DETAILED ACTION
Notice of Pre-AIA or AIA Status
[1] The present application is being examined under the pre-AIA first to invent provisions.
Notice to Applicant
[2] This communication is in response to the amendment filed 21 August 2025. It is noted that this application is a Continuation of United States Patent Application Serial No. 12/040,796 filed 29 February 2008, now United States Patent No. 10,510,025. Claim 20 has been cancelled. Claims 1-3, 11, and 17 have been amended. Claim 21 has been added. Claims 1-19 and 21 are pending.
Response to Remarks/Amendment
[3] Applicant's remarks filed 21 August 2025 have been fully considered but they are not persuasive. The remarks are addressed as follows:
[i] In response to rejection(s) of claim(s) 1-19 (now claims 1-19 and 21 as presented by amendment) under 35 U.S.C. 101 as being directed to non-statutory subject matter as set forth in the previous Office Action mailed 21 May 2025, Applicant provides the following remarks:
"…Features incorporated into independent claims 1, 11, and 17…add efficiencies to the claimed predictive technology and specifically identify fraud-types and records with a fraud tag to help identify/eliminate undesirable activity…These features…improve the model to be more efficient by avoiding the need for ‘full fraud and non-fraud table processing’…”
Applicant further remarks:
“…The implementation details for the frequencies tables…technical improvements integrate the recited subject matter into a practical application because these improvements enhance the functionality of the predictive model and identify fraud records…Similar to Enfish, the claimed technology advances result in improvements in the computer-implemented learning model’s performance by eliminating the need for all data records to be process for the purpose of fraud detection… … "
Applicant further remarks:
“…Similar to Bascom, the claimed combination of technical features and improvements integrate the recited inventive concept into a practical application by using respective frequency tables for different process queues…so that only selected data records…are processed…”
In response, Examiner respectfully disagrees. With respect to considerations under Eligibility Step 2A prong 2: (See MPEP 2106.04(d)):
As presented by amendment, additional elements of claim 1 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject matter include: “computing system” and “data sources”. Claim 1 further indicates, generally, that the claimed method is “for technologically improving a computer-implemented machine-learning model” as designated in the preamble. As presented, claim 1 specifies:
“…the one or more data sources comprising data structures including separate frequency tables adaptively maintained and updated based on associated feature variables to the model, wherein a first frequency table is implemented in a first queue populated by data records identified as belonging to a first classification and a second frequency table is implement as a second queue populated by data records identified as belonging to a second classification…” and “…generating a second score, based on the data records in the first frequency table and not considering the data records in the second frequency table to avoid a full table processing of data records belonging to both the first classification and the second classification…”
As presented by amendment, claim 1 further specifies:
“…the first frequency table and the second frequency table being respectively updated based on counts of data records identified as belonging to the first classification or the second classification, wherein responsive to the first queue or the second queue being full, a history of most recent data records added is maintained by removing an earlier added data record from a respective one of the first queue or the second queue to make room for a newly added data record thereby speeding up feature variable identification, and wherein counts of record are divided into separate individual bins;…”
The above noted limitations serve to clarify aspects of the data sources further indicate that first and second classifications of data records are stored in separate queues and added and removed on a FIFO basis and data is updated based on counts which are stored in bins. While the claim includes a general statement that the FIFO operated queues speed up feature variable selection and, generally, that selectively accessing data from separate stored data avoids having to generate a score using the entirety of the data, the recitations in present form merely describe the data records and the data tables and indicate that counts and data are stored in tables and bins, respectively. The indication that data records, data tables, and counts are stored in tables and bins fails to tie any particular functions to the inventive method, but rather amount to general storage of data records in memory generic structures.
As presented, the function(s) reasonably attributable to the claimed “computing system” are limited to receiving or transmitting data or information via a network (e.g., data from data sources), storing and retrieving information from a memory (e.g., data and models from generic storage partitions, e.g., tables, bins, and queues) and performing tasks that are otherwise performable in the human mind (e.g., generating scores, comparing scores to threshold measures, and determining a likelihood). The claimed executing a mathematical model to generate a score and comparing the score to known data to assess a fraud risk associated with an event benefit from the inherent efficiencies gained by data transmission, data storage, and information display capacities of generic computing devices, but fails to present an additional element(s) which practical integrates the judicial exception into a practical application of the judicial exception (See MPEP 2106.05(f)).
