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 .
The following is a Non-Final Office Action in response to communications received July 28, 2026. Claim(s) 7 and 16 have been canceled. Claims 1-2, 6, 8-11, 15 and 17-20 have been amended. New claims 21-22 have been added. Therefore, claims 1-6, 8-15 and 17-22 are pending and addressed below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 has been entered.
Priority
Application No. 18470555 filed 09/20/2023 Claims Priority from Provisional Application 63525191 , filed 07/06/2023.
Assignee/Applicant: Oracle International Corporation
Inventors: Agrawal, Vikas; Ramanathan, Krishnan; Shishtla, Praneeth Medhatithi; Jagdish Chand.
Response to Amendment/Arguments
Claim Rejections - 35 USC $ 101
Applicant's arguments filed July 28, 2026 have been fully considered but they are not persuasive
In the remarks applicant argues that the claimed subject matter is directed toward statutory subject matter. This is because the claimed limitations are directed toward a technical solution to a technical problem that cannot reasonably be performed using mental concepts. Applicant argument is moot as the previous Office actin did not find the claimed limitations to be directed toward the abstract category of mental concepts.
In the remarks applicant argues that the previous Office Action argues the claimed limitations are directed toward a technical solution to a technical problem that cannot be performed manually. Applicant argues the claimed limitations to increase prediction accuracy of “grace period” such as the delays in all invoices beyond the payment due date as indicative of a late payment disposition. Applicant points to the Ex parte Desjardins which directs consideration of the specification for determination of patent eligibility, specifically pointing to the considerations of improvement to technology. Applicant focuses on the elements of Desjardins which claimed “obtaining second training data, different machine learning task”, “training the machine learning model on the second learning task …to adjust values to optimize performance of the machine learning model on the second machine learning task” which in the specification of Desjardins indicates technical improvements by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving performing across sequential training. Applicant argues the current application similarly requires generating different trained models by changing how delayed payments labels are assigned to the training data, validating the models with test/validation data, using comparative MCC results to control model selection and deployment and causing deployed model to process incoming data and generate the variable prediction. If the models fail to specified quality test, system rejects them without deployment and generates an alert identifying insufficient feature. Applicant repeats the argument that the technical problem identified in set forth in the specification para 0038 “grace periods can reduce …defaults while increasing variability and degrading predictability”. The modification of “training labels according to different grace periods to generate materially different …model variants, evaluates them on…validation data, selects and deploys …best quality model, using model to process incoming data and generate prediction, rejects all models when …comparative results show that deployment would not provide an accurate prediction”. Applicant argues the machine learning system determines whether and how a trained model is used. It prevents an inaccurate model from being deployed and selects a differently trained model only when its measured predictive quality exceeds both the baseline model and the deployment threshold. The mathematical evaluation …applied to a machine learning deployment operation rather than being reported as an end in itself. The claims reflect improvement in the accuracy of a system for optimizing a predicting of a target variable as compared to known solutions improving accuracy of the prediction systems of the ML models improving ML model technology. Applicant’s argument is not persuasive. Applicant’s argument that the claimed limitations provide a technical solution to the technical problem “increase prediction accuracy of “grace period” such as the delays in all invoices beyond the payment due date as indicative of a late payment disposition, is not a problem rooted in technology but rather a problem in the abstract idea of “grace periods” for late payment of invoices. With respect to the ML model adjustment, the adjusting of the model is directed toward the data for analysis of late payments in order to predict late payments not to improve upon the technology of learning models. The specification discloses training a model using training dataset and test/validation dataset (para 0030) where the learning model can be tuned during training by adjusting number layers in neural network, adjusting kernel calculations, used to implement support vector machine. “tuning can include adjusting/selecting features used by the …learning model”. The specification describes that the tuning the training in order to achieve desired performing (e.g. performs predictions at desired level of accuracy, run according to desired resource utilization/time metrics) (para 0032). The specification disclose retraining and updating …learning model with updated training data. The training data can be observed data, labelled data(para 0033). The specification does not disclose a process for improving machine learning model technology but rather the describes improving the accuracy of the model output which is directed toward prediction of late period for payment processing. Accordingly the specification does not disclose a technical process to improve technology but rather the application of a model using training datasets for prediction of payments among customers (specification para 0032-0035). The rejection is maintained.
In the remarks applicant argues that the claimed training models merely involve the abstract idea and are not merely applied pointing to the USPTO Aug 2025 USPTO memo. Applicant argues the claim limitations are similar to concepts found in example 39 rather than example 47. Applicant argues the amended limitations train ML models using historical data with a first trained model having no grace period for target variable and two or more grace periods trained models. Each claimed model having different grace periods for the target variable. The claim limitations similar to example 39 do not set forth any mathematical relationships, formulas or equations using words or mathematical symbols. Applicant’s argument is not persuasive. The “grace periods” are an abstract concept. The “each model” merely limits which model is applied for analysis of the abstract idea. With respect to the mathematical relationships, formulas, the claim limitations recite converting cost of payments to a first z-score and average delay of payment to a second z-score and determining a reliability score comprising determining a Euclidean distance of the first z-score and second z-score, the models claimed merely use the historical data, the grace period parameters in the analysis for calculating values for an abstract concepts. In example 39, the focus was to provide a process for classifying inputs in order to improve upon the ability of technology to detect images where the data includes shifts, distortions, and variations in scale and rotation of patterns in the target images using a combination of features to expand the training set which itself causes a problem in technology itself by generating an increase rate in false positives by applying a second training process to reduces the false positives produced after an image detection has been performed on a set of non-target images. This is not the case of the current application the additional generation of “each model” is not to address a problem rooted in the analysis process of the learning model itself cause by the processing of the learning model analysis. Example 39 is not applicable. The rejection is maintained.
