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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 12, 2026 has been entered.
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 1-4, 6-16, and 18 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-4 and 6 recite a method (process), Claim 7-12 recite a system (machine), and Claims 13-16 and 18 recite a non-transitory computer readable medium (manufacture) and therefore fall into a statutory category.
Step 2A – Prong 1 (Is a Judicial Exception Recited?):
Referring to claims 1-4, 6-16, and 18, the claims are directed to a manner of determining a not-to-exceed limit, which under its broadest reasonable interpretation, covers concepts under the Certain Methods of Organizing Human Activities and Mental Processes grouping of abstract ideas.
The abstract idea portion of the claims is as follows:
(Claim 1) A method for automated facility management system service provider work order processing via improved not-to-exceed (NTE) limit optimization, the method implemented [by one or more work order analysis server devices executing a limit generator application] and comprising:
(Claim 7) [A system for] automated facility management system service provider work order processing via not-to-exceed (NTE) limit optimization, [the system comprising a work order analysis server device, comprising first memory comprising first programmed instructions stored thereon first one or more processors configured to execute the stored first programmed instructions to execute a limit generator application to:]
(Claim 13) [A non-transitory computer readable medium having stored thereon instructions for automated facility management system service provider work order processing via not-to-exceed (NTE) limit optimization comprising executable code which when executed by one or more processors, causes the processors to execute a limit generator application to:]
[training] a [machine learning] model to generate optimized NTE limits for service provider work orders, comprising: [applying an unsupervised algorithm to] a plurality of profile vectors comprising historical invoice data and a plurality of potential target quote rates for a plurality of enterprises and future scenarios to generate pre-processed training data capturing a plurality of points of an empirical conditional distribution of vendor invoice amounts in the historical invoice data;
[and generating the machine learning model based on a supervised or semi-supervised learning algorithm applied to the pre-processed training data] wherein the historical invoice data comprises an amount and associated contextual data for a plurality of invoices issued to the enterprises by service providers in response to service requests;
generating a baseline quote rate for an enterprise based on a plurality of historical invoices, quotes, and NTE values associated with the enterprise and obtained [via one or more communication networks from a work order management application executed by a facility management system associated with the enterprise];
generating a target quote rate for the enterprise based on the baseline quote rate, tolerance data for the enterprise received [via the communication networks from the work order management application], and a stored first set of business rules, wherein the tolerance data comprises a quantitative indication of an efficiency preference of the enterprise with respect to work order review;
applying the [machine learning] model to the target quote rate and work order data extracted from an NTE limit request, received [at an endpoint of the limit generator application, via the communication module, and from the work order management application] to generate a model-recommended NTE limit;
and returning a prescribed NTE limit in response to the NTE limit request to facilitate automated processing of an electronic vendor quote [by the work order management application], wherein the electronic vendor quote is received [by the work order management application from a vendor device via wide area network] and the prescribed NTE limit is generated by applying a stored second set of business rules to the model-recommended NTE limit.
Where the portions not bracketed recite the abstract idea.
Here the claims are directed to both Mental Process (including an observation, evaluation, judgment, or opinion) and Certain Methods of Organizing Activity, in particular managing personal behavior or interactions between people (including following rules or instructions) but for the recitation of generic computer components. In the present application concepts directed to a manner of determining a not-to-exceed limit. (See paragraphs 1-4 and 9)
If a claim limitation, under its broadest reasonable interpretation, covers concepts capable of being performed in managing personal behavior or interactions between people (including following rules or instructions) it falls under the Certain Methods of Organizing Human Activity grouping of abstract ideas. See MPEP 2106.04. If a claim limitation, under its broadest reasonable interpretation, covers concepts capable of being performed in the human mind or via pen and paper it falls under the Mental Processes grouping of abstract ideas. See Id.
Accordingly, the claims recite an abstract idea.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?):
The examiner views the following as the additional elements:
One or more work order analysis server devices. (See paragraph 33)
A limit generator application. (See paragraphs 19 and 25)
A system. (See paragraph 36)
Machine learning. (See paragraphs 25 and 67)
First memory. (See paragraphs 22-23)
First programmed instructions. (See paragraphs 21-22 and 38)
First one or more processors. (See paragraph 21)
A non-transitory computer readable medium. (See paragraph 9)
Executable code. (See paragraph 38)
One or more communication networks. (See paragraphs 31-32)
A work order management application. (See paragraphs 19 and 34)
Facility management system associated with an enterprise. (See paragraph 34)
An endpoint. (See paragraph 70)
A vendor device. (See paragraphs 35-36)
A wide area network. (See paragraphs 32 and 36)
An unsupervised algorithm. (See paragraph 67).
