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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
The pending claims in the present application are claims 1, 3-5, 9-11, 13, 15, and 17-20, of the Amendment dated 28 January 2026.
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, 3-5, 9-11, 13, 15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “method” of claims 1, 3-5, 9-11, and 13 constitutes a process under 35 USC 101, the “system” of claims 15 and 17-19 constitutes a machine under the statute, and the “non-transitory computer-readable medium” of independent claim 20 constitutes a manufacture under the statute. Accordingly, claims 1, 3-5, 9-11, 13, 15, and 17-20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“A method for evaluating performance of operation resources ..., the method comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... receiving ... each of a plurality of performance parameters associated with a set of operation resources; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... determining ... a set of features for each of the plurality of performance parameters, based on the each of a plurality of performance parameters; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... creating ... one or more feature vectors corresponding to each of the plurality of performance parameters, based on the set of features determined for each of the plurality of performance parameters, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... wherein the one or more feature vectors are created ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... iteratively compute a Q value of each of the set of operation resources, and wherein the Q value corresponds to probability of one operation resource from the set of operation resources being preferred over other operation resources from the set of operation resources; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... assessing ... the one or more feature vectors, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... classifying ... the set of operation resources into one of a set of performance categories based on the assessing of the one or more feature vectors; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... evaluating ... the performance of at least one of the set of operation resources, based on an associated category in the set of performance categories, in response to the classifying, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... wherein evaluating further comprises: computing, for each of the set of performance categories, a score for each operation resource from the set of operation resources categorized within an associated performance category, based on a ... determined set of features associated with the each of a plurality of performance parameters for one of the set of performance categories, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... assigning weights to each of the set of features associated with the each of the plurality of performance parameters based on a predefined evaluation criterion, and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... wherein the predefined evaluation criterion comprises one or more of complexity of issues, an expertise level and a resolution quality of issues, and wherein high weights are assigned to one or more features from the set of features associated with the complexity of issues, and the resolution quality of issues as compared to the expertise level; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... ranking each operation resource from the set of operation resources for each of the set of performance categories, based on the computed ranks, to evaluate the performance of each operation resource from the set of operation resources; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... identifying ... at least one operation resource from the set of operation resources for imparting training to the at least one operation resource to bridge technical skill gap, based on the evaluated performance of the at least one operation resource of the set of operation resources.” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... modifying ... with transferable knowledge for a target system to be evaluated, wherein the transferable knowledge corresponds to optimal values associated with the one or more feature vectors corresponding to each of the plurality of performance parameters; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... tuning ... using specific characteristics of the target system to create a target model; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... evaluating the target system performance using the target model to predict system performance of the target system.” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: managing personal behavior or relationships or interactions between people, including social activities, involving evaluating organization resources, which falls under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “receiving” step), and evaluation, judgment, and/or opinion (e.g., the recited “determining,” “creating,” “compute,” “assessing,” “classifying,” “evaluating,” “computing,” “assigning,” “ranking,” “identifying,” “modifying,” “tuning,” and “evaluating” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
The claimed “method” is performed “using Artificial Intelligence (AI)” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “receiving” is performed “by an AI based evaluation system” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “determining” is performed “by the AI based evaluation system” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “creating” is performed “by the AI based evaluation system” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “created” is performed “based on a first pre-trained machine learning model” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
Performing the claimed “compute” is part of “wherein the first pre-trained machine learning model comprises a deep neural network trained using a reinforcement learning algorithm” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “assessing” is performed “by the AI based evaluation system ... based on the first pre-trained machine learning model” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “classifying” is performed “by the AI based evaluation system” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “evaluating” is performed “by the AI based evaluation system” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “computing” is “based on a second machine learning model trained on” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
Performing the claimed “assigning” is part of “wherein training of the second machine learning model further comprises” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “identifying” is performed “by the AI based evaluation system” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “modifying” is of “the first pre-trained machine learning model” and is performed “by the AI based evaluation system” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “tuning” is of “the first pre-trained machine learning model” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, mere automation of manual processes, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 3-5, 9-11, and 13, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “wherein determining a feature from the set of features further comprises: receiving positive feedback data and negative feedback data corresponding to each of the plurality of performance parameters, to determine the feature associated with each of the set of operation resources” of claim 3, the “wherein determining the feature from the set of features further comprises computing one of: a mean, a median and a harmonic mean, based on the positive feedback data and the negative feedback data received corresponding to each of the plurality of performance parameters” of claim 4, the “wherein determining a feature from the set of features further comprises combining two or more features from the set of features determined” of claim 5, the “wherein the one or more performance parameters comprise at least one of type of issues solved, priority of the issues solved, complexity of issues, resolution quality of issues, types of support received from peers, types of support provided to peers, feedback or rating received from managers, expertise level, technical skills, positive feedback data, negative feedback data, neutral feedback data of each of the set of operation resources” of claim 9, the “wherein the set of performance categories includes an excellent performer category, a good performer category, an average performer category, and a bad performer category” of claim 10, the “wherein evaluating the performance of each of the set of operation resources” of claim 11, and the “receive as input an input observation, an input action and to generate an estimated future reward from the input in accordance with each of the plurality of performance parameters associated with the set of operation resources” of claim 13). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “wherein the first pre-trained machine learning model corresponds to a Q network, and wherein the Q network is configured to” of claim 13). Accordingly, claims 3-5, 9-11, and 13 also are rejected as ineligible under 35 USC 101.
