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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/20/2026 has been entered.
Status of Claims
Claims 1-14 are pending and examined herein per Applicant’s amendment filed 01/20/2026. Claim 1, 9, 13, and 14 are amended. Claims 15-27 were previously canceled. No claims are withdrawn or newly added.
Response to Arguments
Applicant's arguments filed with respect to the outstanding 35 USC 101 rejection have been fully considered but they are not persuasive. Applicant argues:
Applicant submits that this analysis improperly oversimplifies the claims by characterizing them as merely "using a trained machine learning model to allocate resources in satisfaction of a contract." This characterization fails to account for the specific technical elements recited in the claims, including the "trained slot allocation machine learning model" that is "configured to generate resource allocations based at least in part on one or more service level agreement (SLA) requirements associated with the service identifier" as recited by claim 1. Remarks p. 6.
Respectfully, it is noted that the limitations referenced in Applicant’s arguments are directed towards newly added limitations that are fully addressed in the updated rejection, below.
Examiner's analysis fails to comply with the recently codified Desjardins framework in the MPEP, which requires examiners to evaluate claims "as a whole" without oversimplifying them. See MPEP § 2106.05(a) as revised by USPTO Memorandum dated Dec. 5, 2025 (MPEP Change Ex Parte Desjardins 2025). The Desjardins framework further prohibits dismissing machine learning elements as generic computer components without considering whether they confer a technological improvement to a technical problem. Remarks p. 6.
Respectfully, the Office disagrees with Applicant’s position. The claims are always considered in part and as a whole under the Alice/Mayo 101 analysis. The recent USPTO 12/05/2025 “Advance notice of changes to the MPEP in light of Ex Parte Desjardins” memorandum, it was stated “These updates are not intended to announce any new USPTO practice or procedure”. With respect to the instant claims even when viewed as a whole they were found to be directed towards an abstract idea without practical application or significantly more. As stated previously, claimed “trained slot allocation machine learning model” is found to be nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. See MPEP 2106.05(f) quoting Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984”.
Ex Parte Desjardins included limitations that trained the model to learn new task while protecting knowledge about previous tasks to overcome the technical problem of catastrophic forgetting. The instant claims simply apply the trained model – there is no improvement to the computer or a technical field, e.g. the claims of Desjarins capture the stated technical improvements of the specification by including the training of the model in the claims. The specification provides the problem the claimed invention seeks to solve is “Complexities arise with respect to allocating resources to slots of a platform management matrix, where the complexities are associated with ensuring a given level of support while also maintaining collaborative contributions to allocation decisions by resources impacted by the decisions.”
The problem to be solved is one of resource allocation – which is a business problem rather than a technical problem. Where the specification describes the claimed resources as “allocate resources according to time slots, service level agreements (SLA), number of users, roles and skills of human team members, volume of alerts (e.g., tickets or data objects), and various other factors associated with the application framework and associated data objects described above supported by the platform management systems. Allocation of resources includes allocation of human resources to time slots.” So while the claimed invention may embody the solution presented in the specification; it is not found to solve a technical problem, an improvement to the functioning of the computer, or technological improvement to a technical problem. For all these reasons the rejection of the previous Office action is maintained.
A human cannot replicate the learned patterns and correlations embedded in a trained machine learning model through mere "evaluation and judgment." Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int 'l, Inc. V. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019). Remarks p. 7.
Respectfully it is noted the claimed invention does not expressly recite any limitations that “replicate the learned patterns and correlations embedded in a trained machine learning model”. As claimed the model is claim at a high level of generality. The Office finds that merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea does not remove the claim from the realm of abstraction. For all these reasons the rejection of the previous Office action is maintained.
The Examiner's reliance on Electric Power Group is misplaced. Unlike the claims in that case, which merely recited "collecting information, analyzing it, and displaying certain results," claim 1 recites specific technical operations: "inputting, to the trained slot allocation machine learning model, the first plurality of preference data structures and slot metadata associated with each slot of the plurality of slots" and "generating, using the trained slot allocation machine learning model, allocations of one or more resource identifiers to each slot of the plurality of slots." These limitations specify a particular technical mechanism for accomplishing the result. Remarks p. 7.