A supervised learning algorithm. (See paragraph 67)
A semi-supervised learning algorithm. (See paragraph 67)
These additional elements are recited at a high-level of generality such that they act to merely “apply” the abstract idea using generic computing components and do not integrate the abstract idea into a practical application. (See MPEP 2106.05 (f))
Referring to claims 1, 7, and 13 the limitation of “training”/“train” and “and generating the machine learning model based on a supervised or semi-supervised learning algorithm applied to the pre-processed training data” the examiner views as a results-oriented solution lacking details and therefore equivalent to merely apply. (See Id., paragraphs 65, 67, and Figure 7 el. 700)
The combination of these additional elements and/or results oriented steps are no more than mere instructions to apply the exception using generic computing components. (See MPEP 2106.05 (f). Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?):
As noted above, the claims as a whole merely describes a method and system that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
Dependent claims 2-4, 6, 9, 12, 15, and 18 further define the abstract idea as identified. Therefore claims 2-4, 6, 9, 12, 15, and 18 are considered to be patent ineligible.
Dependent claims 8 and 10 further define the abstract idea as identified. Additionally, the claim recites the additional elements of first processors (See paragraph 21) and first programmed instructions (See paragraphs 21-22 and 38) at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and therefore does not integrate the abstract idea into a practical application or adds significantly more. Therefore claims 8 and 10 are considered to be patent ineligible.
Dependent claim 11 further defines the abstract idea as identified. Additionally, the claim recites the additional elements of one or more communication networks (See paragraphs 31-32), an endpoint (See paragraph 70), a limit generator application (See paragraphs 19 and 25), one of the work order analysis server devices (See paragraph 33), and work order management application (See paragraphs 19 and 34), and a facility management system associated with the enterprise (See paragraph 34), second memory (See paragraph 34), second programmed instructions (See paragraph 34), second one or more processors (See paragraph 34), user device (See paragraphs 18-19), wide area network (See paragraphs 32 and 36), and vendor device (See paragraphs 35-36), at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and therefore does not integrate the abstract idea into a practical application or adds significantly more. Therefore, claim 11 is considered to be patent ineligible.
Dependent claims 14 and 16 further define the abstract idea as identified. Additionally, the claim recites the additional elements of executable code (See paragraph 38), processors (See paragraph 21), and a limit generator application (See paragraphs 19 and 25) at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and therefore does not integrate the abstract idea into a practical application or adds significantly more. Therefore claims 14 and 16 are considered to be patent ineligible.
In conclusion the claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed January 12, 2026 have been fully considered.
Applicant’s amendments and arguments, on pages 9-15 of the Remarks, regarding the 101 rejection the examiner finds unpersuasive.
Applicant argues that the claims are directed to automated service provider work order processing, in particular, the claimed technology facilitates more effective and efficient automated processing of vendor quote data in accordance with enterprise priorities and optimizes resource utilization, and improves the functioning, of facility management systems while providing a technical solution to this technical problem through generating and applying effective NTE limits in automated work order processing facility management systems as discussed in paragraphs 8-9 of the Specification. According to Applicant, none of the limitations recite an abstract idea of either managing personal behavior or interactions or mental processes. Particularly Applicant contends that claims are directed to automated facility management system service provider work order processing via NTE limit optimization via improved machine learning models trained in a particular manner on specific data, which has nothing to do with interactions between people. Further training and applying machine learning models and exchanging data networks between devices executing particular applications cannot be performed in the human mind or via pen or paper. In particular, applying an unsupervised algorithm to profile vectors comprising historical invoice data and potential target quote rates for enterprise and future scenarios to generate pre-processed training data capturing points of an empirical conditional distribution of vendor invoice amounts in historical invoice data and generating a machine learning model based on a supervised or semi-supervised learning algorithm applied to the pre-processed training data cannot be performed in the human mind.
