DETAILED ACTION
This communication is responsive to the claim amendment filed on 03/04/2026.
Claims 1 and 11 are independent claims.
Claims 1, 11, and 15 are amended.
Claims 1-20 are pending in this application.
This instant application is a “Track One” request for examination.
This Action has been made FINAL.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/04/2026 has been considered and recorded. The submission is in compliance with the provisions of 37 CFR §1.97. See form PTO-1449 singed and attached hereto.
Continuity
This instant application is continuation-in-part (CIP) of applications 17/960,996 filed on 10/06/2022, and application 17/335,135 filed on 06/01/2021.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 6-11, and 16-20 are provisionally rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, 9-10, 11, and 19-20 of co-pending Application No. 17/335,135. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim limitations are directed to classifying resources to niche models as shown in the following compared table:
Instant application: 19/307,559
Co-pending application: 17/335,135
1. A system for classifying resources to niche models, the system comprising: a computing device, wherein the computing device is configured to:
receive a plurality of resource data corresponding to a plurality of resources;
generate a plurality of resource models as a function of the plurality of resource data;
compute a niche model, wherein the niche model comprises a plurality of niche data;
combine the niche model with at least a selected resource model corresponding to a selected resource of the plurality of resources, wherein combining further comprises classifying at least a niche datum of the plurality of niche data to at least a datum of the plurality of resource data; and
automatically select a single resource corresponding to the at least a selected resource model; and
(monitor the niche model using a by-pass engine wherein monitoring is initiated as a function of matching the niche model to the at least a selected resource model. *** this limitation is rejected as double patenting to the limitations recited in claim 26 of co-pending application 17/960,996. See the next comparing table)
(8. The system of claim 1, wherein the computing device is further configured to receive an indication that the selected single resource is no longer available.)
(9. The system of claim 8, wherein the computing device is further configured to select a second resource of the plurality of resource models.)
(10. The system of claim 9, wherein selecting the second resource further comprises:
receiving, from a user associated with the niche model, a set of characteristics of the selected single resource; and
selecting the second resource using the set of characteristics and a classification algorithm.)
2. The system of claim 1, wherein combining the niche model with at least a selected resource model further comprises: generating an attribute score for each of the resource model and the niche model;
aggregating a plurality of attribute scores; and selecting a single resource based on a highest rank of the plurality of attribute scores.
3. The system of claim 1, wherein the computing device is further configured to monitor the niche model after selecting a single resource using a by-pass engine.
4. The system of claim 3, wherein the computing device is further configured to place the niche model in a banning protocol as a function of being flagged by the by-pass engine.
5. The system of claim 4, wherein the system is configured to place the single resource in the banning protocol, wherein the banning protocol comprises: placing the single resource through a confirmation process; and preventing the single resource from being combined with additional niche models as a function of the confirmation process.
6. The system of claim 1, wherein the computing device is configured to combine the niche model to the at least a selected resource model using a classifying machine-learning process.
7. The system of claim 1, wherein the computing device is further configured to combine the niche model to the at least a selected resource model by combining the niche model to a single resource model corresponding to a single resource of the plurality of resources, wherein combining the niche model to the single resource model further comprises: defining a direct-match subset of the plurality of niche models; classifying a set of resource data of the plurality of resource data corresponding to the single resource model to the direct-match subset; and
combining the single resource model with the niche model.
*** Claims 11, and 16-20 are method claims, which recited the similar limitations in claims 1 and 6-10; hence, are rejected to the same reasons, and in comparing to the limitations in claims 11, and 19-20 of co-pending applications 17/335,135.
