DETAILED ACTION
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 .
This action is responsive to Applicant’s Amendment filed on 7/21/2026.
Claims 1-20 are presented for examination. Claims 1-2, 9, 15 and 17 have been amended.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirely as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 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 of this title, 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.
Claims 1-2, 4, 7, 9-10, 12-14 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ghoula et al. (US 20210374164 A1, hereafter Ghoula) in view of Subramanian et al. (US 20170300879 A1, hereafter Subramanian).
Regarding to claim 1, Ghoula discloses: A system for compiling and generating display content for events in an account record (see [0006]-[0012]; “an automated computer-implemented method is provided, for grouping data records for improving the efficiency of a clustering process”, “another aspect, an automated and dynamic system for clustering data records pertaining to different datasets is provided”, “a graphical user interface for receiving as input reconciled data records in a given one of the clusters”. Also see [0004], [0031]-[0032]; “data records from a financial statement can be compared to accounting records of a given account”), the system comprising:
one or more memories; and one or more processors, communicatively coupled to the one or more memories (see [0006]-[0007], [0028]; “The method comprises … generating, by a processor … inputting, by the processor … removing, by the processor, from the dataset” and ““Processing devices” include processing means, such as microcontrollers and/or microprocessors, CPUs, or are implemented on FPGAs, as examples only. The processing means are used in combination with storage medium, also referred to as “memory” or “storage means””), configured to:
identify, using a machine learning model, one or more events from a set of events included in the account record, wherein the one or more events are recurring non-subscription-based events, and wherein the one or more events are identified based on account data associated with the account record and event data associated with the set of events, wherein identifying the one or more events comprises, for an event of the set of events: determining, based on metadata associated with the event, an absence of characteristic of subscription type of events; and identifying the event as a recurring non-subscription-based event based on the absence of the characteristic of subscription type of events (see [0032], [0040]; “Transaction records can be matched one on one, but not necessarily. For example, a transaction record in a financial statement can describe the payment of a balance on a credit card account … a number of different transaction records corresponding to the purchase of different products or services … In the example of the credit card statement, the amount of the payment to the credit card account was negative $100, and the transaction amounts of items purchased using the credit card were recorded as $25, $25 and $50. The four transactions (payment to credit card account, and payment of three items) are reconciled since the sum of the transactions is equal to 0$”, “the training and unreconciled data records, the method can include a step of estimating values of unpopulated or missing fields … transaction amounts treated by the financial institution are debited from the account of a payor … a reconciliation process for a financial institution may consist in the treatment of transactions made by clients in favour of the financial institution, for which an activity may be controlling that mortgage payments are made through a specific system. There can be one classifier model 310 associated with each field (process, type and/or activity), and the raw transaction records generated directly by a source will typically not include the classification fields. The classification fields and associated values can be added to the raw data records in order to improve the efficiency and rapidity in forming clusters of transaction records” and “classifier model 310 … neural networks, SVM or gradient boosting”. Using certain classifier model, such as neural network or machine learning model, to identify or classify certain transaction events from at least one financial account record, such transaction events would include at least multiple credit card payment transactions to items, i.e., claimed recurring non-subscription-based events. Note: it is understood that to identify an given event or transaction record is not a particular type of event or transaction record, then it would require identifying such given event or transaction record missing certain characteristics of such particular type, such as when identifying the three transactions discussed at [0032] is not the mortgage payment discussed at [0040], then it would require to determine these three transactions are absent of some characteristics of the mortgage payment type of event or transaction record);
store, in a database and based on identifying the one or more events, an indication of the one or more events (see [0040]; “The classification fields and associated values can be added to the raw data records”. The values of the classification fields, i.e., claimed indication of the events, are written or stored to the data records);
determine, for entities associated with respective events from the one or more events, one or more entity similarity scores indicating a similarity between two or more entities (see [0006]-[0011]; “generating therefrom a matrix of similarity scores, each similarity score providing an indication of the degree of similarity between the two data records in the pair of the group”. Also see [0041]; “six groups G1-G6 are created, according to the values found in the “process” field, using grouping algorithms 320 … where additional classification fields are used, the groups can be based on more than one field, such as based on “type”, “process” and “activity” fields”);
generate, based on the one or more entity similarity scores, one or more groups of events, from the one or more events, indicating events associated with similar entities (see [0006]-[0011]; “a clustering algorithm for receiving as an input the matrix of similarity scores, and creating therefrom clusters of transaction records”);
generate, for a group of events from the one or more groups of events, display content indicative of information associated with events included in the group, wherein the display content is based on the information associated with the events included in the group and is based on the account data; and provide, for display in association with the account record, the display content (see [0012] and [0053]; “a graphical user interface for receiving as input reconciled data records in a given one of the clusters and for removing reconciled data records from the initial dataset” and “the data records that are members of a cluster can be determined as reconciled automatically, or the members of a cluster can be displayed on a display 190 in a graphical user interface, so that an end user can confirm whether the members are reconciled or not”).
