far as they apply to the amended 103 rejection(s) below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed. Ariad, 598 F.3d at 1350, 94 USPQ2d at 1171; Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002) (holding that generic claim language appearing in ipsis verbis in the original specification did not satisfy the written description requirement because it failed to support the scope of the genus claimed)”. Additionally, original claims may fail to satisfy the written description requirement when the invention is claimed and described in functional language but the specification does not sufficiently identify how the invention achieves the claimed function. Ariad, 598 F.3d at 1349, 94 USPQ2d at 1171.
Claims 60-87 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Representative claims 60, 86, and 87 recite:
“inputting, to the trained at least one model, a data structure comprising first attributes from the first job requisition and second attributes indicating successful and unsuccessful candidate submissions of the first recruiter user in relation to the first attributes;
determining, using the trained at least one model, a first measure of compatibility between the first recruiter user and the first job requisition based on the data structure.”
The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Examiner notes the bolded portion of the representative claims above is new matter. Initially, Examiner notes the bolded portion recites “inputting, to the trained at least one model, a data structure comprising first attributes from the first job requisition and second attributes indicating successful and unsuccessful candidate submissions of the first recruiter user in relation to the first attributes” and “the data structure” which does not appear to be supported by the originally filed disclosure. Examiner notes the closest portions of the original disclosure include [0141, 0144, 0145] which only states an applicant tracking server which is also listed as candidate tracking server
[0145] “In some embodiments, the system may be configured to use information stored in the data structure 1600 to generate an input to one or more models used for matching recruiter users to job requisitions.”;
[0141] “In some embodiments, the system may be configured to use information stored in the data structure 1600 to generate an input to one or more models used for matching recruiter users to job requisitions.”;
[0144] “In some embodiments, the system may be configured to use information stored in the data structure 1600 to generate an input to one or more models used for matching recruiter users to job requisitions.”;
[0147-0148] “FIG. 17 shows a data structure 1700 of a profile for a job requisition.”
None of these portions however disclose the above bolded claim language. Appropriate correction/clarification is required. Claim(s) 61-85 is/are rejected because they depend on claim(s) 60, 86, and 87.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 60-87 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 60, 86, and 87 that, under its broadest reasonable interpretation, is directed to matching recruiters to job openings.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 86 recites a series of steps) and system (claim(s) 60-85 and 87 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s):
Claim 86: tracking activity of a plurality of recruiter users of a talent exchange system, the activity comprising actions performed in the talent exchange system by the plurality of recruiter users in placing candidates for job requisitions;
storing, in a database, records of the activity of the plurality of recruiter users;
training, using at least a portion of the records of the activity, at least one model, the at least one model configured to output a measure of compatibility between a recruiter user and a job requisition; and
matching a first recruiter user of the talent exchange system to a first job requisition at least in part by:
inputting, to the trained at least one model, a data structure comprising first attributes from the first job requisition and second attributes indicating successful and unsuccessful candidate submissions of the first recruiter user in relation to the first attributes;
determining, using the trained at least one model, a first measure of compatibility between the first recruiter user and the first job requisition based on the data structure; and
determining to match the first recruiter user to the first job requisition based on the first measure of compatibility.
Claim(s) 60 and 87: same analysis as claim(s) 86.
Dependent claims 61-85 recite the same or similar abstract idea(s) as independent claim(s) 60, 86, and 87 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with matching recruiters to job openings.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea include a talent exchange system, database, at least one computer hardware processor, at least one non-transitory computer-readable storage medium, data structure (claim(s) 60), computer, one or more computer hardware processors, talent exchange system, database, data structure (claim(s) 86), one non-transitory computer-readable storage medium, processor, talent exchange system, database, data structure (claim(s) 87), database (claim 61), natural language processing (claim 79), natural language processing comprises a transformer (claim(s) 80), natural language processing comprises a recursive neural network (claim(s) 81), recursive neural network (claim(s) 83).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0203]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0203]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Claims 60-75, 82, and 84-87 are rejected under 35 U.S.C. 103 as being unpatentable over Chuang (US 2013/0275321 A1) in view of Ashkenazi et al. (US 2017/0357945 A1).
