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 .
DETAILED ACTION
Claim Rejections- 35 U.S.C § 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.
1. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 17, 20 are directed to a method, device and computer readable medium which are statutory categories of invention. (Step 1: YES).
Representative claim 17 recites the limitations of:
A computing device comprising one or more processors configured to:
receive a request to analyze a bank dispute case, wherein the bank dispute case comprises one or more disputed transactions;
for at least a first disputed transaction of the one or more disputed transactions:
for each of a plurality of data features, determine, based at least in part on one or more details of the respective disputed transaction, a data feature value for the respective data feature;
based at least in part on the respective data feature value of each respective value, determine a denial probability for the respective disputed transaction;
and
based at least in part on the respective data feature value of each respective value, determine an approval probability for the respective disputed transaction; and
in response to receiving an indication of user input selecting the first disputed transaction:
generate a graphical user interface comprising at least: an indication of the denial probability for the first disputed transaction, an indication of the approval probability for the first disputed transaction, and at least one indication of an importance metric for a first data feature of the plurality of data features; and
output, for display on a display device, the graphical user interface.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity.
The claim recites elements that are in bold above, which covers performance of the limitation as a commercial interaction, steps for analyzing disputed transactions
(e.g., receive a request to analyze a bank dispute case, wherein the bank dispute case comprises one or more disputed transactions; for at least a first disputed transaction of the one or more disputed transactions: for each of a plurality of data features, determine, based at least in part on one or more details of the respective disputed transaction, a data feature value for the respective data feature; based at least in part on the respective data feature value of each respective value, determine a denial probability for the respective disputed transaction; and based at least in part on the respective data feature value of each respective value, determine an approval probability for the respective disputed transaction; and in response to receiving an indication of user input selecting the first disputed transaction: an indication of the denial probability for the first disputed transaction, an indication of the approval probability for the first disputed transaction, and at least one indication of an importance metric for a first data feature of the plurality of data features; and output, for display
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a Commercial Interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Claims 1,20 are abstract for similar reasons.
(Step 2A-Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) 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 (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claims 1, 17,20 includes the following additional elements:
-One or more processors
-A graphical user interface
-A display device
-A computing device
- A non-transitory computer readable storage medium
The one or more processors, graphical user interface, display device, computing device and non-transitory computer readable storage medium are recited at a high level of generality and are being used in their ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
Therefore claims 1,17, 20 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited in the claim beyond the judicial exception.
Mere instructions to implement an abstract idea, on or with the use of generic computer components, or even without any computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 1,17,20 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-16, 18-19 further define the abstract idea that is present in their respective independent claims 1,17,20 and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above.
Claim 6, further defines the identified abstract idea as recited in claims 1. The additional element (the second graphical user interface) is recited a high level of generality, operating in its ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f)
Claims 12-13, further defines the identified abstract idea recited in claim 1. The additional element of (the prediction model using a normalization and Shapley values) are recited at a high level of generality, operating in their ordinary capacity and are being used as a tool to implement the steps of the identified abstract idea.
Therefore, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims (2-16, 18-19) are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Claim Rejections- 35 U.S.C § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
2. Claims 1-2, 4, 9-12, 14-17, 19-20 are being rejected under 35 U.S.C 103(a) as being unpatentable over US 2023/0385844 to Laptiev et al, herein Leptiev in view of US 2019/0392538 to SO et al, herein SO.
Regarding claim 1, Laptiev discloses
A method comprising:
receiving, by one or more processors, a request to analyze a bank dispute case, wherein the bank dispute case comprises one or more disputed transactions (At least: [0023]:
[0023] This disclosure describes one or more embodiments of a provisional credit determination system that receives a dispute request, generates a likelihood of approval score by using a dispute-evaluator machine-learning model, and if the likelihood of approval score meets a predetermined threshold, granting a provisional credit to a user account corresponding to the dispute request. To elaborate, the provisional credit determination system uses a dispute-evaluator machine-learning model to generate the likelihood of approval score which predicts the likelihood of a dispute request being approved. The dispute-evaluator machine-learning model uses many different features associated with disputed transactions of a dispute request to generate the likelihood of approval score. The issuance of provisional credit to a user account corresponds with the amount disputed and approved by the provisional credit determination system. After an agent computing device receives the dispute request and actually processes the request, the provisional credit determination system can convert the provisional credit to final credit or reverse the provisional credit based on an approval or denial indication from the agent computing device. Moreover, the provisional credit determination system can use a variety of machine learning models and/or rule-based models to decide in real time whether to grant provisional credit.
