DETAILED ACTION
Status of the Application
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/05/2025 has been entered.
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status
This action is a Non-Final Action on the merits in response to the application filed on 11/05/2025.
Claims 1, 3, 8, 10, and 15 has been amended.
Claims 2, 4-7, 9, 11-14, 16, and 18-20 has been cancelled.
Claims 1, 3, 8, 10, 15, and 17 remain pending in this application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1,3 are directed towards a system, claims 8, 10 are directed towards a computer program product, and claims 15, 17 are directed towards a method, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including normalizing datasets. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1, 3, 8, 10, 15, and 17, the independent claims (claims 1, 8, and 15) are directed to managing performative and predictive data, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
1. (Currently Amended) A system for benchmarking and predictive modeling, the system comprising:
a processing device;
a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of:
receive, via a wireless communication channel, a plurality of unique data packets from one or more third party entities, wherein each unique data packet comprises data associated with an entity account associated with the one or more third party entities;
determining, via the machine learning engine, one or more adjustments associated with a predetermined preferred behavior of the one or more third party entities,
calculating, via the machine learning engine, a confidence degree associated with the one or more adjustments, wherein the confidence degree of each adjustment comprises a probability of the adjustment increasing an overall volume of unique data packets received from the one or more third party systems, and
generating a weighted list of the one or more adjustments, wherein the weighted list is sorted according to the confidence degree; and
these steps fall within and recite an abstract ideas because they are directed to a mathematical concepts which includes mathematical relationships; method of organizing human activity which includes commercial interaction such as business relations (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships or commercial interaction, then it falls within the “Mathematical Concepts”; “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of processing device, storage device, wireless communication channel, database, machine learning engine, (Claim 8 recites computer program product, computer-readable medium, apparatus, processing device, storage device, wireless communication channel, database, machine learning engine; Claim 15 wireless communication channel, database, machine learning engine). The claims recite the steps are performed by the processing device, storage device, wireless communication channel, database, machine learning engine.
The limitations of
normalize one or more subsets of data within each unique data packet based on a standard formatting scheme;
assign at least one standard characteristic associated with each of the one or more normalized subsets of data based on calculating a similarity score of each normalized subsets of data to a plurality of predetermined data categories, wherein the assigned at least one standard characteristic is one of the plurality of predetermined data categories, wherein the similarity score comprises a weight value;
query a database for one or more datasets matching the at least assigned one standard characteristic and append each unique data packet to the one or more datasets matching the at least assigned one standard characteristic, creating a combined dataset;
process the combined dataset via a machine learning engine to predict one or more future behaviors of the entity account, wherein at least one input of the machine learning engine comprises the weight value, and wherein predicting one or more future behaviors of the entity account comprises:
generating a weighted list of the one or more adjustments, wherein the weighted list is sorted according to the confidence degree; and
transmit a notification to a first third party entity system, wherein the notification comprises information associated with the one or more predicted future behaviors and the weighted list of the one or more adjustments.
are mere data processing and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by processing device, storage device, wireless communication channel, database, machine learning engine. The processing device, storage device, wireless communication channel, database, machine learning engine are recited at a high level of generality. In limitation (a), processing device, storage device, wireless communication channel, database, machine learning engine are used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The processing device, storage device, wireless communication channel, database, machine learning engine are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites machine learning engine. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the machine learning model, optimization model. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data processing and outputting. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0023 “use of a machine learning engine to collect a variety of historical information from a plurality of secondary entity systems and predict future performance associated with said secondary entity systems. Each secondary entity may only have access to a limited amount of historical data.”]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
normalize one or more subsets of data within each unique data packet based on a standard formatting scheme;
assign at least one standard characteristic associated with each of the one or more normalized subsets of data based on calculating a similarity score of each normalized subsets of data to a plurality of predetermined data categories, wherein the assigned at least one standard characteristic is one of the plurality of predetermined data categories, wherein the similarity score comprises a weight value;
query a database for one or more datasets matching the at least assigned one standard characteristic and append each unique data packet to the one or more datasets matching the at least assigned one standard characteristic, creating a combined dataset;
process the combined dataset via a machine learning engine to predict one or more future behaviors of the entity account, wherein at least one input of the machine learning engine comprises the weight value, and wherein predicting one or more future behaviors of the entity account comprises:
generating a weighted list of the one or more adjustments, wherein the weighted list is sorted according to the confidence degree; and
transmit a notification to a first third party entity system, wherein the notification comprises information associated with the one or more predicted future behaviors and the weighted list of the one or more adjustments.
