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 .
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 §§ 706.02(l)(1) - 706.02(l)(3) 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).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 4, 6, 7, 10, 11, 13, 15, 16, and 19 of U.S. Patent No. 12,373,795. Although the claims at issue are not identical, they are not patentably distinct from each other for the reasons shown below.
Regarding the independent claims, the subject matter in claim 1 is fully disclosed in claim 1 of the 12,373,795 patent, as shown in the following table:
Instant Application 19/253,290
U.S. Patent 12,373,795
Claim 1.
receive profile attributes of a plurality of users comprising user data, the profile attributes defining a historical progression of actions by each user over a past time period to reach a target state;
cluster, by applying a clustering machine learning model to the profile attributes of the plurality of users to create grouped clusters of users having similar profile attributes within each cluster, each user represented as a node on an output cluster;
provide, subsequent to the clustering, at least one of the grouped clusters of users to a supervised path determination learning model
to apply the profile attributes of a first user from the at least one of the grouped clusters of users associated with a desired target state thereto to estimate a function defining a progression pattern for the historical progression over the past time period to reach the target state;
determine for a second user, from a same grouped cluster as the first user, a recommendation for reaching the target state of the first user based on the estimated function and
the recommendation comprising a series of actions to be performed to progress to the target state of the first user;
and trigger a display of the recommendation on a graphical user interface of a client device and in response to a selection of the recommendation,
update the clustering machine learning model and the supervised path determination learning model in response to the selection for updating subsequent recommendations.
Claim 1.
receive profile attributes of a plurality of users comprising career related information, the profile attributes further defining a historical progression of actions taken online by each user over a past time period to reach a current profile state within an entity;
apply a clustering machine learning model to the profile attributes of the plurality of users;
cluster, using the clustering machine learning model and based on the profile attributes of the plurality of users, to create grouped clusters of users within the entity having similar profile attributes within each cluster, each user represented as a node on an output clustering visualization for similarity processing and clustering;
provide, subsequent to the clustering creating the grouped clusters of users, at least one of the grouped clusters of users to a supervised path determination learning model comprising linear regression;
apply the supervised path determination learning model comprising linear regression to the profile attributes of a first user from the at least one of the grouped clusters of users to estimate a function defining a progression pattern for the historical progression over the past time period to reach the current profile state;
access a database of career positions open for application within the entity and retrieving associated description metadata;
perform natural language processing (NLP) on the description metadata, and the profile attributes of each of the first user and a second user, clustered in a same cluster for being similar to the first user from the at least one of the grouped clusters of users, to determine respective textual context of each;
determine for the second user a recommendation for reaching the current profile state of the first user from an existing state of the second user, the recommendation based on the estimated function provided from the supervised path determination learning model and a determined textual context of the description metadata of a particular career position having more than a predefined degree of match with the first and second user profile attributes and the recommendations comprising a series of online actions to be performed comprising digital applications to access to progress from the existing state to the current profile state of the first user;
and trigger a display of the recommendation on a graphical user interface of a client device for selection thereof and in response to an input indicative of the selection, provide the input to the clustering machine learning model and the supervised path determination learning model for learning from and adjusting the clustering machine learning model comprising clustering and the supervised path determination learning model comprising linear regression in response to the selection for updating subsequent recommendations of online actions.
Although the claims are not identical, they are not patentably distinct from each other because removing limitations and replacing words and phrases with other, interchangeable words and phrases is obvious.
First, as shown in the table above, claim 1 of US Pat. 12,373,795 includes several limitations which claim 1 in the instant application does not but this does not render the claim patentably distinct because removing limitations is obvious.
Second, claim 1 of the present application uses the term a “target state” whereas claim 1 of US Pat. 12,373,795 uses the term a “current profile state”. This difference does not render the claims patentably distinct because the “target state” and the “current profile state” are both used to mean a career goal (e.g., a desired job or title).
Accordingly, claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,373,795. Claims 10 and 19 are similarly rejected on the ground of nonstatutory double patenting as being unpatentable over claims 10 and 19 of U.S. Patent No. 12,373,795 for similar reasons.
Regarding the dependent claims, the subject matter in claims 2-9 is fully disclosed in claims 1, 2, 4, 6, 7, of the 12,373,795 patent, as shown in the following table:
Instant Application 19/253,290
U.S. Patent 12,373,795
Claim 2.
wherein the supervised path determination learning model is a linear regression model
Claim 3.
access a database of available profile states representing potential target states and retrieve associated description metadata; and perform natural language processing (NLP) on the description metadata, and the profile attributes of each of the first user and a second user, clustered in a same cluster for being similar to the first user from the at least one of the grouped clusters of users, to determine respective textual context of each.