for at least a first disputed transaction of the one or more disputed transactions: for each of a plurality of data features, determining, by the one or more processors and based at least in part on one or more details of the respective disputed transaction, a data feature value for the respective data feature (At least: [0072]: To illustrate, the provisional credit determination system 102 can determine that after a specific number of historical provisional credit denials for a user account, the provisional credit determination system 102 automatically denies provisional credit for all subsequent dispute requests from that user account. To further illustrate, if the user account associated with the dispute request 300 previously disputed five transactions and the provisional credit determination system 102 rejected all 5 transactions, then the provisional credit determination system 102 automatically denies granting provisional credit (e.g., provisional credit 210 as discussed in FIG. 2).
based at least in part on the respective data feature value of each respective value, determining by the one or more processors, an approval probability for the respective disputed transaction (At least: [0068], [0097], [0125]:
[0068] As mentioned above, the provisional credit determination system 102 generates a likelihood of approval score via the dispute-evaluator machine learning model 306. For example, the provisional credit determination system 102 generates a first score 320. In particular, as discussed above, the first score 320 indicates the likelihood or probability of the dispute request being approved. The following paragraphs describe the fraud prediction machine learning model 304, the rule-based model 308, and specific example embodiments of rules that contribute to generating a score.
[0097] The final credit threshold 508 is similar to the discussion relating to the predetermined threshold (e.g., predetermined threshold 208 as discussed in FIG. 2). In one or more example embodiments, the provisional credit determination system 102 preestablishes the final credit threshold 508 based on what it considers a significant probability that it will approve the dispute request 500. In particular, in some embodiments, the provisional credit determination system 102 designates the final credit threshold 508 as higher than the predetermined threshold (for provisional credit). To illustrate, if the provisional credit threshold is 0.70, the final credit threshold 508 is greater than 0.70. As illustrated by FIG. 5, the provisional credit determination system 102 generates the likelihood of approval score 506 as 0.99.
[0125] As further illustrated in FIG. 9, the provisional credit determination system 102 provides training features 908 associated with the training dispute request 914 to the dispute-evaluator machine-learning model 904 and utilizes the dispute-evaluator machine-learning model 904 to generate a training likelihood of approval score 912 based on the training features 908. As the name indicates, the training features 908 represent features associated with a training dispute request 914 that are used for training the dispute-evaluator machine-learning model 904. Accordingly, the training features 908 can constitute a feature from any of the feature groups or an individual features described herein. In some embodiments, the dispute-evaluator machine-learning model 904 generates a set of training likelihood of approval predictions, including a predicted likelihood of approval classification with a corresponding probability that the training dispute request 914 will be approved. The training likelihood of approval score 912 can accordingly take the form of any of the likelihood of approval scores described above.
