DETAILED ACTION
This communication is a Final Office Action on the merits in response to communications received on 11/25/2025. Claims 1, 8, and 15 have been amended. Therefore, claims 1-6, 8-13, 15-22 are pending and have been addressed below. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
1. 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.
2. Claims 1-6, 8-13, 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
3. Under Step 1 of the two-part analysis from Alice Corp, claim 1 recites a machine (i.e., a concrete thing, consisting of parts, or of certain devices and combination of devices), claim 8 recites a process (i.e., an act or step, or a series of acts or steps), and claim 15 recites a manufacture (i.e., an article that is given a new form, quality, property, or combination through man-made or artificial means.) Thus, each of the claims fall within one of the four statutory categories.
4. Under Step 2A – Prong One of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
Claims 1, 8, and 15 recite:
“parsing…text of a job description associated with a job for extracting one or more job description skills and generating one or more job description rankings, each job description ranking corresponding to a respective job description skill;”, “determining job description rankings based on storing…keywords corresponding to the job description rankings and analyzing context of each detected job description skill to search for keywords corresponding to a respective job description ranking;”, “parsing…text of a curriculum vitae (CV) of a candidate for extracting one or more candidate skills and generating one or more candidate skill rankings, each candidate skill ranking corresponding to a respective candidate skill;”, “determining candidate skill rankings based on storing…keywords corresponding to the candidate skill rankings and analyzing context of each detected candidate skill to search for keywords corresponding to a respective candidate skill ranking;”, “computing one or more distances for the candidate based on the one or more job description rankings and the one or more candidate skill rankings;”, “updating the one or more distances for the candidate based on: obtaining…text representing notes of communication with the candidate; updating a candidate skill ranking of the one or more candidate skill rankings based on analysis of the text;” and “computing an updated distance between the updated candidate skill ranking and a job description ranking of the one or more job description rankings”
Under the broadest reasonable interpretation, the limitations recite processes for analyzing a candidate skills against job requirements during an interview which encompasses a commercial interaction, (i.e., marketing or sales activities, business relations), managing personal behavior or interactions between people, mathematical concepts (i.e., mathematical calculations/formulas) and mental processes, (i.e., observation, evaluation, judgement, opinion), that fall within the certain methods of organizing human activity, mathematical concepts and mental processes groupings of abstract ideas. See MPEP 2106.04
The Applicant’s Specification in at [0001] The present disclosure generally relates to data processing techniques and provide computer-implemented methods, software, and systems for automating identifications of disparities between job requirements and candidate’s stated skills, automating candidate impression extraction based on interview notes analysis, and recommending actions to be performed with respect to the candidates.
Consistent with the disclosure, the series of steps cover mental processes for collecting and comparing known information from job requirements and curriculum vitaes, computing distances based on the job description rankings and candidate skill rankings and updating the job description rankings and candidate skill rankings. Thus, the limitations cover acts that may be reasonably characterized as mental processes that can be practically performed in the human mind, with or without the use of a physical aid such as pen and paper. Additionally, the step of “computing one or more distances” in the context of the claim recites a mathematical calculation. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." Lastly, the series of limitations pertain to performing business relations and managing interactions between people because the acts are for evaluating resumes and job requirements for recommending questions for a recruiter to ask the candidate during an interview. As such, the claim recites an abstract idea.
5. Under Step 2A – Prong Two of the two-part analysis from Alice Corp, this judicial exception is not integrated into a practical application because the additional elements of: “a system comprising”, “at least one memory storing instructions;”, “at least one hardware processor interoperably coupled with the at least one memory, wherein execution of the instructions by the at least one hardware processor causes performance of operations comprising:”, “, by a text analysis engine”, “by the text analysis engine in a dictionary”, “by the text analysis engine”, “by an action recommendation engine to a client computing device for display thereon” – see claim 1, “a computer-implemented method” – see claim 8, “a non-transitory, computer-readable medium – see claim 15 are recited at a high-level of generality in light of the specification. See Spec, i.e., ¶ 0045, 0051: As used in the present disclosure, the term “computer” is intended to encompass any suitable processing device. For example, the skill analysis system 102, the career server system 130, the action recommendation engine 150, and/or the client 160 can be any computer or processing devices such as, for example, a blade server, general-purpose personal computer (PC), Mac®, workstation, UNIX-based workstation, or any other suitable device. Moreover, although FIG. 1 illustrates a single skill analysis system 102, a single career server system 130, a single action recommendation engine 150, and a single client 160, any one of the skill analysis system 102, the career server system 130, the action recommendation engine 150, and/or the client 160 can be implemented using a single system or more than those illustrated, as well as computers other than servers, including a server pool. In other words, the present disclosure contemplates computers other than general-purpose computers, as well as computers without conventional operating systems.
