DETAILED ACTION
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 .
Status of the Claims
Claims 1-20 were previously pending and subject to a non-final office action mailed 08/28/2025. Claims 1-3 and 11-13 were amended; no claim was cancelled or added in a reply filed 11/20/2025. Therefore claims 1-20 are currently pending and subject to the final office action below.
Response to Arguments
Applicant's arguments filed 11/20/2025 in regards to 101 rejection have been fully considered but they are not persuasive.
Applicant’s argument (reproduced verbatim):
“A. The claims do not recite a judicial exception (Prong One of Step 2A)
The Office asserts that the relevant limitations "cover[] a method of organizing a human activity and mathematical concepts." Office Action p. 3. The Office additionally states that the method "allows for fundamental economic principles or practices... commercial or legal interactions... [and] managing personal behavior or relationships... and mathematical relationships." Office Action, p. 4. For the reasons below, these characterizations do not demonstrate that the claims recite a judicial exception under MPEP § 2106.04 and the USPTO's 2019 and 2024 guidance, further clarified by the USPTO's August 4, 2025 Memorandum to Technology Centers 2100, 2600, and 3600 ("2025 Memorandum").
MPEP § 2106.04 explains that only three groupings qualify as abstract ideas:
mathematical concepts, certain methods of organizing human activity, and mental processes. The 2025 Memorandum emphasizes that Step 2A, Prong One asks whether the claim actually recites subject matter in one of these groupings; a claim does not "recite" an exception merely because it "involves" data, algorithms, or uses of machine learning.
The Office's assertions do not satisfy these requirements. “ (remarks p. 6).
Examiner respectfully disagrees. Applicant’s argument is not persuasive. While the 2025 memorandum correctly instructs that a claim does not “recite” an exception merely because it “involves” data, algorithms, or uses of machine, Applicant misapplies that principle here. The claims are not being characterized as abstract because they involve machine learning or data in some incidental way. The claims are directed to an abstract idea because, when considered as a whole under BRI, the claimed subject matter describes a method of screening job candidates and recommending employment roles based on skills assessment, which is at its core a business practice that organizes human activity in the employment context. Therefore, the Examiner’s characterization is limitation specific that addresses the abstract idea.
Applicant’s argument
“i. The claims do not recite certain methods of organizing human activity
Although the Office states that the limitations "cover[] a method of organizing a human activity," Office Action, p. 3, MPEP §2106.04(a) limits this grouping to specific sub-categories (fundamental economic practices, commercial or legal interactions, and managing personal behavior or relationships). The claims, however, recite computer-implemented data retrieval, data processing, model training, and model-based scoring; none of which fall within those sub- categories. The Deputy's Commissioners 2025 Memorandum for "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" ("2025 Memo") specifically instructs examiners not to oversimplify claim limitations or treat high-level purposes as if they were recited subject matter. On this record, the Office has not identified any claim language that recites a contractual interaction, a financial practice, a sales activity, or a rule for human conduct. The conclusory association with "business relations" and "managing personal behavior" (Office Action, p. 4) is insufficient under §2106.04(a) and inconsistent with the 2025 Memorandum's directive for a limitation-specific analysis. “(remarks p. 7)
Examiner respectfully disagrees. Applicant characterizes the claims as merely reciting “computer-implemented data retrieval, data processing, model training, and model-based scoring,” but this characterization understates what the claims actually recite when read as a whole under BRI. Claim 1 recites:
Receiving a request for consideration for a first job type from a device associated with a prospective job candidate; determining skills relevant to that job type based on aggregated data from a plurality of candidates; training a model using hiring outcomes and role transition events as outcome labels; computing a job seeker skillset match score; and providing a recommendation of a second job type tied to the candidate’s missing skills when the score satisfies a threshold.
These are not bare data-processing steps. They describe the full workflow of evaluating a candidate for employment and recommending an alternate career path, a commercial interaction in the employment domain. MPEP 2106.04(a)(2) expressly identifies “commercial or legal interactions” and “business relations” as sub-categories of “certain methods of organizing human activity.” Matching workers to job types, assessing skill fitness for employment, and recommending alternate positions are squarely business relations and commercial employment practices, regardless of whether they are implemented on a computer.
Applicant is correct that the 2025 Memo instructs against oversimplifying claim limitations. However, the Examiner’s characterization is not a reduction of the claims to an outcome, it is a recognition that the claimed steps, taken in combination, constitute a method of managing commercial employment interactions. The claim does not become a technical solution merely by labeling those interactions as “data retrieval” and “model-based scoring”.
Applicant’s argument:
“ii. The claims do not recite certain mathematical concepts
The Office also asserts that the limitations cover "mathematical concepts" and
"mathematical relationships." Office Action p. 4. Under MPEP §2106.04(a), mathematical concepts encompass mathematical relationships, formulas, equations, or calculations recited in the claims. The claims do not recite any such mathematical expression. This conclusion is supported by the USPTO's own Subject Matter Eligibility Examples and the 2025 Memo.
In Example 39, "Method for Training a Neural Network for Facial Detection," the
USPTO analyzed a claim that recites, among other steps, "training the neural network in a first stage using the first training set" and "training the neural network in a second stage using the
second training set." Subject Matter Eligibility Examples: Abstract Ideas, Example 39, Step 2A, Prong One. The USPTO concluded:
"2A - Prong 1: Judicial Exception Recited? No. The claim does not recite any of the judicial exceptions enumerated in the 2019 PEG. For instance, the claim does not recite any mathematical relationships, formulas, or calculations. While some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims. Further, the claim does not recite a mental process because the steps are not practically performed in the human mind. Finally, the claim does not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between
people. Thus, the claim is eligible because it does not recite a judicial exception." Example 39, Step 2A, Prong One.