In the remarks applicant argues under step 2B analysis the claim limitations as a combination provide unconventional technical process. Applicant argues the generating of differently trained models by changing delayed payment table according to different grace periods validating the models with comparative MCC based selection, threshold controlled deployment use of the selected model on incoming data and rejection of all models upon failed predictive quality testing. The previous Office Action assertation that processors, data warehouses or transformation layers where known established technology does not provide evidence that the claimed order combination was conventional. The examiner respectfully disagrees with the premise of applicant’s argument. The claim limitations recite the technical process as a combination “receiving data”, “training” different models using the data received and parameters related to a grace period of a payment due date. Where trained models for analyzing the incoming data are selected and deployed according to the results of applying Mathews correlation coefficient test where MCC thresholds are compared to the MCC values of each of the different models with different parameters related to grace period of payment due dates in order to determine which model to select and deploy for use to process incoming data, generate a prediction which is merely applying a ML model to receive data, analyze data and output the result which the courts have held under Electric Power Group to be conventional application of technology. The claimed subject matter similar to Recentive Analytics, Inc v Fox Corp, simply uses machine learning to perform a task. The court found that machine learning is now viewed as a common tool rather than a technological breakthrough. Furthermore, similar to Recentive, the “training” steps i) receiving data ii) generate a model iii) training using selective data with different labelled data, iv) testing using MCC thresholds (spec para 0054) v) selecting and deploying models based on MCC threshold criteria, where the specification teaches the learning model trained using training data, makes clear that any suitable machine learning technique (para 0030, para 0033) can be applied which allows data to be inputted and changed based on changes in data (para 0004-0005). The current specification fails to clearly describe a technical problem or explain how the claimed invention improves the “functioning” of the technology, rather than the outcome of applying the technology for analysis.. The “training selected ML algorithm” using different versions of data and parameters related to grace period of a payment due date and testing data” and determining Mathews correlation coefficient (MCC) for the first trained model and for each grace period parameter trained model and when the MCC exceeds MCC threshold and no grace period trained model having higher MCC selected deploying the first trained model- merely focus on the payment due date parameters for weighting the models for use in the prediction analysis of human behavior. According to Recentive “The requirements that the machine learning model dynamically adjusted in the Ma chine Learning Training patents do not represent a technological improvement… training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. See, e.g., Opposition Br. 9 (“[U]sing a machine learning technique[] . . . necessarily includes [an] iterative[] training step . . . .” (internal quotation marks and citation omitted)); Transcript at 26:21–24 (“[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input”).”. Accordingly, the examiner maintains that similar to Recentive the claimed ML limitations do not transform the claimed subject matter into patent eligibility.
In the remarks applicant argues the additional elements are not conventional as disclosed in claims 6, 15 and 20 with the additional elements regarding cloud infrastructure (data plane and control plane) claimed. The examiner respectfully disagrees. See rejection below.
Claim Interpretation
The examiner is interpreting a Z-score by its ordinary meaning in the art a z-score or standard score of a statistical measure that describes a position of a raw score in terms of distance from the mean, measured in standard deviation where a positive z-score indicates a value above a mean and a negative z-score indicates a value below the mean.
Euclidean distance: In mathematics, the Euclidean distance between two points in Euclidean space is the length of the line segment between them. It can be calculated from the Cartesian coordinates of the points using the Pythagorean theorem, and therefore is occasionally called the Pythagorean distance.
The examiner is interpreting the term “data staging area” to be temporary/intermediate storage for data (see specification para 0083, para 0105; FIG. 8 ref # 124).
Data transformation layer- the examiner is giving the computer element its ordinary meaning in the art - A data transformation layer is a component of a data stack. It enables a business to automate the validation and cleansing of data before it is used downstream. The term “data transformation” when considered in light of the application of the data transformation layer is interpreted as - Data transformation involving the conversion, cleaning, and organizing of data into accessible formats.
Data plane- the examiner in light of the specification the examiner is interpreting the data plane to be a user interface (see para 0079-0080, para 0100) which performs API operations (e.g. data extraction, communication)
The Matthews Coefficient is a formula incorporated in the models trained.
[0054]… For the evaluation of models, embodiments use the Matthews' Correlation
Coefficient ("MCC"), which can measure of the quality of binary (two-class)
classifications, such as, for example, whether a customer will pay accounts receivable
in time or not. MCC can be calculated as follows:
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88
598
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where TP is the number of true positives, TN the number of true negatives, FP the
number of false positives and FN the number of false negatives. If any of the four sums
in the denominator is zero, the denominator can be arbitrarily set to one, which results
in a Matthews correlation coefficient of zero, which can be shown to be the correct
limiting value. The MCC determination is close to 1 for perfect correct classification,
close to -1 for incorrect classification, and close to 0 for random classification. In
embodiments, the MCC determinations use the test/validation data 307 split from the
training data 304.
