DETAILED ACTION
Response to Amendment
This action is in response to the response to the amendment filed on 01/30/2026. Claims 21, 22, 24-32, 34-39, and 41-43 have been amended. Claims 21, 22, 24-32, 34-39, and 41-43 are pending and currently under consideration for patentability.
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 .
Inventorship
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
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 21, 22, 24-32, 34-39, and 41-43 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Step 1: In a test for patent subject matter eligibility, claims 21, 22, 24-32, 34-39, and 41-43 are found to be in accordance with Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter. Claims 21, 22, 24-30, 41-43 recite a method, claims 31, 32, 34-37 recite a system, and claims 38, 39 recite a computer-readable media. When assessed under Step 2A, Prong I, they are found to be directed towards an abstract idea. The rationale for this finding is explained below:
Step 2A, Prong I: Under Step 2A, Prong I, claims 21, 31, and 38 are directed to an abstract idea without significantly more, as they all recite a judicial exception. Independent Claims 21, 31, and 38 recite limitations directed to the abstract idea including “intercepting an authorization request for a transaction with a merchant; processing a plurality of data associated with the profile of the user to determine instances of fraudulent behaviors to predict a fraudulent behavior, wherein the instances of fraudulent behaviors includes a purchasing pattern that is an aberration from historical purchasing patterns of the user at the merchant; determining a category spend score exceeds a pre-determined score threshold for predicted fraudulent behavior, wherein the category spend score is calculated for each consumer-merchant relationship based on the historical purchasing patterns of the user at the merchant; identifying the transaction as possibly fraudulent based on determining that the category spend score exceeds the pre-determined score threshold; calculating a consumer value score for a user at the merchant, wherein the consumer value score determines whether the user is a high value consumer or a low value consumer to a merchant based on a historical purchasing pattern between the user and the merchant, wherein the score calculation processor calculates a customized consumer value score for each consumer-merchant relationship; determining the consumer value score exceeds a threshold to indicate the user is a high value consumer to the merchant and the transaction should be authorized regardless of potential fraudulent behavior based on the user being a high value consumer to the merchant; and in response to determining the consumer value score exceeds the threshold, authorizing the transaction identified as possibly fraudulent based on the user being a high value consumer to the merchant based on the consumer value score despite the transaction being identified as possibly fraudulent due to the category spend score exceeding the pre-determined score threshold.” These further limitations are not seen as any more than the judicial exception. Claims 21, 31, and 38 recite additional limitations including “via one or more processors of a fraud detection computing system / a profile computing system / a visualization module / a consumer value score platform / a score calculation processor; from a first interactive interface of a device associated with a user; and profile of the user stored in the profile database;” and “generating a profile of the user and a unique identifier hash recognizing a primary account number (PAN), personally identifiable information (PII), an analysis of spending habits of the user, location information of the user, or a fraudulent activities reports on the PAN; storing the profile of the user and the unique identifier hash in a profile database, wherein the profile is tokenized; and updating the profile with the consumer value score in the profile database, wherein the consumer value score may be continuously updated for each consumer-merchant relationship based additional transaction data to prevent false positives in identifying potentially fraudulent transactions;” and “generating a second interactive interface configured to display the consumer value score indicating a user value to a merchant.” Authorizing a transaction, identified as possibly fraudulent, based on a calculated score compared to a threshold and the predicted fraudulent behaviors is considered to be an abstract idea of certain methods of organizing human activity; such as commercial interactions, advertising, marketing, and sales and mathematical concepts; because authorizing/determining fraudulent transactions has been a well-known merchant problem and the abstract idea is merely appending this well-known merchant problem to the environment of the internet (i.e. using interface to receive input and display analysis/result based on input) and is not a problem necessarily rooted in computer technology. Authorizing a transaction, identified as possibly fraudulent, based on a calculated score compared to a threshold and the predicted fraudulent behaviors is also considered to be an abstract idea of mental processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims recite receiving data (i.e. authorization request) and determining whether a score exceeds a threshold by comparing the score to the threshold which are all concepts that can be performed by a user in their mind with pen and paper and the necessary information. Furthermore, authorizing a transaction, identified as possibly fraudulent, based on a calculated score compared to a threshold and the predicted fraudulent behaviors is also considered to be an abstract idea of Mathematical Concepts such as mathematical relationships, formulas, equations, or calculations because the claims recite – “calculating a consumer value score for a user at the merchant, wherein the consumer value score determines whether the user is a high value consumer or a low value consumer to a merchant, wherein the score calculation processor calculates a customized consumer value score for each consumer-merchant relationship.” Therefore, under Step 2A, Prong I, claims 21, 31, and 38 are directed towards an abstract idea.