Regarding claim 60, 86, and 87 (currently amended), Chuang teaches a computer-implemented method comprising:
using one or more computer hardware processors to perform:
{a talent exchange system for placement of candidates to job openings, the system comprising: a database; at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, causes the at least one computer hardware processor to perform a method, the method comprising: - claim 60}
{a computer-implemented method comprising: using one or more computer hardware processors to perform: - claim 87}
tracking activity of a plurality of recruiter users of a talent exchange system, the activity comprising actions performed in the talent exchange system by the plurality of recruiter users in placing candidates for job requisitions [see at least [0022, 0386] computer implemented method using a distributed computer system which may use the 1Ogen MongoDB database to store talent platform exchange data; [0005, 0088] the staffing talent platforms 104 may comprise one or more web-based staffing aggregators including applicant tracking systems (ATS) and customer relationship management (CRM) systems used by recruiters; [0101] tracking the staffing party's (recruiter users) activity and candidate placement performance];
storing, in a database, records of the activity of the plurality of recruiter users [see at least [0028] receive and store job orders and plurality of candidates, track placement of candidate to job order, and payment to hiring party for placement of candidate to job order; [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0095] store data on various users (profile) including staffing party metrics, job order information, staffing party ID numbers, job order ID numbers, other information (such as in [0017, 0028, 0082, 0239-0241] ); [0386] the 1Ogen MongoDB database may be used to store talent platform exchange data];
a measure of compatibility between a recruiter user and a job requisition; and
matching a first recruiter user of the talent exchange system to a first job requisition at least in part by:
determining a first measure of compatibility between the first recruiter user and the first job requisition; and
the first recruiter user to the first job requisition [for the limitations above, see at least [0080, 0017] for staffing talent platforms A, B and C 104 used by staffing parties A, B and C 106 and agency personnel (e.g., recruiters); [0017, 0237-0238, 0291] determine (match) recruiter for a job based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ) ].
Chuang teaches matching a recruiter to a job requisition based on compatibility metrics but doesn’t/don’t explicitly teach however Ashkenazi discloses
training, using at least a portion of the records of the activity, at least one model, the at least one model configured to output a measure of compatibility between a user and a job requisition [see at least Fig. 2 and [0032] the prediction model 208 (machine learning algorithm) may be trained by the model training module 207 using training data from the training data store 217; [0034] prediction model 208 defined by parameters of the machine learning model to programmatically calculating the likelihood that a user is a match for a job listing; [0005] determining, based on the job prediction score, whether the user is a match for the job listing];
inputting, to the trained at least one model, a data structure comprising first attributes from the first job requisition and second attributes indicating successful and unsuccessful candidate submissions of the first recruiter user in relation to the first attributes;
determining, using the trained at least one model, a first measure of compatibility between the user and the first job requisition based on a data structure [for the limitations above,
as noted in the 112 rejection there is not support for the inputting limitation and is interpreted the trained at least one model,
then see at least [0031] prediction associated with job data that is based on job opening and applicant data “The prediction module 208 may be configured to predict a likelihood that a user is a match for one or more job listings based on work history and job information.”; [0036] “In one embodiment, the matching module 210 eliminates job listings as potential matches based on job preferences (e.g., location, industry, etc.).”]; and
determining to match the user to the first job requisition based on the first measure of compatibility [see at least [0036] preliminary threshold requirement before recommendation “In one embodiment, the matching module 210 eliminates job listings as potential matches based on job preferences (e.g., location, industry, etc.). … The employer application module 204 also may be configured to apply artificial intelligence algorithms to review matches and make offers based upon configuration rules corresponding to selection criteria of potential offerees. The selection criteria may be, for example, threshold score levels, rankings, and/or weightings of particular characteristics of importance.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chuang with Ashkenazi to include the limitation(s) above as disclosed by Ashkenazi. Doing so would provide a recruitment system that automatically determines matches between users and job listings via job matching and prediction scores [see at least Ashkenazi [0003, 0032, 0034] ].
