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 .
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, 6-10, 12-16, 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 7-10 and 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. The Examiner is interpreting the system and non-transitory computer readable medium perform the steps of the method for Examination purposes.
Step 2A – Prong 1 (Is a Judicial Exception Recited?):
Referring to claims 1-4, 6-10, 12-16, and 18, the claims recite concepts covering a manner of determining a limit used in assessing a response of a vendor regarding a service request, 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 improved automated processing pipeline functionality, the method implemented [by a system comprising a server device and a management system, wherein the server device is configured to execute a limit generator application] and comprising:
(Claim 7) [A system with] improved automated processing pipeline functionality, [the system comprising a 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 improved automated processing pipeline functionality 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 limits, comprising:
[applying an unsupervised algorithm to] a plurality of profile vectors comprising historical data and first quote data to generate pre-processed training data capturing a plurality of points of an empirical conditional distribution of invoice amounts in the historical 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 data comprises the invoice amounts and associated contextual data;
generating baseline data based on historical invoice, second quote, and not-to-exceed value data obtained [via one or more communication networks from a management application,] and target data based on the baseline data, tolerance data received [via the communication networks from the management application], and a stored first set of rules, wherein the tolerance data comprises a quantitative indication of an efficiency preference;
applying the [machine learning] model to the target data and order data extracted from a limit request, received [at an endpoint of the limit generator application, via the communication module, and from the management application] to generate a model-recommended limit;
and returning [via the communication networks and to the management application] a prescribed limit in response to the limit request, wherein the prescribed limit is generated by applying a stored second set of rules to the model-recommended limit;
[and the management system is configured to execute the management application to perform steps comprising:]
sending [to the limit generator application, via the communication networks and an application programming interface (API) hosted by the server device], the limit request in response to an order received [from a user device] that comprises the order data;
after receiving the prescribed limit [via the communication networks, from the management application], and in response to the limit request, sending, [via a wide area network (WAN) to a vendor device], a service request comprising at least a portion of the order data;
and after determining that third quote data and invoice data received [via the WAN from the vendor device] in response to the service request satisfy the prescribed limit, automatically sending, [via the WAN], an approval of the third quote data [to the vendor device] and the invoice data [to an external payment system].
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:
A server device. (See paragraph 33)
A management system (See paragraph 34)
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 management application. (See paragraphs 19 and 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)
An API. (See paragraph 70)
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 and 4 further define the abstract idea as identified. Additionally, the claim recites the additional elements of server device (See paragraph 33) and 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 2 and 4 are considered to be patent ineligible.
Dependent claims 3, 6, 9, 12, 15, and 18 further define the abstract idea as identified. Therefore claims 3, 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), first programmed instructions (See paragraphs 21-22 and 38), and 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 8 and 10 are 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 June 24, 2026 have been fully considered.
Applicant’s amendments and arguments, on pages 10-17 of the Remarks, regarding the 101 rejection the Examiner finds unpersuasive.
Applicant argues under Step 2A Prong 1 the claims are directed to a specific, significant, and meaningful claimed invention of improved automated processing pipeline functionality, which in some examples described in the specification relates to improved functionality of facility management system that carry out an automated processing pipeline for service provider work orders. According to Applicant, 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. Applicant contends the claimed technology also provides a technical solution to the technical problem of generating and applying effective limits (e.g., not-to-exceed (NTE) limits) in automated work order processing facility management systems, as explained in paragraph 9 of the Specification that states "this technology optimizes resource utilization on, and improves the functioning of, facility management systems while providing a technical solution to the technical problem of generating and applying effective NTE limits in automated work order processing facility management systems." According to Applicant, the amended claims improve system functioning itself (e.g., the functioning of a facility management system) (i.e., the system's automated processing pipeline) and not merely a business outcome as explained by the Specification that this technology optimizes resource utilization, and improves the functioning of facility management systems while providing a technical solution to the technical problem of generating and applying effective NTE limits in automated work order processing facility management systems and therefore is an improvement is to how the facility management system processes work orders, which is a technical improvement to the automated system, analogous to how improved network monitoring (USPTO's Subject matter Eligibility Example 40) or improved filtering (USPTO's Subject matter Eligibility Example 34) were recognized as technical improvements to computer systems even though they had business utility.
Further, Applicant argues the claims do not cover managing interactions between people, but rather automated processing pipeline functionality via improved machine learning models trained in a particular manner on specific data, which has nothing to do with interactions between people and cannot reasonably be compared to such high-level concepts as budgeting or hedging that have been found abstract by the Office.