With respect to the Court’s findings in Enfish v Microsoft Corp, the claims at issue did not simply employ generic computer elements to store and compare data in a relational database, but rather present a particular way in which the computer performs storage and retrieval operations using bit array generated object identifiers to construct a self-referential storage structure/table. The claims at issue in Enfish v Microsoft Corp recite a specific mechanism as to how the computer constructs an electronic storage structure such that the structure is self-contained and self-referential. In contrast, the identification of separate stored tables, queues, and bins to store data that is used to calculate separate, i.e., first and second scores is limited to a description of the stored data and a general descriptions of tables bins and queues used to store data. While the instant claims serve to improve the predictive scoring generated by a defined mathematical model, the instant claims do not provide for any improvement in the underlying technology. Rather, the instant claims merely store information/data in known structures such that it is accessible for the purpose of executing a mathematical model to generate a score and comparing the score to known data to assess a fraud risk associated with an event. In contrast to the self-referential table of Enfish, the instant invention benefits from computer storage of information, but fail to otherwise improve commercially available technology at the time of the invention.
With respect to the relevant findings of the Court in Bascom Global Internet v. AT&T Mobility LLC, the claimed arrangement was not merely a collection of computing devices arranged in a network configuration. Rather, the invention required that individualized filtering for individual client machines, when positioned in a central server location, provided for improved efficiency and effectiveness in maintained customized filter settings for client devices. In contrast, the identification of separate stored tables to store data that is used to calculate separate, i.e., first and second scores is limited to a description of the stored data and a recognized advantage of using designated subsets of data to calculate scores. While the instant claims serve to improve may improve a prediction of fraud using a defined mathematical model on specified data sets, the instant claims do not provide for any improvement in the underlying technology. Rather, the executing a mathematical model to generate a score and comparing the score to known data to assess a fraud risk associated with an event benefit from, but fail to otherwise improve commercially available technology at the time of the invention.
[ii] Applicant’s remaining remarks are reasonably considered to have been fully addressed in the context of the revised rejection of the claims presented above responsive to the amendments to the subject claims and in accordance with the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register, 17 July 2024.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
[4] Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 as presented by amendment recites “…the learning model updating respective scoring parameters to more accurately detect fraudulent activity…”. There is insufficient antecedent basis for this limitation in the claim. The prior lineage of the claim refers to a “model” but does not introduce a specific “learning model”. Accordingly, there is a lack of antecedent basis for the specific reference to “the learning model”.
NOTE: While Examiner notes that the preamble of the claim indicates that the method is “…for improving a computer-implemented machine-learning model…”, the Specification describes the inventive models as “adaptive models” and presents examples of “adaptive models” including probabilistic neural networks. The Specification further describes updating scoring parameters of the adaptive model. The Specification does not make a specific reference to a model that is described as a “learning model”. The recitation of machine-learning model as presented in the preamble has been treated as an intended purpose, i.e., a category of adaptive models to which the claimed data preparation functions/steps can be applied to make improvements. However, Examiner notes that treatment of the preamble statement as a structural limitation in which the models are a machine-learning model that is distinct from the adaptive models discussed in the Specification could require consideration that the preamble statement introduces new matter to the Specification with respect to the parent Application Serial No. 12,040,796. Examiner suggests amendments to refer to the models as “adaptive models” to align with the terminology used in specification and/or provide explanation that the reference to machine learning is limited to the adaptive models of the supportive disclosure.
For purposes of further examination, Examiner assumes the reference to “learning model” and updating scoring parameters constitutes adjustment to an adaptive mathematical model as described in the Specification. Examiner further assumes a typographical error or oversight to be corrected on the next response.
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.
