DETAILED ACTION
Notice of Pre-AIA or AIA Status
[1] The present application is being examined under the pre-AIA first to invent provisions.
Continued Examination Under 37 CFR 1.114
[2] 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1 April 2026 has been entered.
Notice to Applicant
[3] This communication is in response to the Amendment and the Request for Continued Examination (RCE) filed 1 April 2026. 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, 10-11, 17, and 19 have been amended. Claims 1-19 and 21 are pending.
Response to Remarks/Amendment
[4] Applicant's remarks filed 1 April 2026 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 and 21 under 35 U.S.C. 101 as being directed to non-statutory subject matter as set forth in the previous Office Action mailed 2 December 2025, Applicant provides the following remarks:
"… under prong two of the 2019 guidance, the claims are integrated into a practical application because they impose meaningful limits that are practical and result in improvements to a computing system as claimed… in the pending claims, utilizing separate frequency tables, maintained and updated based on associated feature variables to the model, improves the performance and efficiency of the claimed computer-implemented system. As explained in the specification and as recited in the claims, these features eliminate the need for full fraud and non-fraud table processing and make the fraud detection faster and more efficient. As such, the claimed technological advances in the pending claims result in improvements in the computer-implemented learning model by eliminating the need for all data records to be processed for the purpose of fraud detection…”
Applicant further remarks:
“…the pending claims are directed to a specific technological improvement in the operation of a computer-implemented fraud-detection/machine-learning model, not an abstract idea. The claims recite concrete computing structures-separate frequency tables implemented in separate queues (e.g., FIFO) for different classifications/fraud types, which are adaptively maintained and updated based on feature-variable counts, and which remove least-recent records when full to maintain a bounded ‘most recent history’…even if any claimed scoring aspect is to be construed as mathematical, the claims integrate the math-based features into a practical application and recite significantly more by improving computational efficiency and predictive performance. For example, the second score is generated using the maintained frequency tables without processing all data records, thereby avoiding full-table processing. This reduces computation overhead. As such, the claimed advance is not purely mathematical, rather it is directed to a specific computer-implemented mechanism that solves a computer-centric problem: how to adaptively score streaming records in real time without unbounded memory growth or repeated full-dataset re-computation. The claims solve that problem by maintaining bounded FIFO queues with separate frequency tables that are updated from feedback and used to generate the second score without processing all data records, then blending scores to improve predictive operation…”
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 as presented by amendment specifies:
“…the one or more data sources comprising data structures including separate frequency tables adaptively maintained and updated based on associated feature variables for one or more data records added to the model, wherein a first frequency table is implemented in a first queue populated by data records identified as belonging to the first classification, and a second frequency table is implemented as a second queue populated by data records identified as belonging to a second classification associated with a second fraud type…”
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 added to the model and 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…”
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. While Applicant contends 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 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)).
Applicant further remarks:
“…Similarly, in Enfish, LLC v. Microsoft Corp, claims at issue were deemed patent-eligible because the particularly claimed non-hardware computing elements (i.e., self-referential tables) improved the way that claimed method and system operated and processed data records. The recited frequency tables in the pending claims improve the processing of data records for fraud detection, similar to Enfish's claimed self-referential tables that allowed for more efficient processing of records needed for launching and adaptation of databases… "
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 (e.g., FIFO queues) so that only selected data records (e.g., records identified as fraud-type) are processed. This significantly improves the predictive model's speed and accuracy…”
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 in response to previous rejection(s) of claim(s) 1-19 and 21 under 35 U.S.C. 101 as being directed to non-statutory subject matter are reasonably considered to have been fully addressed in the context of the revised rejection of the claims responsive to the amendments to the subject claims and in consideration of the subject matter eligibility framework 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). Additionally, Applicant substantially rehashes arguments previously presented in the prior response. These arguments are addressed in accordance with Examiner’s response in the prior Office Action(s) mailed 2 December 2025 and 21 May 2025, incorporated in their entirety in 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] Claims 1-19 and 21 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without significantly more.
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). 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 the abstract idea 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, which is reasonably considered to be an inventive concept directed to ineligible Mathematical Concepts (e.g., mathematical relationships, mathematical formulas or equations, and mathematical calculations) and ineligible processes/functions which are performable by Human Mental Processing (e.g., concepts performed or performable in the human mind including observations, evaluations, judgements, or opinions).
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)).
