DETAILED ACTION
The following is a first office action upon examination of application number 18/107475.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/24/2025 has been entered.
Response to Amendment
Claims 1-10 and 12-20 are pending in the application and have been examined on the merits discussed below.
The rejection of claims 1-10 and 12-20 under 35 USC 103 is withdrawn in view of the Applicant’s arguments filed 11/24/2025.
The rejection of claims 1-10 and 12-20 under 35 USC 112 is withdrawn in view of the Applicant’s arguments filed 11/24/2025.
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 1-10 and 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-10 and 12-20 are directed to methods; thus these claims are directed to a process, which is one of the statutory categories of invention.
(Step 2A) The claims recite an abstract idea instructing how to predict customer quality based on historical data, which is described by claim limitations reciting:
determining … scores rating historical customer quality, wherein the scores are determined as a function of multi-source data components, wherein customer quality is a vector quantity determined as a function of a plurality of the multi-source data components;
determining … a customer quality threshold wherein the customer quality threshold is based at least in part on quality scores and market segment preferences and indications received from the user …;
retrieving … historical customer data from multiple sources;
dividing … historical customer data into at least a train partition and test partition;
rating … a plurality of customers based on a quality score wherein said quality score is based at least in part on a function of historical training data associated with said plurality of customers;
determining … a subset of customers from said plurality of customers, wherein said subset of customers comprises one or more customers that do not meet the customer quality threshold;
training a predictive analytic model, … to make determinations as to whether one or more customers of the plurality of customers satisfy the customer quality threshold, wherein said training is based at least in part on cross-validation with the test partition;
testing … the predictive analytic model … to determine if the predictive analytic model is overfit, based at least in part on a function of determining if model prediction error decreased when cross-validating the predictive analytic model against the test partition;
determining the predictive analytic model is not overfit, based on the testing of the predictive analytic model;
updating the train partition based on the testing of the predictive analytic model;
determining … a customer quality improvement for one or more customers of the subset of customers, and a CQIR threshold, wherein the CQIR is a rate at which a customer quality changes over time and the CQIR threshold is a rate of change of CQIR required to determine whether an individual customer meets desired potential customer quality trends, wherein the CQIR and CQIR threshold are based at least in part on use of … said predictive analytic model;
predicting future customer quality for a first set of customer from said subset of customers based at least in part on said CQIR and said CQIR threshold and the predictive analytic model, wherein future customer quality is determined as a trend based on predicted quality evaluated for at least two points in time; and
rejecting said first set of customers from said plurality of customers.
The identified recited limitations in the claims describing predicting customer quality based on historical data (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices and marketing activities. Dependent claims 2-9, 12-16, and 18-20 recite limitations that further narrow the abstract idea; therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the customer ranking engine (CRE), comprising a machine learning or artificial intelligence system comprising program instructions, stored in memory, configured to execute on one or more processors in the CRE, wherein the memory is in electrical communication with the one or more processors and comprises a program memory and a data memory in claim 1; the customer ranking engine (CRE), comprising a machine learning or artificial intelligence system comprising program instructions, stored in a memory, configured to execute on one or more processors in the CRE, wherein the memory is in electrical communication with the one or more processors and comprises a program memory and a data memory in claim 10; and the customer ranking engine (CRE), comprising a machine learning or artificial intelligence system comprising program instructions, stored in a memory, configured to execute on one or more processors in the CRE, wherein the memory is in electrical communication with the one or more processors and comprises a program memory and a data memory in claim 17, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a computer/processor.
Additional elements such as the training … a predictive analytic model comprising a neural network… ; testing…the predictive analytic model via the neural network… and use of said neural network do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only generally link the abstract idea to a technological environment. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Additional elements related to indications received via a user interface only add insignificant extra solution activities (data gathering) and do not provide an improvement to the computer or technology. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see Spec. [0022]). Additional elements related to training … a predictive analytic model comprising a neural network… ; testing…the predictive analytic model via the neural network… and use of said neural network do not provide an improvement to the computer or technology and only generally link the claimed abstract idea to a technological environment. Additional elements related to indications received via a user interface only add insignificant extra solution activities (data gathering) and do not provide an improvement to the computer or technology. With respect to data gathering limitations, the courts have recognized the use of computers to receive and transmit data as a well-understood, routine, and conventional, 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). When taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Response to Arguments
Applicant's arguments filed 11/24/2025 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 101, Applicant argues that the claim do not recite a judicial exception.
Examiner respectfully disagrees. The rejection no longer relies on the Mental Processes grouping of abstract ideas. Examiner notes that the identified recited limitations in the claims describing predicting customer quality based on historical data (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices and marketing activities. The present claims differ from the claim in Example 37 because the present claims recite fundamental economic practices and marketing activities (i.e., an abstract idea).
With respect to the rejection under 35 USC 101, Applicant argues that the claims are integrated into a practical application.
Examiner respectfully disagrees. Examiner respectfully disagrees. The claimed training and application of machine learning systems does not improve machine learning functionality. As per MPEP 2106.05(a), [i]f it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The present specification does not provide any explanation regarding an improvement in the training or use of machine learning models and the present claims only offer general recitation of the 'training of a neural network' without detail regarding how to achieve the result.
The invention in Example 41 improved upon prior art methods for establishing cryptographic communications relying on the difficulty of factoring large integers by computers; the disclosure of the present application does not describe an analogous improvement. Further, Example 41 recited a combination of additional elements limiting the use of the mathematical concepts to the practical application of transmitting the cipher text word signal to a computer over a communication channel. Thus, integrating the abstract idea to a process that secures private network communications between computers by relying on the difficulty of factoring large integers by computers. In contrast, the additional elements such as training … a predictive analytic model comprising a neural network… ; testing…the predictive analytic model via the neural network… and use of said neural network are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a computer/processor.
The asserted improvement in model accuracy and reliability through data partition and cross-validation, and improvement in predictive capability by generating a CQIR slope metric, are not improvements to a technology. Examiner notes that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Additionally, using a general purpose computer to execute a neural network and automate rejection logic does not improve computer functionality; the present claims do not improve computer capabilities and only invoke the computer as a tool.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN TORRICO-LOPEZ whose telephone number is (571)272-3247. The examiner can normally be reached M-F 10AM-5PM.
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/ALAN TORRICO-LOPEZ/ Primary Examiner, Art Unit 3625