Regarding claim 61 (currently amended), modified Chuang teaches the system of claim 60,
and Chuang teaches wherein the at least one computer hardware processor is further configured to:
store a record of activity of the first recruiter user in the database [see at least [0028] receive and store job orders and plurality of candidates, track placement of candidate to job order, and payment to hiring party for placement of candidate to job order; [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0095] store data on various users (profile) including staffing party metrics, job order information, staffing party ID numbers, job order ID numbers, other information (such as in [0017, 0028, 0082, 0239-0241] ); [0386] the 1Ogen MongoDB database may be used to store talent platform exchange data]; and
generate a profile of the first recruiter user based on the record of activity of the first recruiter user,
wherein matching the first recruiter user to the first job requisition is performed at least in part using the profile of the first recruiter user [for the limitations above, see at least [0080, 0017] for staffing talent platforms A, B and C 104 used by staffing parties A, B and C 106 and agency personnel (e.g., recruiters); [0028] receive and store job orders and plurality of candidates, track placement of candidate to job order, and payment to hiring party for placement of candidate to job order; [0386] the 1Ogen MongoDB database may be used to store talent platform exchange data;
[0095] store data on various users (profile) including staffing party metrics, job order information, staffing party ID numbers, job order ID numbers, other information (such as in [0017, 0028, 0082] );
[0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ) ].
Regarding claim 62, modified Chuang teaches the system of claim 61,
and Chuang teaches wherein matching the first recruiter user to the first job requisition comprises generating data using information from the profile of the first recruiter user [see at least [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0028] receive and store job orders and plurality of candidates, track placement of candidate to job order, and payment to hiring party for placement of candidate to job order; [0095] store data on various users (profile) including staffing party metrics, job order information, staffing party ID numbers, job order ID numbers, other information (such as in [0017, 0028, 0082, 0239-0241] ) ].
Modified Chuang doesn’t/don’t explicitly teach but Ashkenazi discloses
generating an input to the trained at least one model using user data [see at least Fig. 2 and [0032] the prediction model 208 may be trained by the model training module 207 using training data from the training data store 217; [0034] prediction model 208 defined by parameters of the machine learning model to programmatically calculating the likelihood that a user is a match for a job listing “by utilizing machine learning system cell weights and biases, projection layer weights and biases, etc. related to title and company data in a combined model, rather than using subjective determinations of a human recruiter”, where weights and biases related to title and company is user data “the candidate work history (e.g., including companies and titles) and job listing (e.g., as defined by a company and title)” ].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Ashkenazi to include the limitation(s) above as disclosed by Ashkenazi. Doing so would provide a recruitment system that automatically determines matches between users and job listings via job matching and prediction scores [see at least Ashkenazi [0003, 0032, 0034] ].
Regarding claim 63, modified Chuang teaches the system of claim 61,
and Chuang teaches wherein generating the profile of the first recruiter user based on the record of activity of the first recruiter user comprises:
determining a measure of performance for the first recruiter user in placing candidates for a job category; and
storing the measure of performance mapped to an identification of the job category in the profile [for the limitations above, see at least [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0237-0241, 0254-0256, 0258-0259] Intrinsic Scoring for Agencies-evaluation of postings and historical performance for filling positions, Extrinsic Scoring for Agencies-prior fills w/company (previously worked on); [0082] placement of different types of candidates with different skill sets such as placement within the legal field specifically; a staffing agency focusing on high-tech employment placement;
[0095] store data on various users (profile) including staffing party metrics, job order information, staffing party ID numbers, job order ID numbers, other information (such as in [0017, 0028, 0082, 0239-0241, 0254-0259] ) ].
Regarding claim 64 (currently amended), modified Chuang teaches the system of claim 60,
and Chuang teaches wherein the at least one computer hardware processor is further configured to:
identify one or more characteristics of one or more job requisitions that the first recruiter user has previously worked on;
obtaining additional information using the identified one or more characteristics; and
generating data based on the obtained additional information [for the limitations above, see at least [0114] display (identify) to the staffing party the job orders previously worked on by the staffing party;
[0082] staffing party can be one or more recruiters; [0237-0238, 0240-0241, 0254-0255, 0258-0259] historical performance for filling positions and prior fills w/company (thus obtain historical performance due to job orders previously worked on);
[0237-0238, 0240-0241, 0254-0255, 0258-0259] scoring based on prior job order (thus generate score data based on historical performance and historical performance based on identified job orders previously worked on) such as Intrinsic Scoring for Agencies-evaluation of postings and historical performance for filling positions and Extrinsic Scoring for Agencies-prior fills w/company].