Applicant additionally contends the claims cannot be performed in the human mind as a person cannot train or apply machine learning models or exchange data over networks between devices executing particular software applications and APIs, as recited by the independent claims. Further according to Applicant, applying an unsupervised algorithm to profile vectors comprising historical data and quote data to generate pre-processed training data capturing points of an empirical conditional distribution of vendor invoice amounts in historical data, and generating a machine learning model based on a supervised or semi-supervised learning algorithm applied to the pre-processed training data, clearly cannot reasonably be performed in the human mind.
The Examiner respectfully disagrees viewing the claims as amended recite concepts for determining a limit based on collected information that is subsequently utilized in assessing a vendor’s response to a service request. The Examiner maintains that that an individual may determine such a limit based on analyzing collected information as claimed, which the Examiner maintains can be performed in the human mind or via pen and paper. Further, an individual or business would subsequently use this limit as part of assessing vendors’ responses to service requests, which the Examiner maintains covers concepts in the certain method of organizing human activity grouping in particular managing personal behavior or interactions between people. The Examiner viewed the training, generation, and application of machine learning, applying an unsupervised algorithm and exchanging data networks between devices executing particular applications, APIs, or software 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 claims to provide for a technical benefit as provided in Example 34 or 40 but rather provide a benefit of automating the analysis of determining a limit based on collected information subsequently used in assessing vendors’ responses to a service request using generic computing components, which the Examiner does not view as technical benefit for example optimizing resource utilization as there is no discussion in the Specification or claims to illustrate how this optimization of resources is achieved. The Examiner views the automated processing pipeline contested by Applicant is the flow of data as claimed by Applicant, which is the identified abstract idea automated using generic computing components. See paragraph 13 and 16 and Figures 3 and 7.
Applicant argues under Step 2A Prong 2 that several limitations of the amended claims are both additional elements and not merely generic computing components, and as the additional element are not recited at a high level of generality. According to Applicant, the Examiner identified machine learning an additional element but the claims detail that the training of the machine learning model includes applying an unsupervised algorithm to a plurality of profile vectors comprising historical data and first quote data to generate pre- processed training data capturing an empirical conditional distribution of invoice amounts in the historical data. Applicant contends the claim limitations cannot be said to lack detail and do not merely or generically recite "machine learning" as a limitation. According to Applicant, the amended claims specify what the machine learning model is trained to do (i.e., generate optimized limits) and how the machine learning model is trained to do it (i.e., by applying an unsupervised algorithm to profile vectors comprising historical data and quote data to generate pre-processed training data and applying a supervised or semi-supervised learning algorithm to the pre-processed training data). Applicant contends the machine learning training limitations cannot reasonably be said to be recited at a high level of generality lacking detail.
The Examiner respectfully disagrees viewing the machine learning model is trained to performed abstract concepts (generate optimized limits), the information used to train the model is abstract (profile vectors comprising historical data and first quote data to generate pre-processed training data capturing an empirical conditional distribution of invoice amounts in the historical data) and how this is 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) the Examiner views as mere instructions to apply the abstract idea using generic computing components that does not integrate the abstract idea into a practical application. The Examiner views the claims only recite the idea of a solution or outcome pertaining to generating the machine learning model as claimed because there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and therefore is equivalent to the apply it and does not integrate the abstract idea into a practical application. See MPEP 2106.05(f).
Applicant argues that SME Eligibility Example 39 made clear that "training the neural network in a first stage using the first training set" limitation does not recite a judicial exception whereas the claims of Example 47 require specific mathematical calculations referred to by name. According to Applicant, the training limitation of the pending independent claims is clearly analogous to the training found to be patent-eligible in Example 39 because it does not require specific mathematical calculations, such as the backpropagation and gradient descent algorithms of Example 47.
The Examiner respectfully disagrees noting that unlike Example 39 the instant claims recite an abstract idea under Step 2A Prong 2. The Examiner viewed the training as claimed as an additional element that amounted to no more than a results-oriented solution lacking details and therefore equivalent to apply it. Further the training as claimed in Example 39 provided a technical improvement to a technical field whereas in the instant claims the training is applied for facilitating the performance of the abstract idea.