Step 2A, Prong II: Step 2A, Prong II is to determine whether any claim recites any additional element that integrate the judicial exception (abstract idea) into a practical application. Claims 21, 31, and 38 recite additional limitations including “via one or more processors of a fraud detection computing system / a profile computing system / a visualization module / a consumer value score platform / a score calculation processor; from a first interactive interface of a device associated with a user; and profile of the user stored in the profile database;” and “generating a profile of the user and a unique identifier hash recognizing a primary account number (PAN), personally identifiable information (PII), an analysis of spending habits of the user, location information of the user, or a fraudulent activities reports on the PAN; storing the profile of the user and the unique identifier hash in a profile database, wherein the profile is tokenized; and updating the profile with the consumer value score in the profile database, wherein the consumer value score may be continuously updated for each consumer-merchant relationship based additional transaction data to prevent false positives in identifying potentially fraudulent transactions;” and “generating a second interactive interface configured to display the consumer value score indicating a user value to a merchant.” Claims 21, 31, and 38 reciting “via one or more processors of a fraud detection computing system / a profile computing system / a visualization module / a consumer value score platform / a score calculation processor; from a first interactive interface of a device associated with a user; and profile of the user stored in the profile database;” are not found to integrate the judicial exception into a practical application. Merely reciting the processor and interface in this manner is seen as 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). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use, i.e. processor/device/database, and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Under Step 2A, Prong II, these claims remain directed towards an abstract idea.
Step 2B: Claims 21, 31, and 38 recite additional limitations including “via one or more processors of a fraud detection computing system / a profile computing system / a visualization module / a consumer value score platform / a score calculation processor; from a first interactive interface of a device associated with a user; and profile of the user stored in the profile database;” and “generating a profile of the user and a unique identifier hash recognizing a primary account number (PAN), personally identifiable information (PII), an analysis of spending habits of the user, location information of the user, or a fraudulent activities reports on the PAN; storing the profile of the user and the unique identifier hash in a profile database, wherein the profile is tokenized; and updating the profile with the consumer value score in the profile database, wherein the consumer value score may be continuously updated for each consumer-merchant relationship based additional transaction data to prevent false positives in identifying potentially fraudulent transactions;” and “generating a second interactive interface configured to display the consumer value score indicating a user value to a merchant.” Claims 21, 31, and 38 reciting the following additional elements “via one or more processors of a fraud detection computing system / a profile computing system / a visualization module / a consumer value score platform / a score calculation processor; from a first interactive interface of a device associated with a user; and profile of the user stored in the profile database;” do not integrate the judicial exception (abstract idea) into a practical application because of the analysis provided in Step 2A, Prong II. Claims 21, 31, and 38 recite the limitation - “generating a profile of the user and a unique identifier hash recognizing a primary account number (PAN), personally identifiable information (PII), an analysis of spending habits of the user, location information of the user, or a fraudulent activities reports on the PAN; storing the profile of the user and the unique identifier hash in a profile database, wherein the profile is tokenized; and updating the profile with the consumer value score in the profile database, wherein the consumer value score may be continuously updated for each consumer-merchant relationship based additional transaction data to prevent false positives in identifying potentially fraudulent transactions;” and “generating a second interactive interface configured to display the consumer value score indicating a user value to a merchant.” However, these additional elements or a combination of elements do not result in the claims amounting to significantly more than the judicial exception because they are not indicative of integration into a practical application:
With respect to the claim limitation; “generating a profile of the user and a unique identifier hash recognizing a primary account number (PAN), personally identifiable information (PII), an analysis of spending habits of the user, location information of the user, or a fraudulent activities reports on the PAN; storing the profile of the user and the unique identifier hash in a profile database, wherein the profile is tokenized; and updating the profile with the consumer value score in the profile database, wherein the consumer value score may be continuously updated for each consumer-merchant relationship based additional transaction data to prevent false positives in identifying potentially fraudulent transactions.” Merely, generating a profile (i.e. fraud detection profile) and storing the profile and associated data (i.e. profile and corresponding consumer value score) in memory (i.e. profile database) in order to retrieve information (via unique hash identifier), wherein the memory is continuously updated does not integrate the claims into a practical application because this is seen as a well-understood, routine, and conventional computer function (See: 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). Furthermore, ¶ [0145] of U.S. Publication 2002/0032718 to Yates teaches that identifying a profile via hash table or identifier is conventional and known in the art (“The server 36 makes use of a software cache and hash table (not shown) to keep track of the profile records. For an address which need to be looked up the address is looked up in the cache in 4 different locations that is by using a four way associative cache (not shown). If the address is not there it is looked up in a conventional hash table. The information in the hash table is the count values for the fields.”). As discussed above with respect to integration of the abstract idea into a practical application, the additional elements listed amount to no more than mere instructions to apply an exception using a generic computer component.