The 2025 Memo expressly uses Example 39 to clarify how machine-learning training should be treated at Step 2A, Prong One. The Memorandum states: "The claim limitation 'training the neural network in a first stage using the first training set' of example 39 does not recite a judicial exception. Even though 'training the neural network' involves a broad array of techniques and/or activities that may involve or rely upon mathematical concepts, the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols. Contrast this with the limitation 'training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm' of claim 2 of example 47. This limitation requires specific mathematical calculations by referring to the mathematical calculations by name, i.e., a backpropagation algorithm and a gradient descent algorithm, and therefore recites a judicial
exception, namely an abstract idea." 2025 Memo, p. 3.
Thus, the USPTO has drawn a clear line under Step 2A, Prong One. A claim that merely recites training a neural network at a functional level, without reciting any specific mathematical formula or algorithm, does not recite a mathematical concept and does not recite a judicial
exception (Example 39). By contrast, a claim that recites training "wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm" is treated as reciting a mathematical concept because it names the mathematical calculations themselves, and therefore recites an abstract idea (Example 47, claim 2; see also Example 47 analysis at Step 2A, Prong One).
Amended claim 1 is squarely on the Example 39 side of this line and not on the Example 47 side. The claim recites, in relevant part, "training, based on the aggregated job candidate data for the first job type and corresponding outcome labels comprising one or more of hiring outcomes and role-transition events, a machine learning model configured to output a job seeker skillset match score as a bounded scalar," and "determining, via the trained machine learning model based on a comparison of current skills possessed by the prospective job candidate and the relevant skills, the job seeker skillset match score." The claim does not identify any training algorithm by name. There is no recitation of a backpropagation algorithm, a gradient descent algorithm, or any other specific optimization or numerical method. The limitation is recited purely at a functional level, i.e., training a model using labeled data and using the trained model to produce a bounded scalar score, which is precisely the type of functional machine-learning recitation that Example 39 and the 2025 Memo confirm does not, by itself, recite a mathematical concept.
Moreover, the same Example 39 analysis confirms that a functionally recited machine- learning pipeline is not a mental process and does not recite a method of organizing human activity, because "the claim does not recite a mental process because the steps are not practically performed in the human mind" and "does not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people." Example 39, Step 2A, Prong One. The present claims similarly recite operations such as retrieving aggregated job-candidate data via an API from ATS/HRIS systems, training a machine-learning model using outcome-labeled datasets for a specific job type, computing a bounded scalar job- seeker skillset match score via the trained model, and providing, to a device, a recommendation of a second job type keyed to missing skills. These are not steps that can practically be performed in the human mind, and they are not methods of organizing human activity under MPEP §2106.04(a).
Accordingly, under the USPTO's own Subject Matter Eligibility Examples and the 2025 Memo, amended claim 1 is treated the same way as Example 39: it does not recite any mathematical relationship, formula, equation, or calculation; it does not recite a mental process; and it does not recite a method of organizing human activity. It therefore does not recite a judicial exception under Step 2A, Prong One, and the Office's characterization of the claim as reciting "mathematical concepts" and "mathematical relationships" is inconsistent with both MPEP §2106.04(a) and the controlling USPTO guidance.” (remarks p. 7-9).
Examiner respectfully disagrees. Applicant’s reliance on Example 39 and the 2025 Memorandum is not persuasive for the following reasons.
First, Applicant applies Example 39 narrowly to only the “training” limitation, but Step 2A, Prong One requires evaluation of the claim as a whole. Even accepting that the training step alone does not name a specific algorithm and is therefore analogous to Example 39, claim 1 expressly recites additional language that does not recite mathematical concepts:
“machine learning model configured to output a job seeker skillset match score as a bounded scalar”, a bounded scalar is a finite range numeral value; reciting that the model must produce a score within a bounded numeric range recites a mathematical relationship;
“determining…the job seeker skillset match score” “based on a comparison of current skills…and the relevant skills”, a comparison that produces a numeric score is a mathematical calculation, distinguishable from Example 39 where no score or quantitative comparison was the output of the claimed training step.
These are not hidden mathematical steps. They are expressed in the claim language using quantitative terms (“bounded scalar”, “comparison”, “ranking”, “quantity”, “amount”). They go beyond the purely functional training-step language of Example 39 and bring the claims closer to Example 47’s mathematical claim language.
Second, the domain context matters. Example 39 involved a claim directed to improving facial detection, a recognized technical field (computer vision/image processing). The claim’s purpose was to improve how the neural network itself performed a technical image-recognition function. Here, the claimed model does not improve a technical computing process; it improves a business decision (which job to recommend to a candidate). The 2025 Memo’s directive to look at claims carefully applies in both directions: just as examiners should not oversimplify, applicant should not transplant eligibility conclusions from one technical domain (computer vision) to a business application domain (employment recommendation) without accounting for the difference in context.
Third, with respect to the mental process argument, the Examiner acknowledges that the specific computing steps (retrieving from ATS/HRIS, training a model) cannot be practically performed in the human mind in their entirety. However, the substance of what the method accomplishes, identifying candidate skill gaps, comparing those gaps to job requirements, and recommending a more suitable job type, is precisely the kind of evaluation and recommendation that human recruiters and career counselors have long performed mentally and on paper. The fact that a computer is required to process large volumes of aggregated data does not remove the evaluation and recommendation concept from the mental process analysis.