1. A system for classifying resources to niche models, the system comprising: a computing device, wherein the computing device is configured to:
receive a plurality of resource data corresponding to a plurality of resources;
generate a plurality of resource models, wherein generating the plurality of resource models further comprises: receiving, for each resource, and from a plurality of resource client devices, a plurality of distributed factors, wherein each distributed factor includes a rating by a peer of the resource; deriving, for each resource and as a function of the plurality of resource data, a merit quantitative field, wherein deriving the merit quantitative field further comprises: generating a training data, wherein the training data comprises at least a resource datum and at least a correlated merit quantitative field datum; training a merit quantitative field machine-learning model as a function of the training data; and deriving the merit quantitative field as a function of the plurality of resource data and the merit quantitative machine-learning model; generating a biasing element; tuning the biasing element as a function of the plurality of distributed factors; and modifying the merit quantitative field as a function of the biasing element; generating a resource model corresponding to the resource as a function of the plurality of resource data and the merit quantitative field, wherein the plurality of resource models are displayed in order of ranking;
compute a niche model, wherein the niche model comprises: a plurality of niche data; and
an output quantitative field, wherein the output quantitative field is generated as a function of a niche quantitative field machine-learning model, wherein generating the output quantitative field comprises: training the niche quantitative field machine-learning model using a training data comprising an output quantitative field data to a niche data; and generating the output quantitative field as a function of the niche quantitative field machine-learning model;
combine the niche model with at least a selected resource model corresponding to a selected resource of the plurality of resources, wherein combining further comprises: classifying the output quantitative field to at least a selected merit quantitative field of the at least a selected resource model; and classifying at least a niche datum of the plurality of niche data to at least a datum of the plurality of resource data;
provide an indication of the at least a selected resource model to a client device of the niche model, wherein providing the indication further comprises:
automatically selecting a single resource; and automatically informing the single resource as a function of the client device;
receive an indication that the selected single resource is no longer available; and
select the a second resource of the plurality of resource models, wherein
selecting the second resource further comprises:
receiving, from a user associated with the niche, a set of characteristics of the selected single resource; and
selecting the second resource using the set of characteristics and a classification algorithm.
9. The system of claim 1, wherein the computing device is configured to combine the niche model to the at least a selected resource model using a classifying machine-learning process,
wherein classifying machine-learning process is configured to: generate a classifier; train the classifier using a classification training data, wherein the classification training data comprises niche models correlated to resource models; and combine the niche model with the at least a resource model as a function of the classifier.
10. The system of claim 1, wherein the computing device is further configured to combine the niche model to the at least a selected resource model by combining the niche model to a single resource model corresponding to a single resource of the plurality of resources, wherein combining the niche model to the single resource model further comprises: defining a direct-match subset of the plurality of niche elements; classifying a set of resource data of the plurality of resource data corresponding to the single resource model to the direct-match subset; classifying the merit quantitative field of the single resource model to the output quantitative field; and
combining the single resource model with the niche model.
*** Claims 11, and 19-20 are method claims, which recited the similar limitations in claims 1 and 9-10. Thus, the claims are rejected to the same reasons set forth above to claims 1 and 9-10, and in comparing to limitations in claims 11 and 16-20 of instant application 19/307559.
Claims 2 and 12 are rejected on the ground of non-statutory double patenting as being unpatentable over of Abbasi Moghaddam, US Pub. No. 2021/0089603 A1 (hereinafter as "Moghaddam") in view of Keppo, WO 2019/108133 (hereinafter as "Keppo"). Please see the below claim rejections under 35 U.S.C. §103.
Claims 3-5 and 13-15 are rejected on the ground of non-statutory double patenting as being unpatentable over of Abbasi Moghaddam, US Pub. No. 2021/0089603 A1 (hereinafter as "Moghaddam") in view of Keppo, WO 2019/108133 (hereinafter as "Keppo"), and further in view of Rudi et al., US Pub. No. 2022/0032199 A1 (hereinafter as “Rudi”). Please see the below claim rejections under 35 U.S.C. §103.
Claims 1, 3-5, 8-11, 13-15, and 18-20 are provisionally rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, 11, 21-24, 26, 30-33, and 35 of co-pending Application No. 17/960,996. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim limitations are directed to classifying resources to niche models using a by-pass engine as shown in the following compared table:
Instant application: 19/307,559
Co-pending application: 17/960,996
1. A system for classifying resources to niche models, the system comprising:
a computing device, wherein the computing device is configured to:
receive a plurality of resource data corresponding to a plurality of resources;
generate a plurality of resource models as a function of the plurality of resource data;
compute a niche model, wherein the niche model comprises a plurality of niche data;
combine the niche model with at least a selected resource model corresponding to a selected resource of the plurality of resources, wherein combining further comprises classifying at least a niche datum of the plurality of niche data to at least a datum of the plurality of resource data; and
automatically select a single resource corresponding to the at least a selected resource model.
2. The system of claim 1, wherein combining the niche model with at least a selected resource model further comprises: generating an attribute score for each of the resource model and the niche model;
aggregating a plurality of attribute scores; and selecting a single resource based on a highest rank of the plurality of attribute scores.
