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 .
Response to Amendment
The following is a FINAL Office action in reply to the Amendments and Arguments received on January 23, 2026.
Status of Claims
Claims 1-20, 23, 30 and 37 remain cancelled.
Claims 21-22, 24-29, 31-36 and 38-41 are currently pending and have been examined.
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 21-22, 24-29, 31-36 and 38-41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 21-22, 24-27 are drawn to methods while claim(s) 28-29, 31-36 and 38-41 are drawn to an apparatus. As such, claims 21-22, 24-29, 31-36 and 38-41 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
Claim 21 (representative of independent claim(s) 28 and 35) recites the following steps:
A method comprising:
generating, a series of prioritized lists of collection customers, wherein the generation of each prioritized list is separated from the generation of a preceding prioritized list by a time interval, and wherein the generation of the prioritized list for one time interval comprises;
inputting a plurality of data inputs, said plurality of data inputs comprising; a list of collection customers; one or more direct ranking variables related to the collection customers, wherein each direct ranking variable quantifies an accounts receivable status of one of the collection customers;
generating with a first machine learning algorithm using the plurality of data inputs a prioritized list of collections customers for one time
configuring, on the computing system, a ranking engine to calculate a collections impact score for each of the collection customers by applying a weighted model, wherein the collections impact score is calculated by:
calculating, one or more derived ranking variables from the one or more direct ranking variables, wherein each derived ranking variable is calculated according to a formula;
applying a weighting to selected direct ranking variables and selected derived ranking variables using the weighted model to create weighted direct ranking variables and weighted derived ranking variables;
inputting a plurality of the weighted direct ranking variables and the weighted derived ranking variables to calculate the collections impact score for each of the collection customers; and generating the prioritized list of the collection customers in a first iteration, with the collection customers ordered on the prioritized list according to the calculated collection impact score for each collection customer;
monitoring, one or more of the direct ranking variables to determine the accounts receivable status of the collection customers over multiple time intervals;
training a second algorithm to identify a positive trend or a negative trend in the accounts receivable status of the collection customers;
executing the second algorithm to autonomously identify a positive trend or a negative trend in the accounts receivable status of the collection customers;
training the second algorithm to autonomously modify the first algorithm based upon the positive trend or the negative trend in the accounts receivable status of the collection customers identified by the second algorithm
wherein the modification to the first model comprises changing the formula used to calculate at least one of the weighting of selected direct ranking variables and selected derived ranking variables to create adjusted weighted direct ranking variables and adjusted weighted derived ranking variables;
executing the second algorithm autonomously modify the first algorithm by changing the formula used to calculate at least one of the weighting of selected direct ranking variables and selected derived ranking variables
wherein autonomously modifying the first algorithm occurs between the first iteration and a subsequent iteration
wherein the modification of the first algorithm the generat[ion] of subsequent prioritized collection customer lists in the subsequent iteration; and
wherein the adjusted weighted direct ranking variables and the adjusted weighted derived ranking variables cause the subsequent prioritized collection customer lists in the subsequent iteration to be different than the prioritized collection customer lists in the first iteration;
delivering the subsequent prioritized collection customer lists generated based on the
modification to the first algorithm to collection agents
These steps, under its broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) ranking customers of a collection agency to create an optimized prioritized list (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the "mental processes" subject matter grouping of abstract ideas.
Alternatively, these steps, under its broadest reasonable interpretation, encompass mathematical relationships and mathematical formulas/ equations. These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 21 recites an abstract idea (Step 2A - Prong One: YES).
Independent claim(s) 28 and 35 is determined to recite an abstract idea under the same analysis.
Step 2A - Prong Two:
This judicial exception is not integrated into a practical application. The claim(s) recite the additional elements/limitations of:
a computing system
ranking engine
training a second machine learning algorithm
executing the second machine learning algorithm
a first machine learning algorithm
identifying by the machine learning algorithm;
a graphical user interface on each of the collection agent's device using the computing system
A non-transitory computer readable medium comprising computer executable instructions:
one or more processors;
A computer system comprising: one or more processors; a non-transitory memory communicatively coupled to the one or more processors and storing instructions
The requirement to execute the claimed steps/functions listed above is equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. This/these limitation(s) do/does not impose any meaningful limits on producing the abstract idea and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A -Prong Two: NO).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above in "Step 2A - Prong 2", the requirement to execute the claimed steps/functions listed above is equivalent to adding the words "apply it" on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as "significantly more" (see MPEP 2106.05 (f)).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Regarding Dependent Claims:
Dependent claims 22, 24-27, 29, 31-34 and 36, 38-41 include additional limitations that are part of the abstract idea except for:
Training second the machine learning algorithm
identified by the machine learning algorithm
executing the machine learning algorithm
analyzed by the computing system as the machine learning algorithm is trained
by the computing system
The additional elements of the dependent claims are equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
Response to Arguments
Applicant's arguments filed with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
Applicant Argues: The above amendments reflect similar language that was found allowable in 18/089,972.