Accordingly, the claims recite a judicial exception under step 2A, Prong One, comprising at least a certain method of organizing human activity (commercial employment interactions) and mathematical concepts (bounded scalar scoring, skill comparisons, and ranking by quantity). Therefore, the claims are directed towards an abstract idea.
Applicant’s argument:
“i. The claims provide an improvement to technology
The MPEP states that "improvements in technology beyond computer functionality may demonstrate patent eligibility." See MPEP § 2106.05(a)(II). For example, MPEP § 2106.04(d)(I) explains that "[l]imitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include: [a]n improvement in the functioning of a computer, or an improvement to other technology or technical field. "To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method." Id. The MPEP also states, "Consideration of improvements is relevant to the eligibility analysis regardless of the technology of the claimed invention[,]" and "the consideration applies equally whether it is a computer-implemented invention, an invention in the life sciences, or any other technology." See MPEP § 2106.05(a)(II). The present claims represent an improvement both in computer technology and the life sciences, as expressly identified by the MPEP.
The Office states "[t]his judicial exception is not integrated into a practical application,""[e]ach of the additional limitations is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component, and concludes "these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea." Office Action, pp. 3-4.
The 2025 Memo reaffirms that the analysis under Prong Two must consider the claim as a whole, including the specific recited data flows, system interactions, and architectural features, and must not reduce the claim to an outcome. The 2025 Memo also instructs that machine learning elements recited at a functional level should not be dismissed as "generic" merely because they are expressed in functional language. Instead, examiners must evaluate whether the recited operations contribute to a particular technical implementation.
When viewed as a whole, the present claim applies whatever exception the Office alleges in a manner that meaningfully limits it. The recited subject matter is not a generic instruction to perform an abstract idea on a computer. Rather, the claim directs a specific computing architecture to operate on enterprise data sources through defined interfaces, to transform structured and unstructured records into training inputs for a model, to use labeled outcomes associated with job types, and to generate a bounded value through model-based inference that is then used by the system in producing a subsequent action. These are not generalized invocations of computing components but recitations that confine the manner in which data is acquired, processed, and acted upon within the described technical environment.
This is consistent with the reasoning of McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016). In McRO, the Federal Circuit determined that the claims were not directed to an abstract idea because they used a specific technical method for transforming input data into animated output that had not previously been achievable. Here, as in McRO, the claim invokes a particular technical method for processing structured information, generating a learned representation, and using that representation to produce a subsequent system output. The technological implementation is central, not incidental.
The claims also resemble those found eligible in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014), where the Federal Circuit upheld claims rooted in a specific computing architecture designed to solve a problem unique to computer networks. The present claim likewise recites an architecture configured to acquire, transform, and apply data in a defined technical framework, rather than invoking computing devices for the sake of operating a conceptual idea.
A similar principle appears in BASCOM Global Internet Services, Inc. v. AT&TMobility LLC, 827 F.3d 1341 (Fed. Cir. 2016) and Amdocs (Israel) Ltd. v. Openet Telecom, Inc., 841 F.3d 1288 (Fed. Cir. 2016), where the Federal Circuit recognized eligibility based on the non- conventional and non-generic arrangement of known computing components. The present claim recites a concrete arrangement of data interfaces, training workflows, and model-based inference pathways that are tied together in a specific order within a defined technological system. That structure meaningfully limits the use of any alleged exception.
The PTAB has applied the same reasoning in Ex Parte Smith (Appeal2018-000064), an informative decision concerning a trading platform that used timers, event-driven workflows, and cross-system routing to address latency and execution problems in electronic trading. The Board held that the claims were patent-eligible because they implemented a specific technological solution within a particular architecture. The present claim likewise recites a defined technological solution that operates through coordinated system interactions rather than generalized computer implementation.
The 2025 Memo additionally instructs that Prong Two favors eligibility where "a claim covers a particular solution to a problem or a particular way to achieve a desired outcome," especially in machine-learning contexts where the processing is tied to specific system inputs, outputs, or data structures. The claim here recites defined machine-learning operations integrated into an architecture that relies on coordinated data sources and produces a system-driven action. This is a meaningful limitation that does not allow the alleged exception to be performed in any technological context or on any general-purpose computer.
Accordingly, even assuming arguendo that Step 2A, Prong One were satisfied, the record supports that the claim integrates any alleged exception into a practical application under Step 2A, Prong Two.
Accordingly, Applicant respectfully submits that the recite a judicial exception, and even if they did, the judicial exception is integrated into a practical application. Accordingly, the rejection of claims 1-20 under 35 U.S.C. § 101 should be reconsidered and withdrawn.” (remarks p. 10-13).
Examiner respectfully disagrees.
Applicant argues that the claim “directs a specific computing architecture to operate on enterprise data sources through defined interfaces, to transform structured and unstructured records into training inputs for a model,” and that this constitutes an improvement to technology. However, the claim language does not support this characterization at the level of specificity required.
MPEP 2106.05(a) requires that, to demonstrate a technical improvement, the claims must “recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method.” The claim recites no such details. It does not describe any new data transformation technique, any novel pre-processing workflow, any unconventional model architecture, or any technical mechanism by which the API retrieval from an ATS/HRIS operates differently than conventional database queries. The recitation of “an API” as the retrieval mechanism is a generic functional reference to a well-known and conventional integration method, not a specific technical contribution. The recitation of a “a machine learning model configured to output a job seeker skillset match score as a bounded scalar” is equally generic; it identifies a desired output characteristic without specifying any new or unconventional technical means of producing it.