With respect to the claim limitation; “generating a second interactive interface configured to display the consumer value score indicating a user value to a merchant.” Merely reciting generating an interface that displays results (i.e. consumer value scores) is well-understood, routine, and conventional in the art since 1985 (See Wikipeida: User Interface – “In 1985, with the beginning of Microsoft Windows and other graphical user interfaces, IBM created what is called the Systems Application Architecture (SAA) standard which include the Common User Access (CUA) derivative.”) (See also: Richard, Stéphane. "Text User Interface Development Series Part One – T.U.I. Basics". Archived from the original on 16 November 2014. Retrieved 13 June 2014.) Therefore, receiving authorization request, displaying options for selection of category of products with each category corresponding to a spend score, and displaying updated purchase behavior associated with selection of products and corresponding scores via user interface of the device is seen as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) and generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). As discussed above with respect to integration of the abstract idea into a practical application, the additional elements listed amount to no more than mere instructions to apply an exception using a generic computer component.
In addition, the applicant’s specifications describe “general-purpose CPU” elements, ¶ [0067], for implementing the computer system, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Under Step 2B in a test for patent subject matter eligibility, these claims are not patent eligible.
Dependent claims 22, 24-30, 41-43, Dependent claims 31, 32, 34-37, and Dependent claim 39 further recite the method, system, and computer-readable medium of independent claims 21, 31, and 38, respectively. Dependent claims 22, 24-30, 32, 34-37, 39, and 41-43 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation fail to establish that the claims are not directed to an abstract idea.
Under Step 2A, Prong I, these additional claims only further narrow the abstract idea set forth in claims 21, 31, and 38. For example, dependent claims 22, 24-30, 32, 34-37, 39, and 41-43 further describe the calculating a score of a user based on a plurality of data associated with the user in order to authorize a potential fraudulent transaction – which is only further narrowing the scope of the abstract idea recited in the independent claims.
Under Step 2A, Prong II, for dependent claims 22, 24-30, 32, 34-37, 39, and 41-43, there are no additional elements introduced. Thus, they do not present integration into a practical application, or amount to significantly more.
Under Step 2B, the dependent claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
Allowable Subject Matter
Claims 21, 22, 24-32, 34-39, and 41-43 allowable over the prior art:
The Applicant has provided arguments on 01/30/2025; “First, the Office Action appears to rarely on Chetal’s “wasteful spending engine” as corresponding to “calculating, via one or more processors of a score calculation processor, a consumer value score, wherein the consumer value score determines whether the user is a high value consumer or a low value consumer to a merchant based on a historical purchasing pattern between the user and the merchant,” “determining, via the one or more processors of the consumer value score platform, the consumer value score exceeds a threshold to indicate the user is a high value consumer to the merchant and the transaction should be authorized regardless of potential fraudulent behavior based on the high consumer value to the merchant.” (Office Action, pp. 51-53.) Specifically, the Office Action states, “the Chetal reference teaches determining that the spending score is above a threshold then determining a combined/overall spend score and determining that the overall/combined spend score exceeds or is below a threshold indicating the consumer is at risk for financial waste or is of high importance/value to the provider.” (Office Action, p. 53.) However, Chetal states the wasteful spending engine “may indicat[e] that the consumer is at-risk for, or committing, financial waste.” (Chetal, ¶ [0068].) Chetal states the system “may detect if a consumer's transactions are wasteful (i.e., sending more money than is necessary, and/or conduction transactions against company policy causing financial waste.)” (Chetal, ¶ [0058.) Chetal states, “wasteful spending engine 158 may be configured to monitor and/or evaluate transactions, such as travel expenses, to determine if a consumer (e.g., an employee of the company) is engaging in wasteful transactions (i.e., unnecessary transaction that are against company policy.)’ (Chetal, ¶ [0058] (emphasis added).) Further, Chetal describes, “the compliance scores from payment risk engine 152 (delinquent risk score 250), noncompliance engine 156 (consumer-level and/or transaction-level noncompliance score 350), and/or wasteful spending engine 158 (combined spending score 450) may be used in method 800 to produce an overall compliance score.” (Chetal, ¶ [0071].) Chetal states that individual compliance scores may be weighted because “one compliance score may be a stronger indicator of compliance with company policy by a consumer.” (Chetal, ¶ [0071].) Chetal describes a system designed to analyze transaction to determine how employees of a company spend company money on things like air, travel, food and beverage, etc. and compare the spending with company policy. However, this does not disclose, “calculating, via one or more processors of a score calculation processor, a consumer value score, wherein the consumer value score determines whether the user is a high value consumer or a low value consumer to a merchant based on a historical purchasing pattern between the user and the merchant,” “determining, via the one or more processors of the consumer value score platform, the consumer value score exceeds a threshold to indicate the user is a high value consumer _to the merchant and the transaction should be authorized regardless of potential fraudulent behavior based on the high consumer value to the merchant,” “in response to determining the consumer value score exceeds the threshold, authorizing, via the one or more processors of the consumer value platform, the transaction based on the user being a high value consumer to the merchant based on the consumer value score despite the calculated spend score exceeding the pre-determined score threshold,” as recited by amended claim 21. (Emphasis added.) Second, the Office Action appears to rely on the spending type user interface described in Chetal as corresponding to “generating, via the one or more processors utilizing the visualization module, a third interactive interface of a purchase behavior during a specific timeframe comprising the selection of the array of product categories, the corresponding category spend scores, and the consumer value score indicating a user value to a merchant,” as recited by amended claim 21. (Office Action, pp. 53- 55.) However, this does not disclose “generating, via the one or more processors utilizing the visualization module, a third interactive interface of a purchase behavior during a specific timeframe comprising the selection of the array of product categories, the corresponding category spend scores, and the consumer value score indicating a user value to a merchant,” as recited by amended claim 21.”
The Examiner agrees and would also like to note that the independent claims, as filed of 01/30/2025, recite – “calculating, via one or more processors of a score calculation processor, a consumer value score for a user at the merchant, wherein the consumer value score determines whether the user is a high value consumer or a low value consumer to a merchant based on a historical purchasing pattern between the user and the merchant, wherein the score calculation processor calculates a customized consumer value score for each consumer-merchant relationship; determining, via the one or more processors of the consumer value score platform, the consumer value score exceeds a threshold to indicate the user is a high value consumer to the merchant and the transaction should be authorized regardless of potential fraudulent behavior based on the user being a high value consumer to the merchant; in response to determining the consumer value score exceeds the threshold, authorizing, via the one or more processors of the consumer value score platform, the transaction identified as possibly fraudulent based on the user being a high value consumer to the merchant based on the consumer value score despite the transaction being identified as possibly fraudulent due to the category spend score exceeding the pre-determined score threshold; and generating, via the one or more processors utilizing a visualization module, a second interactive interface configured to display the consumer value score indicating a user value to a merchant.” The Chetal reference at most discloses authorizing transactions for consumers or employees despite being indicated as being high risk for fraudulent transactions against company policy but does not disclose determining whether the user is a high value consumer or a low value consumer to a merchant based on a historical purchasing pattern between the user and the merchant. The Chetal, Winters, Ranft, and Das references do not disclose in response to determining the consumer value score exceeds the threshold, authorizing, via the one or more processors of the consumer value platform, the transaction based on the user being a high value consumer to the merchant based on the consumer value score despite the calculated spend score exceeding the pre-determined score threshold. Therefore, the rejection(s) of claim(s) 21, 22, 24-32, 34-39, and 41-43 under 35 U.S.C. § 103 has been previously withdrawn.