Modified Chuang doesn’t/don’t explicitly teach but Ashkenazi discloses
generating based on the user data [see at least Fig. 2 and [0032] the prediction model 208 may be trained by the model training module 207 using training data from the training data store 217; [0034] prediction model 208 defined by parameters of the machine learning model to programmatically calculating the likelihood that a user is a match for a job listing “by utilizing machine learning system cell weights and biases, projection layer weights and biases, etc. related to title and company data in a combined model, rather than using subjective determinations of a human recruiter”, where weights and biases related to title and company is user data “the candidate work history (e.g., including companies and titles) and job listing (e.g., as defined by a company and title)” ].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Ashkenazi to include the limitation(s) above as disclosed by Ashkenazi. Doing so would provide a recruitment system that automatically determines matches between users and job listings via job matching and prediction scores [see at least Ashkenazi [0003, 0032, 0034] ].
Regarding claim 65, modified Chuang teaches the system of claim 64,
and Chuang teaches wherein the one or more characteristics include identities of one or more employers that the first recruiter user has worked with, textual job descriptions of the one or more job requisitions, job skills specified in the one or more job requisitions, and/or employment categories of the one or more job requisitions [see at least [0114] a) display (identify) to the staffing party the job orders previously worked on by the staffing party and b) displaying (identify) a sortable list of job orders, where the job orders list may include the company providing the job order (identities of one or more employers that the first recruiter user has worked with) and category (e.g. IT, Banking. Accounting, Customer Service, etc.), where category is employment categories of the one or more job requisitions; [0057] various parts of prior art can be combined;
[0020, 0114] textual job descriptions of the one or more job;
[0114, 0305, 0057, 011] job skills specified in the one or more job requisitions:
[0114] a) display (identify) to the staffing party the job orders previously worked on by the staffing party and b) displaying (identify) a sortable list of job orders, where the job orders list may include data };
[0305] job order includes job title, job industry,
[0057] various parts of prior art can be combined].
Regarding claim 66, modified Chuang teaches the system of claim 60,
and Chuang teaches wherein matching the first recruiter user to the first job requisition further comprises: generating data based on values of one or more measures of performance for the first recruiter user [see at least [0080, 0017] for staffing talent platforms A, B and C 104 used by staffing parties A, B and C 106 and agency personnel (e.g., recruiters); [0017, 0237-0238, 0291] determine (match) recruiter for a job based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] );
[0237-0238, 0240-0241, 0254-0255, 0258-0259] scoring based on prior job order such as Intrinsic Scoring for Agencies-evaluation of postings and historical performance for filling positions and Extrinsic Scoring for Agencies-prior fills w/company].
Modified Chuang doesn’t/don’t explicitly teach but Ashkenazi discloses
generating an input to the at least one model based on user data [see at least Fig. 2 and [0032] the prediction model 208 may be trained by the model training module 207 using training data from the training data store 217; [0034] prediction model 208 defined by parameters of the machine learning model to programmatically calculating the likelihood that a user is a match for a job listing “by utilizing machine learning system cell weights and biases, projection layer weights and biases, etc. related to title and company data in a combined model, rather than using subjective determinations of a human recruiter”, where weights and biases related to title and company is user data “the candidate work history (e.g., including companies and titles) and job listing (e.g., as defined by a company and title)” ].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Ashkenazi to include the limitation(s) above as disclosed by Ashkenazi. Doing so would provide a recruitment system that automatically determines matches between users and job listings via job matching and prediction scores [see at least Ashkenazi [0003, 0032, 0034] ].
Regarding claim 67, modified Chuang teaches the system of claim 66,
and Chuang teaches wherein the one or more measures of performance include a submission acceptance rate, hiring rate, response time, number of hires, time taken to hire for one or more job requisitions for which the first recruiter user has placed candidates, and/or experience level specified by the one or more job requisitions [see at least [0303] number of hires (for a particular date range); [0303] time taken to hire for one or more job requisitions for which the first recruiter user has placed candidates: number of hires for a particular date range, average days to fill; [0164] experience level specified by the one or more job requisitions: candidate information such as, an experience summary].
Regarding claim 68, modified Chuang teaches the system of claim 60,
and Chuang teaches wherein matching the first recruiter user to the first job requisition further comprises: generating data based on whether the first recruiter user has a prior relationship with a hiring party that submitted the first job requisition [see at least [0080, 0017] for staffing talent platforms A, B and C 104 used by staffing parties A, B and C 106 and agency personnel (e.g., recruiters); [0017, 0237-0238, 0291] determine (match) recruiter for a job based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] )].