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 1-6, 8-15 and 17-22 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
In reference to Claims 1-6 and 8-9:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 1 and the dependent claims. Such methods fall under the statutory category of "process." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. Method claim 1 recites a method steps (1) receiving data (2) generating a plurality of trained model (3) training a selected ML algorithm using data and due date parameters for invoice payments (4) training selected ML algorithm using different labelled versions of data related to invoice payment as when payment occurs beyond the payment due date (5) using text/validation data and determining MCC for the trained model and for each grace period (6) when MCC for each grace period model is higher than MCC and exceeds threshold MCC, selecting and deploying grace period model having highest MCC (7) when MCC for trained model exceeds threshold MCC and no grace period having higher MCC is selected and deployed (8) when MCC for trained model is low and MCC for each model is lower than MCC for first trained model rejecting plurality of trained ML model without deploying any of the plurality of trained ML models and generating an alert indicating data lacks sufficient distinguishability to provide accurate prediction of target variable (9) determining cost of a delayed payment (10) determining average delay of payments (11) converting cost of delayed payment to a first z-score and average delay of payments to second z-score (12) determining reliability score comprising Euclidean distance of first Z-score and second Z-score (13) displaying score.
The claimed limitations which under its broadest reasonable interpretation, covers performance of mathematical concepts of applying Mathews correlation coefficients and z-score mathematical tools to test model results of the analysis. The limitations when considered as a whole the claimed subject matter is directed toward the calculation of a reliability score. The additional limitation to the selecting step include “determining a Matthews’ Correlation Coefficient for the first model”, “determining the MCC for each …trained model” and based on a condition “deploying the first trained model”. The wherein clause therefore recites the abstract concept of a mathematical concept.(spec ¶ 0054).
Furthermore, the specification makes clear that the focus of the invention is to receive customer historical data corresponding to transactions with an organization and targeting a variable including number of days of delayed payment for each transaction and the average delay of payment of the customer. (para 0006). The specification discloses that “reliability score” is calculated to measure variations of customers by distribution of delays in payment for each customer by incorporating grace periods for each customer (para 0015) where the determination of the customer reliability score is a result of analyzing customers based on accounts receivable (para 0061) and how much such delays cost companies when compared to their peers (para 0062). The specification makes clear that the purpose of the reliability score. Such concepts can be found in the abstract category of risk mitigation and sales activities/behaviors. The claim limitations do not focus on the technology for data formatting but instead focuses on the calculation of the score which is a mathematical concept. As discussed above, the specification that the purpose of the analysis and the calculation of the score is to analyze and measure human behavior as it related to cost which is a process directed toward commercial activity, a sub-category of the abstract category of methods of organizing human activity. These concepts are enumerated in Section I of the 2019 revised patent subject matter eligibility guidance published in the federal register (84 FR 50) on January 7, 2019) is directed toward abstract category of mathematical concepts and methods of organizing human activity.
STEP 2A Prong 2: The additional elements recited in the claim beyond the abstract idea include a plurality of ML models, a cloud based system. Although, the preamble of the claim recites “using a system” comprising “plurality of different machine learning machine learning (ML) models, the steps recited in the claim are not tied to any particular system technical process. The limitations similar to “Trading Technologies International, Inc, v IBG LLC Interactive Brokers LLC,” the claims do not recite a technological feature that directed toward a technical solution to a problem rooted in technology. The claimed limitations recite at a high level the steps “receiving…data” and “displaying…the …score” which according to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) the courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) where technology is merely applied to perform the abstract idea or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)
The limitations “generating a plurality of trained ML models”, “training a selected ML algorithm using historical data with an invoice payment labeled as delayed when payment occurs beyond payment due date…”, “training a selected ML models using …differently labeled versions of the historical data…invoice payment is labeled as delayed only when payment occurs beyond payment due date plus a respective grace period…” recite high level functions which expected outcomes. The training of the models do not recite any processes directed toward improving upon “training” models. The specification teaches the learning model trained using training data, makes clear that any suitable machine learning technique can be applied which allows data to be inputted and changed based on changes in data.
The limitations ”using test/validation data split from historical data, determining …an MCC for …model and for each grace period trained model”, “when …MCC for a grace period trained model is higher than the MCC for the first trained model and exceeds a threshold MCC, selecting and deploying the grace period trained model having highest MCC”, “when the MCC for first trained model exceeds the threshold MCC and no grace period trained model having higher MCC is selected, deploying the first trained model”, “using the deployed first trained model or grace period trained model to process incoming data and generate prediction of target variable for …customer”, “when the MCC for the first trained model is low and the MCC for each grace period trained model is lower than the MCC for the first trained model, rejecting the plurality of trained ML models without deploying any of the plurality of trained ML models and generating an alert indicating that the historical data lacks sufficient distinguishability to provide accurate prediction of the target variable for the first customer” which are directed toward the data acted upon to train the plurality of models and the use of MCC thresholds for selection and deployment of models used for processing incoming data and generation of target variable for customer. The limitations do not recite a technical solution to a technical problem because the purpose of the generation of models disclosed is to process incoming data related to payment dates of invoices and the prediction of late payment and grace periods, which is business problem, not a technical one.
The limitations “determining cost of delayed payment”, “determining average delay of payments” which is directed toward analyzing human behavior using technology.
The limitations “converting cost of delayed payments to a first z-score and average of delay payments to second z-score” and “determining a reliability score of the customer comprising a Euclidean distance of the first z-score and second z-score” is directed toward assigning a value to data for calculating a score representing human behavior.