Ghoula does not disclose: characteristic of subscription type of events is a subscription indicator including at least one of a subscription identifier, a renewal date, or an automated billing cycle.
However, Subramanian discloses: wherein identifying the one or more events comprises, for an event of the set of events: determining, based on metadata associated with the event, an absence of a subscription indicator including at least one of subscription identifier, a renewal date, or an automated billing cycle; and identifying the event as a [recurring] non-subscription-based event based on the subscription indicator (see [0060]; “To differentiate between subscribing and non-subscribing acquiring banks, the transaction message may include a flag or other subscription indicator to indicate whether the transaction message originates from a subscribing acquiring bank. The subscription indicator may take various forms. For example, in some instances, the subscription indicator may be a flag in a subscription indicator field of the transaction message. In other instances, data in an IRD field of the transaction message may constitute the subscription indicator. For example, a null or blank value in an IRD field may indicate that the acquiring bank is a subscriber and that the IRP computing device should perform interchange qualification and provide an IRD. In contrast, if the acquiring bank populates the IRD field with a non-null or non-blank value, the IRP computing device may treat the inclusion of data in the IRD field as an indicator that the acquiring bank is not a subscriber to the interchange qualification service”).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the identifications of missing fields of the raw transaction records by classifier model from Ghoula by including identifying whether a given transaction record is related to a subscription type transaction or not based on an subscription indicator from Subramanian, and thus the combination of Ghoula and Subramanian would disclose the missing limitations from Ghoula, since it would provide a specific type of characteristic to distinguish whether it is subscription related event or not (see [0022] and [0060] from Subramanian; “the inclusion of data in the IRD field as an indicator that the acquiring bank is not a subscriber to the interchange qualification service”).
Regarding to Claim 2, the rejection of Claim 1 is incorporated and further the combination of Ghoula and Subramanian discloses: wherein the one or more processors, to identify the one or more events, are configured to: identify the one or more events further based on: the one or more events being associated with an entity or event type that is associated with multiple events from the set of events, the one or more events being separately initiated, and the one or more events being user initiated events (see [0032] and [0040] from Ghoula; “a transaction record in a financial statement can describe the payment of a balance on a credit card account … a number of different transaction records corresponding to the purchase of different products or services … In the example of the credit card statement … The four transactions (payment to credit card account, and payment of three items)” and “a reconciliation process for a financial institution may consist in the treatment of transactions made by clients in favour of the financial institution, for which an activity may be controlling that mortgage payments are made through a specific system. There can be one classifier model 310 associated with each field (process, type and/or activity), and the raw transaction records generated directly by a source will typically not include the classification fields. The classification fields and associated values can be added to the raw data records”. It is understood that identifying the different transaction records at the credit statement is based on the entity or event type and user separately initiated, such as whether it is payment or charge to items/products/services that the user/client separately initiated or it is payment to the credit card account that the user/client separately initiated).