Modified Chuang doesn’t/don’t explicitly teach but Ashkenazi discloses
generating an input to the at least one model based on user data [see at least Fig. 2 and [0032] the prediction model 208 may be trained by the model training module 207 using training data from the training data store 217; [0034] prediction model 208 defined by parameters of the machine learning model to programmatically calculating the likelihood that a user is a match for a job listing “by utilizing machine learning system cell weights and biases, projection layer weights and biases, etc. related to title and company data in a combined model, rather than using subjective determinations of a human recruiter”, where weights and biases related to title and company is user data “the candidate work history (e.g., including companies and titles) and job listing (e.g., as defined by a company and title)” ].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Ashkenazi to include the limitation(s) above as disclosed by Ashkenazi. Doing so would provide a recruitment system that automatically determines matches between users and job listings via job matching and prediction scores [see at least Ashkenazi [0003, 0032, 0034] ].
Regarding claim 69 (currently amended), modified Chuang teaches the system of claim 60,
and Chuang teaches wherein the at least one computer hardware processor is further configured to match the first recruiter user to the first job requisition based at least in part on an indication of how busy the first recruiter user is [see at least [0080, 0017] for staffing talent platforms A, B and C 104 used by staffing parties A, B and C 106 and agency personnel (e.g., recruiters); [0017, 0237-0238, 0291] determine (match) recruiter for a job based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] );
[0114] display job orders previously saved by the staffing party, but which no candidates have been matched as a separated tab labeled “My Open Job Orders without Matches.”].
Regarding claim 70 (currently amended), modified Chuang teaches the system of claim 69,
and Chuang teaches wherein the at least one computer hardware processor is further configured to determine the indication of how busy the first recruiter user is based on a number of job requisitions, different from the first job requisition, that the first recruiter user is assigned to in the system [see at least [0114] display job orders previously saved by the staffing party, but which no candidates have been matched as a separated tab labeled “My Open Job Orders without Matches.”].
Regarding claim 71 (currently amended), modified Chuang teaches the system of claim 60,
and Chuang teaches wherein the at least one computer hardware processor is further configured to match the first recruiter user to the first job requisition based at least in part on a measure of urgency for finding a candidate for the first job requisition [see at least [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0239-0241, 0244] Intrinsic Scoring for Agencies Evaluation of both historical performance for filling positions and start date/urgency].
Regarding claim 72 (currently amended), modified Chuang teaches the system of claim 71,
and Chuang teaches wherein the at least one computer hardware processor is further configured to determine the measure of urgency based on a response time of a hiring party associated with the first job requisition [see at least [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0239-0241, 0244] Intrinsic Scoring for Agencies Evaluation of both historical performance for filling positions and start date/urgency].
Regarding claim 73, modified Chuang teaches the system of claim 60,
and Chuang teaches wherein matching the first recruiter user to the first job requisition further comprises: generating data using information specified by the first job requisition [see at least [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0239-0240, 0253] Intrinsic Scoring for Agencies Evaluation of keyword match of candidate skills and position (job requisition); [0095] job order information including position data such as full time, part-time, contract or contingent labor basis, position location].
Modified Chuang doesn’t/don’t explicitly teach but Ashkenazi discloses
generating an input to the at least one model using information specified by the first job requisition [see at least Fig. 2 and [0032] the prediction model 208 may be trained by the model training module 207 using training data from the training data store 217; [0034] prediction model 208 defined by parameters of the machine learning model to programmatically calculating the likelihood that a user is a match for a job listing “by utilizing machine learning system cell weights and biases, projection layer weights and biases, etc. related to title and company data in a combined model, rather than using subjective determinations of a human recruiter”, where weights and biases related to title and company is user and job listing data “the candidate work history (e.g., including companies and titles) and job listing (e.g., as defined by a company and title)” ].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Ashkenazi to include the limitation(s) above as disclosed by Ashkenazi. Doing so would provide a recruitment system that automatically determines matches between users and job listings via job matching and prediction scores [see at least Ashkenazi [0003, 0032, 0034] ].