Respectfully the Office disagrees with Applicant’s position. While Applicant’s claim may define the data, e.g. preference data, slot metadata, the inputs are data, the content of the data is not relevant to the Alice/Mayo analysis. Where the courts have said information regardless of its particular content is within the realm of abstract ideas. See, e.g., Internet Patents, 790 F.3d at 1349; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347 (Fed. Cir. 2014); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011) also see MPEP 2106.05(g). Thus the inputting step is found to be a step of data gathering – insignificant extra solution activity.
Further the generating step is the outputting (post-solution activity) of the analysis – the solution. In Flook, the Court reasoned that "[t]he notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance. A competent draftsman could attach some form of post-solution activity to almost any mathematical formula". 437 U.S. at 590; 198 USPQ at 197; Id. (holding that step of adjusting an alarm limit variable to a figure computed according to a mathematical formula was "post-solution activity"). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 79, 101 USPQ2d 1961, 1968 (2012) (additional element of measuring metabolites of a drug administered to a patient was insignificant extra-solution activity).
For all these reasons the rejection of the previous Office action is maintained.
The Examiner's Step 2A, Prong 2 analysis incorrectly characterizes the machine learning model as merely "applied to the received data" without meaningful limitation. However, claim 1 recites that the trained slot allocation machine learning model is "configured to generate resource allocations based at least in part on one or more service level agreement (SLA) requirements associated with the service identifier." This is not an instruction to "apply" a generic model; it specifies a model with particular configuration and training that constrains how the allocation is performed. Remarks p. 8.
Respectfully, the Office disagrees with Applicant’s position. The model is already trained. The trained model is simply applied to the data, the model is apply the same way regardless of the data entered. The model is not learning as claimed, the technology is not improved – it is simply applied. For all these reasons the rejection of the previous Office action is maintained.
Dependent claims 2-12 further specify technical details including "alert frequency data structures" (claim 2), "skill data structures" (claim 3), "trained predictive models trained based on historical alert frequency data" (claim 5), and training "based historical alert frequency data and historical support matrices" (claim 9). These claims are allowable for the same reasons as claim 1, and additionally because they further specify the technical nature of the claimed system. Remarks p. 8.
Respectfully, the Office disagrees with Applicant’s position. For all the reason given above with respect to claim 1 the rejection of these claims is also maintain.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. certain methods of organizing human activity and mental processes) without practical application or significantly more when the elements are considered individually and as an ordered combination.
Step 1: Is the claimed invention to a process, machine, manufacture or composition of matter?
Yes, the claims fall within at least one of the four categories of patent eligible subject. Claims 1-12 are to an apparatus (machine), claims 13 at a method (process), and computer readable medium (manufacture).
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Yes, the claims are found to recite an abstract idea. Specifically, the abstract idea of certain methods of organizing human activity and mental processes. Where certain methods of organizing human activity include 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); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II). Where mental processes relates to concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Claim 1 (as a representative claim) recites the following, where the limitations found to contain elements of the abstract idea are in bold italics:
1. (Original) An apparatus comprising one or more processors and one or more storage devices storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to:
receive a first plurality of preference data structures, each preference data structure of the first plurality of preference data structures comprising one or more first support control requirements associated with (i) a supported platform and (ii) allocation of resources to a support matrix associated with the supported platform, the support matrix comprising (1) a plurality of slots and (2) associated with a service identifier;
generate, using trained slot allocation machine learning model, allocations of one or more resource identifiers to each slot of the plurality of slots by;
inputting, to the trained slot allocation machine learning model, the first plurality of preference data structures and slot metadata associated with each slot of the plurality of slots, wherein the trained slot allocation machine learning model is configured to generate resource allocations that satisfy one or more service level agreement (SLA) requirements associated with the service identifier by allocating resources having skills matching skill requirements associated with each slot and by allocating a number of resources to each slot based on predicted alert frequency data for that slot; and
generating by the trained slot allocation machine learning model, the allocations of the one or more resource identifiers to each slot of the plurality of slots;
generate, based on the allocations, a modified support matrix configured for rendering via a display device of a computing device; and
cause rendering of the modified support matrix via the display device of the computing device.