Thus, because the specification describes the additional elements in general terms without describing the particulars, the additional elements may be broadly but reasonably construed as reciting generic computer components performing conventional computer functions in light of the applicant’s specification. The additional elements recited in the claim add the words “apply it” with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely use a computer processor as a tool to perform the abstract idea as discussed in MPEP 2106.05 (f).
The other additional elements of: “outputting…candidate-specific query data determined based on the updated distances for the candidate.” adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05 (g)
The other additional elements of: “digitized”, “dynamically” and “real-time” are merely an attempt to limit the claimed invention to a particular technological environment or field of use which does not add meaningful limitations to the claim, as discussed in MPEP 2106.05 (h)
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
6. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: “a system comprising”, “at least one memory storing instructions;”, “at least one hardware processor interoperably coupled with the at least one memory, wherein execution of the instructions by the at least one hardware processor causes performance of operations comprising:”, “by a text analysis engine”, “by the text analysis engine”, “by an action recommendation engine to a client computing device for display thereon” – see claim 1, “a computer-implemented method” – see claim 8, “a non-transitory, computer-readable medium – see claim 15 at best amounts to nothing more than mere instructions in which to apply the judicial exception and cannot provide an inventive concept.
The other additional elements of: “outputting…candidate-specific query data determined based on the updated distances for the candidate.” was considered extra-solution activity, which for purposes of Step 2A Prong Two is considered insignificant. At Step 2B, the evaluation insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well-known. As discussed in MPEP 2106.05 (d)(II), the Symantec, TLI Communications, OIP Techs, Alice court decisions held that “arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price”, and “presenting offers and gathering statistics” are well-understood, routine and conventional claimed in a generic manner. As such, the limitations remain insignificant extra solution activity even upon reconsideration, and do not amount to significantly more. Even when considered in combination, the additional elements represent mere instructions to apply an exception and insignificant extra solution activity which cannot provide an inventive concept.
7. Claims 2-6, 9-13, and 16-22 are dependent of claims 1, 8, and 15
Claims 2, 9, and 16 recite “wherein generating the one or more job description rankings comprises: identifying one or more words comprised in the job description associated with description of a job description skill; determining whether the one or more words correspond to a job description ranking; and in response to determining that the one or more words correspond to a job description ranking, assigning the job description ranking to the job description skill.” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Claims 3, 10, 17 recite “wherein the one or more candidate skill rankings are generated based on at least one of: a number of times a candidate skill is comprised in the CV; a number of jobs in the CV comprising the candidate skill; or a font used to encode a candidate skill in the CV.” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Claims 4, 11, and 18 recite “wherein computing the one or more distances based on the one or more job description rankings and the one or more candidate skill rankings comprises: in response to determining that none of the one or more candidate skills corresponds to a job description skill, setting a distance associated with the job description skill to a predetermined maximum value; in response to determining that a candidate skill ranking of a candidate skill is higher than or equal to a job description ranking of a job description skill, setting a distance between the candidate skill and the job description skill to a predetermined minimum value; or in response to determining that a candidate skill ranking of a candidate skill is lower than a job description ranking of a job description skill, setting a distance between the candidate skill and the job description skill to a difference between the job description ranking and the candidate skill ranking.” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Claims 5, 12, and 19 recite “generating an aggregate score based on the one or more distances” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Claims 6, 13, and 20 recites “wherein the aggregate score is used to determine at least one of: whether to perform the communication with the candidate; a duration of the communication with the candidate; or a hiring decision of the candidate.” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Claims 21 and 22 recite “wherein the one or more distances for the candidate are computed using a machine learning model” serves to further narrow how the abstract idea may be performed. Using a machine learning model is recited nominally in the claimed and amounts to mere instructions to apply the abstract idea on a computer. The additional elements recited in the dependent claims use computer components or other machinery in their ordinary capacity for economic or other tasks (e.g., to receive, store, or output data) and/or simply adds generic computer components after the fact to the abstract idea. When viewed individually and as an ordered combination, the limitations recited in the claim does not integrate the judicial exception into a practical application or provide an inventive concept.