are recited at a high level of generality. These elements amount to transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a processing device, storage device, wireless communication channel, database, machine learning engine to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 3, 10, 17 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claim 3 recite processing device; claim 10 recite apparatus. The dependent claims 3, 10, 17 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 3, 10, 17 recites processing device, storage device, wireless communication channel, database, machine learning engine which are considered an insignificant extra-solution activities of processing and analyzing data; see MPEP 2106.05(g). Claims 3, 10, 17 recites processing device, storage device, wireless communication channel, database, machine learning engine, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 3, 10, 17 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 8, and 15. Therefore claims 3, 10, 17 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Reasons for Removing the Prior Art Rejection
The rejections under 35 U.S.C. 103 as to claim 1, 3, 8, 10, 15, and 17 are removed in light of Applicant's claims and remarks of 11/05/2025, which are deemed persuasive as to independent claim 1. The reasons for withdrawal of the rejections under 35 U.S.C. 103 can be found at the following claim limitations of 11/05/2025 at claim 1, 8, and 15 as follows:
Claim 1
A system for benchmarking and predictive modeling, the system comprising:
a processing device;
a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of:
receive, via a wireless communication channel, a plurality of unique data packets from one or more third party entities, wherein each unique data packet comprises data associated with an entity account associated with the one or more third party entities;
normalize one or more subsets of data within each unique data packet based on a standard formatting scheme;
assign at least one standard characteristic associated with each of the one or more normalized subsets of data based on calculating a similarity score of each normalized subsets of data to a plurality of predetermined data categories, wherein the assigned at least one standard characteristic is one of the plurality of predetermined data categories, wherein the similarity score comprises a weight value;
query a database for one or more datasets matching the at least assigned one standard characteristic and append each unique data packet to the one or more datasets matching the at least assigned one standard characteristic, creating a combined dataset;
process the combined dataset via a machine learning engine to predict one or more future behaviors of the entity account, wherein at least one input of the machine learning engine comprises the weight value, and wherein predicting one or more future behaviors of the entity account comprises:
determining, via the machine learning engine, one or more adjustments associated with a predetermined preferred behavior of the one or more third party entities,
calculating, via the machine learning engine, a confidence degree associated with the one or more adjustments, wherein the confidence degree of each adjustment comprises a probability of the adjustment increasing an overall volume of unique data packets received from the one or more third party systems, and
generating a weighted list of the one or more adjustments, wherein the weighted list is sorted according to the confidence degree; and
transmit a notification to a first third party entity system, wherein the notification comprises information associated with the one or more predicted future behaviors and the weighted list of the one or more adjustments.
Applicant’s Remarks of 11/05/2025 at pg. 11-13 as follows:
“Independent claims 1, 8 and 15 recite, in one form or another: receiving, via a wireless communication channel, a plurality of unique data packets from one or more third party entities, wherein each unique data packet comprises data associated with an entity account associated with the one or more third party entities; normalizing one or more subsets of data within each unique data packet based on a standard formatting scheme; assigning at least one standard characteristic associated with each of the one or more normalized subsets of data based on calculating a similarity score of each normalized subsets of data to a plurality of predetermined data categories, wherein the assigned at least one standard characteristic is one of the plurality of predetermined data categories, wherein the similarity score comprises a weight value; querying a database for one or more datasets matching the at least assigned one standard characteristic and append each unique data packet to the one or more datasets matching the at least assigned one standard characteristic, creating a combined dataset; processing the combined dataset via a machine learning engine to predict one or more future behaviors of the entity account, wherein at least one input of the machine learning engine comprises the weight value, and wherein predicting one or more future behaviors of the entity account comprises: determining, via the machine learning engine, one or more adjustments associated with a predetermined preferred behavior of the one or more third party entities, calculating, via the machine learning engine, a confidence degree associated with the one or more adjustments, wherein the confidence degree of each adjustment comprises a probability of the adjustment increasing an overall volume of unique data packets received from the one or more third party systems, and generating a weighted list of the one or more adjustments, wherein the weighted list is sorted according to the confidence degree; and transmitting a notification to a first third party entity system, wherein the notification comprises information associated with the one or more predicted future behaviors and the weighted list of the one or more adjustments.