The Examiner respectfully disagrees, viewing that the additional elements as discussed in the Step 2A Prong 2 Analysis are mere instructions to apply the abstract idea using generic computing components. The Examiner views that the improvement is to a business process in this instance facilitating the interactions between vendors and enterprises and is not a technical improvement. The Examiner maintains the claims outline a manner of computing the NTE limit for an entity which is subsequently used in the facilitating of processing of vendor quotes. The Examiner viewed the training, generation, and application of machine learning, applying an unsupervised algorithm and exchanging data networks between devices executing particular applications as additional elements that are mere instructions to apply the abstract idea using generic computing components as discussed in the Step 2A Prong 2 Analysis. The Examiner does not view the concept of generating pre-processed training data as claimed as an additional element but as a concept a person can do based on the analysis of the collected information. (See also paragraph 67)
Applicant argues under Step 2A Prong 2 that the claims recite training a machine learning model to generate optimized NTE limits for service provider work orders by applying an unsupervised algorithm to profile vectors comprising historical invoice data and potential target quote rates for enterprises and future scenarios to generate pre-processed training data capturing points of an empirical conditional distribution of vendor invoice amounts in the historical invoice data. According to Applicant, the machine learning model training requires generating the machine learning model based on a supervised or semi-supervised learning algorithm applied to the pre-processed training data and the claims also specify that the historical invoice data comprises the vendor invoice amounts and contextual data for invoices issued to the enterprises by service providers in response to service requests.
The Examiner respectfully disagrees reiterating they viewed the additional elements identified in Applicant’s Step 2A Prong 1 arguments as mere instructions to apply the abstract idea using generic computing components and do not integrate the abstract idea into a practical application. The Examiner views the concept related to the generation of pre-processed data as claimed is part of the recited abstract idea as discussed previously. The Examiner does not view what the historical invoice data comprises as an additional element but rather further defining the abstract idea.
Applicant contends the claims specify what the machine learning model is trained to do (generate optimized NTE limits for service provider work orders) and how the machine learning model is trained to do it (by applying an unsupervised algorithm to profile vectors comprising historical invoice data and potential target quote rates for enterprises and future scenarios to generate pre-processed training data and applying a supervised or semi-supervised learning algorithm to the pre-processed training data) that cannot be said to be recited at a high level of generality lacking details. According to Applicant, this training limitation is analogous to that of the training in Example 39.
The Examiner respectfully disagrees the Examiner views the machine learning model is trained to perform abstract concepts (generate optimized NTE limits for service provider work, what information is used to train the model is abstract (profile vectors comprising historical invoice data and potential target quote rates for enterprises and future scenarios to generate pre-processed training data) and how this applied via generic computing components (the machine learning model, applying an unsupervised model, and applying a supervised or semi-supervised learning algorithm to the pre-processed training data” which the Examiner views as mere instructions to apply the abstract idea. Unlike the claims in Example 39, where the claims do not recite an abstract idea and provides a technical improvement to training a neural network, the present claims recite an abstract idea and merely applies this abstract idea using generic computing components for facilitating the performance of the abstract idea.
Applicant contends the claims recite applying the machine learning model to the target quote rate and work order data extracted from an NTE limit request, received at an endpoint of the limit generator application, via the communication networks, and from the work order management application to generate a model-recommended NTE limit. According to Applicant the machine learning model is specially trained and the limit generator and work order management applications are not generic computer components but are instead specially programmed software applications that carry out particular claimed functions. The pending claims are clearly directed to a particular practical application as explained in paragraph 9 of the Specification illustrating the claims provide a technical solution to a technical problem and facilitate more effective and efficient automated processing by facility management systems of vendor quote data and vendor invoices.
The Examiner respectfully disagrees, reiterating that they view the improvement is not to a technical field or any of the other considerations enumerated under MPEP 2106.04 (d) but rather to improving a business process. The Examiner does not view that the machine learning model is trained in a specific manner but rather only uses a specific type of data. The claims nor the specification describe an improvement to machine learning and the claims do not provide steps through which machine learning is improved. The Examiner maintains that the additional elements as proffered by Applicant in view of associated paragraphs in the Specification are generic computing components.
Applicant argues under Step 2B that the additional elements of the claims add significantly more. According to Applicant these additional elements that correspond to:
generating a target quote rate for the enterprise based on the baseline quote rate, tolerance data for the enterprise received via the communication networks from the work order management application, and a stored first set of business rules, wherein the tolerance data comprises a quantitative indication of an efficiency preference of the enterprise with respect to work order review
According to Applicant, these limitations would not be necessary for a claim that merely recited a computing device configured to apply the abstract idea of determining an NTE limit.
The Examiner respectfully disagrees maintaining the limitations as identified in the Step 2A Prong 1 Analysis recite an abstract idea and that the additional elements are mere instructions to apply the abstract idea using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more under the Step 2A Prong 2 Analysis and Step 2B Analysis respectively.