[5] Previous rejection(s) of claims 1-19 (now claims 1-19 and 21 as presented by amendment) under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without significantly more has/have not been overcome by the amendments to the subject claims and is/are maintained. The revised statement of rejection presented below is necessitated by amendment and addresses the present amendments to the pending claims.
The following analysis is based on the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register, 17 July 2024. Claim(s) 1-19 and 21 as a whole is/are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might serve to impede, rather than promote, innovation. Still, inventions that integrate the building blocks of human ingenuity into something more by applying the abstract idea in a meaningful way are patent eligible (See MPEP 2106.04).
Consistent with the findings of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. ineligible abstract ideas are defined in groups, namely: (1) Mathematical Concepts (e.g., mathematical relationships, mathematical formulas or equations, and mathematical calculations; (2) Mental Processes (e.g., concepts performed or performable in the human mind including observations, evaluations, judgements, or opinions); and (3) Certain Methods of Organizing Human Activity. Groupings of Certain Methods of Organizing Human Activity include three sub-categories within the group, namely: (1) fundamental economic principles or practices; (2) commercial or legal interactions (e.g., agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); (3) managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions) (See MPEP 2106.04(a).
Eligibility Step 1: Four Categories of Statutory Subject Matter (See MPEP 2106.03): Independent claims 1, 11, and 17 are directed to a method, a system, and non-transitory computer-readable storage medium, respectively, and are reasonably understood to be properly directed to one of the four recognized statutory classes of invention designated by 35 U.S.C. 101; namely, a process or method, a machine or apparatus, an article of manufacture, or a composition of matter. While the claims, generally, are directed to recognized statutory classes of invention, each of method/process, system/apparatus claims, and computer-readable media/articles of manufacture are subject to additional analysis as defined by the Courts to determine whether the particularly claimed subject matter is patent-eligible with respect to these further requirements. In the case of the instant application, each of claims 1, 11, and 17 are determined to be directed to ineligible subject matter based on the following analysis/guidance:
Eligibility Step 2A prong 1: (See MPEP 2106.04): In reference to claim 1, the claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do/does not amount to significantly more than an abstract idea. The claim(s) is/are directed to abstract Mathematical Concepts (e.g., mathematical relationships, mathematical formulas or equations, and mathematical calculations) and processes performable by Human Mental Processing (e.g., concepts performed or performable in the human mind including observations, evaluations, judgements, or opinions). In particular, the general subject matter to which the claims are directed executing a mathematical model to generate a score and comparing the score to known data to assess a fraud risk associated with an event, which is an ineligible concept encompassing abstract mathematical processes and observations/comparisons performable using human mental processing.
The courts have previously identified subject matter limited to the implementation of Mathematical Concepts as ineligible abstract ideas (See at least Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972); and Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ2d 193, 195 (1978)). Further, the courts consider steps/processes performable by Human Mental Processing and/or by a human using pen and paper to be ineligible abstract ideas (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011). Further, mental processes or concepts performed in the human mind including observation and evaluation are considered to be ineligible abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for a recitation of generic computer components, then the claim is still to be grouped as a mental process unless the limitation cannot practically be performed in the human mind (See MPEP 2106.04(a)(2)).
With respect to functions/steps limited to Mathematical Concepts and/or processes performable by Human Mental Processing and/or by a human using pen and paper, representative claim 1 recites:
“…receiving… at least a first data record…”, “…generating a first score representing a first likelihood that the first data record is associated with a first classification…”, “…generating a second score, based on the data records in the first frequency table and not considering the data records in the second frequency table to avoid a full table processing of data records belonging to both the first classification and the second classification to represent a second likelihood that the first data record is associated with the first classification, in response to the first score being higher than a threshold value…”, “…the feedback comprising information about at least a second data record received by the model prior to the model receiving the first data record, the second data record being identified as associated with at least one of the first classification or a second classification according to the feedback…”, and “…applying a blend of the first score and the second score to indicate likelihood of occurrence of an event associated with the first classification…wherein the event associated with the first classification is identified as a fraudulent event, and wherein additional segmentation of the model along additional fraud types is achieved by identifying various fraud types and obtaining at least one additional score per fraud type to realize the blend of the first score and the second score across various fraud types thereby improving the model’s performance…”
Respectfully, absent further clarification of the processing steps executed by the recited at least one computing system implementing the model, one of ordinary skill in the art would readily understand that calculating scores using input data and an applicable mathematical model are mathematical processes as reasonably understood from the associated mathematical formulas and equations identified in the supportive disclosure as applied to generate the subject scores. By extension, given scores and applicable thresholds/comparisons, one of ordinary skill would be capable of comparing scores to a threshold and/or blending scores to determine a likelihood of an event employing by the human mental processing, i.e., observations and judgements based on comparisons of scores to thresholds (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011) (“a method that can be performed by human thought alone is merely an abstract idea and is not patent eligible under 35 U.S.C 101).