None of the cited references anticipate, teach, or suggest the above-presented features of independent claims 1, 8, and 15. Specifically, none of Kurniadi, Peterson, Alexander, and Gross anticipate, teach, or suggest the following amended claim recitation: calculating, via the machine learning engine, a confidence degree associated with the one or more adjustments, wherein the confidence degree of each adjustment comprises a probability of the adjustment increasing an overall volume of unique data packets received from the one or more third party systems. The Office cites Gross at [0076] as teaching "wherein the confidence degree of each adjustment is a probability of the adjustment increasing an overall volume of unique data packets received from the one or more third party systems." Office Action, 27. The cited portion of Gross teaches "evaluating duplicate copies of the same page or part (form) in a document." The data values within the duplicate copies are then compared to one another in order to validate the data values and subsequently "increase[] data confidence values." In other words, the cited portion of Gross describes a validation process of comparing data found in multiple, duplicate forms, and determining whether the data is consistent across the multiple forms. If the data is consistent, then the likelihood of the data being correct (i.e., the "confidence value") is increased. Other than the use of the term "confidence," there is no correlation between the data validation process described by Gross and the present claim recitation. The claimed confidence degree is not a degree of confidence of whether a particular data value is correct (determined by comparing data values in duplicate documents, as described by Gross). Rather, the claimed confidence degree is a probability of an adjustment increasing an overall volume of unique data packets received from one or more third party systems. None of the other cited references remedy this deficiency of Gross.”
Page 9 of 13
Response to Arguments
Applicant’s arguments filed 11/05/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 11/05/2025.
Regarding the 35 U.S.C. 101 rejection, at pg. 9-11 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
1. A system for benchmarking and predictive modeling, the system comprising:
a processing device;
a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of:
receive, via a wireless communication channel, a plurality of unique data packets from one or more third party entities, wherein each unique data packet comprises data associated with an entity account associated with the one or more third party entities;
normalize one or more subsets of data within each unique data packet based on a standard formatting scheme;
assign at least one standard characteristic associated with each of the one or more normalized subsets of data based on calculating a similarity score of each normalized subsets of data to a plurality of predetermined data categories, wherein the assigned at least one standard characteristic is one of the plurality of predetermined data categories, wherein the similarity score comprises a weight value;
query a database for one or more datasets matching the at least assigned one standard characteristic and append each unique data packet to the one or more datasets matching the at least assigned one standard characteristic, creating a combined dataset;
process the combined dataset via a machine learning engine to predict one or more future behaviors of the entity account, wherein at least one input of the machine learning engine comprises the weight value, and wherein predicting one or more future behaviors of the entity account comprises:
determining, via the machine learning engine, one or more adjustments associated with a predetermined preferred behavior of the one or more third party entities,
calculating, via the machine learning engine, a confidence degree associated with the one or more adjustments, wherein the confidence degree of each adjustment comprises a probability of the adjustment increasing an overall volume of unique data packets received from the one or more third party systems, and
generating a weighted list of the one or more adjustments, wherein the weighted list is sorted according to the confidence degree; and
transmit a notification to a first third party entity system, wherein the notification comprises information associated with the one or more predicted future behaviors and the weighted list of the one or more adjustments.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for managing performative and predictive data, which constitutes methods related to mathematical relationships or commercial interaction which are still considered an abstract idea under the 2019 PEG. The database is comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 8 and 9 that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps and arguments at pg. 9 and 10 of:
“The Examiner's characterization of the present claims as merely a "computer-aided mental process" disregards the claim limitations that specify, for example, normalizing one or more subsets of data within each unique data packet based on a standard formatting scheme (which requires programmatic manipulation of datasets using computational rules), querying a database for one or more datasets matching the at least assigned one standard characteristic and append each unique data packet to the one or more datasets matching the at least assigned one standard characteristic, creating a combined dataset (which requires executing computer-based retrieval and storage operations across physical memory structures), and calculating, via a machine learning engine, a confidence degree associated with the one or more adjustments, wherein the confidence degree of each adjustment comprises a probability of the adjustment increasing an overall volume of unique data packets received from the one or more third party systems (which employs algorithmic model execution and numerical optimization well beyond any human cognitive capability).