When considered as a combination, the claim limitations recite the technical process as a combination “receiving data”, “training” different models using the data received and parameters related to a grace period of a payment due date. Where the trained models are selected and deployed according to the results of applying Mathews correlation coefficient test where MCC thresholds are compared to the MCC values of each of the different models with different parameters related to grace period of payment due dates in order to determine which model to select and deploy for use to process incoming data used and deployed for analyzing the incoming data, generate a prediction which is merely applying a ML model to receive data, analyze data and output the result which the courts have held under Electric Power Group to be conventional application of technology. The claimed subject matter similar to Recentive Analytics, Inc v Fox Corp, simply uses machine learning to perform a task. The court found that machine learning is now viewed as a common tool rather than a technological breakthrough. Furthermore, similar to Recentive, the “training” steps i) receiving data ii) generate a model iii) training using selective data with different labelled data, iv) testing using MCC thresholds (spec para 0054) v) selecting and deploying models based on MCC threshold criteria, where the specification teaches the learning model trained using training data, makes clear that any suitable machine learning technique (para 0030, para 0033) can be applied which allows data to be inputted and changed based on changes in data (para 0004-0005). The current specification fails to clearly describe a technical problem or explain how the claimed invention improves the “functioning” of the technology, rather than the outcome of applying the technology for analysis.. The “training selected ML algorithm” using different versions of data and parameters related to grace period of a payment due date and testing data” and determining Mathews correlation coefficient (MCC) for the first trained model and for each grace period parameter trained model and when the MCC exceeds MCC threshold and no grace period trained model having higher MCC selected deploying the first trained model- merely focus on the payment due date parameters where MCC threshold criteria are applied for selection and deployment of models for use in the prediction analysis of human behavior.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea. This is because the claimed subject matter when considered as a whole is directed toward collecting, organizing and manipulating data that is loaded into a system for analysis where the analysis is a mathematical process for determining a reliability score and outputting the results.
The current application similarly does not recite a technical solution to a technical problem because the problem disclosed in the is the need to predict and provide recommendations related to lending decisions based on the analysis of borrower related data, which is business problem, not a technical one.
The functions recited in the claims recite the concept of receiving customer data, manipulating and loading collected data merely provides the variables for use in the mathematical process of determining cost of payment delay, determining average delay of payment, converting data into z-values which are applied to calculate a reliability score, which as a combination of parts or as a whole are a process directed toward any of the underlying technology which provides indication of patent eligibility but instead directed toward analyzing and scoring customer risk and mathematical concepts.
Because the claim limitations lack technical disclosure and merely mentions the environment where the method is performed (system) and the generated models without technical details of implementation there is no integration of elements for improving upon technology or improve upon computer functionality or capability in how computers carry out one of their basic functions. There is no integration of elements do not provide a process that allows computers to perform functions that previously could not be performed. The integration of elements do not provide a process which applies a relationship to apply a new way of using an application. The instant application, therefore, still appears only to implement the abstract idea in a broadly claimed field of use environment for calculating values related to a business practice and mathematical concepts. The steps are still a combination made to perform a mathematical process to calculate a value measuring a business metric and does not provide any of the determined indications of patent eligibility set forth in the 2019 USPTO 101 guidance. The functions are is recited at a high-level of generality without any additional elements beyond the identified abstract idea. The claim limitations lacks technical disclosure and therefore fail to provide any indication of patent eligible subject matter under step 2A prong 2. Taking the claim elements separately, the operation performed by the method at each step of the process is purely in terms of results desired and devoid of technical implementation of the claimed process. Technology is not integral to the process as the claimed subject matter fails to mention any technology. Furthermore, the claimed functions do not provide an operation that could be considered as sufficient to provide a technological implementation or application of/or improvement to this concept (i.e. integrated into a practical application). The additional elements only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, an particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The claim is directed to an abstract idea.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the identified abstract idea include a system without providing any correlation between the functions of the system and the body of the claim.
The generating of the trained models is high level and do not provide any unconventional technological process or technical details as to the process for training and generating the models other than the data acted upon. the trained models are selected and deployed according to the results of applying Mathews correlation coefficient test where MCC thresholds are compared to the MCC values of each of the different models with different parameters related to grace period of payment due dates in order to determine which model to select and deploy for use to process incoming data used and deployed for analyzing the incoming data, generate a prediction which is merely applying a ML model to receive data, analyze data and output the result which the courts have held under Electric Power Group to be conventional application of technology. The claimed subject matter similar to Recentive Analytics, Inc v Fox Corp, simply uses machine learning to perform a task. The court found that machine learning is now viewed as a common tool rather than a technological breakthrough. Furthermore, similar to Recentive, the “training” steps i) receiving data ii) generate a model iii) training using selective data with different labelled data, iv) testing using MCC thresholds (spec para 0054) v) selecting and deploying models based on MCC threshold criteria, where the specification teaches the learning model trained using training data, makes clear that any suitable machine learning technique (para 0030, para 0033) can be applied which allows data to be inputted and changed based on changes in data (para 0004-0005). .
Taking the claim elements separately, the steps performed by the method at each step of the process is purely conventional. As a result, none of the hardware recited by the method claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers. None of the limitations recite technological implementation details for any of these steps, but instead recite only results desired to be achieved by any and all possible means. … and therefore amounts to no more than mere instructions to apply an exception using a generic computer component which cannot provide an inventive concept.