5. The system of claim 4, wherein the system is configured to place the single resource in the banning protocol, wherein the banning protocol comprises:
placing the single resource through a confirmation process; and
preventing the single resource from being combined with additional niche models as a function of the confirmation process.
(8. The system of claim 1, wherein the computing device is further configured to receive an indication that the selected single resource is no longer available.)
(9. The system of claim 8, wherein the computing device is further configured to select a second resource of the plurality of resource models.)
(10. The system of claim 9, wherein selecting the second resource further comprises:
receiving, from a user associated with the niche model, a set of characteristics of the selected single resource; and
selecting the second resource using the set of characteristics and a classification algorithm.)
3. The system of claim 1, wherein the computing device is further configured to monitor the niche model after selecting a single resource using a by-pass engine.
4. The system of claim 3, wherein the computing device is further configured to place the niche model in a banning protocol as a function of being flagged by the by-pass engine.
6. The system of claim 1, wherein the computing device is configured to combine the niche model to the at least a selected resource model using a classifying machine-learning process.
7. The system of claim 1, wherein the computing device is further configured to combine the niche model to the at least a selected resource model by combining the niche model to a single resource model corresponding to a single resource of the plurality of resources, wherein combining the niche model to the single resource model further comprises: defining a direct-match subset of the plurality of niche models; classifying a set of resource data of the plurality of resource data corresponding to the single resource model to the direct-match subset; and
combining the single resource model with the niche model.
*** Claims 11, 13-15, and 18-20 are method claims, which recited the similar limitations in claims 1, 3-5, and 8-10; hence, are rejected to the same reasons, and in comparing to the limitations in claims 11, 13-15, and 18-20 of the co-pending application 17/960,966.
1. A system for monitoring a niche model using a by-pass engine, the system comprising:
a computing device, wherein the computing device is configured to:
generate a plurality of resource models, wherein generating the plurality of resource models comprises:
receiving, for each resource, a plurality of distributed factors, wherein each distributed factor includes a rating by a peer of a resource; and
deriving, for each resource and as a function of a plurality of resource data, a merit quantitative field, wherein deriving the merit quantitative field comprises:
deriving a merit quantitative field as a function of the plurality of resource data;
generating a biasing element; tuning the biasing element as a function of the plurality of distributed factors; and modifying the merit quantitative field as a function of the biasing element;
select a resource model from a plurality of resource models;
receive compute a niche model, wherein the niche model comprises a niche quantitative field;
combine the niche model with the selected resource model as a function of the merit quantitative field and the niche quantitative field; and
provide an indication of the at least a selected resource model to a niche client device of the niche model, wherein providing the indication further comprises:
automatically selecting a single resource; and automatically informing the single resource as a function of the client device.
21. The system of claim 1, wherein the computing device is further configured to place the single resource in a banning protocol, wherein the banning protocol includes:
placing a candidate through a confirmation process as a function of the indication of the at least a selected resource model; and preventing the single resource from being combined with additional resource models as function of confirmation process.
22. The system of claim 1, wherein the computing device is further configured to receive an indication that the selected single resource is no longer available.
23. The system of claim 22, wherein the computing device is further configured to select a second resource of the plurality of resource models.
24. The system of claim 23, wherein selecting the second resource further comprises:
receiving, from a user associated with the niche model, a set of characteristics of the selected single resource; and
selecting the second resource using the set of characteristics and a classification algorithm.
26. The system of claim 1, wherein the computing device is further configured to monitor the niche model after the combination of the niche model with the selected resource model using a by-pass engine. *** this limitation is the same as the amended limitation in claim 1 of application no. 19/307,559)
** Claims 11, and 30-33, and 35 are method claims, which recited the similar limitations in claims 1, 21-24, and 26. Thus, the claims are rejected to the same reasons, and in comparing to limitations in claims 11, 13-15, and 18-20 of instant application 19/307,559.
Claims 2, 6-7, 12, and 16-17 are rejected on the ground of non-statutory double patenting as being unpatentable over of Abbasi Moghaddam, US Pub. No. 2021/0089603 A1 (hereinafter as "Moghaddam") in view of Keppo, WO 2019/108133 (hereinafter as "Keppo"). Please see the below claim rejections under 35 U.S.C. §103.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 3 and 13, the claims recite the term "a by-pass engine" in line 2 which render the claims indefinite because it's unclear that “a by-pass engine” in claims 3 and 13 to be the same as or different from the amended “a by-pass engine” in claims 1 and 11. In addition, Applicant’s specification discloses “a by-pass engine 172” at paragraphs [0037] and [0038], but the element “172” is not shown in Applicant’s Drawing, in particular, Figure 1 and/or other Figures as well. Appropriate clarification or correction is required.