Response to Arguments
Applicant’s arguments see pages 16-21 of the Remarks disclosed, filed on 01/30/2026, with respect to the 35 U.S.C. § 101 rejection(s) of claim(s) 21, 22, 24-32, 34-39, and 41-43 have been considered but are not persuasive. The Applicant asserts “Here, the Specification recites an improvement to the functioning of a computer, or an improvement to other technology or technical field by advancing technologies to accurately and securely process electronic transactions. For example, creating customized category spend scores and consumer value score allows fraud detection technology to be personalized to apply consumer and merchant specific data to each transaction analysis which more accurately determines whether fraudulent behavior is likely or has occurred…Here, the amended claims recite this improvement to processing and security in in electronic transaction processing systems in at least, a "determining, via the one or more processors of a consumer value score platform, a category spend score exceeds a pre-determined score threshold for predicted fraudulent behavior, wherein the category spend score is calculated for each consumer-merchant relationship based on the historical purchasing patterns of the user at the merchant," "calculating, via one or more processors of a score calculation processor, a consumer value score for a user at the merchant, wherein the consumer value score determines whether the user is a high value consumer or a low value consumer to a merchant based on a historical purchasing pattern between the user and the merchant, wherein the score calculation processor calculates a customized consumer value score for each consumer-merchant relationship, and "updating, via the one or more processors of the consumer value score platform, the profile with the consumer value score in the profile database, wherein the consumer value score may be continuously updated for each consumer-merchant relationship based additional transaction data to prevent false positives in identifying potentially fraudulent transactions." (Emphases added.) These features are not certain methods of organizing human activity and instead are additional elements that must be considered for "integration into practical application." (MPEP § 2106.04(d)).” The Examiner respectfully disagrees:
“Accurately and securely process electronic transactions” by “creating customized category spend scores and consumer value score allows fraud detection technology to be personalized to apply consumer and merchant specific data to each transaction analysis which more accurately determines whether fraudulent behavior is likely or has occurred” further recites the abstract idea of certain methods of organizing human activity; such as commercial interactions, advertising, marketing, and sales and mathematical concepts; because authorizing/determining fraudulent transactions has been a well-known merchant problem and the abstract idea is merely appending this well-known merchant problem to the environment of the internet (i.e. using interface to receive input and display analysis/result based on input) and is not a problem necessarily rooted in computer technology.
Furthermore, the claim limitation; “determining, via the one or more processors of a consumer value score platform, a category spend score exceeds a pre-determined score threshold for predicted fraudulent behavior, wherein the category spend score is calculated for each consumer-merchant relationship based on the historical purchasing patterns of the user at the merchant,” is directed to an abstract idea of certain methods of organizing human activity; such as commercial interactions, advertising, marketing, and sales and mathematical concepts; because authorizing/determining fraudulent transactions has been a well-known merchant problem and the abstract idea is merely appending this well-known merchant problem to the environment of the internet (i.e. using interface to receive input and display analysis/result based on input) and is not a problem necessarily rooted in computer technology and is also directed to an abstract idea of mental processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims recite determining whether a score exceeds a threshold by comparing the score to the threshold which are all concepts that can be performed by a user in their mind with pen and paper and the necessary information. The claim limitation; “calculating, via one or more processors of a score calculation processor, a consumer value score for a user at the merchant, wherein the consumer value score determines whether the user is a high value consumer or a low value consumer to a merchant based on a historical purchasing pattern between the user and the merchant, wherein the score calculation processor calculates a customized consumer value score for each consumer-merchant relationship,” is directed to an abstract idea of Mathematical Concepts such as mathematical relationships, formulas, equations, or calculations because the claims recite “calculating a consumer value score”.