Laptiev discloses does not disclose, SO in the same field of endeavor discloses based at least in part on the respective data feature value of each respective value, determining, by the one or more processors, a denial probability for the respective disputed transaction (At least: Fig 12 and associated text; Fig 11 and associated text; [0046], [0064], [0067])
[0046] The predictions module 310 may be configured to predict propensity of the dispute information 102 to be valid 108 or invalid 110 (as shown in FIG. 1). The propensity may be indicated via a probability, a numeral or any other format as per feasibility and requirement. The predictions module 310 may employ one or more machine learning techniques, for predicting or determining the propensity of the dispute information 102. As such, the one or more machine learning techniques are configured to ingest the dispute information 102 provided by the customer 202 and provide the predictions for each dispute. In one implementation, the predictions of the predictions module 310 may be raw predictions, which are probability indicating the propensity for a particular dispute to be deemed invalid after an investigation process
[0064] Similarly, the row 1104e is configured with an ‘invalid’ outcome and an ‘auto-reject’ action. Thus, when a dispute is categorized in row 1104e, the system 208 automatically rejects the dispute. As another example, the row 1104c is configured with a ‘valid’ outcome and a ‘prioritize investigation action’. Thus, when the dispute is categorized in the row 1104c, the system 208 automatically indicates the parties to prioritize investigation for the dispute. The investigation may be prioritized based on the scores or any other parameter as per feasibility and requirement. A reason may also be provided (not shown in Figures) by the system 208, for auto-rejecting or auto-clearing the dispute. In one implementation, the system 208 may include a machine learning algorithm that assigns weights and/or parameters to each of the variables in the prediction model (such as model 906 and model 912 shown in FIG. 9), used for computing the score. Various parameters related to the dispute are considered for inclusion in the predictive model, such as transaction amount, products disputed, or historical behavior of the disputing party. As such, the system 208 may assign appropriate weights and/or parameters for each of the variables in the model, the net effect of which is to produce the overall score.
[0067] Similarly, for scores that fall between “2-4”, the ‘investigate’ action may be provided, which signifies that the dispute can be investigated and indicates probability of invalidity. Further, for the score of “5” the ‘auto-reject’ action may be provided, which suggests that the dispute is invalid and can be auto-cleared. For each dispute, the score band 1202 (also referred to as workflow bucket) is provided to the customer 202 who uses it to drive various workflows. The customer 202 may therefore auto-clear and auto-reject the disputes appropriately, while using the remaining buckets for dispute investigation prioritization. In other words, the customer concentrates on those disputes that are ranked the highest (i.e. with highest scores) and then spends time on the next group and so on. Thereby, the customer does not waste time on the least likely invalid dispute and spends most time on the likeliest to be invalid.
in response to receiving an indication of user input selecting the first disputed transaction: generating, by the one or more processors, a graphical user interface comprising at least: an indication of the denial probability for the first disputed transaction (At least: [0067], Fig 12 and associated text)
Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify Laptiev’s invention to include based at least in part on the respective data feature value of each respective value, determining, by the one or more processors, a denial probability for the respective disputed transaction and in response to receiving an indication of user input selecting the first disputed transaction:
generating, by the one or more processors, a graphical user interface comprising at least: an indication of the denial probability for the first disputed transaction in order to ensure that the parties are automatically informed about the dispute and are able to prioritize their investigation into the disputed transactions (SO: [0064]).
Laptiev further discloses:
in response to receiving an indication of user input selecting the first disputed transaction:
generating, by the one or more processors, a graphical user interface comprising at least: (At least: [0040], [0171]:
[0040] Relatedly, as used herein, the term “disputed transaction” refers to a user-identified transaction that the client device associated with the user adds to a dispute request. In particular, the client device can provide for display via a graphical user interface, to a user, a list of recent transactions. To illustrate, using the client device, a user can select a transaction from the list and generate a dispute request for the selected transaction. The dispute request can include any number of disputed transactions. Moreover, a disputed transaction corresponds to a single transaction on the user account.
[0171] The I/O interface 1308 may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output providers (e.g., display providers), one or more audio speakers, and one or more audio providers. In certain embodiments, interface 1308 is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation.
an indication of the approval probability for the first disputed transaction, and at least one indication of an importance metric for a first data feature of the plurality of data features (At least: [0065], [0066], [0106],[0068], [0097], [0112]);
[0065] As just discussed, monitoring historical results of dispute requests associated with a user account can improve the rate of correctly determining whether granting provisional credit is appropriate. In one or more example embodiments, the provisional credit determination system 102 can prioritize receiving and identifying historical dispute request results in response to receiving the dispute request 300. In particular, the provisional credit determination system 102 can use intelligent incorporation of historical data when receiving the dispute request 300. To illustrate, intelligent incorporation of historical data would determine whether the provisional credit determination system 102 should receive historical dispute results based on factors such as dispute amount, or merchant associated with dispute request 300. Based on balancing different factors such as amount and merchant, the provisional credit determination system 102 can then retrieve historical data of the user account.