With respect to the data sources, claim 1 further specifies:
“…the one or more data sources comprising data structures including separate frequency tables adaptively maintained and updated based on associated feature variables to the model, wherein a first frequency table is implemented in a first queue populated by data records identified as belonging to a first classification and a second frequency table is implement as a second queue populated by data records identified as belonging to a second classification…”
While the amended limitations serve to clarify aspects of the data sources, the recitation merely describes the data tables but fails to tie any particular functions to the inventive method other than to state, generally, that selectively accessing data from separate stored data avoids having to generate a score using the entirety of the data. These functions provide a description of a potential efficiency gained by accessing a filtered subset of data, but fails to present functions performed by the inventive method or system.
The technical elements identified in claim 1 include: “computing system” and “data sources”. Claim 1 further indicates, generally, that the claimed method is “for technologically improving a computer-implemented machine-learning model” as designated in the preamble. Claim 1 further specifies:
“…the one or more data sources comprising data structures including separate frequency tables adaptively maintained and updated based on associated feature variables to the model, wherein a first frequency table is implemented in a first queue populated by data records identified as belonging to a first classification and a second frequency table is implement as a second queue populated by data records identified as belonging to a second classification…” and “…generating a second score, based on the data records in the first frequency table and not considering the data records in the second frequency table to avoid a full table processing of data records belonging to both the first classification and the second classification…”
As presented by amendment, claim 1 further specifies:
“…the first frequency table and the second frequency table being respectively updated based on counts of data records identified as belonging to the first classification or the second classification, wherein responsive to the first queue or the second queue being full, a history of most recent data records added is maintained by removing an earlier added data record from a respective one of the first queue or the second queue to make room for a newly added data record thereby speeding up feature variable identification, and wherein counts of record are divided into separate individual bins;…”
With respect to these potential additional elements, the claimed “computing system” is identified as implementing the model. The claimed “data sources” are identified as communicating feedback data with the computing system and are further arranged into first and second frequency tables. The amended limitation(s) clarify aspects of the data sources further indicate that first and second classifications of data records are stored in separate queues and added and removed on a FIFO basis and data is updated based on counts which are stored in bins.
These technical elements and the recited functions constitute technical features which have been considered at each step of Examiner’s analysis but are determined to constitute generic computing structures executing generic computing functions previously identified by the courts, as further analyzed under Step 2A prong 2 and Step 2B below.