With respect to functions/steps limited to Mathematical Concepts, as presented by amendment, representative claim 1 recites:
“…generating a first score representing a first likelihood that the first data record is associated with a first classification, and wherein the first classification is associated with a first fraud type, in response to feedback received from one or more data sources…generating a second score, based on the data records in the first frequency table and the second frequency table without processing all data records belonging to both the first classification and the second classification due to removal of least recent data records from the first frequency table and the second frequency table, wherein the second score represents a second likelihood that the first data record is associated with a fraud type; and applying a blend of the first score and the second score to determine with higher predictability whether the first record is fraudulent…”
Considered in light of the supportive disclosure at paragraph [0033]-[0037], the claimed generating scores and a blend of scores are reasonable understood to be performed by the disclosed scoring formulas and mathematical processes in the noted sections of the Specification as originally filed.
Further limitations are directed to ineligible processes/functions which are performable by Human Mental Processing and/or or by a human using pen and paper (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011). The courts have previously identified subject matter limited to 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, 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 processes performable by Human Mental Processing and/or by a human using pen and paper, as presented by amendment, representative claim 1 recites:
“…receiving…at least a first data record; the one or more data sources…including separate frequency tables adaptively maintained and updated based on associated feature variables for one or more data records added…, wherein a first frequency table is implemented in a first queue populated by data records identified as belonging to the first classification, and a second frequency table is implemented as a second queue populated by data records identified as belonging to a second classification associated with a second fraud type, the first frequency table and the second frequency table being respectively updated based on counts of data records added to the model and 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…”
Respectfully, absent further clarification of the processing steps executed by the recited computer, processors, and executable code, one of ordinary skill in the art, given scores and applicable thresholds/comparisons 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)
Claims 1, 11, and 17 recite technical elements 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 technical elements of claim 1 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject are limited to: “computer”, “data sources”, “frequency tables”, and “queues”. Claim 1 further indicates, generally, that the claimed method is directed to improving a “computer-implemented model” as designated in the preamble. Claims 11 and 17, directed to a computer-readable medium and a computer-implemented system introduce a “processor” and processor-executable “instructions” as engaged in a general manner in the performance of each of the recited steps/functions. With respect to these potential additional elements:
(1) The “computer”, “processor”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “data sources” are identified as comprising data structures which include the recited “frequency tables”.
(3) The “frequency tables” are identified as being implemented by the recited first and second “queues”.
With respect to the indication 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, 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 the tables are implemented as queues. The indication that data records, data tables, and counts are stored in tables and implemented in a queue or list fails to tie any particular functions to the inventive method, but rather amount to general storage of data records in memory generic structures.
With respect to the above noted functions attributable to the identified additional elements, MPEP 2106.05 stipulates that: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f); and/or Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) serve as indications that the use of the technology recited does not indicate integration into a practical application of the judicial exception.
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:
(1) The “computer”, “processor”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “data sources” are identified as comprising data structures which include the recited “frequency tables”.
(3) The “frequency tables” are identified as being implemented by the recited first and second “queues”.
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: 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.
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 specifies 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 specifies 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. 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).
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...”. Claim 21 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.
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. 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.
Cited NON-PATENT Literature:
Quah et al.: Quah et al., Real Time Credit Card Fraud Detection using Computational Intelligence, 2007-08-01, 2007 International Joint Conference on Neural Networks (2007, Page(s): 863-868): Relevant Teachings: Quah discloses a system/method that provides an overview of fraud detection techniques as applied to online financial transactions. The publication establishes at least that computational modelling using trained neural networks provides an effective and economic mechanism for detecting fraud associated with e-commerce financial transactions as common practice in the art.
Cited PATENT Literature:
Gavan et al., System And Method For Detecting And Managing Fraud, United States Patent No. 6,601,048, column(s) 3 and 4: Relevant Teachings: Gavan discloses a system/method that provides functionality to calculate a probability of fraud associated with credit card and debit card usage. The inventive system used AI pattern recognition to model potential fraud probabilities based on transaction patterns.
Baker et al., System And Method Of Detecting Fraud, United States Patent Application Publication No. 2007/0106582, paragraphs [0021]-[0024]: Relevant Teachings: Baker discloses a system/method that includes steps/functions including scoring of fraud risk using a model executed by a programmatic scorer.
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/ROBERT D RINES/Primary Examiner, Art Unit 3625
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