Claim 4.
generate the recommendation further based on a determined textual context of the description metadata of a particular role having more than a predefined degree of match with the profile attributes between the first and the second user.
Claim 5.
update an existing state of the second user to the target state of the first user.
Claim 6.
career related progression patterns for each of the users to move from a first profile state to another along with associated timing information;
training status of each of the users obtained over the past time period; and
performance metrics of each of the users within an entity and provided on the graphical user interface and certifications taken by each of the users.
Claim 7.
wherein the recommendation is further generated based on a similarity score determined by applying natural language processing to the profile attributes of the second user and a description metadata of the target state.
Claim 8.
wherein the recommendations include attributes from the first user's historical progression indicative of a career journey from a prior state to the target state for the first user, the attributes including an indication of actions performed comprising: certifications completed, training status changes, and performance metrics.
Claim 9.
generate a reasoning for the recommendation and corresponding online actions, the reasoning including features of the historical progression of the first user and an indication of a degree of similarity between the first user and the second user.
Claim 1.
…a supervised path determination learning model comprising linear regression…
Claim 1.
…access a database of career positions open for application within the entity and retrieving associated description metadata;
perform natural language processing (NLP) on the description metadata, and the profile attributes of each of the first user and a second user, clustered in a same cluster for being similar to the first user from the at least one of the grouped clusters of users, to determine respective textual context of each…
Claim 1.
…determine for the second user a recommendation for reaching the current profile state of the first user from an existing state of the second user, the recommendation based on the estimated function provided from the supervised path determination learning model and a determined textual context of the description metadata of a particular career position having more than a predefined degree of match with the first and second user profile attributes…
Claim 2.
…update the existing state to a target state defined by the current profile state of the first user…
Claim 4.
…career related progression patterns for each of the users to move from a first profile state to another along with associated timing information;
training status of each of the users obtained over the past time period; and
performance metrics of each of the users within the entity and provided on the graphical user interface and certifications taken by each of the users…
Claim 1.
…perform natural language processing (NLP) on the description metadata…
..the recommendation based on the estimated function provided from the supervised path determination learning model and a determined textual context of the description metadata of a particular career position having more than a predefined degree of match with the first and second user profile attributes...
Claim 6.
wherein the recommendations include attributes from the first user's historical progression indicative of a career journey from a prior state to the current profile state for the first user, the attributes including an indication of actions performed comprising: certifications completed, training status changes, and performance metrics.
Claim 7.
generate a reasoning for the recommendation and corresponding online actions, the reasoning including features of the historical progression of the first user and an indication of a degree of similarity between the first user and the second user.
Although the claims are not identical, they are not patentably distinct from each other because removing limitations and replacing words and phrases with other, interchangeable words and phrases is obvious.
Accordingly, claims 2-9 is are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 4, 6, and 7 of U.S. Patent No. 12,373,795. Claims 11-18 are similarly rejected on the ground of nonstatutory double patenting as being unpatentable over claims 10, 11, 13, 15, 16 of U.S. Patent No. 12,373,795 for similar reasons.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention. Applying Step 1 to the claims it is determined that: claims 1-9 and 19 are directed to a machine; and claims 10-18 are directed to a process.
Independent Claims
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
The independent claims recite an abstract idea. Specifically, independent claim 1 recites an abstract idea in the limitations (emphasized):
…receive profile attributes of a plurality of users comprising user data, the profile attributes defining a historical progression of actions by each user over a past time period to reach a target state;
cluster, by applying a clustering machine learning model to the profile attributes of the plurality of users to create grouped clusters of users having similar profile attributes within each cluster, each user represented as a node on an output cluster;
provide, subsequent to the clustering, at least one of the grouped clusters of users to a supervised path determination learning model to apply the profile attributes of a first user from the at least one of the grouped clusters of users associated with a desired target state thereto to estimate a function defining a progression pattern for the historical progression over the past time period to reach the target state;
determine for a second user, from a same grouped cluster as the first user, a recommendation for reaching the target state of the first user based on the estimated function and the recommendation comprising a series of actions to be performed to progress to the target state of the first user; and
trigger a display of the recommendation on a graphical user interface of a client device and in response to a selection of the recommendation, update the clustering machine learning model and the supervised path determination learning model in response to the selection for updating subsequent recommendations.