Claims 2-10 and 12-20 are also rejected because of dependency to claims 1 and 11, respectively.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 6-12, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Abbasi Moghaddam, US Pub. No. 2021/0089603 A1 (hereinafter as "Moghaddam") in view of Keppo, WO 2019/108133 (hereinafter as "Keppo") and further in view of Adeli-Nadjafi, US Pub. No. 2022/0383186 A1 (hereinafter as “Adeli-Nadjafi”).
Regarding claim 1, Moghaddam teaches: a system for classifying resources to niche models, the system comprising: a computing device (par. [0071] “Computer system 400 includes a processor 402, memory 404, storage 406, and/or other components found in electronic computing devices.” The system comprising: a computing device, wherein the computing device is configured to is taught as Computer system includes a processor, and memory), wherein the computing device is configured to:
receive a plurality of resource data corresponding to a plurality of resources (par. [0039] “A feature-processing apparatus 204 uses data 202 from event streams 200 and/or data repository 134 to calculate sets of features for users and/or entities in the online network”, such that receive a plurality of resource data corresponding to a plurality of resources is taught as the sets of features for users and/or entities in the online network);
compute a niche model, wherein the niche model comprises a plurality of niche data (see par. [0033] “Jobs data 218 includes structured and/or unstructured data for job listings and/or job descriptions that are posted and/or provided by members of the online network”, such that niche data is taught as the job data for job listings; and par. [0050] “one or more embodiments, machine learning models 238 generate output related to the compatibility of the candidate with the jobs”, such that compute a niche model, wherein the niche model comprises is taught as machine learning models 238 generate output related to the compatibility of the candidate with the job);
combine the niche model with at least a selected resource model corresponding to a selected resource of the plurality of resources (par. [0054] “Next, management apparatus 206 inputs the vector, along with optional candidate features 222, job features 226, and/or candidate-job features 224, into the global version. Management apparatus 206 also inputs one or more candidate features 222 into the job-specific model for the job pair and one or more job features 226 into the user-specific model for the candidate. Management apparatus 206 then combines the output of the global version, job-specific model, and user-specific version into a match score between the candidate and job.” Combine the niche model with at least a selected resource model corresponding to a selected resource of the plurality of resources is taught as Management apparatus then combines job-specific model, and user-specific version into a match score between the candidate and job. Niche model is taught as the job-specific model combined with the user-specific model (i.e. Resource model). For additional analysis refer to paragraph [0059]), wherein combining further comprises classifying at least a niche datum of the plurality of niche data to at least a datum of the plurality of resource data (par. [0057] “By training a first tree-based model 208 to predict outcomes between users and jobs (or other entities) based on features for the users and jobs, the disclosed embodiments transform the features into a smaller set of feature interactions 228. Interactions 228 can then be inputted into one or more additional machine learning models 238 that generate recommendations 244 and/or other output related to the users and jobs”, such that classifying at least a niche datum of the plurality of niche data to at least a datum of the plurality of resource data is taught as the predicted outcomes matching users and their data to jobs that are related based on features for the jobs); and
automatically select a single resource corresponding to the at least a selected resource model (see par. [0025] “The feedback may be stored in data repository 134 and used as training data for one or more machine learning models, and the output of the machine learning model(s) may be used to display and/or otherwise recommend jobs, advertisements, posts, articles, connections, products, companies, groups, and/or other types of content, entities, or actions to members of online network 118”, wherein provide an indication of the at least a selected resource model to a client device of the niche model is taught as the output of the machine learning models may be used to display and or recommend jobs to people or entities, and par. [0015]).
Moghaddam does not explicitly teach “generate a plurality of resource models as a function of the plurality of resource data.”
