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 .
Status of the Application
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/27/2025 has been entered.
Status of Claims
Claims 1, 10, and 19 are currently amended.
Claims 1-20 are currently pending following this response.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/22/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
New matter
No new matter has been added to the amended claims.
Response to Arguments - 35 USC § 101
The arguments have been fully considered, but they are not persuasive.
Regarding applicant’s arguments on pages 9-13
The examiner respectfully disagrees.
Claims can recite an abstract idea even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer’).
Collecting data, recognizing certain data within the collected data set, and storing that recognized data in a memory in Content Extraction is according to the court an abstract idea that is similar to other concepts that have been identified as abstract by the courts. Present claim 1 is collecting and analyzing data using a generic computer processor. Therefore, it is reasonable to conclude based on the similarity of the idea described in this claim to several abstract ideas found by the courts that claim 1 is directed to an abstract idea (arguments page 10).
The present claims are seeking segmentation of transaction population and applying different ML models for low, medium, or high target variation customers. This is applying different ML models to different sets of data wherein applying "grace period" in machine learning context can refer to a period of time where the model's behavior is adjusted or handled differently. Any improvement in this regard is a result of business improvement and not a technical improvement (arguments page 9, payments predictability accuracy). The Federal Circuit has repeatedly stated that simply applying existing machine learning techniques to new data environments, without improvements to the underlying machine learning models, is not sufficient for patent eligibility 35 U.S.C. § 101. This means that simply using a known machine learning algorithm on a new dataset or in a new application, without any actual innovation to the algorithm itself, is not considered an inventive concept.
This principle was highlighted in the Recentive Analytics, Inc. v. Fox Corp. case, where the court affirmed that patents claiming the use of generic machine learning models for scheduling and network map creation were ineligible for patent protection. The court emphasized that iterative training and dynamic adjustment, which are inherent to machine learning, do not constitute a patentable improvement. According to legal publications, to be eligible for patent protection, machine learning inventions must demonstrate a technological improvement to the underlying model itself, not just a new application of the model (arguments page 12, improvement in the technical field of machine learning).
The proposed amendments to the claims do not help overcoming the 101 rejections. The generating a first prediction of the target variable using either the trained regular ML model or the trained two or more grace period ML models; and retraining either the trained regular ML model or the trained two or more grace period ML models using additional historical data that comprises at least an outcome of the first prediction. Model retraining enables the model in production to make the most accurate predictions with the most up-to-date data. Model retraining does not change the parameters and variables used in the model. It adapts the model to the current data so that the existing parameters give healthier and up-to-date outputs. Further, (“[Merely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea”); 2019 Revised Guidance at 55. See also Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1090 (Fed. Cir. 2019) (“This invention makes the trader faster and more efficient, not the computer. This is not a technical solution to a technical problem. See MPEP 2106.05(f) (2)
In Bascom, (arguments page 10), the claims solved an internet centric problem of filtering internet content. The internet as computer technology as well as the computers are a necessary component of the claims. In other words, it is impossible to perform the claims without the use of the internet because no analog version of the problem existed prior to the internet.
On the other hand, the computing device in the current claims is not a necessary component to perform the abstract idea described in the claims. As stated above and as tedious as it can be, the current claims represent a process that can be done by a human analog that is merely linked to the computing device to solve the problem of payment predictability faced by merchants. The removal of the computing device from the current claims and the use of pre-internet/pre-computer technology does not affect the performance of the abstract idea of " predicting a target variable for accounts receivable ". Therefore, Bascom is distinguishable from the present claims because the present claims are merely linking the combination of the conventional and routine computer components (old elements) to the abstract idea in order to solve a business problem, while in Bascom, the abstract idea is necessarily rooted in the combination of the conventional and routine computer components.
In conclusion, the Examiner maintains the rejections of the pending claims under 35 USC § 101 in the present office action.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1-20 even if they were directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of predicting a target variable for accounts receivable.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “A method of predicting a target variable the method comprising: receiving historical data corresponding to a plurality of transactions corresponding to a plurality of customers, the historical data comprising, for each of the transactions, the target variable; segmenting each of the customers based on the historical data corresponding to each of the customers, the segmenting comprising determining a measure of variability of the target variable for each customer and, based on the measure of variability, classifying each customer as having a low variation, a medium variation, or a high variation; for each low variation customer, creating a regular model without a grace period that is trained and tested using the historical data; and for each medium variation customer, creating the regular model and creating two or more grace period models, each grace period model adding a different grace period to the target variable and trained and tested using the historical data with the grace period; generating a first prediction of the target variable using either the trained regular ML model or the trained two or more grace period ML models; and retraining either the trained regular ML model or the trained two or more grace period ML models using additional historical data that comprises at least an outcome of the first prediction wherein receiving the historical data comprises retrieving the historical data as a dataset in response to an activation plan, the activation plan: extracting the historical data; transforming the historical data into a data warehouse format by a data transformation layer”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite Mental Process because an ordinary person can analyze transaction data, create a model without grace period for low variation customer and create a model with grace period for a medium variation customer. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 10 and 18 recite substantially similar limitations to those presented with respect to claim 1. As a result, claims 10 and 18 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 2-9, 11-18, and 20 recite a Mental Process because the claimed elements describe a process for analyzing access data. As a result, claims 2-9, 11-18, and 20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “using a machine learning (ML)”, “in a cloud based analytics system for a tenant of the cloud based analytics system”, “of the tenant”, “ML”, “from a transactional database corresponding to the tenant”, “to a data staging area corresponding to the tenant”, “and loading the transformed historical data into a data warehouse for the cloud based analytics system corresponding to the tenant”. When considered in view of the claim as a whole, the step of “receiving” does not integrate the abstract idea into a practical application because “receiving” is an insignificant extra solution activity to the judicial exception. When considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 10 and 18 recite substantially similar limitations to those recited with respect to claim 1. Although claim 10 further recites “A computer readable medium” and claim 18 further recites “A cloud based machine learning (ML) model generating system for predicting a target variable for accounts receivable, the system comprising: one or more processors”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 10 and 18 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-9, 11-18, and 20 do not include any additional elements beyond those recited by independent claims 1, 10, and 18. As a result, claims 2-9, 11-18, and 20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “using a machine learning (ML)”, “in a cloud based analytics system for a tenant of the cloud based analytics system”, “of the tenant”, “ML”, “from a transactional database corresponding to the tenant”, “to a data staging area corresponding to the tenant”, “and loading the transformed historical data into a data warehouse for the cloud based analytics system corresponding to the tenant”. The step of “receiving” does not amount to significantly more than the abstract idea because “receiving” is well-understood, routine, and conventional computer function in view of MPEP 2106.05(d)(ll). The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 10 and 18 recite substantially similar limitations to those recited with respect to claim 1. Although claim 10 further recites “A computer readable medium” and claim 18 further recites “A cloud-based machine learning (ML) model generating system for predicting a target variable for accounts receivable, the system comprising: one or more processors”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 10 and 18 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-9, 11-18, and 20 do not include any additional elements beyond those recited by independent claims 1, 10, and 18. As a result, claims 2-9, 11-18, and 20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Any inquiry concerning this communication from the examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-3734.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623