Claim Rejections - 35 USC § 103
8. 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.
9. The factual inquiries 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 non-obviousness.
10. Claim(s) 1-3, 8-10, and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Au (US 2015/0039291 A1) in view of Ahmed (WO 2022/251970 A1) in further view of Buhrmann (US 2016/0232160 A1).
With respect to claims 1, 8, and 15, Au discloses
a system (¶ 0052: discloses a data processing system 1), a computer-implemented method (¶ 0055: discloses the system 1 is set to run on aa computing device), a non-transitory computer-readable medium (¶ 0055) comprising:
at least one memory storing instructions (¶ 0055, 0057: discloses memory 310)
at least one hardware processor interoperably coupled with the at least one memory (¶ 0055: discloses a computing device 10), wherein execution of the instructions by the at least one hardware processor causes performance of operations comprising:
parsing, by a text analysis engine, digitized text of a job description associated with a job for extracting one or more job description skills (¶ 0059-0060, 0067, 0099, 0115: discloses the system parses the Job Spec to identify contextual phrases. The system 1 extracts commonly used contextual phrases from a group of job descriptions.), and generating one or more job description rankings (¶ 0062, 0065, 0124: discloses the system 1 will accumulate all weights of matching contextual phrases to produce score of the Job Spec.), each job description ranking corresponding to a respective job description skill (¶ 0100: discloses assigns weights to each contextual phase parsed from the Job Spec);
determining job description rankings based on storing, by the text analysis engine in a dictionary (¶ 0060: discloses a Key Texts Library, i.e., KTL), keywords corresponding to the job description rankings (¶ 0027, 0058: discloses the system 1 uses a group of Job Descriptions in a database to establish a Key Texts Library of contextual phrases against which can be matched, scored, and ranked.) and
analyzing context of each detected job description skill to search for keywords corresponding to a respective job description ranking (¶ 0111: discloses the system 1 will match and score a specific Job Spec against the KTL);
parsing, by the text analysis engine, digitized text of a curriculum vitae (CV) of a candidate for extracting one or more candidate skills (¶ 0060, 0069, 0070, 0087: discloses the system 1 parses each CV. The system extracts commonly used phrases from a group of CVs.) and generating one or more candidate skill rankings (¶0063-0064, 0095: discloses the system ranks each CV), each candidate skill ranking corresponding to a respective candidate skill (¶ 0095-0096);
determining candidate skill rankings based on storing, by the text analysis engine in a dictionary (¶ 0060: discloses a Key Texts Library, i.e., KTL), keywords corresponding to the candidate skill rankings (¶ 0058: discloses the system 1 uses a group of CVs in a database to establish a Key Texts Library of contextual phrases against which can be matched, scored, and ranked.) and analyzing context of each detected candidate skill to search for keywords corresponding to a respective candidate skill ranking (¶ 0086, 0091, 0095-0096: discloses the system 1 will match and score each CV against the KTL. The system allocates the weights of the contextual phrases in KTL to matching contextual phrases in the CV.);
computing one or more distances for the candidate based on the one or more job description rankings and the one or more candidate skill rankings (¶ 0096, 0097, 0126: discloses the system 1 will match, score, and rank [which represents computing one or more distances for the candidate] a group of CVs against a specific set of job descriptions. The system offers a candidate an effective tool to compare his/her CV with words and phrases commonly used in the market for similar job domains. The system can offer a recruiter an efficient and informed method to prepare a Job Spec by comparing with words and phrases commonly used in the market for similar job domains.);
computing an updated distance between the updated candidate skill ranking and a job description ranking of the one or more job description rankings (¶ 0078, 0097: discloses the system 1 updates KTL to identify additional contextual phrases for each new document added to the KTL. Thus, system 1 may update, match, score, and rank a group of CVs against a specific set of job descriptions.) dynamically updating the one or more distances for the candidate (¶ 0078, 0096, 0097, 0126)