Eligibility Step 2A prong 2: (See MPEP 2106.04(d)): Under step 2A prong two, Examiners are to consider additional elements recited in the claim beyond the judicial exception and evaluate whether those additional elements integrate the exception into a practical application. Further, to be considered a recitation of an element which integrates the judicial exception into a practical application, the additional elements must apply, rely on, or use the judicial exception in a manner that imposes meaningful limits on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
Additional elements of claim 1 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject matter include: “computing system” and “data sources”. Claim 1 further indicates, generally, that the claimed method is “for technologically improving a computer-implemented machine-learning model” as designated in the preamble. Claim 1 further specifies:
“…the one or more data sources comprising data structures including separate frequency tables adaptively maintained and updated based on associated feature variables to the model, wherein a first frequency table is implemented in a first queue populated by data records identified as belonging to a first classification and a second frequency table is implement as a second queue populated by data records identified as belonging to a second classification…” and “…generating a second score, based on the data records in the first frequency table and not considering the data records in the second frequency table to avoid a full table processing of data records belonging to both the first classification and the second classification…”
As presented by amendment, claim 1 further specifies:
“…the first frequency table and the second frequency table being respectively updated based on counts of data records identified as belonging to the first classification or the second classification, wherein responsive to the first queue or the second queue being full, a history of most recent data records added is maintained by removing an earlier added data record from a respective one of the first queue or the second queue to make room for a newly added data record thereby speeding up feature variable identification, and wherein counts of record are divided into separate individual bins;…”
With respect to these potential additional elements, the claimed “computing system” is identified as implementing the model. The claimed “data sources” are identified as communicating feedback data with the computing system and are further arranged into first and second frequency tables. The amended limitation(s) clarify aspects of the data sources further indicate that first and second classifications of data records are stored in separate queues and added and removed on a FIFO basis and data is updated based on counts which are stored in bins.
The above noted limitations added by amendment serve to clarify aspects of the data sources further indicate that first and second classifications of data records are stored in separate queues and added and removed on a FIFO basis and data is updated based on counts which are stored in bins. While the claim includes a general statement that the FIFO operated queues speed up feature variable selection and, generally, that selectively accessing data from separate stored data avoids having to generate a score using the entirety of the data, the recitations in present form merely describe the data records and the data tables and indicate that counts and data are stored in tables and bins, respectively. The indication that data records, data tables, and counts are stored in tables and bins fails to tie any particular functions to the inventive method, but rather amount to general storage of data records in memory generic structures.
As presented, the function(s) reasonably attributable to the claimed “computing system” are limited to receiving or transmitting data or information via a network (e.g., data from data sources), storing and retrieving information from a memory (e.g., data and models from generic storage partitions, e.g., tables, bins, and queues) and performing tasks that are otherwise performable in the human mind (e.g., comparing scores to threshold measures and determining a likelihood). The claimed executing a mathematical model to generate a score and comparing the score to known data to assess a fraud risk associated with an event benefit from the inherent efficiencies gained by data transmission, data storage, and information display capacities of generic computing devices, but fails to present an additional element(s) which practical integrates the judicial exception into a practical application of the judicial exception (See MPEP 2106.05(f)).
Each of the above noted limitations states a result (e.g., information is received by a model, scores are calculated using the model, scores are applied to indicate a likelihood of a fraud event etc.) as associated with a respective “computing system” or “data source”. Beyond the general statement that the model is implemented on a computing system and data sources communicate feedback data, the limitations provide no further clarification with respect to the functions performed by the “computing system/model” and “data sources” in producing the claimed result. A recitation of “implemented on” or “by a computing system or data source”, absent clarification of particular processing steps executed by the underlying technology to produce the result are reasonably understood to be an equivalent of “apply it”. The technology as engaged is solely identified as storing and retrieving information, performing tasks that are otherwise performable in the human mind (e.g., comparing scores to threshold measures and determining a likelihood of fraud based on the comparisons), and sending and receiving information over a network. (See MPEP 2106.05(f)).
Accordingly, claim 1 is reasonably understood to be conducting standard, and formally manually performed process of executing a mathematical model to generate a score and comparing the score to known data to assess a fraud risk associated with an event using the generic devices as tools to perform the abstract idea. The identified functions of the recited additional elements reasonably constitute a general linking of the abstract idea to a generic technological environment, e.g., generic devices capable of storing and retrieving information from memory and transmitting data or information over a computer network. The claimed executing a mathematical model to generate a score and comparing the score to known data to assess a fraud risk associated with an event benefit from the inherent efficiencies gained by data transmission, data storage, and information display capacities of generic computing devices, but fails to present an additional element(s) which practical integrates the judicial exception into a practical application of the judicial exception.