In the same field of endeavor (i.e., data processing), Keppo teaches: “generate a plurality of resource models as a function of the plurality of resource data” (Page 2, line 4, e.g., “Preferably, the method comprises identifying a plurality of requirements from a job description for the job position; wherein building the data model comprises building a plurality of data models, each data model corresponding to a requirement of the plurality of requirements and quantifies the relevance of the individual characteristic to the requirement.” Generate a plurality of resource models is taught as building a plurality of data models; and Page 1, line 35, e.g., “identifying a plurality of individual characteristics from an individual profile data set; identifying a job demographic profile data set, wherein the job demographic profile data set comprises historical data associated with the plurality of individual characteristics of the job position; building a data model based on the job demographic profile data set; inputting the identified plurality of individual characteristics into the data model; and computing a score for the individual based on the input into the data model, wherein the score is used to determine the suitability of the individual for the job position”, wherein a function of the plurality of resource data is taught as the data model being built on the individual characteristics from an individual’s profile (i.e. resource data), and line 40 “the data model is optimized through a weight cost function”)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified stacking ensemble models of Moghaddam with the data models of Keppo in order to allow simplifying and improving the recruitment process by utilizing the information, thereby determining the most suitable candidates for an available job position (Keppo: Page 5, Line 35 “The platform allows organizations to simplify and improve their recruitment process by utilizing available information to find the most suitable candidates for an available job position,”).
Moghaddam and Keppo do not explicitly teach the amended limitation: “monitor the niche model using a by-pass engine wherein monitoring is initiated as a function of matching the niche model to the at least a selected resource model.”
In the same field of endeavor (i.e., data processing), Adeli-Nadjafi teaches:
monitor the niche model using a by-pass engine wherein monitoring is initiated as a function of matching the niche model to the at least a selected resource model (par. [0090]: “system 100 may track location of resource as a function of time. Tracking may include detecting that resource has logged onto a workstation and/or has “punched in” and/or “punched out” to determine that resource is located at a worksite corresponding to niche. Tracking may include one or more geolocation and/or geofence determinations, where an approximate or exact geographical location of resource at a given time may be determined…”, wherein the technique of “tracking” is interpreted as the monitoring, and the “system 100” is interpreted as the “by-pass engine”; further in claim 1 disclosed: “niche models” in line 1, and the determined=selected “a resource model” corresponding to the resource as a function of the resource data; and pars. [0022] “Elements of resource data 112 used in resource model 116 may include one or more elements to be used in matching resource model 116 to a niche model 140…” and [0023]; and par. [0031] e.g., “computing device 104 is configured to combine niche model 140 with at least a selected resource model 116 corresponding to a selected resource of the plurality of resources. “Combination,” as used herein, refers to matching and/or associating niche model 140 with at least a selected resource model 116...”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Adeli-Nadjafi with the teachings of Moghaddam and Keppo for allowing a skill artisan in motivation to perform monitoring the niche model which is initiated as the function of matching the niche model to the selected/determined resource model (Adeli-Nadjafi: pars. [0022-23], [0090]).
Regarding claim 2, Moghaddam and Keppo, in combination, teach: wherein combining the niche model with at least a selected resource model further comprises:
generating an attribute score for each of the resource model and the niche model (Moghaddam: Abstract: “produce a score”, par. [0015] and par. [0027] “the machine learning model(s) generate scores based on similarities between the candidates' profile data with online network 118 and descriptions of the opportunities. The model(s) further adjust the scores based on social and/or other validation of the candidates' profile data (e.g., endorsements of skills, recommendations, accomplishments, awards, patents, publications, reputation scores, etc.). The rankings are then generated by ordering candidates 116 by descending score.”);
aggregating a plurality of attribute scores (Moghaddam: par. [0052] via “combined to generate a match score (e.g., match scores 240)” using equation; and Keppo: Page 2, line 13 “computing a final score by summing the plurality of individual data model scores”); and
selecting a single resource based on a highest rank of the plurality of attribute scores (see par. [0068]).
Regarding claim 3, Adeli-Nadjafi teaches: wherein the computing device is further configured to monitor the niche model after selecting a single resource using a by-pass engine ((par. [0090]: “system 100 may track location of resource as a function of time. Tracking may include detecting that resource has logged onto a workstation and/or has “punched in” and/or “punched out” to determine that resource is located at a worksite corresponding to niche. Tracking may include one or more geolocation and/or geofence determinations, where an approximate or exact geographical location of resource at a given time may be determined…”, wherein the technique of “tracking” is interpreted as the monitoring, and the “system 100” is interpreted as the “by-pass engine”; further in claim 1 disclosed: “niche models” in line 1, and the determined=selected “a resource model” corresponding to the resource as a function of the resource data; and par. [0023]; and par. [0031] e.g., “computing device 104 is configured to combine niche model 140 with at least a selected resource model 116 corresponding to a selected resource of the plurality of resources. “Combination,” as used herein, refers to matching and/or associating niche model 140 with at least a selected resource model 116...”).