The Au reference does not explicitly disclose the following limitations. In the same field of endeavor, the Ahmed reference is related to an assessment module configured to evaluate a transcript for a particular skill deemed necessary or particularly relevant (¶ 0062) and teaches:
obtaining, in real-time via a client computing device, digitized text representing notes of communication with the candidate (¶ 0065-0066, 0074-0075: discloses interviewee responses may be in text or audio formats. The responses may be processes through automated speech recognition and converted to a text transcript 520.);
dynamically updating a candidate skill ranking of the one or more candidate skill rankings based on analysis of the digitized text (¶ 0070, 0074-0075: discloses after transcript 520 is analyzed the output is used to calculate one or more skill scores and rankings. The assessment module scores 305 may output to a scoring dashboard, i.e., a graphical user interface indicating the candidates score and a breakdown of the basis for the score.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and methods of Au, to include obtaining, in real-time via a client computing device, digitized text representing notes of communication with the candidate; dynamically updating a candidate skill ranking of the one or more candidate skill rankings based on analysis of the digitized text, as disclosed by Ahmed to achieve the claimed invention. As disclosed by Ahmed, the motivation for the combination would have been to provide advantages for recruiters by clarifying the relationship between data extracted from interview responses and the applicant’s scores. (¶ 0028)
The combination of Au and Ahmed do not explicitly disclose the following limitations. The Buhrmann reference teaches:
outputting, by an action recommendation engine to a client computing device for display thereon, candidate-specific query data determined based on the updated distances for the candidate. (¶ 0117, 0118, 0138, 0141, 0143, 0151-0152, 0163, 0198, 0205: discloses a hiring manager seeks to gauge the potential qualification of a job applicant based on the resume compared to a job description. The strength is presented to the manager as a score. The manager may use the ordered list of applicants and associated scores to predict how qualified a particular applicant is for the job at hand. The hiring manager is able to utilize and customize any combination of textual information that is relevant to “employment practices” to differentiate information. The invention provides for insight gleaned from candidate resumes as input for providing proposed questions for the hiring manager to ask during the interview.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the combination Au and Ahmed to include outputting, by an action recommendation engine to a client computing device for display thereon, candidate-specific query data determined based on the updated distances for the candidate, as disclosed by Buhrmann to achieve the claimed invention. As disclosed by Buhrmann, the motivation for the combination would have been to reduce time wasted and allow hiring manager to better gauge a candidate’s potential and make more informed hiring decisions. (¶ 0205)
With respect to claims 2, 9, and 16, the combination of Au, Ahmed, and Buhrmann discloses the system, computer-implemented method, and non-transitory computer-readable medium,
wherein generating the one or more job description rankings comprises:
identifying one or more words comprised in the job description associated with description of a job description skill (¶ 0115: Au discloses the system 1 parses the Job Spec to identify contextual phrases.);
determining whether the one or more words correspond to a job description ranking (¶ 0116: Au discloses the system will match each contextual phrase identified from the Job Spec with contextual phrases in KTL); and in response to determining that the one or more words correspond to a job description ranking, assigning the job description ranking to the job description skill. (¶ 0120: Au discloses weights of the contextual phrases in KTL are allocated to matching contextual phrases in the Job Spec by system 1.)
With respect to claims 3, 10, and 17, the combination of Au, Ahmed, and Buhrmann discloses the system, computer-implemented method, and non-transitory computer-readable medium, wherein the one or more candidate skill rankings are generated based on at least one of:
a number of times a candidate skill is comprised in the CV (¶ 0087-0094: Au discloses system 1 matches each contextual phrase identified from each CV with contextual phrases in KTL.); a number of jobs in the CV comprising the candidate skill; or a font used to encode a candidate skill in the CV.