When the claims are taken as a whole, as an ordered combination, the combination of steps does not add “significantly more” by virtue of considering the steps as a whole, as an ordered combination. All of these recited steps performed at a high level are generic, routine, conventional computer activities that are performed only for their conventional uses. See Elec. Power Grp. v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). Also see In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011) Absent a possible narrower construction of the terms “receiving…data”, “generating plurality of trained ML models”, “training selected ML model using …data”, “training selected models using differently labeled versions of historical data”, test/validating data, determining MCC” and applying conditions for selection and deployment of models based on MCC criteria, “determining …cost”, “determining …average…payments”, “converting …cost to a …first z-score and average payment to a …second z-score”, “determining reliability score…determining Euclidean distance…”, “displaying …score” ... are functions can be achieved by any general purpose computer without special programming. None of these activities are used in some unconventional manner nor do any produce some unexpected result. In short, each step does no more than require a generic computer to perform generic computer functions.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-transmission is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). The ordering of the steps is therefore ordinary and conventional. The analysis concludes that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
For evidence the examiner directs the Applicant to Electric Power Group and Recentive Analytic v Fox Corp decisions.
The specification discloses the determination of cost of delayed customer payment, average delay of payment for a given customer and the converting process as mathematical process without any details related to a technical process:
[0006]… Embodiments convert the cost of delayed payments to a first Z-score
and the average delay of payments to a second Z-score. Embodiments then determine
a reliability score of the customer comprising determining a Euclidean distance of the
first Z-score and the second Z-score.
[0063] At 602, the cost of a delayed payment from the customer is determined as
follows as a dynamic penalty:
Dynamic Cost of Late Paying Customer to Company =
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552
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[0066] At 604, the average delay for a given customer is determined as follow:
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88
298
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where "n" is the total number of invoices for a customer. This gives an amount of
weighted delay for the customer, including an overall average delay for the customer.
For example, if the customer has paid a large amount of $100 invoices early, even a
short delay on a $1 M invoice will dominate the average delay.
[0067] At 606, the cost from 602 and the delay from 604 is converted into a ZScore
(i.e., a statistical measurement of a score's relationship to the mean in a group of
scores). For the specific customer's average delay at 604, the Z-Score, Zd = (Specific
Customer's Avg. Delay - Avg of Customer Avg Delay)/Std Dev of Customer Avg. Delay.
This determines how far this customer's average delay is from their peers.
[0068] The Z-Score of Cost of a Specific Customer to the Company (from 602) =
Zc = (Specific Customer Cost to Company - Avg of Customer Cost to Company)/Std
Dev of Cost of Customer to Company. This determines how much more expensive is
this customer compared to their peers.
[0069] At 608, the Euclidean distance in the Z-score space is determined to
generate the Customer Relative Reliability Score ("CRRS") at 610 as follows:
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60
224
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If the score is close to 1, this is a good customer. If the score is beyond 2-3, then this is
a risky customer. If the score is greater than 4, then this is a bad customer that should
The claimed subject is nothing but a series of mathematical calculations based on selected information. The court also has “treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 2-6 and 8-9 these dependent claim have also been reviewed with the same analysis as independent claim 1. Dependent claim 2 is directed toward establishing values for performing a calculation (cost of delayed payment comprises penalty based on sum of number of days delayed times weighed cost) and recording usage over time -mathematical concepts and amount of storage space for use (insignificant extra solution activity. Dependent claim 3, 4 and 5 are directed toward a mathematical formula and calculation using specific variables- mathematical concept. Dependent claim 6 is directed toward further limiting the system of method claim 1 to comprising a data plane and control plane, software components operating the data plane and providing access to data warehouse- well understood computer architecture prevalent in the art and limiting the data plane (software) to comprise data pipeline or process that maintains data analytics schema for each customer (e.g. data management), limiting the data plane to comprise data transformation layer software applied to format data understood by the cloud system (mere data manipulation) and the limit the claimed system to provide customer schema for each tenant and utilize data within warehouse for analysis- - which merely provides a field of use to manage, store data for utilization in analysis of a business process. The “data plane” applied to perform the operations of “extract …data”, “loading …data”, and “control plane” applied to perform the “displaying …score of the customer”, which according to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) the courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) where technology is merely applied to perform the abstract idea or as insignificant extra-solution activity. Dependent claim 8 is directed toward when first trained model or selected grace period model has mid-range MCC, segmenting transactions for first customer, segmenting comprising determining a measure of variability of targe variable for each transaction and based on measure of variability classifying each transaction having low, medium or high variation- data analysis, manipulation and organization and mathematical concepts for a business practice.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2-6 and 8-9 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 10-15 and 17-18:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a non-transitory computer readable medium, as in independent Claim 10 and the dependent claims. Such mediums fall under the statutory category of "manufacture." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. Manufacture claim executed instructions corresponds to steps of claim 1. Therefore, claim 10 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mathematical concepts and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: Manufacture claim executed instructions corresponds to steps of claim 1. Therefore, claim 10 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1. The additional elements recited in the claim beyond the abstract idea include a “non-transitory computer readable medium having instructions stored executable by one or more processors to optimize target variable (abstract concept) using a system comprising a plurality of learning model, a cloud based analytics system for tenant of the cloud based analytics system. Although the preamble of the claim recites the additional elements the body of the claim does not tie any of the technology to the claimed limitations in the body of the claim. The training, selecting and deploying of the learning models operations and generating correspond to the analysis of the steps of claim 1. Therefore, the “learning model” limitations have been analyzing in the analysis of claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. “non-transitory computer readable medium having instructions stored executable by one or more processors to optimize target variable (abstract concept) using a system comprising a plurality of learning model, a cloud based analytics system for tenant of the cloud based analytics system. Although the preamble of the claim recites the additional elements the body of the claim does not tie any of the technology to the claimed limitations in the body of the claim. The training, selecting and deploying of the learning models operations and generating correspond to the analysis of the steps of claim 1. Therefore, the “learning model” limitations have been analyzing in the analysis of claim 1. The limitations of manufacture claim 10 corresponds to steps of method claim 1. Therefore, claim 10 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