Regarding pending claims 15 and 17-19, while the claims are of different scope relative to claims 1 and 3-5, the claims recite limitations similar to the limitations of claims 1 and 3-5. As such, the rejection rationales applied to reject claims 1 and 3-5 also apply for purposes of rejecting claims 15 and 17-19. Limitations recited by claims 15 and 17-19 that do not appear to have a counterpart in claims 1 and 3-5, such as the recited “system,” “processor,” “memory,” and “executable instructions” of claim 15, fail to warrant a finding of eligibility, because such limitations amount to additional elements that fail to meet the criteria of Step 2A, Prong Two and Step 2B, for the same reasons as the additional elements of claims 1 and 3-5. Claims 15 and 17-19 are, therefore, also rejected as ineligible under 35 USC 101.
Regarding claim 20, while the claim is of different scope relative to claims 1 15, the claim recites limitations similar to the limitations of claim 1 and 15. As such, the rejection rationales applied to reject claims 1 and 15 also apply for purposes of rejecting claim 20. Claims 20 is, therefore, also rejected as ineligible under 35 USC 101.
Examiner Remarks
The pending claims distinguish over the closest US, foreign, and NPL prior art references of record. For example, the cited Dubey (U.S. Pat. App. Pub. No. 2017/0359273 A1), Sanderson (CN Pat. App. Pub. No. 111340246 A), Arulkumaran (NPL), Upadhyay (U.S. Pat. App. Pub. No. 2021/0174307 A1), Ng (NPL), and Weiss (NPL) references (see the Final Rejection dated 19 November 2024, at pp. 12 and 24-26), whether taken alone or in combination, fail to disclose or suggest the sequence of steps recited by the claims. More specifically, they fail to recite the combined “creating ... feature vectors” and “assessing ... the ... feature vectors,” involving the “first pre-trained machine learning model” with “Q value;” and the “classifying ... into ... performance categories based on the assessing” and “evaluating ... based on an associated categories” steps, involving the “second machine learning model” with “weights” limitations of amended independent claim 1. Dubey’s disclosure of “dynamic resource assessment” (abstract) involving “machine learning” (para. [0074]), Sanderson’s teaching of a “pre-trained machine learning model” and “feature vector” (abstract), and Arulkumaran’s teaching of a “neural network” and “Q-value” (p. 32), recite some of the claimed features, but not their specified relationships or sequences. The cited Upadhyay, Ng, and Weiss references do not remedy the above-noted deficiencies of the cited Dubey, Sanderson, and Arulkumaran references, and were not cited for such a purpose.
Response to Arguments
On pp. 15-39 of the Amendment, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. With respect to Step 2A, Prong One of the eligibility analysis, the applicant contends that the claimed invention is not directed to an abstract idea because the examiner’s characterization does not capture the technological nature of the claimed invention, including the AI and ML aspects and processes. (Amendment, pp. 18 and 19.) The applicant emphasizes functions implemented by hardware components, software modules, and databases in the system architecture. (Amendment, p. 19.) The applicant also contends that the encodings, embeddings, vector representations, feature vectors, pre-trained models, Q-values, and the reinforcement-learning Q-network are technical processes that cannot be performed mentally. (Amendment, p. 20.) The applicant also lists alleged technical improvements of the claimed invention. (Amendment, pp. 20 and 21).