These limitations recite two abstract ideas. First, the limitations of clustering the profile attributes and providing the profile attributes to estimate a function defining a progression pattern (i.e., performing linear regression) recite an abstract idea because the Specification as filed explicitly identifies clustering as a mathematical operation, see ¶¶[0040], [0054], and because linear regression is a technique that fits a linear equation to data that the Specification as filed explicitly describes as a mathematical model, see ¶[0045]. Claims that recite mathematical operations and models fall within the “Mathematical Concepts” grouping of abstract ideas.
Second, the limitations of determining a recommendation for reaching the target state recite an abstract recites an abstract idea because career path recommendations are a method of managing personal behavior. Claims that recite managing personal behavior fall within the “Certain Methods of Organizing Activity” grouping of abstract ideas. Further, Examiner finds the presence of these two abstract ideas does not render the claims non-abstract, see MPEP 2106.04.I discussing Recognicorp, LLC v. Nintendo Co., Ltd., 855 F. 3d 1322, 1327 (stating combining “one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”). Claims 1, 10, and 19 recite an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of the independent claims do not integrate the abstract idea into a practical application. Claim 1 recites the additional elements (emphasized):
…receive profile attributes of a plurality of users comprising user data, the profile attributes defining a historical progression of actions by each user over a past time period to reach a target state;
cluster, by applying a clustering machine learning model to the profile attributes of the plurality of users to create grouped clusters of users having similar profile attributes within each cluster, each user represented as a node on an output cluster;
provide, subsequent to the clustering, at least one of the grouped clusters of users to a supervised path determination learning model to apply the profile attributes of a first user from the at least one of the grouped clusters of users associated with a desired target state thereto to estimate a function defining a progression pattern for the historical progression over the past time period to reach the target state;
determine for a second user, from a same grouped cluster as the first user, a recommendation for reaching the target state of the first user based on the estimated function and the recommendation comprising a series of actions to be performed to progress to the target state of the first user; and
trigger a display of the recommendation on a graphical user interface of a client device and in response to a selection of the recommendation, update the clustering machine learning model and the supervised path determination learning model in response to the selection for updating subsequent recommendations.
The additional elements of the independent claims do not integrate the abstract ideas into a practical application for the following reasons. First, the additional elements of the clustering machine learning model, the supervised path determination learning model, and updating the models in response to the selections, when considered individually or in combination, do not integrate the abstract idea because the use of machine learning is claimed too broadly and generally to be more than mere inclusions to apply the exception.
Second, the additional elements of displaying the recommendation on a graphical user interface of a client device, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of displaying data, see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Third, claims 1 and 19 further recite “a processor; a non-transient computer-readable medium comprising instructions” and a “non-transitory computer-readable medium containing computer program code”, respectively. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Claims 1, 10 and 19 are directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claims 1, 10 and 19 are not patent eligible.
Dependent Claims
The dependent claims are rejected under 35 USC 101 as directed to an abstract idea for the following reasons.
Claims 2 and 11 recite the same abstract idea as the independent claims because linear regression is a mathematical concept.
Claims 3, 7, 12 and 16 recite the additional elements of accessing a database and performing natural language processing. These additional elements of applying a first machine learning model, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (i.e., natural language processing) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claims 4-6, 8, 9, 13-15, 17 and 18 recite the same abstract idea as the independent claims because generating the recommendation, updating user states, the various claimed information about the users, and generating a reasoning for the recommendation are all still a part of providing career advice.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1, 5, 6, 8-10, 14, 15, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xie et al, US Pub. No. 2019/0303798, herein referred to as “Xie” in view of Fang, US Pub. No. 2017/0286914, herein referred to as “Fang”.