Regarding claim 74, modified Chuang teaches the system of claim 73,
and Chuang teaches wherein the information specified by the first job requisition includes one or more of: an indication of a location, an employer name, an indication of salary, a placement fee, a job title, an indication of a level of responsibility, a number of employees that are to report to a position, a size of an employer, and/or representation of a textual job description [see at least [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0239-0240, 0253] Intrinsic Scoring for Agencies Evaluation of keyword match of candidate skills and position (job requisition); [0095] job order information including position data such as full time, part-time, contract or contingent labor basis, position location; [0114] display (textual description) additional information relating to each job order in one or more data fields, such as the title of the job order and the company providing the job order; [0115] search for job orders by Title, Skill, Location, Company, Salary Range and Fee, etc; [0082] C-level candidates (an indication of a level of responsibility); ]
Modified Chuang doesn’t/don’t explicitly teach but Ashkenazi discloses
a size of an employer [see at least [0026] company and job information including employee count (size of employer); [0029] user data such as company size preference; [0031] match a user to an employer based on user job information and user information].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Ashkenazi to include the limitation(s) above as disclosed by Ashkenazi. Doing so would provide a recruitment system that automatically determines matches between users and job listings via job matching and prediction scores [see at least Ashkenazi [0003, 0032, 0034] ].
Regarding claim 75, modified Chuang teaches the system of claim 60,
and Chuang teaches determine the measure of compatibility between the recruiter user and the job requisition based on performance of the first recruiter user in placing candidates for each of one or more job categories associated with the first job requisition [see at least [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0239-0241, 0255, 0259] Intrinsic Scoring for Agencies Evaluation of historical performance for filling positions (job requisition); Fig. 3 and [0114] job (position) information including category and as noted in Fig. 3 a job can have categories of Banking/Mortgage, Accounting and Finance, etc.].
Modified Chuang doesn’t/don’t explicitly teach but Ashkenazi discloses
wherein the at least one model is configured to determine the measure of compatibility between the first user and the first job requisition based on user data [see at least Fig. 2 and [0032] the prediction model 208 may be trained by the model training module 207 using training data from the training data store 217; [0034] prediction model 208 defined by parameters of the machine learning model to programmatically calculating the likelihood that a user is a match for a job listing “by utilizing machine learning system cell weights and biases, projection layer weights and biases, etc. related to title and company data in a combined model, rather than using subjective determinations of a human recruiter”, where weights and biases related to title and company is user and job listing data “the candidate work history (e.g., including companies and titles) and job listing (e.g., as defined by a company and title)” ].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Ashkenazi to include the limitation(s) above as disclosed by Ashkenazi. Doing so would provide a recruitment system that automatically determines matches between users and job listings via job matching and prediction scores [see at least Ashkenazi [0003, 0032, 0034] ].
Regarding claim 82, modified Chuang teaches the system of claim 60,
and Chuang teaches wherein the measure of compatibility comprises an indication of a probability that the first recruiter user will place a candidate for the first job requisition [see at least [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0239-0240, 0253] Intrinsic Scoring for Agencies Evaluation of keyword match of candidate skills and position (job requisition); [0016] agencies match candidates to job orders (job requisition) based on compatibility of probability; [0057] various parts of prior art can be combined].
Modified Chuang doesn’t/don’t explicitly teach but Spiering discloses
a recursive neural network [see at least [pg 2] recursive neural network].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Spiering to include the limitation(s) above as disclosed by Spiering. Doing so would provide a recruitment system that automatically determines matches between users and job listings via a more robust method of a recursive neural network.
Regarding claim 84 (currently amended), modified Chuang teaches the system of claim 60,
and Chuang teaches wherein the at least one computer hardware processor is further configured to match a candidate submitted by the recruiter user to the job requisition based on the measure of compatibility [see at least [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0239-0240, 0253] Intrinsic Scoring for Agencies Evaluation of keyword match of candidate skills and position (job requisition); [0016] agencies match candidates to job orders (job requisition) based on compatibility; [0057] various parts of prior art can be combined].
Regarding claim 85, modified Chuang teaches the system of claim 84,
and Chuang teaches wherein determining the first measure of compatibility between the first recruiter user and the first job requisition further comprises determining a compatibility score between the first recruiter user and an employer that submitted the job requisition [see at least [0080, 0017] for staffing talent platforms A, B and C 104 used by staffing parties A, B and C 106 and agency personnel (e.g., recruiters); [0017, 0237-0238, 0291] determine (match) recruiter for a job order based on measures of compatibility including etc (such as in [0028, 0082, 0095, 0239-0241] ); [0239-0241, 0255, 0259] Intrinsic Scoring for Agencies Evaluation of historical performance for filling positions (job requisition) and Extrinsic Scoring for Agencies evaluation of prior fills/company]
Claims 76-78 are rejected under 35 U.S.C. 103 as being unpatentable over Chuang in view of Ashkenazi as applied to claim(s) 60 above and further in view of Bonissone et al. (US 2014/0188768 A1).