The claim limitation; “updating, via the one or more processors of the consumer value score platform, the profile with the consumer value score in the profile database, wherein the consumer value score may be continuously updated for each consumer-merchant relationship based additional transaction data to prevent false positives in identifying potentially fraudulent transactions” was analyzed under step 2B as an additional limitation that does not integrate the claims into a practical application. Merely, generating a profile (i.e. fraud detection profile) and storing the profile and associated data (i.e. profile and corresponding consumer value score) in memory (i.e. profile database) in order to retrieve information (via unique hash identifier), wherein the memory is continuously updated does not integrate the claims into a practical application because this is seen as a well-understood, routine, and conventional computer function (See: 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). Furthermore, ¶ [0145] of U.S. Publication 2002/0032718 to Yates teaches that identifying a profile via hash table or identifier is conventional and known in the art (“The server 36 makes use of a software cache and hash table (not shown) to keep track of the profile records. For an address which need to be looked up the address is looked up in the cache in 4 different locations that is by using a four way associative cache (not shown). If the address is not there it is looked up in a conventional hash table. The information in the hash table is the count values for the fields.”). As discussed above with respect to integration of the abstract idea into a practical application, the additional elements listed amount to no more than mere instructions to apply an exception using a generic computer component. Therefore, the rejection(s) of claim(s) 21, 22, 24-32, 34-39, and 41-43 under 35 U.S.C. § 101 is maintained above with an updated analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The following reference are cited to further show the state of the art:
U.S. Publication 2019/0287182 to Chetal for disclosure of the system may be configured to perform operations including receiving a transaction history for a consumer having transaction information associated with a plurality of transactions; detecting within the transaction information for each transaction a characteristic, resulting in a plurality of characteristics; calculating a respective value associated with each characteristic, wherein the respective value is at least one of a number or percentage of transactions having the characteristic; assigning a respective weight to each characteristic, producing an assigned respective weight for each characteristic; applying the assigned respective weight to the respective value associated with each characteristic to produce a respective weighted value for each characteristic; combining the respective weighted values of the plurality of characteristics; and/or producing a compliance score in response to the combining the respective weight values.
U.S. Publication 2011/0231305 to Winters for disclosure of a system includes a transaction handler to process transactions, a data warehouse to store transaction data recording the transactions, a portal configured to determine online activity tracking data, and at least one processor coupled with the data warehouse and the portal and configured to identify, using the transaction data and the online activity tracking data, first users who have not been to a website of a first merchant within a predetermined period of time, identify a set of transactions of the first users, and determine a spending pattern in the set of transactions of the first users.
U.S. Publication 2016/0232546 to Ranft for disclosure of among other things, there is regularly received through a communication network from providers of financial products or from an aggregator or both, current information about transactions that occur in accounts of consumers of financial products that are maintained with providers of the financial products. The received current transaction information is stored in a database of information about the respective consumers. Machine learning is applied to the stored transaction information and other information about the consumers in the database to generate model profiles of transactions in accounts of corresponding categories of consumers for corresponding financial products. As current information about transactions is received, transactions that have occurred in the accounts of the consumers of the financial products are analyzed using the model profiles for the applicable categories of customers and financial products. Each of the consumers for whom transactions occurred that did not conform to the corresponding model profile is alerted through a communication network.
U.S. Publication 2022/0051294 to Das for disclosure of a system includes a network interface configured to communicate with a user device and a processing circuit. The processing circuit is configured to receive, via the network interface, a cookie identifier and a device identifier associated with the user device and identify a profile associated with at least one of the cookie identifier and the device identifier by searching a profile database with at least one of the cookie identifier and the device identifier. The processing circuit is further configured to generate an advertisement based on the profile and transmit the advertisement to the user device to be displayed. The processing circuit prompts a user of the user device to input user identification into a user identification prompt, receives user identification from the user device, and determines that the user identification is associated with the profile by matching the user identification with information stored in the profile.
U.S. Publication 2010/0317420 to Hoffberg for disclosure of a system and method providing for communication and reolution of utility functions between participants, wherein the utility function is evaluated based on local information at the recipient to determine a cost value thereof. A user interface having express representation of both information elements, and associated reliability of the information. An automated system for optimally conveying information based on relevance and reliability.
U.S. Publication 2015/0073981 to Adjaoute for disclosure of a merchant data breach process comprises processing daily payment transaction data with a risk and compliance platform to obtain a fraud score for each constituent transaction. Constituent transactions with high risk fraud scores are sorted into a table according to the transaction date, cardholder, and merchant. The table data is scored according to suspected card visits, highly probable visits, and all card visits. The scores are normalized according to merchant size grouping through the use of multipliers. The normalized scores are summed together day-by-day into a final score. A timely warning of an underlying and expanding security rupture caused by a merchant data breach is issued for damage control and law enforcement.
THIS ACTION IS MADE FINAL. 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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Azam Ansari, whose telephone number is (571) 272-7047. The examiner can normally be reached from Monday to Friday between 8 AM and 4:30 PM.
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Applicants are invited to contact the Office to schedule either an in-person or a telephonic interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
/AZAM A ANSARI/
Primary Examiner, Art Unit 3621
February 18, 2026