Regarding claim 4, Adeli-Nadjafi teaches: wherein the computing device is further configured to place the niche model in a banning protocol as a function of being flagged by the by-pass engine (pars. [0069] “ Data entries in a master production database 504 may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational master production database 504…”).
Regarding claim 6, Moghaddam and Adeli-Nadjafi, in combination, teach: wherein the computing device is configured to combine the niche model to the at least a selected resource model using a classifying machine-learning process (Adeli-Nadjafi: see Fig. 4 via training data classifying machine-learning process; and Moghaddam: see par. [0054] “Next, management apparatus 206 inputs the vector, along with optional candidate features 222, job features 226, and/or candidate-job features 224, into the global version. Management apparatus 206 also inputs one or more candidate features 222 into the job-specific model for the job pair and one or more job features 226 into the user-specific model for the candidate. Management apparatus 206 then combines the output of the global version, job-specific model, and user-specific version into a match score between the candidate and job.” Combine the niche model with at least a selected resource model corresponding to a selected resource of the plurality of resources is taught as Management apparatus then combines job-specific model, and user-specific version into a match score between the candidate and job. Niche model is taught as the job-specific model combined with the user-specific model (i.e. Resource model). For additional analysis refer to paragraph [0059], and par. [0050] “one or more embodiments, machine learning models 238 generate output related to the compatibility of the candidate with the jobs.” Using a classifying machine-learning process is taught as machine learning models generate output related to the compatibility of the candidate with the job).
Regarding claim 7, Moghaddam and Keppo, in combination, teach: wherein the computing device is further configured to combine the niche model to the at least a selected resource model by combining the niche model to a single resource model corresponding to a single resource of the plurality of resources (Moghaddam: par. [0054] “Next, management apparatus 206 inputs the vector, along with optional candidate features 222, job features 226, and/or candidate-job features 224, into the global version. Management apparatus 206 also inputs one or more candidate features 222 into the job-specific model for the job pair and one or more job features 226 into the user-specific model for the candidate. Management apparatus 206 then combines the output of the global version, job-specific model, and user-specific version into a match score between the candidate and job.” Combining the niche model to a single resource model corresponding to a single resource of the plurality of resources, wherein combining the niche model to the single resource model is taught as Management apparatus then combines job-specific model, and user-specific version into a match score between the candidate and job. Niche model is taught as the job-specific model combined with the user-specific model (i.e. Resource model). For additional analysis refer to par. [0059]; and Keppo: Page 2, line 41 “the data model is built by blending two or more statistical models”), wherein combining the niche model to the single resource model 3urther comprises:
defining a direct-match subset of the plurality of niche models (par. [0050] “After machine learning models 238 are trained machine learning models 238 generate match scores 240 ranging from 0 to 1. Each match score represents the likelihood of a positive outcome between a candidate and a job.” Defining a direct-match subset of the plurality of niche data/models is taught as generating a match score between a candidate (i.e. resource model) and a job (i.e. niche model(s)));
classifying a set of resource data of the plurality of resource data corresponding to the single resource model to the direct-match subset (par. [0054] “Next, management apparatus 206 inputs the vector, along with optional candidate features 222, job features 226, and/or candidate-job features 224, into the global version. Management apparatus 206 also inputs one or more candidate features 222 into the job-specific model for the job pair and one or more job features 226 into the user-specific model for the candidate. Management apparatus 206 then combines the output of the global version, job-specific model, and user-specific version into a match score between the candidate and job.” Classifying a set of resource data of the plurality of resource data corresponding to the single resource model to the direct-match subset is taught as the candidate features (i.e. salary of the candidate) matched to the jobs data which includes salary range.)); and
combining the single resource model with the niche model (par. [0054] “Next, management apparatus 206 inputs the vector, along with optional candidate features 222, job features 226, and/or candidate-job features 224, into the global version. Management apparatus 206 also inputs one or more candidate features 222 into the job-specific model for the job pair and one or more job features 226 into the user-specific model for the candidate. Management apparatus 206 then combines the output of the global version, job-specific model, and user-specific version into a match score between the candidate and job.” Combining the single resource model with the niche model is taught as Management apparatus then combines job-specific model, and user-specific version into a match score between the candidate and job. Niche model is taught as the job-specific model combined with the user-specific model (i.e. Resource model). For additional analysis refer to par. [0059]).