These functions are integral to the claimed system and are not mental steps, but rather operations that require electronic data processing, memory management, and model computation, which are all activities that improve how a computing system performs machine learning tasks.”
seems to describe a “particular way” of managing performative and predictive data. “ The Applicant is basically relying on the system elements as integrating the abstract idea into a practical application but those system elements aren't really utilized in any particular manner.
In regards to Ex Parte Desjardins, the instant claims are not similar to Ex Parte Desjardins, Examiner finds the Board determined the improvements in Desjardins to be directed to addressing problems arising in the context of a technical improvements to machine learning systems, which overcome a problem specifically arising in the realm of AI and machine learning inventions. There is no similar technological problem or solution here, as the current claims are just using typically know actions/steps of a machine learning model and no improvements.
Additionally, the Examiner would like to point the Applicant to the 2019 PEG, in which managing agricultural information will fall under. The 2019 PEG which states:
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).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Additionally, please refer above to the 35 U.S.C. 101 rejection for further explanation and rationale, a revised 101 rejection is now presented.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO2006099081A2 teaching Method and system for managing account information Jiang et al, US 20190163925 A1 Jiang teaches: A system for benchmarking and predictive modeling, the system comprising: a processing device; a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: / A computer program product for benchmarking and predictive modeling, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to: / A method for benchmarking and predictive modeling, the method comprising:
- “receive a plurality of unique data packets from one or more third party entities, wherein each unique data packet comprises data associated with an entity account associated with the one or more third party entities”; (Jiang ¶ [0035] 1st senetnce: three-party software behavior monitor is a data packet monitor installed on E-Commerce website, the 3rd party payment platform and the user client, used to monitor, in real time, data packets transmitted between the 3 parties in a complete transaction, and extract and integrate necessary parameter information (comprising URL address and a parameter and the like) in the data packets, to send key info to the real-time software behavior verification system)
- “determine a set of standard characteristics of each unique data packet”;
(Jiang ¶ [0006] the method for monitoring and verifying software behavior comprises receiving, by a software behavior verification system based on a physical hardware system, legal [or standard] user behavior data containing user activities performed during legal electronic transactions and storing the legal user behavior data as a software behavior model)
- “query a database for one or more datasets matching the set of standard characteristics”
(Jiang ¶ [0028] 2nd sentence: real-time software behavior verification system authenticates a user behavior interaction sequence against the [stored] software behavior model in real time according to a global unique order number…) “and append each unique data packet to the one or more datasets matching the set of standard characteristics, creating a combined dataset” (Jiang ¶ [0028] 1st sentence the real-time software behavior verification system … integrates the key sequence and information in the data packets. Specifically, per, ¶ [0029] the software behavior certificate is formed according to interaction modes between the three parties, that is, the E-Commerce website, the third-party payment platform, and the user client, comprising the interaction modes between any 2 of them; the software behavior certificate is manually created by a professional, and is stored in a server in the format of an XML file. ¶ [0031] input is a key parameter (URL and the like) received by any of the 3 parties (user, E-Commerce website, and 3rd party payment platform); and output is a key parameter sent by the current party; the interaction information represents a software behavior sequence);
- “process the combined dataset” “to predict one or more future behaviors of the entity account”. (Jiang ¶ [0036] After receiving data packets of interaction information in the transaction that are respectively submitted by the three-party software behavior monitor, the real-time software behavior verification system extracts and integrates key sequences and information in the data packets, and compares a user behavior interaction sequence with the software behavior model in real time according to a global unique order number, and sends an alarm and terminates the transaction in the case of illegal behaviors comprising disorder and identity spoofing
US 20220391435 A1 teaching Dual deep learning architecture for machine-learning systems
US 20160171494 A1 reciting at ¶ [0011] 1st sentence: The three-party software behavior monitor is a data packet monitor installed on the E-Commerce website, the third-party payment platform and the user client, and is used to monitor, in real time, data packets transmitted between the three parties in a complete transaction, and extract and integrate necessary parameter information (comprising URL address and a parameter and the like) in the data packets, so as to send key information to the real-time software behavior verification system. Similarly ¶ [0025] 1st sentence: The three-party software behavior monitor: a data packet monitor installed on an E-Commerce website, a third-party payment platform, and a user client, and used to monitor, in real time, data packets transmitted between the three parties in a complete transaction, and extract and integrate necessary parameter information in the data packets, so as to send key information to the real-time software behavior verification system