[0066] The next few paragraphs describe the dispute-evaluator machine learning model 306. The provisional credit determination system 102 can accomplish this by inputting the feature groups into machine learning models. For example, the dispute-evaluator machine learning model 306 can receive feature groups that drive the likelihood of approval for the dispute request 300. In some instances, feature groups that drive the likelihood of approval score includes transaction type, transaction details, account information, and/or merchant information. In particular, the provisional credit determination system 102 can utilize the dispute-evaluator machine learning model 306 to analyze the dispute request 300 and the information associated with disputed transaction(s) 302. For example, the provisional credit determination system 102 can encode information associated with the dispute request 300 and/or the information associated with disputed transaction(s) 302, e.g., feature groups (e.g., using one hot encoding, an encoding layer, or a vector mapping) and then process the encoding utilizing the dispute-evaluator machine learning model 306 to generate the likelihood of approval score (e.g., likelihood of approval score 206 as discussed in FIG. 2).
and
outputting, by the one or more processors and for display on a display device, the graphical user interface (At least: [0171]).
Regarding claim 2, Laptiev and SO discloses the method of claim 1. Laptiev further discloses wherein the first data feature comprises a data feature of the plurality of data features having a highest value for the importance metric (At least: [0097]).
Regarding claim 4, Laptiev and SO discloses the method of claim 1. Leptiev does not disclose, SO discloses further discloses further comprising: based at least in part on the denial probability for of the first disputed transaction and the approval probability for the first disputed transaction, determining, by the one or more processors, a transaction score for the first disputed transaction, wherein the graphical user interface further includes an indication of the transaction score for the first disputed transaction (At least: [0064]).
Therefore it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Laptiev’s invention to include further comprising: based at least in part on the denial probability for of the first disputed transaction and the approval probability for the first disputed transaction, determining, by the one or more processors, a transaction score for the first disputed transaction, wherein the graphical user interface further includes an indication of the transaction score for the first disputed transaction in order to ensure that the parties are automatically informed about the dispute and are able to prioritize their investigation into the disputed transactions (SO: [0064]).
Regarding claim 9, Laptiev and SO discloses the method of claim 1. Laptiev further discloses wherein the plurality of data features comprise two or more of:
a total dollar value of the respective disputed transaction (At least: [0023]);
a number of disputed transactions in the one or more disputed transactions;
a program name of the respective disputed transaction;
a program type of the respective disputed transaction;
a cardholder state for the respective disputed transaction;
a merchant state for the respective disputed transaction;
a number of days since the respective disputed transaction;
a good classification code for the respective disputed transaction;
a network identification for the respective disputed transaction;
a dispute reason for the respective disputed transaction (At least: [0141])
a point-of-sale entry mode for the respective disputed transaction; and
a transaction code for the respective disputed transaction.
Regarding claim 10, Laptiev and SO discloses the method of claim 1. Laptiev further discloses wherein the indication of the data feature value comprises a hyperlink that, when the one or more processors receive an indication of user input selecting the hyperlink, the method further comprises updating, by the one or more processors, the graphical user interface to include details the data feature indicated by the respective indication (At least: [0178]).
Regarding claim 11, Laptiev and SO discloses the method of claim 1. Laptiev further comprising: outputting, by the one or more processors, in the graphical user interface, a recommended disposition for the bank dispute case based at least in part on the denial probability and the approval probability for at least the first disputed transaction (At least: [0146], [0151], [0171]).
Claim 19 is being rejected using the same rationale as claim 11.
Regarding claim 12, Laptiev and SO discloses the method of claim 1. Laptiev further discloses wherein determining the denial probability and determining the approval probability are further based at least in part on a prediction model (At least: Abstract).