Examiner respectfully disagrees. The referenced allowed application goes far beyond the steps represented in the instant amended claims set. While the claims have been amended to be similar they are not reflective of the needed technical improvement.
Applicant Argues: Firstly, in response to the Examiner's assertion that steps disclosed in the independent claims falls within "methods that can be performed in the human mind' or "mathematical relationships" grouping of abstract ideas - the applicant argues that the Examiner's analysis mischaracterizes the claimed invention by oversimplifying its technical contributions and overlooking the specific, unconventional computational architecture recited in the claims.
Examiner respectfully disagrees. Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer,” and that “courts have found requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind” (see p. 8 of the October 2019 Update: Subject Matter Eligibility). The Examiner also notes that “both product claims (e.g., computer system, computer-readable medium, etc.) and process claims may recite mental processes (see p. 8 of the October 2019 Update: Subject Matter Eligibility). Furthermore the claims make reference to an algorithm which is considered a mathematical relationship. The claims are interpreted to be abstract and the next step of the analysis determines if the steps integrate the claims into a practical application.
Applicant Argues: The claims are not merely directed to an abstract idea implemented on a generic computer, the claims are directed to a specific, self-optimizing technological system that improves the functioning of the computer itself by creating a dynamic feedback loop that autonomously reconfigures its own underlying logic….Further, the Examiner's central point is that the invention does not solve a "technological solution" and is not an "improvement in computers as tools." This overlooks the most critical aspect of the invention. The key improvement is the autonomous, self-reconfiguring system.
Examiner respectfully disagrees. As recently explained by the Federal Circuit in Recentive Analytics, Inc. V. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025), the requirements that the machine learning algorithm is " autonomously modified" are incident to the very nature of machine learning and, as such, do not represent a technological improvement.
Applicant Argues: The computer system itself, through the second ML algorithm, analyzes performance outcomes and re-writes its own operational parameters. It changes the very mathematical formulas it uses. This transforms the computer from a static tool into an adaptive, evolving machine.
Examiner respectfully disagrees. As recently explained by the Federal Circuit in Recentive Analytics, Inc. V. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025), the Federal Circuit reaffirmed that iteratively training a machine learning model on data does not transform an abstract idea into a patent-eligible invention.
Applicant Argues: The system generates better, more effective worklists in the next cycle because its internal logic has been technologically improved by the feedback loop. This is an improvement to the computer's functionality as a data-generating system, not just an improvement in the abstract idea of collections.
Applicant’s alleged improvement is not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. A showing that a claim is directed to any improvement does not automatically mean a claim is patent eligible (e.g., an improved business function or an improved idea itself is not patent eligible). In this case, ranking customers of a collection agency to create an optimized prioritized list is an abstract idea, and an “improved” way of ranking customers of a collection agency to create an optimized prioritized list, if anything, an improvement to the idea itself.
Applicant Argues: Currently amended claims provide a specific solution, not a generic tool: The ML algorithms are not just "tools" in the abstract sense. They are integrated components in a specific architecture designed to achieve this self-modification.
Examiner respectfully disagrees. Examiner notes that most machine learning models are inherently trained on large, often complex datasets to generate predictions or classifications. However, this alone is routine and well-understood in the field. Although prior to Recentive one could argue that the trained machine learning model represents a technological improvement, the Recentive decision makes clear that such arguments are insufficient unless the claims specifically describe how the technological improvement is achieved. The Federal Circuit reaffirmed that iteratively training a machine learning model on data does not transform an abstract idea into a patent-eligible invention.
Applicant Argues: This is a tangible improvement in the quality of the computer's output, stemming directly from the process claimed. The claims are thus practical applications that improves an existing technology, consistent with DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014).