Regarding claim 76, modified Chuang teaches the system of claim 60,
and Chuang teaches job requisition and one or more job categories [see at least [0082, 0114] job order is in a category of “category (e.g. IT, Banking. Accounting, Customer Service, etc.)” and legal field and high-tech].
Modified Chuang doesn’t/don’t explicitly teach but Bonissone discloses
wherein the at least one model comprises at least one first model configured to classify the input data into one or more categories [see at least [0028-0029, 0035, 006] for local models are applied to parts of the query which is/are the subsets of data to determine clusters (categories) for data, where a query is classification or regression; [0090] the models are applied to a query such as text processing but not limited to text processing.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Bonissone to include the limitation(s) above as disclosed by Bonissone. Doing so would provide a method to improve the problem being addressed by modified Chuang (Chuang) [0008] “extremely inefficient at placing the most qualified candidates to open positions” by improving the selection of recruiters via classification models trained by cluster analysis [see at least Bonissone [0002-0005] applying a model using determined data which includes determined clusters/classification (from local models [0028-0029, 0035] ) on new queries; [0029, 0035] where the model can be chosen from a collection of models (including the local model above) or other local or global models].
Additionally, modified Chuang teaches matching a recruiter to a job requisition based on compatibility metrics using a model. Bonissone teaches a known technique of data categorization utilizing classification models trained by cluster analysis. It would have been obvious to one of ordinary still in the art to include in the matching a recruiter to a job requisition of modified Chuang the ability to further classify via utilizing classification models trained by cluster analysis as taught by Bonissone since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 77, modified Chuang teaches the system of claim 76, as well as
the one or more job categories that the job requisition is classified into by the at least one first model and
determining the measure of compatibility.
Modified Chuang doesn’t/don’t explicitly teach but Bonissone discloses
wherein the at least one model comprises comprises at least one second model for determining the measure of compatibility based on the one or more categories that the data is classified into by the at least one first model [see at least [0027-0029, 0035, 0006, 0046] for local models are applied to parts of the query which is/are the subsets of data to determine clusters (categories) and/or to determine a prediction, where a query is classification and/or regression; [0090] the models are applied to a query such as text processing but not limited to text processing].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Bonissone to include the limitation(s) above as disclosed by Bonissone. Doing so would provide a method to improve the problem being addressed by modified Chuang (Chuang) [0008] “extremely inefficient at placing the most qualified candidates to open positions” by improving the selection of recruiters via classification models trained by cluster analysis [see at least Bonissone [0002-0005] applying a model using determined data which includes determined clusters/classification (from local models [0028-0029, 0035] ) on new queries; [0029, 0035] where the model can be chosen from a collection of models (including the local model above) or other local or global models].
Additionally, modified Chuang teaches matching a recruiter to a job requisition based on compatibility metrics using a model. Bonissone teaches a known technique of data categorization utilizing classification models trained by cluster analysis. It would have been obvious to one of ordinary still in the art to include in the matching a recruiter to a job requisition of modified Chuang the ability to further classify via utilizing classification models trained by cluster analysis as taught by Bonissone since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 78 (currently amended), modified Chuang teaches the system of claim 76,
and Chuang teaches wherein the at least one computer hardware processor is further configured to the stored records of activity of the plurality of recruiter users [see at least [0080, 0017] for staffing talent platforms A, B and C 104 used by staffing parties A, B and C 106 and agency personnel (e.g., recruiters); [0095] store data on various users (profile) including staffing party metrics, job order information, staffing party ID numbers, job order ID numbers, other information (such as in [0017, 0028, 0082] ); [0028] receive and store job orders and plurality of candidates, track placement of candidate to job order, and payment to hiring party for placement of candidate to job order;
receive (obtain) job orders from hiring parties; Fig. 3 and [0020, 0114] textual job descriptions of the one or more job including categories and as noted in Fig. 3 a job can have categories of Banking/Mortgage, Accounting and Finance, etc.].