Regarding claim 14, Laptiev and SO discloses the method of claim 1. Laptiev further discloses wherein the indication of the data feature value for the first data feature comprises a plain-language rationale explaining the data feature value for the first data feature (At least: [0066]).
Regarding claim 15, Leptiev and SO discloses the method of claim 1. Leptiev further discloses further comprising: automatically sending, by the one or more processors, a disposition for the bank dispute case to a dispute processing system based at least in part on the denial probability and the approval probability for at least the first disputed transaction (At least: [0146], [0151]).
Regarding claim 16, Leptiev and SO discloses the method of claim 1. Leptiev further discloses wherein the graphical user interface includes indications of each data feature of the plurality of data features with a non-zero value as the importance metric (At least: [0106]).
Regarding claim 17, Laptiev discloses a computing device comprising one or more processors configured to (At least: [0021]:
receive a request to analyze a bank dispute case, wherein the bank dispute case comprises one or more disputed transactions (At least: [0023]);
for at least a first disputed transaction of the one or more disputed transactions:
for each of a plurality of data features, determine, based at least in part on one or more details of the respective disputed transaction, a data feature value for the respective data feature (At least:[0072]).
Laptiev discloses does not disclose, SO in the same field of endeavor discloses based at least in part on the respective data feature value of each respective value, determine a denial probability for the respective disputed transaction (At least: Fig 12 and associated text; Fig 11 and associated text; [0046], [0064], [0067]).
Laptiev further discloses:
based at least in part on the respective data feature value of each respective value, determine an approval probability for the respective disputed transaction (At least: [0068], [0097], [0125]).
Laptiev does not disclose, SO discloses in response to receiving an indication of user input selecting the first disputed transaction: generate a graphical user interface comprising at least: an indication of the denial probability for the first disputed transaction (At least: [0067], Fig 12 and associated text).
Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify Laptiev’s invention to include based at least in part on the respective data feature value of each respective value, determine a denial probability for the respective disputed transaction, in response to receiving an indication of user input selecting the first disputed transaction: generate a graphical user interface comprising at least: an indication of the denial probability for the first disputed transaction in order to ensure that the parties are automatically informed about the dispute and are able to prioritize their investigation into the disputed transactions (SO: [0064]).
Laptiev further discloses in response to receiving an indication of user input selecting the first disputed transaction: generate a graphical user interface comprising at least (At least: [0040],[0171]) , an indication of the approval probability for the first disputed transaction, and at least one indication of an importance metric for a first data feature of the plurality of data features (At least: 0065], [0066], [0106],[0068], [0097], [0112]); and output, for display on a display device, the graphical user interface (At least: [0171]).
Regarding claim 20, Laptiev discloses a non-transitory computer-readable storage medium having stored thereon instructions, that when executed, cause one or more processors of a computing device to (At least: [0152], [0158]:
receive a request to analyze a bank dispute case, wherein the bank dispute case comprises one or more disputed transactions (At least: [0023]);
for at least a first disputed transaction of the one or more disputed transactions:
for each of a plurality of data features, determine, based at least in part on one or more details of the respective disputed transaction, a data feature value for the respective data feature (At least:[0072]).
Laptiev discloses does not disclose, SO in the same field of endeavor discloses based at least in part on the respective data feature value of each respective value, determine a denial probability for the respective disputed transaction (At least: Fig 12 and associated text; Fig 11 and associated text; [0046], [0064], [0067]).
Laptiev further discloses:
based at least in part on the respective data feature value of each respective value, determine an approval probability for the respective disputed transaction (At least: [0068], [0097], [0125]).
Laptiev does not disclose, SO discloses in response to receiving an indication of user input selecting the first disputed transaction: generate a graphical user interface comprising at least: an indication of the denial probability for the first disputed transaction (At least: [0067], Fig 12 and associated text).
Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify Laptiev’s invention to include based at least in part on the respective data feature value of each respective value, determine a denial probability for the respective disputed transaction, in response to receiving an indication of user input selecting the first disputed transaction: generate a graphical user interface comprising at least: an indication of the denial probability for the first disputed transaction in order to ensure that the parties are automatically informed about the dispute and are able to prioritize their investigation into the disputed transactions (SO: [0064]).