11. Claim(s) 5-6, 12-13, and 19-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Au in view of Ahmed in view of Buhrmann in further view of Kenthapadi (US 2021/0142293 A1).
With respect to claims 5, 12, and 19, the combination of Au, Ahmed, and Buhrmann discloses the system, computer-implemented method, and non-transitory computer-readable medium (¶ 0055-0057 – See Au), the operations comprising:
The combination of Au, Ahmed, and Buhrmann does not explicitly disclose the following limitations. With in the same field of endeavor, the Kenthapadi reference is related to techniques for ranking candidates for job, positions, roles, and/or other opportunities (¶ 0010) and teaches:
generating an aggregate score based on the one or more distances. (¶ 0014, 0052: discloses scores for attributes of the job are combined with corresponding scores for attributes of each candidate to produce overall scores between the candidates and the job. For example, an overall score between a candidate and the job is calculated as a Euclidean distance.)
Therefore, 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 the combination of Au, Ahmed, and Buhrmann to include the step for generating an aggregate score based on the one or more distances, as disclosed by Kenthapadi to achieve the claimed invention. As disclosed by Kenthapadi, the motivation for the combination would have been to provide advantages with guiding the decisions of candidates and/or moderators involved in screening for or placing the opportunities. (¶ 0028)
With respect to claims 6, 13, and 20, the combination of Au, Ahmed, and Buhrmann discloses the system, computer-implemented method, and non-transitory computer-readable medium,
wherein the aggregate score is used to determine at least one of: whether to perform the communication with the candidate; a duration of the communication with the candidate; or a hiring decision of the candidate. (¶ 0011, 0028, 0054: Kenthapadi discloses rankings based on the scores guide the decisions of candidates and/or moderators involved in screening for or placing the opportunities. The overall score 238 may be used to output multiple sets of different candidate rankings for a recruiter.)
With respect to claims 21 and 22, the combination of Au, Ahmed, and Buhrmann discloses the system and computer-implemented method
wherein the one or more distances for the candidate are computed (¶ 0096, 0097, 0126: discloses the system 1 will match, score, and rank a group of CVs against a specific set of job descriptions. The system offers a candidate an effective tool to compare his/her CV with words and phrases commonly used in the market for similar job domains. The system can offer a recruiter an efficient and informed method to prepare a Job Spec by comparing with words and phrases commonly used in the market for similar job domains.)
The combination of Au, Ahmed, and Buhrmann does not explicitly disclose the following limitations. However, Kenthapadi discloses:
using a machine learning model. (¶ 0027, 0052: discloses machine learning models may output scores representing strengths of candidates with respect to opportunities and/or qualifications related to the opportunities. The score between the job and each candidate is calculated using Euclidean distance.)
Therefore, 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 the combination of Au, Ahmed, and Buhrmann, to add the features for using a machine learning model, as disclosed by Kenthapadi to achieve the claimed invention. As disclosed by Kenthapadi, the motivation for the combination would have been to provide advantages in improving the quality of candidates, recommendations of opportunities to candidates, and recommendations of candidates for opportunities. (¶ 0027-0028)
12. Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Au in view of Ahmed in view of Buhrmann in further view of Zoia (US 2017/0147984 A1).
With respect to claims 4, 11, and 18, the combination of Au, Ahmed, Buhrmann discloses the system, computer-implemented method, and non-transitory computer-readable medium (¶ 0055-0057 – See Au), wherein computing the one or more distances based on the one or more job description rankings and the one or more candidate skill rankings comprises: (¶ 0096, 0097, 0126: discloses the system 1 will match, score, and rank a group of CVs against a specific set of job descriptions. The system offers a candidate an effective tool to compare his/her CV with words and phrases commonly used in the market for similar job domains. The system can offer a recruiter an efficient and informed method to prepare a Job Spec by comparing with words and phrases commonly used in the market for similar job domains.)