Eligibility Step 2B: (See MPEP 2106.05): Analysis under step 2B is further subject to the Revised Examination Procedure responsive to the Subject Matter Eligibility Decision in Berkheimer v. HP, Inc. issued by the United States Patent and Trademark Office (19 April 2018). Examiner respectfully submits that the recited uses of the underlying computer technology constitute well-known, routine, and conventional uses of generic computers operating in a network environment. In support of Examiner’s conclusion that the recited functions/role of the computer as presented in the present form of the claims constitutes known and conventional uses of generic computing technology, Examiner provides the following:
In reference to the Specification as originally filed, Examiner notes paragraphs [0083]-[0090]. In the noted disclosure, the Specification provides listings of generic computing systems, e.g., a general computing platform including exemplary servers, network configurations and various processor configuration which are identified as capable and interchangeable for performing the disclosed processes. The disclosure does not identify any particular modifications to the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that this disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
While the above noted disclosure serves to provide sufficient explanation of technical elements required to perform the inventive method using available computing technology, the disclosure does not appear to identify any particular modifications or inventive configurations of the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that the disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Further, absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
The claims specify that the above identified generic computing structures and associated functions/routines include: a “computing system” identified as implementing a model and “data sources”. Claim 1 further indicates, generally, that the claimed method is “for technologically improving a computer-implemented machine-learning model” as designated in the preamble. With respect to the recited data sources, claim 1 further specifies:
“…the one or more data sources comprising data structures including separate frequency tables adaptively maintained and updated based on associated feature variables to the model, wherein a first frequency table is implemented in a first queue populated by data records identified as belonging to a first classification and a second frequency table is implement as a second queue populated by data records identified as belonging to a second classification…” and “…generating a second score, based on the data records in the first frequency table and not considering the data records in the second frequency table to avoid a full table processing of data records belonging to both the first classification and the second classification…”
As presented by amendment, claim 1 further specifies:
“…the first frequency table and the second frequency table being respectively updated based on counts of data records identified as belonging to the first classification or the second classification, wherein responsive to the first queue or the second queue being full, a history of most recent data records added is maintained by removing an earlier added data record from a respective one of the first queue or the second queue to make room for a newly added data record thereby speeding up feature variable identification, and wherein counts of record are divided into separate individual bins;…”
With respect to these potential additional elements, as noted, the claimed “computing system” is identified as implementing the model. The claimed “data sources” are identified as communicating feedback data with the computing system and are further arranged into first and second frequency tables. The amended limitation(s) clarify aspects of the data sources further indicate that first and second classifications of data records are stored in separate queues and added and removed on a FIFO basis and data is updated based on counts which are stored in bins. The amendments merely describe locations/structures in which the designated data records are stored but fails to link the separate storage partitions to any functions performed by the system other than to access records stored in computer memory in a FIFO manner.
The claims specify that the above identified generic computing structures and associated functions/routines include: (1) receive and store data records; (2) generated score using a model applying defined mathematical formulae; (3) comparing data to classifications and thresholds; (4) receive feedback data; and (5) apply the scores to indicate a likelihood of a fraud event. While Examiner acknowledges that the noted limitations are computer-implemented, Examiner respectfully submits that, in aggregate (e.g., “as a whole”) they do not amount to significantly more than the abstract idea/ineligible subject matter to which the claimed invention is primarily directed.
While utilizing a computer, the claimed invention is not rooted in computer technology nor does it improve the performance of the underlying computer technology. The computer-implemented features of the claimed invention noted above are reasonably limited to: (1) receiving and sending data via a computer network (e.g., data records and feedback data); (2) storing and retrieving information and data from a generic computer memory (e.g., data records stored in tables, bins and queues and models and feedback data stored in memory); and (3) performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., generating scores, comparing scores to threshold measures, and determining a likelihood).
The above listed computer-implemented functions are distinguished from the generic data storage, retrieval, transmission, and data manipulation/processing capacities of the generic systems identified in the Specification solely by the recited identification of particular data elements that are of utility to a user performing the specific method of executing a mathematical model to generate a score and comparing the score to known data to assess a fraud risk associated with an event. In summary, the computer of the instant invention is facilitating non-technical aims, i.e., executing a mathematical model to generate a score and comparing the score to known data to assess a fraud risk associated with an event, because it has been programmed to store, retrieve, and transmit specific data elements and/or instructions that is/are of utility to the user. The non-technical functions of executing a mathematical model to generate a score and comparing the score to known data to assess a fraud risk associated with an event benefit from the use of computer technology, but fail to improve the underlying technology.