Laptiev further discloses in response to receiving an indication of user input selecting the first disputed transaction: generate a graphical user interface comprising at least (At least: [0040],[0171]) , an indication of the approval probability for the first disputed transaction, and at least one indication of an importance metric for a first data feature of the plurality of data features (At least: 0065], [0066], [0106],[0068], [0097], [0112]); and output, for display on a display device, the graphical user interface (At least: [0171]).
3. Claim 13 is being rejected under 35 U.S.C 103(a) as being unpatentable over Laptiev in view of SO and further in view of US 2023/0229735 to Jain et al, herein Jain.
Regarding claim 13, Laptiev and SO discloses the method of claim 12. Laptiev does not disclose, Jain discloses further comprising: developing, by the one or more processors, the prediction model using a normalization pipeline and Shapley values (At least: [0097], [0112], [0126]).
Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify Laptiev’s invention to include further comprising: developing, by the one or more processors, the prediction model using a normalization pipeline and Shapley values in order to ensure that by using the machine learning models, a high likelihood of merchant chargebacks, and disputed transactions can be determined (Jain: [0112]).
No Prior Art for claims 3, 5-8, 18
Based on prior art search results, the prior art of record neither anticipates nor renders obvious the claimed subject matter of the instant application as a whole either taken alone or in combination, in particular, the prior art does not teach the limitations:
Claim 3: “ the first data feature having the highest value for the importance metric indicates that the first data feature has a most extreme positively evaluated data feature value or a most extreme negatively evaluated data feature value compared to each other data feature of the plurality of data features, wherein the first data feature having the most extreme positively evaluated data feature value comprises the data feature value having a greatest influence on either decreasing the denial probability or increasing the approval probability compared to the data feature values for each other data feature of the plurality of data features, wherein the first data feature having the most extreme negatively evaluated data feature value comprises the data feature value having a greatest influence on either increasing the denial probability or decreasing the approval probability compared to the data feature values for each other data feature of the plurality of data features”
Claims 5,18 : further comprising for each of the one or more disputed transactions: for each of the plurality of data features, determining, by the one or more processors and based at least in part on the one or more details of the respective disputed transaction, the data feature value for the respective data feature; based at least in part on the respective data feature value of each respective value, determining, by the one or more processors, the denial probability for the respective disputed transaction;
based at least in part on the respective data feature value of each respective value, determining, by the one or more processors, the approval probability for the respective disputed transaction; and based at least in part on the denial probability for of the respective disputed transaction and the approval probability for the respective disputed transaction, determining, by the one or more processors, a transaction score for the respective disputed transaction; and based at least in part on the transaction score for each of the one or more disputed transactions, determining, by the one or more processors, a case score for the bank dispute case.
Claim 6,: further comprising: generating, by the one or more processors, a second graphical user interface comprising at least: an indication of the case score; and one or more of: an indication of a disputed transaction of the plurality of transactions having a most extreme denial probability; an indication a disputed transaction of the plurality of transactions having a most extreme approval probability; an indication of an importance metric for a data feature of the plurality of data features across each of the one or more disputed transactions; and outputting, by the one or more processors and for display on the display device, the second graphical user interface.
Claim 7: wherein the indications comprise a hyperlink that, when the one or more processors receive an indication of user input selecting the hyperlink, the method further comprises updating, by the one or more processors, the second graphical user interface to include details of the disputed transaction or the data feature indicated by the respective indication.
Claim 8: wherein determining the case score is further based at least in part on one or more case-level data features, wherein the one or more case-level data features comprise one or more of: a total dollar value for the bank dispute case; a total number of transactions in the bank dispute case; a program name for the bank dispute case; a program type for the bank dispute case; a cardholder state for the bank dispute case; an indication of the cardholder participating in a government welfare or child support program; and a number of days between transaction settlements and a report date for the bank dispute case.
CONCLUSION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5.
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/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 10/3/2025