Applicant’s assertion that the claims represent an improvement “in the life sciences” is not supported by the record. The claims address employment recommendation and career path analysis. There is no plausible connection to life sciences. This argument is unpersuasive.
In McRo, the Federal Circuit found eligibility because the claims recited specific rules that defined how morph-weight sets and transition parameters were derived from timed sequences, and the use of those specific rules produced a technical result (more accurate 3D lip synchronization) that could not be achieved by prior methods. The court emphasized that that the claimed rules were not merely stated as a desired outcome, but defined a specific, previously unavailable technical procedure.
Here, by contrast, the claims do not recite any specific rules, parameters, or technical procedures for training the model, deriving the match score, or selecting a second job type. The training step is recited purely at the level of outcome (“configured to output a job seeker skillset match score as a bounded scalar”) without specifying how that output is derived. Unlike McRo, the present claims do not use the alleged abstract idea “in a specific way” that achieves a previously unavailable technical result. The claimed method merely applies a generic ML model to an HR recommendation task using conventional data sources, which is not the kind of specific technical procedure that supported eligibility in McRo.
In DDR Holdings, the claims addressed a problem specifically arising in the context of computer networks, the “click-away” problem unique to Internet commerce, and solved it through a specific architectural modification to how web servers generated and served composite web pages. The eligibility rested on the claim addressing a problem unique to the Internet/computer network environment and solving it in a technically specific way.
The present claims do not solve a problem unique to computing or network environments. The problem being solved, identifying skill gaps in job candidates and recommending alternate career paths, exists entirely in the human/business domain of human resources. Computers are used as tools to process the data and generate the recommendation, but the problem and solution are not rooted in any technical challenge specific to computing infrastructure, network architecture, or software systems. Therefore, the current claims are distinguishable from DDR Holdings.
In Bascom, the Federal Circuit found eligibility based on the “non-conventional and non-generic arrangement” of an Internet content filter at a specific location (the ISP server) rather than a local or remote server, which addressed a specific technical tradeoff between flexibility and user side implementation. In Amdocs, the claims recited a specific distributed system architecture in which each network device gathered and processed its own data near the source to solve a network data volume problem.
The present claims contain no analogous non-conventional arrangement of components. The architecture described, a device, an API, an ATS/HRIS data store, an ML model, and a display device, is a conventional client server data processing arrangement. There is no specific placement, distribution, or interconnection of components that solves a technical problem arising from the architecture itself. The components are invoked at a high level of generality and used in their conventional roles. The arrangement is not “non-conventional” within the meaning of Bascom and Amdocs.
In Ex Parte Smith, the PTAB found eligibility for a trading platform that used specific timers, event driven workflows, and cross system order routing to address latency and execution problems in high-speed electronic trading, a problem that was technical in nature and specific to the electronic trading environment. The eligibility was grounded in the claim’s recitation of a specific technical mechanism to address a specifically technical computing/network performance problem.
The present claims address a business problem, recommending suitable jobs to candidate, not a technical problem arising from computing or network performance. The use of APIs, ML models, and ATS/HRIS systems is described at a generic level. There is no parallel to the timer-based, event-driven, latency-sensitive architecture that distinguished Ex Parte Smith.
Applicant cites the 2025 Memo’s instruction that “prong Two favors eligibility where a claim covers a particular solution to a problem or a particular way to achieve a desired outcome.” However, the 2025 Memo’s instruction must be read in context. A “particular solution” in the Prong Two sense refers to a specific technical implementation that meaningfully limits the exception, not merely any defined sequence of functional steps. The claims here recite a sequence of functional operations (receive, retrieve, train, score, recommend) using generic components (device, API, ATS/HRIS, ML model) without specifying the technical particulars of how any step is accomplished. That is not a “particular solution” within the meaning of the 2025 Memo’s guidance; it is a high level of functional description of an abstract HR workflow.
Accordingly, the rejection of claims 1-20 under 35 USC 101 is maintained.
Applicant’s arguments with respect to 103 rejection 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.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1/11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim “receiving a request for consideration for a first job type for the job candidate; determining, based on aggregated job candidate data of a plurality of job candidates having the first job type, one or more relevant skills possessed by the plurality job candidates; retrieving at least a portion of the aggregated job candidate data; determining, via machine learning model, based on a comparison of current skills possessed by the prospective job candidate and the relevant skills, a job seeker skillset match score; and based on the skillset match score satisfying a threshold, providing [prospective job candidate] a recommendation of a second job type relevant to at least one of the one or more relevant skills missing from the current skills of the prospective job candidate.”
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing a human activity and mathematical concepts. That is, the method allows for fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and mathematical relationships.
This judicial exception is not integrated into a practical application. In particular, the claim recites a device associated with a prospective job candidate, an application programming interface (SPI), an applicant tracking system (ATS) and/or a human resource information system (HRIS), “training, based on the aggregated job candidate data for the first job type and corresponding outcome labels comprising one or more of hiring outcomes and role- transition events, a machine learning model configured to output a job seeker skillset match score as a bounded scalar “ (claim 1), and a device associated with a prospective job candidate configured to provide a request for consideration for a first job type for the job candidate; and a career management co-op platform (CMCP) system, an application programming interface (SPI), an applicant tracking system (ATS) and/or a human resource information system (HRIS), “train, based on the aggregated job candidate data for the first job type and corresponding outcome labels comprising one or more of hiring outcomes and role- transition events, a machine learning model configured to output a job seeker skillset match score as a bounded scalar “ (claim 11). Each of the additional limitations is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements, alone or in combination, 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, alone or in combination, are nothing more than mere instructions to apply the exception on a general computer.