The claimed computer readable medium is analogous to a Beauregard claim. In the court decision, In re Beauregard, 53 F.3d 1583 (Fed.Cir.1995) is a claim to a computer readable medium (e.g., a disk, hard drive, or other data storage device) containing program instructions for a computer to perform a particular process. The claimed medium is analogous with the Beauregard claim, where the Federal Circuit held that even though the claim is directed to a manufacture, the claim is not "truly drawn to a specific" computer readable medium, but rather is directed toward the method of generating, selecting and deploying ML models for use in determining a reliability score representing customer predicted behavior for late payment. Simply reciting the use of a computer to execute an algorithm that can be performed entirely in the human mind will not change the analysis. The Beauregard claim was then treated as a method claim. This claim was determined not to meet the Alice/May 2A and 2B test. Furthermore, the "incidental use" of a computer did not allow the claim to meet the Alice 2A or 2B requirements. The court noted that even though the method may require the use of a computer, methods that can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas "even when performed by a computer" has not met its burden to demonstrate that claim 2 is "truly drawn to a specific" computer readable medium, rather than to the underlying method of credit card fraud detection. Similar to the Beauregard decision, the current limitations, as a general matter, are not drawn to a specific computer, instead the claim limitations are directed toward programming a general purpose processor to perform high level functions of generating, selecting and deploying ML models for use analysis of customer behavior, determining a cost, determining average delay of payments, converting determined cost and average payment delay to z-values and then using the z-values to determine a reliability score. Thus, claim recites a Beauregard claim format, and thus is patent ineligible under step 2B.
When the claims are taken as a whole, as an ordered combination, the combination of steps does not add “significantly more” by virtue of considering the steps as a whole, as an ordered combination. All of these computer functions are generic, routine, conventional processor activities that are performed only for their conventional uses for performing the abstract idea. See Elec. Power Grp. v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). Also see In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011) Absent a possible narrower construction of the terms “receiving data”, “generating ML models”, “training ML models”, “selecting…deploying …models” based on MCC criteria, “using models …”, “determine cost of delayed payment”, “determine average delay of payments”, “converting cost of delay payments to first z-score and average delay of payments to second z-score”, “determining reliability score” using Euclidean distance between first and second z-score”... are executed instructions that can be achieved by any general purpose processor without special programming. None of these activities are used in some unconventional manner nor do any produce some unexpected result. In short, each step does no more than require a generic processor to perform generic processor functions.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-transmission is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). The ordering of the steps is therefore ordinary and conventional. The analysis concludes that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
The claimed “cloud based analytics system” is nominally mentioned in the preamble and is not tied to any of the limitations in the body of the claim. Accordingly the “cloud-based analytics system” is merely a field of use recitation (MPEP 2106.05 (h)). Therefore, the “cloud-based analytics system” fails to impose meaningful limits upon the identified conceptual idea discussed above. With respect to the application of the “transformation layer” to perform the step “transforming data” the claim limitation and specification lacks technical details on how the transformation layer performs the step and thus is broad enough to encompass the transformation layer transforming data in its ordinary capacity as such technology was designed to operation. Application of such transformation layer software is known in the art for use in formatting data -WO 2020/039198 A1 by Barnett-“ connect to one or more external databases or APIs and extract data of relevance to the model. These APIs or data sources could be open source or freely available and not in a data format which is readily usable by the model. Therefore, once extracted in raw format or in a format specific to the remote system, the data is transformed in to data structures compatible with the connected graph model representation via a data transformation layer. Once transformed, data is loaded into the model directly via an optional data service layer 17.” (FIG. 3); WO 2010/006187 A2 by Rajaraman et al – “The data transformation layer 204 may include code that forms the outlet for custom or platform-specific data manipulation and formatting. This layer 204 may be used for data in both inbound and outbound message Queues.”…(FIG. 2); US Pub No. 2022/0301031 A1 by Iyer-“ the data processing engine 202 instantiates a data transformation layer that maps and transforms data received from a source. For example, the data transformation layer transforms non-XML data format (e.g., of a data source 135) to XML data format for storage. In some implementations, the data processing engine 202 processes, correlates, integrates, and synchronizes the received data streams from disparate devices 115, servers 120, 140, and data sources 135 into a consolidated data stream to perform the functionalities as described herein:“ Using a data transformation layer to transform data----is the most basic functions of such software.
The specification discloses the determination of cost of delayed customer payment, average delay of payment for a given customer and the converting process as mathematical process without any details related to a technical process:
[0006]… Embodiments convert the cost of delayed payments to a first Z-score
and the average delay of payments to a second Z-score. Embodiments then determine
a reliability score of the customer comprising determining a Euclidean distance of the
first Z-score and the second Z-score.
[0063] At 602, the cost of a delayed payment from the customer is determined as
follows as a dynamic penalty:
Dynamic Cost of Late Paying Customer to Company =
PNG
media_image2.png
430
552
media_image2.png
Greyscale
[0066] At 604, the average delay for a given customer is determined as follow:
PNG
media_image3.png
88
298
media_image3.png
Greyscale
where "n" is the total number of invoices for a customer. This gives an amount of
weighted delay for the customer, including an overall average delay for the customer.