In support, the courts have previously found that utilization of a computer to receive or transmit data and communications over a network and/or employing generic computer memory and processor capacities store and retrieve information from a computer memory are insufficient computer-implemented functions to establish that an otherwise unpatentable judicial exception (e.g. abstract idea) is patent eligible. With respect to the determinations of the Courts regarding using a computer for sending and receiving data or information over a computer network and storing and retrieving information from computer memory, see at least: 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; sending messages over a network OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); receiving and sending information 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); storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 and see performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; and Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) with respect to the performance of repetitive calculations does not impose meaningful limits on the scope of the claims.
Independent claims 11 and 17, directed to an apparatus/system and computer-executable instructions stored on computer-readable media for performing the method steps are rejected for substantially the same reasons, in that the generically recited computer components in the apparatus/system and computer readable media claims add nothing of substance to the underlying abstract idea.
Dependent claim 2 has been amended to specify that “…counts of records stored in the separate individual bins...”. The indication that the counts of records are stored in bins is limited to a general operation of storing and retrieving information in computer memory.
Dependent claim 3 has been amended to specify that “…a fraud tag is generated based on the blended score and assigned to the first record…”. The claimed fraud tag is reasonably understood to be a label assigned to a record to identify the record as potentially indicative of fraud and further storing the record. Accordingly, this is reasonably understood to be an addition al process performable by mental processing, i.e., observing/comparing a score to determine potential fraud and general storage of data records in computer memory. Claim 3 has been amended to further include “…the learning model updating respective scoring parameters to more accurately detect fraudulent activity…”. This limitation is reasonably understood to constitute a step/function limited to performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., generating scores and comparing scores to threshold measures, and adjusting mathematical scoring formula based on the observations) (See rejection under 35 U.S.C. 112(b) above).
Newly added claim 21 further specifies, “…wherein probabilities of the second data record associated with the first classification are computed, the first data record being compared with at least the second data record, wherein the second likelihood that the first data record is associated with the first classification is computed based on results of comparing of the first data record with at least the second data record; and wherein a second likelihood is combined with probabilities of at least a second data record associated with the first classification to calculate marginal probabilities of a first data record associated with the first classification...”. The newly added claim presents additional steps limited to performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., generating scores, comparing scores to threshold measures, and determining a likelihood).
Dependent claims 2-10, 12-16, 18-19 and 21, when analyzed as a whole are held to be ineligible subject matter and are rejected under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claimed invention is not directed to an abstract idea.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
In accordance with all relevant considerations and aligned with previous findings of the courts, the technical elements imparted on the method that would potentially provide a basis for meeting a “significantly more” threshold for establishing patent eligibility for an otherwise abstract concept by the use of computer technology fail to amount to significantly more than the abstract idea itself. For further guidance and authority, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al. 573 U.S.____ (2014)) (See MPEP 2106).
Allowable Subject Matter
[6] Claims 1-19 and 21 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) and under 35 U.S.C. 101 as set forth in this Office action.
Conclusion
[7] The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Herbrich et al., EVENT PREDICTION, United States Patent Application Publication No. 2009/0046593, paragraphs [0037]-[0043]: Relevant Teachings: Herbrich discloses a system/method that applies statistical models to determine probability of credit card fraud.
Lewis et al., METHOD AND APPARATUS FOR EVALUATING FRAUD RISK IN AN ELECTRONIC COMMERCE TRANSACTION, United States Patent Application Publication No. 2008/0140576 paragraphs [0052]-[0054]: Relevant Teachings: Lewis discloses a system/method that provides models to assess fraud risk associated with financial transactions.
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.
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/ROBERT D RINES/Primary Examiner, Art Unit 3625
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