Dependent claims 2-10 and 11-20 are also directed to an abstract idea without significantly more because they further narrow the abstract idea described in relation to claim 1/11 without successfully integrating the exception into a practical application (application tracking system is recited at a high level of generality and amounts to apply it instructions) or providing significantly more limitations.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 10-13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mitchell (US 7593860) in view of Garg (US 10803421) and Crow (WO 2005/038584).
As per claim 1/11, Mitchell discloses a system comprising:
a device associated with a prospective job candidate configured to provide a request for consideration for a first job type for the job candidate; a career management co-op platform (CMCP) system configured to:
receive the request (1:24-27, “(8) receiving by said computing apparatus, job candidate data, said job candidate data comprising a job candidate and a second list comprising a second plurality of required skills related to a job held by said job candidate;”);
determining one or more relevant skills possessed by the plurality of job candidates (3:49-53, “Job related data 16 comprises a list of specified job titles (e.g., computer programmers, data architect, network architects, etc) and associated required skills necessary to perform each specified job title.” 3:60-4:4, “A market valued skill is defined herein as a critical "hot" market place skill that is required to perform specified job and linked with business unit skills aligned to the achievement of business priorities (e.g., growth in revenue and innovation). A market valued skill comprises a job-related skill that when applied to a job will produce significant revenue, drive significant innovation, and is aligned with a market direction and particular business strategy for an entity or business. Market valued skills are dynamic and change as new products and services are introduced into the market place (e.g., as technology changes at a rapid pace market valued skills also change at a rapid pace).”);
Retrieving at least a portion of the aggregated job candidate data (claim 1, “wherein said first internal market data comprises first capacity planning data associated with an entity and first workforce management data associated with said entity,”, 4:25-30, “Internal market related data may comprise any market related data known to a person of ordinary skill in the art including, inter alia, resource and capacity planning data for an entity, workforce management data for an entity such as attrition and demand growth in specific environment areas (products/services/offerings), etc”);
A job-candidate match score is implemented as a bounded scalar with threshold comparison (6:45-52, “(18) Equation 1 will calculate analysis scores comprising a range of 0 to 2 with 0 indicating that the candidate is not a match for a specific job and 2 indicating that the candidate is a perfect match for a specific job.”, 6:49-59, “The analysis score is compared to a predetermined value to determine an action performed for the candidate (e.g., recommend that the candidate is a match for a specific job, reject a candidate for a specific job, recommend a training program to prepare the candidate for a specific job). For example, any analysis score comprising a range of greater than or equal to 1 (i.e., 1 is the predetermined value) is considered to be a good match for a specific job because the candidate is determined to possess an adequate number of required skills and therefore any training time required to move from a current job position to a desired job position would be minimal.”)
Comparing candidate’s current skills to required skills to produce a score (1:32-38, “(11) comparing by said career analysis tool, said first plurality of required skills with said second plurality of required skills to determine a first set of common skills between said first plurality of required skills and said second plurality of skills; (12) calculating, by said career analysis tool, a first score for said candidate by dividing the number of said first set of common skills by the number of said first plurality of required skills;”, 4:32-36, “(7) Career analysis tool 18 in system 2 of FIG. 1 allows job related data 16 to be compared to candidate input data 22 and a weighting factor(s) may be applied to the comparison to analyze a candidate's qualifications for specified job titles applied for by the candidate.”);
and based on the job seeker skillset match score satisfying a threshold, providing, to the device, a recommendation of a second job type relevant to at least one of the one or more relevant skills missing from the current skills of the prospective job candidate (6:60-63, “Any analysis score comprising a range of less than or equal to 1 (i.e., 1 is the predetermined value) is not considered to be a good match for a specific job because the candidate is not determined to possess an adequate number of required skills.”, 6:15-19, “If in step 44 it is determined that the analysis score is not greater than or equal to 1.0 (i.e., less than 1.0), then step 36 is repeated to perform another comparison between job related skills for a job title held by a candidate and required skills necessary for another desired job title.”, 7:43-48, “(19) Career analysis tool 18 may be used for (and the implementation example with respect to Tables 1-3 may be applied to) calculating several analysis scores for a candidate for several job titles and specific job titles may be recommended by career analysis tool 18 based on a comparison of each the analysis scores to each other.”, 6:45-7:4, “(18) Equation 1 will calculate analysis scores comprising a range of 0 to 2 with 0 indicating that the candidate is not a match for a specific job and 2 indicating that the candidate is a perfect match for a specific job. The analysis score is compared to a predetermined value to determine an action performed for the candidate (e.g., recommend that the candidate is a match for a specific job, reject a candidate for a specific job, recommend a training program to prepare the candidate for a specific job). For example, any analysis score comprising a range of greater than or equal to 1 (i.e., 1 is the predetermined value) is considered to be a good match for a specific job because the candidate is determined to possess an adequate number of required skills and therefore any training time required to move from a current job position to a desired job position would be minimal. Any analysis score comprising a range of less than or equal to 1 (i.e., 1 is the predetermined value) is not considered to be a good match for a specific job because the candidate is not determined to possess an adequate number of required skills. However, if there is no time limitation related to training time then candidates comprising an analysis score of less than 1 may be considered as long as required skills are determined to be considered market valued skills. Therefore, based of the implementation example with respect to Tables 1-3, the candidate was determined to have an analysis score of 1.3 (i.e., greater than or equal to 1) and therefore the candidate is considered to be a good match for the Network Architect job.”).