The combination of Au, Ahmed, Buhrmann does not explicitly disclose the following limitations. In the same field of endeavor, the Zoia reference is related to techniques for measuring the Levenshtein distance between texts of job descriptions of candidate profile and job profile (¶ 0060) and teaches:
in response to determining that none of the one or more candidate skills corresponds to a job description skill (¶ 0054: discloses if the match value between the candidate profile and job profile does not exceed the threshold), setting a distance associated with the job description skill to a predetermined maximum value (¶ 0054);
in response to determining that a candidate skill ranking of a candidate skill is higher than or equal to a job description ranking of a job description skill (¶ 0049-0050: discloses the candidate profile may be matched to the job profile and it may be determined at step 510 whether the match score exceeded a threshold.),
setting a distance between the candidate skill and the job description skill to a predetermined minimum value (¶ 0050: discloses the threshold value may be set to a minimum value.);
Therefore, 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 the combination of Au, Ahmed, and Buhrmann to include the job matching techniques, as disclosed by Zoia to achieve the claimed invention. As disclosed by Zoia, the motivation for the combination would have been to provide advantages that accurately surface job matches that fit the candidate’s employment interests. (¶ 0065)
Response to Arguments
Applicant's arguments filed 25 November 2025 have been fully considered but they are not persuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “The Applicant submits that amended claim 1, as a whole, integrates the recited features into a practical application. In particular, the Applicant submits that amended claim 1 is eligible under Prong Two of Step 2A of USPTO's 2019 Revised Patent Subject Matter Eligibility Guidance ("2019 Guidance"). Claim 1, as amended, recites the elements of: parsing, by a text analysis engine, digitized text of a job description associated with a job for extracting one or more job description skills; determining job description rankings based on storing, by the text analysis engine in a dictionary, keywords corresponding to the job description rankings and analyzing context of each detected job description skill to search for keywords corresponding to a respective job description ranking; parsing, by the text analysis engine, digitized text of a curriculum vitae (CV) of a candidate for extracting one or more candidate skills; obtaining, in real-time via a client computing device, digitized text representing notes of communication with the candidate; determining candidate skill rankings based on storing, by the text analysis engine in a dictionary, keywords corresponding to the candidate skill rankings and analyzing context of each detected candidate skill to search for keywords corresponding to a respective candidate skill ranking; dynamically updating a candidate skill ranking of the one or more candidate skill rankings based on analysis of the digitized text; outputting, by an action recommendation engine to a client computing device for display thereon, candidate-specific query data determined based on the dynamically updated distances for the candidate.”
“These elements of amended claim 1 represent improvements to a system of document processing and digital data analysis. The computing system of claim 1 is configured to integrate automated parsing of digitized text in documents and computation of distances representing perceived disparities between job requirements and candidate skills into a process for dynamically updating a feedback-based model for candidate assessment and action recommendation and outputting recommendations using the model. More particularly, the computing system outputs, to client computing devices for display thereon, at least one of interview scheduling data or candidate-specific query data determined based on dynamically updated "distances" for the candidate.”
The claimed features enable improved methods for unstructured textual data analysis and model-based production of action recommendations in connection with candidate interviews. The recited elements of claim 1 do not merely link the features of amended claim 1 to a technical field, but instead add a meaningful limitation in that they provide technical aspects of a solution, implemented by a computing system, for converting automated processing of digital documents to generating and dynamically updating a feedback-based model that is used for candidate assessment and action recommendations. A person of ordinary skill in the art would recognize that these elements of claim 1, in combination with the other claim limitations, reflect technical advantages of enhancements to a system for automated digital data processing and dynamic model-based output of recommendations data.” The Examiner respectfully disagrees.
Contrary to the remarks, the claims remain ineligible under Step 2A Prong Two of the two-part analysis. It is important for Applicant to note that merely reproducing the combination of limitations as recited in claim 1 does not automatically make the claim eligible. The remarks rely upon “a feedback based model” and “using the model” which are features not reflected in the claim 1.