The instant Specification [3] discloses, “data structures comprising one or more first support control requirements associated with a supported platform and allocation of resources to a support matrix associated with the supported platform”, Specification [20] “Allocation of resources includes allocation of human resources to time slots” and Specification [105] “a platform management matrix (e.g., a calendar)”/[107] “an automated platform support matrix (e.g., on-call schedule, created through a recommendation engine)”. Where the resources are – “resources includes allocation of human resources”. Specification [20].
Applicant’s claims are directed toward using a trained machine learning model to allocate resources in satisfaction of a contract (service level agreement) – allocating humans to time slots based on characteristic of the human. As claimed but for the nominal use of a processor the claimed limitations could be practically be performed in the human mind using ones evaluation and judgement. See Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) in MPEP2106.04(A)(2) were the Court found a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind.
Where adhering to a contract such as the claimed service level agreement falls within the abstract idea of certain methods of organizing human activity. Where a SLA is common understood to mean a contract between a service provider and a customer that defines the specific services to be provided and the expected level of performance. A contract is a commercial or legal interactions, which including agreements in the form of contracts or legal obligations. Further the claim assigns resources (humans) to slot (schedule times) which under the broadest reasonable interpretation in the light of the specification is the management of managing personal behavior in so much as the matrix is a calendar or schedule of user assign times – where a schedule is an instruction to the user where and when to do something.
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claimed invention does not recite additional elements that integrate the abstract idea into a practical application. Where a practical application is described as integrating the abstract idea by applying it, relying on it, or using the abstract idea in a manner that imposes a meaningful limit on it such that the claim is more than a drafting effort designed to monopolize it, see October 2019: Subject Matter Eligibility at p. 11.
In the claimed invention, the trained model is simply applied to the received data. The MPEP provides “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984”, see MPEP 2106.05(f). The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743. See MPEP 2106.05(f).
The identified judicial exception is not integrated into a practical application. In particular, the claims recites the additional limitations see non-bold-italicized elements above. The receiving, obtaining, generating and rending elements are determined to be insignificant extra-solution activity. Where 2106.05(g) MPEP states, “term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.”
The Office finds that merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea; adding insignificant extra solution activity to the judicial exception; or only generally linking the use of the abstract idea to a particular technological environment or field is not sufficient to integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
No, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and as part of the ordered combination. Further the hardware components are found to be general and generic in nature, see specific at for example [5], [26-29], and [83], rather than specific or special.
Where 2106.05(d)(I)(2) of the MPEP states, “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").”
These limitations do NOT offer an improvement to another technology or technical field; improvements to the functioning of the computer itself; apply the judicial exception with, or by use of, a particular machine; effect a transformation or reduction of a particular article to a different state or thing; add a specific limitation other than what is well-understood, routine and conventional in the field, or add unconventional steps that confine the claim to a particular useful application; or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, these additional limitations when considered individually or in combination do not provide an inventive concept that can transform the abstract idea into patent eligible subject matter.
The other independent claims recite similar limitations and are rejected for the same reasoning given above.
The dependent claims do not further limit the claimed invention in such a way as to direct the claimed invention to statutory subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zhang et al (US 2020/0250626 A1) teaches a fixed number of time slots are allotted for each period of time. When all the time slots for a period of time are selected, the user may select a time slot for another period of time or may instead choose to cancel the order.
Dasgupta et al (US 2017/0039879 A1) teaches The task manager 218 assigns the one or more tasks as per the skill set possessed by the one or more crowdworkers. In an embodiment, the expertise gap of the one or more crowdworkers in the respective skill set tends to zero (i.e., γ.fwdarw.0). Further, the second time interval for the crowdworker to complete a task in the respective skill set approaches the ideal time interval (i.e., T.sub.n′.fwdarw.T.sub.1). As the one or more tasks are assigned as per the skill set possessed by the one or more crowdworkers, the number of instances where the SLA is violated is minimized. In an embodiment, each of the one or more crowdworkers has a first set of tasks pertaining to the skill sets possessed by the one or more crowdworkers.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FOLASHADE ANDERSON whose telephone number is (571)270-3331. The examiner can normally be reached Monday to Thursday 12:00 P.M. to 6:00 P.M. CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached at (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FOLASHADE ANDERSON/Primary Examiner, Art Unit 3623