Regarding claim 1, Xie teaches:
a processor; a non-transient computer-readable medium comprising instructions that when executed by the processor cause the processor to (processor, memory and instructions, e.g., ¶¶ [0068]-[0069], [0073] and Figs. 8 and 9):
receive profile attributes of a plurality of users comprising user data (obtains information from profile database, e.g., ¶¶[0046], [0051] and Figs. 3 and 4; see also ¶[0038] noting profile data includes career related information),
the profile attributes defining a historical progression of actions by each user over a past time period to reach a target state (profile data includes job title, seniority, major, job start date, job send date, years after graduation, etc., ¶[0018], and educational background (e.g., schools, majors, matriculation and/or graduation dates, etc.), employment history, skills, professional organizations, ¶[0038]; see also ¶[0020] discussing identifying career paths taken by users in the training);
cluster, by applying a clustering machine learning model to the profile attributes of the plurality of users to create grouped clusters of users having similar profile attributes within each cluster (clustering algorithm is applied to identify general career paths, ¶¶ [0023], [0047], [0065] and Figs. 3 and 6),
provide, subsequent to the clustering, at least one of the grouped clusters of users to a supervised path determination learning model (clustered notes are passed to career path model component, ¶[0048] and Fig. 3, and career path model component is a supervised machine learning algorithm, ¶¶[0051]-[0052] and Fig. 4)
to apply the profile attributes of a first user from the at least one of the grouped clusters of users associated with a desired target state thereto to estimate a function defining a progression pattern for the historical progression over the past time period to reach the target state (models careers paths, ¶¶[0052]-[0053]; see also ¶[0048] noting model predicts likelihood of candidates proceeding to a career path and ¶[0028] noting recommendations are based on users’ profile and mentors’ profile);
determine for a second user, from a same grouped cluster as the first user, a recommendation for reaching the target state of the first user based on the estimated function and the recommendation comprising a series of actions to be performed to progress to the target state of the first user (uses model to recommend one or more activities designed to enhance the candidate, ¶¶[0048]; see also ¶[0027] noting activities include increasing number of publications in the users profile and adding skills to the profile; and ¶[0059] showing an example of determining how to progress from one position to another);
and trigger a display of the recommendation on a graphical user interface of a client device (recommendations are presented to the user, e.g., ¶¶ [0049], [0055]; see also e.g., ¶ [0073] discussing user devices)
and in response to a selection of the recommendation, update the clustering machine learning model and the supervised path determination learning model in response to the selection for updating subsequent recommendations (alters candidate profile based on activity and resubmits profile to model to revise prediction, ¶¶[0066]-[0067] and Fig. 6).
However Xie does not teach but Fang does teach:
each user represented as a node on an output cluster (includes each member profile is a node, ¶¶[0068]-[0069]; see also, ¶[0036] discussing clustering).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the career path recommendations of Xie with the social graphs of Fang because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized modeling career paths would likely be improved by analyzing the connections of the user in additional to their own skills and accordingly would have modified Xie to include the social graphs of Fang.
Regarding claim 5, the combination of Xie and Fang teaches all the limitations of claim 1 and Xie further teaches:
update an existing state of the second user to the target state of the first user (alters candidate profile based on activity and resubmits profile to mode to revise prediction, ¶¶ [0066]-[0067] and Fig. 6; e.g., recommends ¶[0027]).
Regarding claim 6, the combination of Xie and Fang teaches all the limitations of claim 1 and Xie further teaches:
wherein the profile attributes for the users provide metadata characterizing a career profile for each user and further comprises: career related progression patterns for each of the users to move from a first profile state to another along with associated timing information (profile data includes job title, seniority, major, job start date, job send date, years after graduation, etc., ¶ [0018], and graduations dates and employment history, ¶[0038]; see also ¶¶[0019]-[0020] discussing labeling and identifying the career paths in the training data);
training status of each of the users obtained over the past time period (profile data includes educational background, ¶[0038]);
and performance metrics of each of the users within an entity and provided on the graphical user interface and certifications taken by each of the users (analyzed data includes activities weighted based on career path, e.g., publications are weighted more heavily for a professor than a project manager, ¶[0024]).
Regarding claim 8, the combination of Xie and Fang teaches all the limitations of claim 1 and Xie further teaches:
wherein the recommendations include attributes from the first user's historical progression indicative of a career journey from a prior state to the target state for the first user (uses career paths taken by users in the training data, ¶[0020]),
the attributes including an indication of actions performed comprising: certifications completed, training status changes, and performance metrics (profile data includes job title, seniority, major, job start date, job send date, years after graduation, etc., ¶[0018], and graduations dates and employment history, ¶[0038]).
Regarding claim 9, the combination of Xie and Fang teaches all the limitations of claim 1 and Xie further teaches:
generate a reasoning for the recommendation and corresponding online actions (determines recommendation by optimizing likelihood of a user corresponding to the candidate user profile progressing down a career path corresponding to the given career path model, ¶[0066]),
the reasoning including features of the historical progression of the first user and an indication of a degree of similarity between the first user and the second user (determines recommendations based on similarities of career paths, ¶[0023]; see also ¶¶[0062]-[0063] discussing identifying a similar cohort).