For example, if the customer has paid a large amount of $100 invoices early, even a
short delay on a $1 M invoice will dominate the average delay.
[0067] At 606, the cost from 602 and the delay from 604 is converted into a ZScore
(i.e., a statistical measurement of a score's relationship to the mean in a group of
scores). For the specific customer's average delay at 604, the Z-Score, Zd = (Specific
Customer's Avg. Delay - Avg of Customer Avg Delay)/Std Dev of Customer Avg. Delay.
This determines how far this customer's average delay is from their peers.
[0068] The Z-Score of Cost of a Specific Customer to the Company (from 602) =
Zc = (Specific Customer Cost to Company - Avg of Customer Cost to Company)/Std
Dev of Cost of Customer to Company. This determines how much more expensive is
this customer compared to their peers.
[0069] At 608, the Euclidean distance in the Z-score space is determined to
generate the Customer Relative Reliability Score ("CRRS") at 610 as follows:
PNG
media_image4.png
60
224
media_image4.png
Greyscale
If the score is close to 1, this is a good customer. If the score is beyond 2-3, then this is
a risky customer. If the score is greater than 4, then this is a bad customer that should
The claimed subject is nothing but a series of mathematical calculations based on selected information. The court also has “treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category. According to the Gottschalk v. Benson, 409 U. S. 63; Parker v., decision, a mathematical formula are patent ineligible. This was because the mathematical formula involved here has no substantial practical application except in connection with a computer, similarly, the determining of z-values and reliability scores as claimed using an algorithm that has no impact upon the computer, its functionality or field its functionality or field is patent ineligible. As discussed above, the claimed subject matter is directed toward mathematical concepts. Claims can recite a mental process 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"). The current limitations similarly recite as being executed by a process to perform mathematical process is an abstract idea because it can be performed by computers without a computer. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 11-15 and 17-18 these dependent claim have also been reviewed with the same analysis as independent claim 10. The instructions of Dependent claim 11 corresponds to steps of method claim 2. Therefore, claim 11 has been analyzed and rejected as previously discussed with respect to claim 2. The instructions of Dependent claim 12 corresponds to steps of method claim 3. Therefore, claim 12 has been analyzed and rejected as previously discussed with respect to claim 3. The instructions of Dependent claim 13 corresponds to steps of method claim 4. Therefore, claim 13 has been analyzed and rejected as previously discussed with respect to claim 4. The instructions of Dependent claim 14 corresponds to steps of method claim 5. Therefore, claim 14 has been analyzed and rejected as previously discussed with respect to claim 5. The instructions of Dependent claim 15 corresponds to steps of method claim 6. Therefore, claim 15 has been analyzed and rejected as previously discussed with respect to claim 6. Therefore, claim 16 has been analyzed and rejected as previously discussed with respect to claim 7. The instructions of Dependent claim 17 corresponds to steps of method claim 8. Therefore, claim 17 has been analyzed and rejected as previously discussed with respect to claim 8. The instructions of Dependent claim 18 corresponds to steps of method claim 9. Therefore, claim 18 has been analyzed and rejected as previously discussed with respect to claim 9.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 10. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 11-18 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 19-22:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a system, as in independent Claim 19 and the dependent claims. Such systems fall under the statutory category of "machine." Therefore, the claims are directed to a statutory eligibility category.
. STEP 2A Prong 1. The functions of machine claim 19 corresponds to steps of claim 1. Therefore, claim 19 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mathematical concepts and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The functions of machine claim 19 corresponds to steps of claim 1. Therefore, claim 19 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1. The additional elements recited in the claim beyond the abstract idea include a “cloud based analyzer system” comprising a plurality of different machine learning models in a cloud based analytics system the system comprising “one or more processors executing instructions” configured to perform the functions corresponding to the steps of method claim 1. The training, selecting and deploying of the learning models operations and generating correspond to the analysis of the steps of claim 1. Therefore, the “learning model” limitations have been analyzing in the analysis of claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements “cloud based analyzer system” comprising a plurality of different machine learning models in a cloud based analytics system the system comprising “one or more processors executing instructions”- are some of the most basic hardware of a computer system. The learning models are recited as a high level according to the training dataset acted upon and for use in predicting human behavior.
The training, selecting and deploying of the learning models operations and generating correspond to the analysis of the steps of claim 1. Therefore, the “learning model” limitations have been analyzing in the analysis of claim 1. The limitations of system claim 19 corresponds to steps of method claim 1. Therefore, claim 19 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
The claimed “cloud based analytics system” is nominally mentioned in the preamble and is not tied to any of the limitations in the body of the claim. Accordingly the “cloud-based analytics system” is merely a field of use recitation (MPEP 2106.05 (h)). Therefore, the “cloud-based analytics system” fails to impose meaningful limits upon the identified conceptual idea discussed above. With respect to the application of the “transformation layer” to perform the step “transforming data” the claim limitation and specification lacks technical details on how the transformation layer performs the step and thus is broad enough to encompass the transformation layer transforming data in its ordinary capacity as such technology was designed to operation. Application of such transformation layer software is known in the art for use in formatting data -WO 2020/039198 A1 by Barnett-“ connect to one or more external databases or APIs and extract data of relevance to the model. These APIs or data sources could be open source or freely available and not in a data format which is readily usable by the model. Therefore, once extracted in raw format or in a format specific to the remote system, the data is transformed in to data structures compatible with the connected graph model representation via a data transformation layer. Once transformed, data is loaded into the model directly via an optional data service layer 17.” (FIG. 3); WO 2010/006187 A2 by Rajaraman et al – “The data transformation layer 204 may include code that forms the outlet for custom or platform-specific data manipulation and formatting. This layer 204 may be used for data in both inbound and outbound message Queues.”…(FIG. 2); US Pub No. 2022/0301031 A1 by Iyer-“ the data processing engine 202 instantiates a data transformation layer that maps and transforms data received from a source. For example, the data transformation layer transforms non-XML data format (e.g., of a data source 135) to XML data format for storage. In some implementations, the data processing engine 202 processes, correlates, integrates, and synchronizes the received data streams from disparate devices 115, servers 120, 140, and data sources 135 into a consolidated data stream to perform the functionalities as described herein:“ Using a data transformation layer to transform data----is the most basic functions of such software.