The additional elements recited in claim 1 include “a system comprising”, “at least one memory storing instructions;”, “at least one hardware processor interoperably coupled with the at least one memory, wherein execution of the instructions by the at least one hardware processor causes performance of operations comprising:”, “, by a text analysis engine”, “by the text analysis engine in a dictionary”, “by the text analysis engine”, “by an action recommendation engine to a client computing device for display thereon”. The description, i.e., [See Spec, i.e., ¶ 0045, 0051, 0057 - a text analysis engine 108. The text analysis engine 108 can be any application, program, other component, or combination thereof that, when executed by the processor 106, enables automatic analysis of job descriptions and CVs.], of the additional elements evidences they are generic elements that are used as tools to perform the abstract idea. Thus, Applicant has not shown that any of the features of claim 1 improve the way the recited generic “system” executes instructions, or otherwise stores and retrieves data, in a manner that improves the functionality or efficiency of any recited generic computer components or otherwise changes the way the claimed generic computer components functions.
In the instant case, Applicant purports improvements to “a system of document processing and digital data analysis” and/or “improved methods for unstructured textual data analysis and model-based production of action recommendations in connection with candidate interviews”, however, the courts have previously held an improved abstract idea (even if novel and nonobvious) is still an abstract idea. See Mayo Collaborative Services v. Prometheus Labs., Inc., 566 U.S. 66, 90 (2012) (holding that a novel and nonobvious claim directed to a purely abstract idea is, nonetheless, patent-ineligible); see also Solutran, Inc. V. Elavon, Inc., 931F.3d1161, 1169 (Fed. Cir. 2019) ("[E]ven if the idea is novel and non-obvious [it] is not enough to save it from ineligibility"). For these reasons, the rejections under 101 are being maintained.
Applicant further argues “As described above, amended claim 1 recites the elements of: parsing, by a text analysis engine, digitized text of a job description associated with a job for extracting one or more job description skills; determining job description rankings based on storing, by the text analysis engine in a dictionary, keywords corresponding to the job description rankings and analyzing context of each detected job description skill to search for keywords corresponding to a respective job description ranking; parsing, by the text analysis engine, digitized text of a curriculum vitae (CV) of a candidate for extracting one or more candidate skills; obtaining, in real-time via a client computing device, digitized text representing notes of communication with the candidate; determining candidate skill rankings based on storing, by the text analysis engine in a dictionary, keywords corresponding to the candidate skill rankings and analyzing context of each detected candidate skill to search for keywords corresponding to a respective candidate skill ranking; dynamically updating a candidate skill ranking of the one or more candidate skill rankings based on analysis of the digitized text; outputting, by an action recommendation engine to a client computing device for display thereon, candidate-specific query data determined based on the dynamically updated distances for the candidate. The claimed system represents an improvement in digital data processing technology. The combination of steps recited in the claim is not routine or conventional activity in the field of automated digital document processing. In particular, the limitations in claim 1 are not considered to be insignificant. Amended claim 1, as a whole, amounts to significantly more than one or more alleged abstract ideas.
For at least the reasons presented above, the Applicant respectfully submits that amended claim 1 is patent eligible. For similar reasons, independent claims 8 and 15, which contain similar features as amended claim 1, are also eligible, as are all pending dependent claims.” The Examiner respectfully disagrees.
Contrary to the remarks, the claimed invention remains ineligible under Step 2B of the two-part analysis. It is important for applicant to note because Step 2A Prong Two overlaps with Step 2B many of the considerations need not to be re-evaluated because the answer will be the same. Here, Applicant discusses and points to the same features argued above for Step 2A Prong Two, thus the reply does not change the analysis. As for improvements in digital data processing technology, the remarks are an attempt to limit the claimed invention to a technological field or particular field of use which cannot provide the inventive concept. The ordered combination of limitations are using results focused functional claim language which is a frequent feature of ineligible claims, especially those that claim the use of generic computer components and network technology to carry out business processes. At best, the ordered combination of limitations merely narrow how the abstract idea is to be performed and the additional elements behave in their ordinary or normal capacity to aid in performing the abstract idea recited by the claim. Accordingly, since claims 8 and 15 recite substantially similar subject matter they are being held ineligible under the same rationales. For these reasons, the rejections under 101 are being maintained.
With Respect to Rejections Under 35 USC 103
Applicant’s arguments with respect to claim(s) 1-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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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/EHRIN L PRATT/Examiner, Art Unit 3629
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626