Modified Chuang doesn’t/don’t explicitly teach but Ashkenazi discloses
train the at least one first model based on the records of activity [see at least Fig. 2 and [0032] the prediction model 208 (machine learning algorithm) may be trained by the model training module 207 using training data from the training data store 217; [0034] prediction model 208 defined by parameters of the machine learning model to programmatically calculating the likelihood that a user is a match for a job listing; [0005] determining, based on the job prediction score, whether the user is a match for the job listing].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Ashkenazi to include the limitation(s) above as disclosed by Ashkenazi. Doing so would provide a recruitment system that automatically determines matches between users and job listings via job matching and prediction scores [see at least Ashkenazi [0003, 0032, 0034] ].
Claims 79-81 are rejected under 35 U.S.C. 103 as being unpatentable over Chuang in view of Ashkenazi and Bonissone as applied to claim(s) 76 above and further in view of Spiering (reference U on the Notice of References Cited).
Regarding claim 79, modified Chuang teaches the system of claim 76,
and Chuang teaches textual information in the job requisition [see at least [0114] display (textual description) additional information relating to each job order in one or more data fields, such as the title of the job order and the company providing the job order].
Modified Chuang doesn’t/don’t explicitly teach but Bonissone discloses
wherein the at least one first model comprises a language processing to process textual information in the query [see at least [0028-0029, 0035, 006] for local models are applied to parts of the query which is/are the subsets of data to determine clusters (categories) for data, where a query is classification or regression; [0090] the models are applied to a query such as text processing but not limited to text processing].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Bonissone to include the limitation(s) above as disclosed by Bonissone. Doing so would provide a method to improve the problem being addressed by modified Chuang (Chuang) [0008] “extremely inefficient at placing the most qualified candidates to open positions” by improving the selection of recruiters via classification models trained by cluster analysis [see at least Bonissone [0002-0005] applying a model using determined data which includes determined clusters/classification (from local models [0028-0029, 0035] ) on new queries; [0029, 0035] where the model can be chosen from a collection of models (including the local model above) or other local or global models].
Modified Chuang doesn’t/don’t explicitly teach but Spiering discloses
a natural language processing model [see at least [pg 1] natural language processing;
[pg 2] recursive neural network;
[pg 3] transformer].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Spiering to include the limitation(s) above as disclosed by Spiering. Doing so would provide a recruitment system that automatically determines matches between users and job listings via a more robust method of natural language processing.
Regarding claim 80, modified Chuang teaches the system of claim 79, .
Modified Chuang doesn’t/don’t explicitly teach but Spiering discloses
wherein the natural language processing model comprises a transformer [see at least [pg 1] natural language processing;
[pg 2] recursive neural network;
[pg 3] transformer].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Spiering to include the limitation(s) above as disclosed by Spiering. Doing so would provide a recruitment system that automatically determines matches between users and job listings via a more robust method of natural language processing.
Regarding claim 81, modified Chuang teaches the system of claim 79, .
Modified Chuang doesn’t/don’t explicitly teach but Spiering discloses
wherein the natural language processing model comprises a recursive neural network [see at least [pg 1] natural language processing;
[pg 2] recursive neural network;
[pg 3] transformer].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Spiering to include the limitation(s) above as disclosed by Spiering. Doing so would provide a recruitment system that automatically determines matches between users and job listings via a more robust method of natural language processing.
Claim 83 is rejected under 35 U.S.C. 103 as being unpatentable over Chuang in view of Ashkenazi as applied to claim(s) 60 above and further in view of Spiering (reference U on the Notice of References Cited).
Regarding claim 83, modified Chuang teaches the system of claim 60, .
Modified Chuang doesn’t/don’t explicitly teach but Ashkenazi discloses
wherein the at least one model comprises a machine learning [see at least Fig. 2 and [0032] the prediction model 208 (machine learning algorithm) may be trained by the model training module 207 using training data from the training data store 217; [0034] prediction model 208 defined by parameters of the machine learning model to programmatically calculating the likelihood that a user is a match for a job listing; [0005] determining, based on the job prediction score, whether the user is a match for the job listing].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Chuang with Ashkenazi to include the limitation(s) above as disclosed by Ashkenazi. Doing so would provide a recruitment system that automatically determines matches between users and job listings via job matching and prediction scores [see at least Ashkenazi [0003, 0032, 0034] ].
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WEBB whose telephone number is (313)446-6615. The examiner can normally be reached on M-F 10-3.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.W./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624