The specification discloses the determination of cost of delayed customer payment, average delay of payment for a given customer and the converting process as mathematical process without any details related to a technical process:
[0006]… Embodiments convert the cost of delayed payments to a first Z-score
and the average delay of payments to a second Z-score. Embodiments then determine
a reliability score of the customer comprising determining a Euclidean distance of the
first Z-score and the second Z-score.
[0063] At 602, the cost of a delayed payment from the customer is determined as
follows as a dynamic penalty:
Dynamic Cost of Late Paying Customer to Company =
PNG
media_image2.png
430
552
media_image2.png
Greyscale
[0066] At 604, the average delay for a given customer is determined as follow:
PNG
media_image3.png
88
298
media_image3.png
Greyscale
where "n" is the total number of invoices for a customer. This gives an amount of
weighted delay for the customer, including an overall average delay for the customer.
For example, if the customer has paid a large amount of $100 invoices early, even a
short delay on a $1 M invoice will dominate the average delay.
[0067] At 606, the cost from 602 and the delay from 604 is converted into a ZScore
(i.e., a statistical measurement of a score's relationship to the mean in a group of
scores). For the specific customer's average delay at 604, the Z-Score, Zd = (Specific
Customer's Avg. Delay - Avg of Customer Avg Delay)/Std Dev of Customer Avg. Delay.
This determines how far this customer's average delay is from their peers.
[0068] The Z-Score of Cost of a Specific Customer to the Company (from 602) =
Zc = (Specific Customer Cost to Company - Avg of Customer Cost to Company)/Std
Dev of Cost of Customer to Company. This determines how much more expensive is
this customer compared to their peers.
[0069] At 608, the Euclidean distance in the Z-score space is determined to
generate the Customer Relative Reliability Score ("CRRS") at 610 as follows:
PNG
media_image4.png
60
224
media_image4.png
Greyscale
If the score is close to 1, this is a good customer. If the score is beyond 2-3, then this is
a risky customer. If the score is greater than 4, then this is a bad customer that should
The claimed subject is nothing but a series of mathematical calculations based on selected information. The court also has “treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category.
According to the Gottschalk v. Benson, 409 U. S. 63; Parker v., decision, a mathematical formula are patent ineligible. This was because the mathematical formula involved here has no substantial practical application except in connection with a computer, similarly, the determining of z-values and reliability scores as claimed using an algorithm that has no impact upon the computer, its functionality or field its functionality or field is patent ineligible. As discussed above, the claimed subject matter is directed toward mathematical concepts. 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"). The current limitations similarly recite as being executed by a process to perform mathematical process is an abstract idea because it can be performed by computers without a computer
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claim 20-22 these dependent claim has also been reviewed with the same analysis as independent claim 19. The processor functions of claim 20 corresponds to steps of method claim 2. Therefore, claim 20 has been analyzed and rejected as previously discussed with respect to claim 2.
Dependent claim 21 is directed toward limiting the cloud based system data plane as a data pipeline that maintains a data analytics schema that is updated on a periodic basis and includes a data transformation layer that the is used to transform transaction data into a model format and wherein the cloud based analytics system provides for each tenant of the plurality of tenants, a customer schema which allows the tenant to supplement and utilize data within their own data warehouse instance whose contents are controlled by the tenant. The specification describes the control plane as software to provide control for cloud/software products offered within SaaS/cloud environments such as off the shelf Oracle Analytics Clouse environment (para 0074). he control plane, data plane and transformation layer is merely software per se, amounting to no more than mere instructions to perform the access claimed. The specification describes the “data plane” as altering/updating frequency of extraction, loading and transforming data edited in the data warehouse, where the software includes a process layer, transformation layer that form the software used to extract data from business applications for use in the data analysis. The control plane and data plane (layered software) are not directed toward a process to improve any of the underlying technology or the models applied to analyze “grace periods” of a business process. Dependent claim 22 discloses the condition “when the first trained model or selected grace period model” has mid-range MCC, segmenting each of the transaction for the customer, the segmenting comprising determining measure of variability of the target variable for each transaction, classifying each transaction having low variation, medium variation or high variation which is merely applying a condition for data analysis.
The dependent claim(s) has been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 19. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 20-22 is directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
Conclusion
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 MARY M GREGG whose telephone number is (571)270-5050. The examiner can normally be reached M-F 9am-5pm.
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, Christine Behncke can be reached at 571-272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARY M GREGG/Examiner, Art Unit 3695
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