Regarding to Claim 4, the rejection of Claim 1 is incorporated and further the combination of Ghoula and Subramanian discloses: wherein the one or more processors, to determine the one or more entity similarity scores, are configured to: determine the one or more entity similarity scores based on category code information associated with the entities (see [0006]-[0011] and [0041] from Ghoula; “generating therefrom a matrix of similarity scores, each similarity score providing an indication of the degree of similarity between the two data records in the pair of the group” and “six groups G1-G6 are created, according to the values found in the “process” field, using grouping algorithms 320 … where additional classification fields are used, the groups can be based on more than one field, such as based on “type”, “process” and “activity” fields”. The similarity scores based on the fields of type, process, activity, i.e., claimed category code information).
Regarding to Claim 7, the rejection of Claim 1 is incorporated and further the combination of Ghoula and Subramanian discloses: wherein the one or more processors, to identify the one or more events, are configured to: detect, based on the event data, that the one or more events are associated with a trigger category; and identify the one or more events based on detecting that the one or more events are associated with the trigger category (see [0032] and [0040] from Ghoula; “the training and unreconciled data records, the method can include a step of estimating values of unpopulated or missing fields … transaction amounts treated by the financial institution are debited from the account of a payor … a reconciliation process for a financial institution may consist in the treatment of transactions made by clients in favour of the financial institution, for which an activity may be controlling that mortgage payments are made through a specific system. There can be one classifier model 310 associated with each field (process, type and/or activity), and the raw transaction records generated directly by a source will typically not include the classification fields. The classification fields and associated values can be added to the raw data records in order to improve the efficiency and rapidity in forming clusters of transaction records”. Detecting trigger category indicating the fields of process, type, activity as claimed trigger category and then identifying or classifying each event based on these fields).
Regarding to Claim 9, Ghoula discloses: A method of compiling and generating display content for events in an account record (see [0006]-[0012]; “an automated computer-implemented method is provided, for grouping data records for improving the efficiency of a clustering process”, “another aspect, an automated and dynamic system for clustering data records pertaining to different datasets is provided”, “a graphical user interface for receiving as input reconciled data records in a given one of the clusters”. Also see [0004], [0031]-[0032]; “data records from a financial statement can be compared to accounting records of a given account”), comprising:
monitoring, by a device, the account record, wherein the account record indicates a set of events associated with an account (see [0040]; “the training and unreconciled data records, the method can include a step of estimating values of unpopulated or missing fields … transaction amounts treated by the financial institution are debited from the account of a payor … There can be one classifier model 310 associated with each field (process, type and/or activity), and the raw transaction records generated directly by a source will typically not include the classification fields. The classification fields and associated values can be added to the raw data records in order to improve the efficiency and rapidity in forming clusters of transaction records”. Observing or monitoring the raw transactions of the account record to identify or classify certain events);
aggregating, by the device and based on monitoring the account record, one or more events from the set of events that satisfy one or more aggregation criteria, wherein the one or more events are identified based on account data associated with the account record and event data associated with the set of events (see [0006]-[0011], [0040]; “generating therefrom a matrix of similarity scores, each similarity score providing an indication of the degree of similarity between the two data records in the pair of the group” and “There can be one classifier model 310 associated with each field (process, type and/or activity), and the raw transaction records generated directly by a source will typically not include the classification fields”. Also see [0041]; “six groups G1-G6 are created, according to the values found in the “process” field, using grouping algorithms 320 … where additional classification fields are used, the groups can be based on more than one field, such as based on “type”, “process” and “activity” fields”), wherein the one or more aggregation criteria include a recurring non-pattern-based event criterion satisfied by an event, of the set of events, based on: determining, based on metadata associated with the event, an absence of characteristic of subscription type of events; and identifying the event as a recurring non-subscription-based event based on the absence of the characteristic of subscription type of events (see [0032], [0040]; “Transaction records can be matched one on one, but not necessarily. For example, a transaction record in a financial statement can describe the payment of a balance on a credit card account … a number of different transaction records corresponding to the purchase of different products or services … In the example