The examiner disagrees with the above contentions, or at least disagrees with the notion that the contentions establish conditions for eligibility. Step 2A, Prong One looks at abstract idea elements, not technology whether in the form of hardware components, software modules, databases, system architecture, AI, ML and the like. (MPEP 2106.04(a).) Such technology is viewed as additional elements and are set aside for later steps of the eligibility analysis. (MPEP 2016.04(d) and 2106.05.) The presence of additional elements in claims plays virtually no role in determining whether claims recite abstract idea elements. Once those limitations are set aside, the remaining limitations are considered at Step 2A, Prong One. Looking at the pending claims, the remaining limitations thereof fall under one or more of the enumerated groupings of abstract ideas because they are steps associated with evaluating resources (e.g., employees), and involve consideration of, at most, a few data elements. Vector representations, under broadest reasonable interpretation, can be nothing more than a set of values (three numbers, perhaps). Any contemplating of a scenario is a form of modeling. Further, the claim does not recite encodings or embeddings (not that such recitations would warrant eligibility). The processing of these simplistic forms of data are well within the realm of mental processing. For all of these reasons, the claims fail to meet the criteria for eligibility at Step 2A, Prong One.
With respect to Step 2A, Prong Two of the eligibility analysis, the applicant contends that the claims are directed to an improvement in the field of AI-based model evaluation, emphasizing converting parameters into NN encodings, computing Q-values, computing category-specific scores, modifying and tuning pre-trained models through transfer learning. (Amendment, p. 27.) The applicant also contends that the additional elements improve how AI systems process and adapt ML models to new environments, emphasizing data processing improvements, reinforcement learning based evaluation, trained and weighted scoring and ranking, transfer learning, modifying ML models using transferable knowledge, tuning models to create target models, and evaluating target system performance using target models. (Amendment, pp. 28 and 29.) According to the applicant, these aspects achieve a real-world technical outcome. (Amendment, p. 29.) The applicant also contends that the claimed invention is implemented by a specific machine architecture that constitutes a particular machine. (Amendment, pp. 29 and 30.) The applicant also points to eligibility rationales of Amdocs as grounds for eligibility of the pending claims. (Amendment, pp. 30 and 31.)
The examiner disagrees with the above contentions, or at least disagrees with the notion that the contentions establish conditions for eligibility. The applicant’s claims and specification establish the use of (mere application of) technology, not improvements to the technology. Even if the claimed technology is useful, or is advantageous, merely applying such useful or advantageous technology is not improving the technology. What exactly about the technology is improved? The claims, specification, and the applicant’s remarks appear to describe use of conventional forms of technology. Almost all AI and ML models are trained. Many conventional ML models have fine tuning of weights. Transfer learning is a conventional concept. Amdocs involves a distributed network architecture operating in an unconventional fashion. The applicant’s claims involve evaluating employee performance using a distributed network architecture operating conventionally. The examiner contends that the steps of the claimed invention that relate to evaluating employee performance do not in any way improve technology. The structure of the claims themselves, and the applicant’s specification (para. [0002]), make clear that the invention involves evaluating employee performance using technology so it does not have to be performed manually. Technology is used in the process, but the technology is not improved. Evaluating performance of employees is improved, but that is an improvement to the abstract idea, and unworthy of eligibility. (MPEP 2106.05(a)(II).) Further, additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. (MPEP 2106.05(b)(II).) For all of these reasons, the claims fail to meet the criteria for eligibility at Step 2A, Prong Two.
With respect to Step 2B of the eligibility analysis, the applicant contends that the claimed invention provides significantly more through non-conventional technical elements including pre-trained model-based feature-vector generation and neural encodings, reinforcement-learning Q-network for computing Q-values, second trained model with predefined evaluation criteria and differential weights, and transfer learning and target-model creation; ordered combination of a coordinated ML pipeline; elements that are not routine, generic, or conventional; non-generic automation; specific machine configuration, and inventive concepts. (Amendment, pp. 31-38.)
The examiner disagrees with the above contentions, or at least disagrees with the notion that the contentions establish conditions for eligibility. Initially, some of the features emphasized by the applicant are not explicitly claimed, and thus, contentions based thereon are not persuasive. Pre-trained models, feature vectors, neural networks, reinforcement learning, Q-networks and Q-values, weights, transfer learning, ML pipelines (like ensemble learning), and the like, are conventional. Merely applying conventional technology is not grounds for eligibility. (MPEP 2106.05(f).) Features of the claimed technology, or advantages or benefits associated with their use, are not improvements to the technology. The examiner also contends that the type of employee evaluation data handled by the technological (additional) elements does not establish eligibility, especially when the data has analog counterparts. The claimed forms of data, while descriptive and jargon-heavy, appear to be nothing more than several values. For all of these reasons, the claims fail to meet the criteria for eligibility at Step 2B.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS Y. HO, whose telephone number is (571)270-7918. The examiner can normally be reached Monday through Friday, 9:30 AM to 5:30 PM Eastern.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor, can be reached at 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624
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