Examiner respectfully disagrees and maintains the previous response. The computer components recited in claim 21 include a computing system; ranking engine; training a second machine learning algorithm; and a graphical user interface on each of the collection agent's device using the computing system. The additional elements to perform the steps of generating; monitoring, processing and delivering. But generating; monitoring, processing and delivering are generic computing functions. See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016). Furthermore, those generic computing functions are performed using generic computer components. The Specification describes these components as generic, general purpose computer components used as tools to perform the abstract idea (paragraph [0147]). The Specification discloses the recited functions at a high-level of generality, devoid of additional meaningful details indicating that the functions could not have been performed by a general-purpose computer.
Applicant Argues: This is a tangible improvement in the quality of the computer's output, stemming directly from the process claimed. The claims are thus practical applications that improves an existing technology, consistent with DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014).
Examiner respectfully disagrees. The Specification fails to clearly evidence how the use of a machine learning algorithms being trained is an actual technological improvement over, or differs from, the expected general concept of applying the machine learning algorithm. It is unclear how the machine learning algorithm are being integrated in any specialized manner that serves any specialized technical purpose/solution.
The claims are not rooted in machine learning technology, and the claims do not solve a technical problem that only arises in AI or machine learning. MPEP § 2106.05(a).
The amended limitations being referred to simply apply data analysis to train the generic machine learning algorithm and do nothing more than use computational instructions to be implemented in a computer processing environment, simply to "apply it" without any improvement to the computer functionality or technology itself.
Applicant Argues: The autonomous modification of ranking engine's formulas/weighted models/weightage factor and re-programing the first machine learning algorithms is a specific improvement, not a generic computer function.
Examiner respectfully disagrees. The amended claims can be broadly, but reasonably, interpreted as encompassing a mathematical algorithm computing mathematical calculations and manipulating particular information, i.e., values of certain parameters to train a machine learning model, similarly to the claims in Gottschalk v. Benson, 409 U.S. 63, 67 (1972). Information as such is intangible, and data analysis and algorithms are abstract ideas. See, e.g., Microsoft Corp. V. AT&T Corp., 550 U.S. 437, 451 n. 12 (2007); Alice, 134 S. Ct. at 2355; Parker v. Flook, 437 U.S. 584, 594- 95 (1978) ("Reasoning that an algorithm, or mathematical formula, is like a law of nature, Benson applied the established rule that a law of nature cannot be the subject of a patent."); Gottschalk, 409 U.S. at 71-72. Similarly, information collection and analysis, including when limited to particular content, is within the realm of abstract ideas. See, e.g., Internet Patents Corp. V. Active Network, Inc., 790 F.3d 1343, 1349 (Fed. Cir. 2015); 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). That is, "[w]ithout additional limitations, a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible." Digitech, 758 F.3d at 1349-51 ("Data in its ethereal, non-physical form is simply information that does not fall under any of the categories of eligible subject matter under section 101.").
Applicant Argues: The present claims are analogous to Diamond v. Diehr. Just as the claims in Diehr used a well Known mathematical equation (the Arrhenius equation) in a specific way to improve a technical process (curing rubber), the claims here use a known concept (machine learning) in a specific and unconventional architecture to improve a technical process: the computer's own method for generating and optimizing data outputs.
Examiner respectfully disagrees. Examiner does not agree that the instant amended claims are analogous to Diamond v. Diehr as there is not an improvement to any other technology or technical field nor an effecting of a transformation or reduction of a particular article to a different state or thing as required by MPEP 2106. Producing a more optimized list using known machine learning techniques does not represent patent eligible subject matter.
Applicant Argues: The amended claims do not preempt any abstract idea, as the claims are narrowly tailored to a specific, improved machine-learning method that protects a specific technical improvement in the field of computerized generation of personalized collections agent worklists, which is precisely the sort of innovation that the patent system is designed to encourage.
Examiner respectfully notes the following: The issue of preemption is certainly not the only consideration and/or standard used in the 101 analysis, nor does it supersede other considerations and/or standards required by both Alice and the "Interim Guidance" that have been applied to the claims in question. Even assuming arguendo that there may not be preemption of the specific abstract idea as Applicant purports (which the Examiner does not concede), the claims in question are still only limiting the abstract idea, and does not impart patent eligible subject matter into the claim. Thus, the basis of Applicant's arguments (i.e. preemption) is not persuasive for at least this reason.
Conclusion
THIS ACTION IS MADE FINAL. 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 RASHIDA R SHORTER whose telephone number is (571)272-9345. The examiner can normally be reached Monday- Friday from 9am- 530pm. 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, Jessica Lemieux can be reached at (571) 270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RASHIDA R SHORTER/Primary Examiner, Art Unit 3626