Regarding claims 10, 14, 15, and 17-19, claims 10, 14, 15, and 17-19 recite similar limitations as claims 1, 5, 6, 8, and 9 and accordingly are rejected for similar reasons as claims 1, 5, 6, 8, and 9.
Claim(s) 2 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Xie and Fang, further in view of Schreiber et al, US Pub. No. 2017/0236095, herein referred to as “Schreiber”.
Regarding claim 2, the combination of Xie and Fang teaches all the limitations of claim 1 and does not teach but Schreiber does teach:
wherein the supervised path determination learning model is a linear regression model (linear regression is performed to choose assess transitions and choose a career path from a source profession to a target profession, ¶¶[0099]-[0100] and Fig. 6).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the career path recommendations of Xie and Fang with the linear regression to determine a career of Schreiber because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Xie teaches performing the analysis based on logistic regression, ¶[0052]. One of ordinary skill would have modified the teachings of Xie to use linear regression, e.g., as taught by Schreiber, for situations where linear regression is more appropriate (e.g., when trying to predict of continuously distributed outcomes, as opposed to binary outcomes).
Regarding claim 11, claim 11 recites similar limitations as claim 2 and accordingly is rejected for similar reasons as claim 2.
Claim(s) 3, 4, 7, 12, 13, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Xie and Fang, further in view of Bolte, et al, US Pub. No. 2018/0039946, herein referred to as “Bolte”.
Regarding claim 3, the combination of Xie and Fang teaches all the limitations of claim 1 and does not teach but Bolte does teach:
access a database of available profile states representing potential target states and retrieve associated description metadata (access job sites to obtain job profiles, ¶¶[0065]-[0066]);
and perform natural language processing (NLP) on the description metadata, and the profile attributes of each of the first user and a second user, clustered in a same cluster for being similar to the first user from the at least one of the grouped clusters of users, to determine respective textual context of each (uses natural language processing to extract information and key words from a candidate's resume and from a job description, ¶¶[0112], [0116]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the career path recommendations of Xie and Fang with the natural language processing based recommendations of Bolte because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized data about people’s career histories and job descriptions would likely include unstructured text data and accordingly would have used natural language processing to analyze the data when suggesting career paths, e.g., as taught by Bolte.
Regarding claim 4, the combination of Xie, Fang and Bolte teaches all the limitations of claim 3 and Fang further teaches:
generate the recommendation further based on a determined textual context of the description metadata of a particular role having more than a predefined degree of match with the profile attributes between the first and the second user (determines a degree a resumes matches evaluation data constituting search terms to a job pipeline, ¶[0029]; see also ¶[0025] noting “job pipeline” is a relatively fine grained descriptor of a job classification).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the career path recommendations of Xie with the matching job descriptions and resumes Fang because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized modeling career paths would likely be improved by directly comparing job and user resumes, e.g., as taught by Fang and accordingly would have modified Xie to do so.
Regarding claim 7, the combination of Xie and Fang teaches all the limitations of claim 1 and does not teach but Bolte does teach:
wherein the recommendation is further generated based on a similarity score determined to the profile attributes of the second user and a description metadata of the target state (determines a degree a resumes matches evaluation data constituting search terms to a job pipeline, ¶[0029]; see also ¶[0025] noting “job pipeline” is a relatively fine grained descriptor of a job classification).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the career path recommendations of Xie with the matching job descriptions and resumes Fang because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized modeling career paths would likely be improved by directly comparing job and user resumes, e.g., as taught by Fang and accordingly would have modified Xie to do so.
However the combination of Xie and Fang does not teach but Bolte does teach:
by applying natural language processing (uses natural language processing to extract information and key words from a candidate's resume and from a job description, ¶¶[0112], [0116]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the career path recommendations of Xie and Fang with the natural language processing based recommendations of Bolte because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized data about people’s career histories and job descriptions would likely include unstructured text data and accordingly would have used natural language processing to analyze the data when suggesting career paths, e.g., as taught by Bolte.
Regarding claims 12, 13, and 16, claims 12, 13, and 16 recite similar limitations as claims 3, 4, and 7 and accordingly are rejected for similar reasons as claims 3, 4, and 7.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached at (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626
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