US 20060204051 A1 reciting at ¶ [0045] Fig. 3 shows a flow diagram depicting the actions taken by the participants in one embodiment of the invention when locking a credit file and processing a new account request. The consumer first initiates a lock on the credit file by providing the trusted third party the authentication information required by the credit bureau to access the consumer's credit file 301. The trusted third party attempts to set a lock on the consumer's credit file 302. The credit bureau then inserts a fraud alert, as well as information identifying the trusted third party and the lock identifier in the consumer's credit file 303. The trusted third party confirms the lock status 304 and the consumer confirms that the lock is set 305. When processing a new account request, the account provider, such as the carrier used in the example in FIG. 1, receives an application for an account and requests the credit file from the credit bureau 306. The credit bureau provides the credit file with the lock information to the account provider 307. The account provider then initiates a service request to the trusted third party to open a credit account 308. The trusted third party initiates an outbound communication to the consumer 309. Once presented with the new account request 310, the consumer can either approve or deny the request 311. The third party receives notification of the consumer's approval decision 312 and communicates the decision to the account provider 313. [0046] FIG. 4 shows a flow diagram depicting how the actions taken by the participants in one embodiment of the invention when the consumer's locked credit profile is updated. In this embodiment of the invention, before the consumer's profile data contained in the credit file or account 404 of a trusted third party subscriber 100 may be updated by a party other than the subscriber 100, the party requesting to update the credit file or account 404 must receive approval from the subscriber 100. When a creditor 401 or other party 402 desires to update a subscriber's credit file 404 at a credit bureau 403, the creditor 401 or other party 402 makes a request to the credit bureau 403. The credit bureau 403 notifies the trusted third party 102 of the request and, in turn, the trusted third party 102 authenticates the subscriber and obtains the subscriber's approval for the update. The trusted third party 102 notifies the credit bureau 403 of the approval whereupon the credit bureau 403 updates the credit file or account 404 as requested by the creditor 401 or other third party 402.
US 11228592 B1 column 22 lines 4-56: However, on the other hand, if the social security number received by the reel hub corresponds to a subscriber of the consent-based authorization system, while the credit check authorization had been initialized by an individual who was not a subscriber of the consent-based authorization system (e.g., as determined in block 1504), then the credit bureau can alert the third party that the social security number belongs to a consent-based authorization system subscriber, and thus the request could be potentially fraudulent (block 1524). This allows the third-party agency to be alerted and determine an appropriate response to the credit request from the individual (block 1526). In some cases, the third-party agency determines to assume that the request was fraudulent and to inform the individual as such and decline the credit check request (block 1528), and end the authorization process (block 1522). However, on the other hand, if at block 1562, the reel hub determines that the subscriber authorization is not valid, the credit bureau can alert the third-party agency that the request could be fraudulent (block 1572). The credit bureau can send that information to the third-party agency which then determines an appropriate response to the credit request (block 1574), which may including informing the requesting organization that the individual's credit check request is declined (block 1576), at which point the authorization process is ended (block 1568). Furthermore, if the individual is a subscriber to the consent-based authorization system, they are alerted of a potential fraud (block 1596, illustrated in FIG. 15D).
US 20130173447 A1 ¶ [0009] 3rd - 4th sentences: The credit bureau notifies a trusted third party of the request and, in turn, the trusted third party authenticates the subscriber and obtains the subscriber's approval for the update. The trusted third party notifies the credit bureau of the approval whereupon the credit bureau updates the credit file or account as requested by the creditor or other third party.
US 6532450 B1 column 5 lines 50-66 The system 14 can also automatically send a notification 32 to the debtor 28. At the same time the system 14 optionally notifies a credit bureau 78 of the delinquent debt referral using a referral data file 80 and if desired a notification letter 82. At this time the financial management system 14 can provide data concerning the debtor to other destinations. For example, a procurement system or a vendor database 84 can be notified that the debtor has been found delinquent allowing the procurement system 84 to select the debtor for purchases that can be later offset. An enterprise wide data warehouse 86 can store the data for use in statistical analysis of debt transactions. Third parties 88, such as the U.S. Internal Revenue Service, can be informed about the writeoff. The reporting capability also allows a warning to be provided to other third parties that can allow them to avoid establishing business relationships with the debtor
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/UCHE BYRD/Examiner, Art Unit 3624
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