Applicant argues the independent claims recite applying the machine learning model to the quote and order data extracted from a limit request, received at an endpoint of a limit generator application, via the communication networks, and from a management application, to generate a model-recommended limit. According to Applicant, the claimed machine learning model is specially-trained and the limit generator and management applications are not generic computer components but are instead specially programmed software applications that carry out particular claimed functions. Applicant contends the claims are clearly directed to a particular practical application as explained in paragraph 9, for example: "this technology optimizes resource utilization on, and improves the functioning of, facility management systems while providing a technical solution to the technical problem of generating and applying effective NTE limits in automated work order processing facility management systems" and therefore, the claims provide a technical solution to a technical problem and facilitate more effective and efficient automated processing pipelines, functionality implemented by a facility management system with respect to vendor quote data and vendor invoices in the example described in detail in the specification of the above-identified application.
The Examiner respectfully disagrees reiterating they do not view the manner of training of machine learning as model as specific as discussed prior but rather as mere instructions to apply the abstract idea. The Examiner views the endpoint of a limit generator application, the communication networks, and the management application as generic computing components that amount to no more than mere instructions to apply the abstract idea. This is further supported by the paragraphs identified by the Examiner in the Step 2A Prong 2 Analysis. The Examiner does not view the claims to provide for a technical benefit or technical solution to a technical problem, for example resource optimization or automated processing pipelines, but rather provides a benefit of automating the analysis of determining a limit based on collected information subsequently used in assessing vendors’ responses to a service request which the Examiner views as a business problem not a technical problem.
Applicant argues the amended claims also impose significant meaningful limits on practicing any abstract idea because the claims require multiple devices within a system interacting over networks to facilitate improved, more efficient end-to-end order processing and automated payment. According to Applicant, the claims provide significant specificity and impose substantial limits on any abstract idea and therefore the claims cannot reasonably be said to preempt any abstract idea or fail to impose meaningful limits on practicing an abstract idea, which is "the concern underlying the judicial exceptions." Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052 (Fed. Cir. 2016).
The Examiner respectfully disagrees viewing the required multiple devices within a system interacting over networks are mere instructions to apply the abstract idea using generic computing components for purposes of automating the abstract idea. Therefore, the Examiner does not view the claims to impose meaningful limits on the claims. Further regarding preemption, the Examiner cites to 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
Applicant argues under Step 2B that the additional elements add significantly more to the abstract idea, for example, the independent claims recite generating target data based on the baseline data, tolerance data received via the communication networks from a management application, and a stored first set of rules, wherein the tolerance data comprises a quantitative indication of an efficiency preference 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 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 as another example, the claims recite apply the machine learning model to the target data and order data extracted from a limit request, received at an endpoint of the limit generator application, via the communication networks, and from the management application, to generate a model-recommended limit and after determining that third quote data and invoice data received via the WAN from the vendor device in response to the service request satisfy the prescribed limit, automatically send, via the WAN, an approval of the third quote data to the vendor device and the invoice data to an external payment system, in addition to reciting training a machine learning model to generate optimized limits by applying an unsupervised algorithm to a plurality of profile vectors comprising historical data and first quote data to generate pre-processed training data capturing an empirical conditional distribution of invoice amounts in the historical 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 data comprises the invoice amounts and contextual data. According to Applicant, the limitations of the amended claims are not merely applying any concepts or exceptions, are not recited at a high level of generality, and invoke 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 do 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’s contested specificity regarding the additional elements the Examiner views as further defining the abstract idea rather than the additional elements as discussed previously.
Applicant argues the Federal Circuit determined the claims in Bascom did not "preempt all ways of filtering content on the Internet; rather, they recite a specific, discrete implementation of the abstract idea of filtering content" and thus recited something "significantly more" than the abstract idea. According to Applicant, the claimed technology in BASCOM, the pending 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 processing pipeline functionality. Applicant contends the claimed technology does not merely implement conventional functions, but instead requires a specially programmed computing device with particular network connectivity to server devices, 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.
Applicant argues the Office's assertion that some limitations of the currently pending claims may be implemented by generic computing structures, such as a processor, does not by itself render the currently pending claims ineligible for patent protection, as explained with reference to Example 4 of the 2014 Update. See also, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2358-9 (2014) ('There is no dispute that a computer is a tangible system (in § 101 terms, a 'machine'), or that many computer-implemented claims are formally addressed to patent-eligible subject matter.") According to Applicant, whether the claimed invention can be carried out by a general-purpose computing device (though it cannot) is simply not determinative of subject matter eligibility.
The Examiner respectfully disagrees because the claims only provide an improvement to a business problem by optimizing the limit for subsequent use in assessing responses to service requests and not to a technical field or other consideration enumerated under MPEP 2106.04 (d) unlike in Example 4 or in the Bascom decision.
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.
Kwok et al. (US 20220215332) -directed to using machine learning to predict impacts to a supply chain by analyzing current events.
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 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
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