However, Mitchell does not disclose but Garg discloses retrieving, via an application programming interface (API), at least a portion of the aggregated job candidate data from an applicant tracking system (ATS) and/or a human resource information system (HRIS) (claim 2, “obtaining the talent profile from a human resource (HR) database of the first data source of the enterprise, wherein the talent profile of the candidate comprises at least one of: an employment role held by the candidate and skills associated with the role”, )
training, based on the aggregated job candidate data for the first job type and corresponding outcome labels comprising one or more of hiring outcomes and role-transition events, a machine learning model configured to output a job seeker skillset match score (claim 1, “a neural network module trained by adjusting at least one parameter associated with the neural network module based on a training enriched talent profile, a training calibrated job profile, and a training match score between the training enriched talent profile and the training calibrated job profile;”, 12:54-60, “(88) The system also has its own proprietary data 1090 with its history of matching enriched talent profiles to calibrated job positions across a variety of organizations. In certain embodiments, this data is used to train the DNN, obtain peer data, identify ideal and unqualified candidate for a calibrated job profile, and create vector representations of values for roles, skills, companies, schools, and fields of study.”, 13:10-16, “The matching engine 1040 matches enhanced talent profiles to calibrated job positions using DNN 1050 and outputs one or more hiring-related predictions for each candidate-job pair inputted into the engine. The match score calculation 1060 calculates match scores for candidate/job pairs using the hiring-related predictions outputting by the matching engine 1040.”);
determining, via the trained machine learning model based on a comparison of current skills possessed by the prospective job candidate and the relevant skills, the job seeker skillset match score (12:8-14, “The DNN 750 outputs one or more hiring-related predictions (980) for each pair, resulting in a match score for each of the inputted candidates at Organization X. Examples of hiring-related predictions are the probability of the candidate being interviewed, the probability of the candidate being hired, and the probability of the candidate being successful in a job after a period of time (e.g., 1 year).”, 13:10-16, “The matching engine 1040 matches enhanced talent profiles to calibrated job positions using DNN 1050 and outputs one or more hiring-related predictions for each candidate-job pair inputted into the engine. The match score calculation 1060 calculates match scores for candidate/job pairs using the hiring-related predictions outputting by the matching engine 1040.”))
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Garg in the teaching of Mitchell, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
However, Mitchell does not disclose but Crow discloses determining, based on aggregated job candidate data of a plurality of job candidates having the first job type, one or more relevant skills possessed by the plurality of job candidates ((page. 28, ln 20-31, “The method can be used to identify and extract skills from job candidate data (e.g., the job candidate data 122 of FIG. 1). At 1920, skills lists are identified, and at 1930, skills are extracted from the identified skills lists. The skills so extracted may then be added, for example, as skills with a confidence score. The confidence score can be compared with the confidence scores of the same concepts extracted by the other speculative extractors such as the other heuristic extractors or the parsing extractors. The confidence score for a particular concept can be added to the concept space responsive to determining that the confidence score reaches or exceeds the set threshold.”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Crow in the teaching of Mitchell, in order to find job candidate matches having characteristics similar to those of a desirable candidate (please see Crow abstract).
As per claim 2/12, Mitchell discloses wherein the CMCP system is further configured to, based on the job seeker skillset match score satisfying a threshold, determine the second job type relevant to the at least one of the one or more relevant skills missing from the current skills of the prospective job candidate (col. 6:45 to col. 7:15, “18) Equation 1 will calculate analysis scores comprising a range of 0 to 2 with 0 indicating that the candidate is not a match for a specific job and 2 indicating that the candidate is a perfect match for a specific job. The analysis score is compared to a predetermined value to determine an action performed for the candidate (e.g., recommend that the candidate is a match for a specific job, reject a candidate for a specific job, recommend a training program to prepare the candidate for a specific job). For example, any analysis score comprising a range of greater than or equal to 1 (i.e., 1 is the predetermined value) is considered to be a good match for a specific job because the candidate is determined to possess an adequate number of required skills and therefore any training time required to move from a current job position to a desired job position would be minimal. Any analysis score comprising a range of less than or equal to 1 (i.e., 1 is the predetermined value) is not considered to be a good match for a specific job because the candidate is not determined to possess an adequate number of required skills. However, if there is no time limitation related to training time then candidates comprising an analysis score of less than 1 may be considered as long as required skills are determined to be considered market valued skills. Therefore, based on the implementation example with respect to Tables 1-3, the candidate was determined to have an analysis score of 1.3 (i.e., greater than or equal to 1) and therefore the candidate is considered to be a good match for the Network Architect job. Additionally, prior to calculating the analysis score, a first fraction of common skills scores between the two job titles (i.e., Common skills*1/Total required skills from Equation 1) may be compared to a first predetermined value and a second fraction of market valued skills with respect to a total number of required skills for the requested job title (i.e., Weighting factor from Equation 1) may be compared to a second predetermined value. In this instance, the first predetermined value would have to be equal to or exceeded by the first fraction and the second predetermined value would have to be equal to or exceeded by the second fraction in order to calculate an analysis score.”). However, Mitchell does not disclose but Garg discloses using the trained machine learning model to generate a match score and determine appropriate job candidate pairings as disclosed in claim 1. (please see claim 1 rejection for combination rationale).