Regarding claim 8, Keppo teaches: wherein the computing device is further configured to receive an indication that the selected single resource is no longer available (Keppo: Page 5, lines 6-7 “removing an individual from consideration if the score is below a qualifying score” Removing an individual with data as single resource is no longer available for consideration).
Regarding claim 9, Moghaddam teaches: wherein the computing device is further configured to select a second resource of the plurality of resource models (par. [0013] “using a stacking model to generate and/or select jobs (or other entities) as recommendations to users. The stacking model includes a tree-based model that is trained to predict outcomes between pairs of users and jobs, based on features for the users and/or jobs.”).
Regarding claim 10, Moghaddam and Keppo, in combination, teach: wherein selecting the second resource further comprises:
receiving, from a user associated with the niche model, a set of characteristics of the selected single resource (Moghaddam: par. [0027] “After candidates 116 are identified, profile and/or activity data of candidates 116 are inputted into the machine learning model(s), along with features and/or characteristics of the corresponding opportunities (e.g., required or desired skills, education, experience, industry, title, etc.)…”; and Keppo: Page 2, lines 10-13); and
selecting the second resource using the set of characteristics and a classification algorithm (Moghaddam: par. [0012] “selecting recommendations”, and par. [0034] via “classifier attributes in profile data 216 and/or jobs data218” and “organized into a hierarchical taxonomy” of characteristics are embedded to the classification algorithm).
Claims 11-14, and 16-20 are rejected in the analysis of above claims 1-4, 6-10; and therefore, the claims are rejected on that basis.
Claim 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable
over Moghaddam, Keppo, and Adeli-Nadjafi, and further in view of Rudi et al., US Pub. No. 2022/0032199 A1 (hereinafter as “Rudi”).
Regarding claim 5, the claim is rejected by the same reasons set forth above to claims 1 and 3-4. However, Moghaddam, Keppo, and Adeli-Nadjafi do not explicitly teach: “place the single resource in the banning protocol, wherein the banning protocol comprises: placing the single resource through a confirmation process; and preventing the single resource from being combined with additional niche models as a function of the confirmation process.”
In the same field of endeavor (i.e., data processing), Rudi teaches: “place the single resource in the banning protocol, wherein the banning protocol comprises: placing the single resource through a confirmation process” (Fig. 6B, element 613 via Verify, and par. [0138] such that the acknowledgement by the system may confirm in response to a report, and pars. [0153-154]); and “preventing the single resource from being combined with additional niche models as a function of the confirmation process” (pars. [0038] “In particular, restriction on game play of players that exhibit undesirable behavior may be enforced (e.g., placing those players in timeout sessions, or banning the player for a period of time). This leads to increased bandwidth of the cloud gaming system that is available for constructive game play during one or more gaming sessions, as players exhibiting undesirable behavior are prevented from taking up valuable bandwidth, and targets of the undesirable behavior need not waste time and effort in their game play to address undesirable behavior.”, and [0153-156] disclose the confirmation process).
Thus, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have modified a local AI model of Rudi
with the stacking ensemble models of Moghaddam, the data models of Keppo, and monitoring the niche model with the selected resource model for allowing a skill artisan in motivation to place the resource through a confirmation with preventing it from being combined with niche model efficiently.
Claim 15 is rejected in the analysis of above claim 5; and therefore, the
claim is rejected on that basis.
Response to Arguments
Referring to Double Patenting rejections, upon the various searches, the amended limitation as of “monitor the niche model using a by-pass engine wherein monitoring is initiated as a function of matching the niche model to the at least a selected resource model” is rejected in comparing to limitation in claim 26 of co-pending application 17/960,996. Therefore, the rejections are still maintained.
Referring to Claim Rejections under 35 U.S.C. 103, Applicant’s arguments to the amended limitation in claim 1 (see Remarks, pages 3-4) have been fully considered, but are moot in view of the new grounds of rejection necessitated by applicant's amendment to the claims. Applicant's newly amended features are taught implicitly, expressly, or impliedly by the prior art of record.
Prior Arts
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275,277 (CCPA 1968)); Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989).
Conclusion
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.
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/Jessica N Le/Examiner, Art Unit 2169 /MD I UDDIN/ Primary Examiner, Art Unit 2169