Applicant also contends:
As another example, the amended independent claims recite applying the machine learning model to the target quote rate and work order data extracted from an NTE limit request, received at an endpoint of the limit generator application, via the communication networks, and from the work order management application, to generate a model-recommended NTE limit and returning via the communication networks and to the work order management application a prescribed NTE limit in response to the NIE limit request to facilitate automated processing of an electronic vendor quote by the work order management application, wherein the electronic vendor quote is received by the work order management application from a vendor device via wide area network and the prescribed NTE limit is generated of by applying a stored second set
of business rules to the model-recommended NTE limit.
According to Applicant, these elements amount to more than mere instructions to apply any exception as they invoke computing components and machine learning models to do much more than facilitate or implement any abstract idea.
The Examiner respectfully disagrees maintaining the limitations as identified in the Step 2A Prong 1 Analysis recite an abstract idea and that the additional elements identified are mere instructions to apply the abstract idea using generic computing components and merely serve for facilitating the performance of the abstract idea rather than integrating the abstract idea into a practical application or adds significantly more under the Step 2A Prong 2 Analysis and Step 2B Analysis respectively.
Applicant contends the claims further recite:
applying the machine learning model to the target quote rate and work order data extracted from an NTE limit request, received at an endpoint of the limit generator application, via the communication networks, and from the work order management application, to generate a model-recommended NTE limit and returning via the communication networks and to the work order management application a prescribed NTE limit in response to the NTE limit request to facilitate automated processing of an electronic vendor quote by the work order management application, wherein the electronic vendor quote is received by the work order management application from a vendor device via wide area network and the prescribed NTE limit is generated by applying a stored second set of business rules to the model-recommended NTE limit, which clearly amount to more than mere instructions to apply any exception…training a
machine learning model to generate optimized NTE limits for service provider work orders by applying an unsupervised algorithm to profile vectors comprising historical invoice data and potential target quote rates for enterprises and future scenarios to generate pre-processed training data capturing points of an empirical conditional distribution of vendor invoice amounts in the historical invoice data and applying a supervised or semi-supervised learning algorithm to the pre-processed training data.
According to Applicant, these limitations are not mere instructions to apply the abstract idea using generic computing components as these steps are not recited at a high-level of generality but rather invokes computing components and machine learning models to do much more than facilitate or implement any abstract idea.
The Examiner respectfully disagrees reiterating they view the limitations as identified in the Step 2A Prong 1 Analysis to recite an abstract idea and that the additional elements are mere instructions to apply the abstract idea using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more, under the Step 2A Prong 2 Analysis and Step 2B Analysis respectively.
Applicant argues that the claims do not preempt all ways of determining an NTE limit, but instead impose meaningful limits on any abstract idea and recite a particular arrangement of limitations that provides an inventive concept and technical improvement over conventional automated facilitate management system work order processing.
The Examiner respectfully disagrees citing MPEP 2106.04:
While preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052 (Fed. Cir. 2016). Instead, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test referred to by the Office as Steps 2A and 2B). Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1150 (Fed. Cir. 2016); Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015). It is necessary to evaluate eligibility using the Alice/Mayo test, because while a preemptive claim may be ineligible, the absence of complete preemption does not demonstrate that a claim is eligible. Diamond v. Diehr, 450 U.S. 175, 191-92 n.14 (1981) (“We rejected in Flook the argument that because all possible uses of the mathematical formula were not pre-empted, the claim should be eligible for patent protection”).
Applicant argues that similar to Bascom the claims do not preempt all ways of determining an NTE limit, and instead impose meaningful limits on any abstract idea and recite a particular arrangement of limitations that provides an inventive concept and technical improvement over conventional automated facilitate management system work order processing. According to Applicant, the claimed technology does not merely implement conventional functions, and instead requires a specially programmed computing device with particular network connectivity to user devices, vendor devices, and facility management systems.
The Examiner respectfully disagrees viewing that the present claims provide an improvement to a business process as discussed above and not a technical improvement as provided in Bascom. The Examiner maintains that the additional elements identified by Applicant are mere instructions to apply the identified abstract idea using generic computing components that does not integrate the abstract idea into a practical application or add significantly more to the abstract idea.
Therefore, for the foregoing reasons the Examiner has maintained the 101 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bauders et al. (US 20230394426) – directed to supply chain management.
Choudhury et al. (US 20220129844) – directed to a supply chain visibility platform.
Okada (US 20230360073) – directed to predicting as a contract price a price of a purchase target to be provided by a supplier to a buyer.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J MONAGHAN whose telephone number is (571)270-5523. The examiner can normally be reached on Monday- Friday 8:30 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached on (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J. Monaghan/Examiner, Art Unit 3629