As per claim 3/13, Mitchell discloses determining the second job type based on a highest ranked skill of the one or more relevant skills missing from the current skills of the prospective job candidate (col. 7:29-42, “additionally, each of the market valued skills and each of the common skills may be weighted (i.e., a weighting factor applied) before an analysis score is calculated. As a first example, each of the common skills may be weighted according to a relative importance (e.g., by an expert in the field) to the requested job (e.g., for a Network Architect it may be determined that an Apply IT standards skill is more important that a communications skill and therefore the Apply IT standards skill is weighted higher). As a second example, each of the market valued skills may be weighted according to an amount of time that the market valued skill has been determined to be a market valued skill (e.g., the greater the amount of time that the market valued skill has been determined to be a market valued skill, the higher the weighting), col. 7:47-62, “Additionally, career analysis tool 18 may determine a skills delta between job related skills associated with a job held by the candidate and required skills necessary to obtain a desired position (i.e., required skills missing from the candidate's current job). Career analysis tool 18 may determine a training program for the candidate for obtaining the missing required skills determined by the skills delta. For example, with respect to the implementation example with respect to Tables 1-3, the candidate would be determined to have a skills delta of 3 (i.e., 3 missing required skills as indicated by "No" in Column 3, rows 3, 6, and 9 of Table 3). Career analysis tool 18 may develop a training program for the candidate so that the candidate could acquire the 3 missing required skills.” The highest ranked skill among the missing skills is simply the missing skill with the greatest weight). However, Mitchell does not disclose but Garg discloses using the trained machine learning model to generate a match score and determine appropriate job candidate pairings as disclosed in claim 1. (please see claim 1 rejection for combination rationale).
As per claim 10/20, Mitchell does not disclose but Crow discloses wherein the CMCP system is configured to receive aggregated job candidate data of a plurality of job candidates from an application tracking system (p.49:11-17, p.71:1-5, “The task of storing detailed information about a candidate can be left to the Applicant Tracking Software (ATS) that is the client of the Match Service. A set of CVOs can be returned from the match and clone methods of the Match Service API. It can also be the input to the clone method… Integration into Applicant Tracking Software System Any of the technologies described herein can be integrated into applicant tracking software system.”(please see claim 1 rejection for combination rationale).
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mitchell (US 7593860) in view of Garg (US 10803421) and Crow (WO 2005/038584), as disclosed in the rejection of claim 13, in further view of Cai (US 20150161566).
As per claim 4/14, Mitchell in view of Crow and Garg does not disclose but Cai discloses wherein the CMCP system is further configured to rank the list of relevant skills based on a quantity of the plurality of job candidates having a respective skill, an amount of experience developing a respective skill, or a combination thereof (paragraph 45-46, “The demand analyzer 232 may aggregate the required skills of the job openings and sort them according to, for example, user-defined categories (e.g., by industry, city, etc.). The skills that occur more frequently in the job data may be indicative of higher demand for those skills. A filter may be implemented to remove duplicate job openings [0046] Supply analyzer 230 may further be employed to generate a supply report indicating the skills that are currently supplied by job candidates. Such supply report may be generated based on the profile data provided by the profile manager 202. In some implementations, each individual profile is associated with a set of profile features, including a set of acquired skills. The supply analyzer 230 may aggregate the acquired skills of the individual profiles of candidates in the profile data, and sort them according to, for example, user-defined categories (e.g., by industry, city, etc.). The acquired skills that occur more frequently in the profile data may be indicative of higher supply for those skills.”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Cai in the teaching of Mitchell, in order facilitate workforce planning and analytics (please see Cai abstract).
Claim(s) 5-6 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mitchell (US 7593860) in view of Garg (US 10803421) and Crow (WO 2005/038584), as disclosed in the rejection of claim 1, in further view of Champaneria (US 20190114593) hereinafter “Champ” and Bixler (US 20130325734).
As per claim 5/15, Mitchell does not disclose but Champ discloses determining a job seeker skillset match score based on multiple criteria (paragraph 89, 93, 97).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Champ in the teaching of Mitchell, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
However, Champ does not disclose a potential cultural match score as part of the criteria but Bixler discloses wherein the CMCP system is configured to determine the job seeker skillset match score based on a potential cultural match (PCM) score (paragraph 47, “[0047] In various embodiments, presently disclosed embodiments are directed to systems and methods that correlate, inter alia, candidates' current situation and career aspirations, as well as candidates' history of past hires to calculate an aggregate "temperature" of such candidates. Also, such systems and methods can correlate the cultural fit of the candidate(s) and calculate an aggregate "temperature" of such a candidate(s). In an embodiment, a candidate's temperature is a metric that reflects the likelihood that the candidate will consider new employment opportunities. Among other things, a change in a candidate's temperature may indicate a passive candidate is becoming, or will soon become, a potential candidate, and thus, should be contacted by a recruiter from a prospective employer before the candidate enters the open job market (i.e., becomes an active candidate).”, paragraph 158, “[0158] In an embodiment, a temperature calculation score is impacted by each answer to each question in the candidate's current situation. Online activity in the system (block 1017) can be assessed to increase the temperature score. Online activity in other systems (block 1018), such as updating a professional social networking profile (e.g., a LinkedIn profile) can also be assessed to increase the temperature score. Information gathered from other systems (block 1015) also can be aggregated and be used in the temperature score. All of these factors are then weighted and combined into an overall score. The score is then categorized into Hot, Warm or Cold ranges that is displayed to recruiters.”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Bixler in the teaching of Mitchell in view of Champ, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 6/16, Mitchell in view of Crow, Garg, Champ and Bixler discloses all the limitation of claim 5/15. Mitchell does not disclose but Bixler further discloses wherein the PCM score includes at least one soft skill score (paragraph 113, “n an embodiment, the questionnaire can additionally or alternatively include questions that assess cultural and behavioral responses to various situations. In other embodiments, candidates can be asked to rank or order various criteria which can be used to determine the ideal or preferred culture in which they would like to work. This cultural fit information can be used to assess the quality of the cultural match of the candidate to the culture of the companies or employers in the system.”, paragraph 142-158)(please see claim 5 rejection for combination rationale).
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mitchell (US 7593860) in view of Garg (US 10803421) and Crow (WO 2005/038584), as in further view of “Champ” and Bixler (US 20130325734), as disclosed in the rejection of claim 6/16, in further view of Pande (US 11120403).
As per claim 7/17, Mitchell in view of Champ and Bixler does not disclose but Pande discloses wherein the at least one soft skill score includes an ethics score, leadership score, a teamwork score, a communication score, or any combination thereof (col. 6:52 to col. 7:20, “(44) Based on a users profile, the set of soft skills, functional and industry relevant skills are chosen which are most important. These are pulled from the database of job roles/profiles. For instance a student would mainly focus on 5 core soft skills as communication, problem-solving, teamwork, leadership and initiative and some skills relevant to the degree they are pursuing. Where as a job seeker in the healthcare industry would be rated on soft skills, functional and industry relevant skills that correspond to their current profile. (45) To obtain the skills score the following is done: (46) To provide a score on skills the analytics engine takes all data stored in the user’s profile and identifies what job role/function/industry the user fits into for job seekers, and for students—degree discipline, desired function/company. 1. This data is then matched with skills matching database that contains keywords, phrases, job roles, patterns that determine whether someone has a skill or not. 2. For each skill—a count of matches is generated with weights for each match is calculated 3. Count of matches and weight is added up and matched to benchmarks for that “type” of profile. The score is then normalized on a 0 to 1 scale. Benchmarks determine whether the user is high, medium or low on a skill 4. This is repeated for all skills stored in the database for that particular profile of user.”)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Pande in the teaching of Mitchell in view of Champ and Bixler, in order to assess a career profile of a candidate (Pande, abstract).
Claim(s) 8-9 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mitchell (US 7593860) in view of Garg (US 10803421) and Crow (WO 2005/038584), as disclosed in the rejection of claim 1/11, as in further view of “Champ”.
As per claim 8/18, Mitchell does not disclose but Champ discloses wherein the CMCP system is configured to determine the job seeker skillset match score based on a potential skills match (PSM) score (paragraph 94, “ The résumés of searched candidates may be scored based on their match to one or more elements extracted from the job description, which may include one or more key points, concepts, or requirements that are outlined in the job description.”, paragraph 97, “[0097] In an exemplary embodiment, résumés may be scored by a semantic engine utilizing machine learning concepts. The semantic engine may apply weight to certain requirements, which may be specified by a hiring organization or recruiter or may be derived from the job description. For example, these requirements may include (but may not be limited to) a title search (i.e. a search of job titles), the date on which the résumé was last updated, particular skills (or synonyms of those skills) that may be listed in the résumé, a number of required years of experience in a particular field, a number of required years of experience in a particular industry, a number of years of experience associated with a skill or with a particular set of skills, employment continuity, salary history, salary requirements, geographical proximity, social footprint (for example, the connections of the user on one or more social media websites), activity of social media (for example, the postings of the user on one or more social media websites), willingness to relocate, and any additional requirements that have been provided by the hiring company or which have been deemed relevant to the process. In an exemplary embodiment, some or all of these criteria may be given different levels of weights from one another. In an exemplary embodiment, particular criteria may be mutually exclusively given weight, or may weigh against each other; for example, if a candidate is found who is geographically proximate to a hiring company, it may not matter that the candidate is willing to relocate, and the willingness of the candidate to relocate may not be scored or may be given different weight.”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Champ in the teaching of Mitchell, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 9/19, Mitchell does not disclose but Crow discloses a skill score provided by the prospective job candidate (3:10-12, “The extracted concepts can be associated with a concept score. Such a concept score can, for example, generally indicate the candidate's level of experience with respect to the associated concept. Via the concept scores, conceptualized job candidate data can be represented by a point in n-dimensional space, sometimes called the "concept space."” Examiner interprets this as candidate provided skill score)(please see claim 1/11 rejection for combination rationale).
However, Mitchell in view Crow does not disclose but Champ discloses herein the CMCP system is configured to determine the PSM score based on a comparison of different criteria and one or more skill scores provided by recruiters or hiring managers (paragraph 16, “For example, according to an exemplary embodiment, a hiring manager may specifically provide the top three to top five skills or types/level of experience that is needed for the job, along with the job description; in some exemplary embodiments, skills, experience, or other such qualities that have been indicated by a hiring manager as being important may be given additional weight in a matching and scoring process, if desired. The method may next include generating, on the automated recruitment system, a first data point matrix, the data point matrix based on the one or more job requirements. The method may next include automatically posting, with the automated recruitment system, on a network, one or more posts comprising the job description. The method may next include automatically maintaining, with the automated recruitment system, the one or more posts, which may be continued over a period of time.” The weighing of the different criteria is taken as comparison).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Champ in the teaching of Mitchell in view of Crow, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
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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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3628