of the credit card statement, the amount of the payment to the credit card account was negative $100, and the transaction amounts of items purchased using the credit card were recorded as $25, $25 and $50. The four transactions (payment to credit card account, and payment of three items) are reconciled since the sum of the transactions is equal to 0$”, “There can be one classifier model 310 associated with each field (process, type and/or activity), and the raw transaction records generated directly by a source will typically not include the classification fields. The classification fields and associated values can be added to the raw data records in order to improve the efficiency and rapidity in forming clusters of transaction records … for which an activity may be controlling that mortgage payments are made through a specific system” and “classifier model 310 … neural networks, SVM or gradient boosting”. Aggregating or grouping transactions in one of the reasonable embodiments to distinguish non-subscription credit card transactions, i.e., the “payment of three items” discussed at [0032] and subscription transactions, i.e., “mortgage payments” discussed at [0042], and thus such distinguishing would require identifying the “payment of three items” from [0032] based on such “payment of three items” does not include characteristic of mortgage payment);
generating, by the device and based on the event data, one or more groups of events, from the one or more events, indicating events associated with respective event parameters (see [0006]-[0011]; “a clustering algorithm for receiving as an input the matrix of similarity scores, and creating therefrom clusters of transaction records”. Also see [0041]; “six groups G1-G6 are created, according to the values found in the “process” field, using grouping algorithms 320 … where additional classification fields are used, the groups can be based on more than one field, such as based on “type”, “process” and “activity” fields”);
generating, by the device and for a group of events from the one or more groups of events, display content indicative of information associated with events included in the group of events, wherein the display content is based on the information associated with the events and is based on the account data; and providing, by the device, the display content for display (see [0012] and [0053]; “a graphical user interface for receiving as input reconciled data records in a given one of the clusters and for removing reconciled data records from the initial dataset” and “the data records that are members of a cluster can be determined as reconciled automatically, or the members of a cluster can be displayed on a display 190 in a graphical user interface, so that an end user can confirm whether the members are reconciled or not”).
Ghoula does not disclose: characteristic of subscription type of events is a subscription indicator including at least one of a subscription identifier, a renewal date, or an automated billing cycle.
However, Subramanian discloses: a [recurring] non-pattern-based on event criterion satisfied by an event, of the set of events, based on: determining, based on metadata associated with the event, an absence of a subscription indicator including at least one of subscription identifier, a renewal date, or an automated billing cycle; and identifying the event as a [recurring] non-subscription-based event based on the subscription indicator (see [0060]; “To differentiate between subscribing and non-subscribing acquiring banks, the transaction message may include a flag or other subscription indicator to indicate whether the transaction message originates from a subscribing acquiring bank. The subscription indicator may take various forms. For example, in some instances, the subscription indicator may be a flag in a subscription indicator field of the transaction message. In other instances, data in an IRD field of the transaction message may constitute the subscription indicator. For example, a null or blank value in an IRD field may indicate that the acquiring bank is a subscriber and that the IRP computing device should perform interchange qualification and provide an IRD. In contrast, if the acquiring bank populates the IRD field with a non-null or non-blank value, the IRP computing device may treat the inclusion of data in the IRD field as an indicator that the acquiring bank is not a subscriber to the interchange qualification service”).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the identifications of missing fields of the raw transaction records by classifier model from Ghoula by including identifying whether a given transaction record is related to a subscription type transaction or not based on an subscription indicator from Subramanian, and thus the combination of Ghoula and Subramanian would disclose the missing limitations from Ghoula, since it would provide a specific type of characteristic to distinguish whether it is subscription related event or not (see [0022] and [0060] from Subramanian; “the inclusion of data in the IRD field as an indicator that the acquiring bank is not a subscriber to the interchange qualification service”).
Regarding to Claim 10, the rejection of Claim 9 is incorporated and further the combination of Ghoula and Subramanian discloses: wherein the respective event parameters include an entity similarity score, and wherein generating the one or more groups of events comprises: determining, for entities associated with respective events from the one or more events, one or more entity similarity scores indicating similarities between two or more entities; and grouping an event, from the one or more events, into the group of events based on an entity similarity score associated with the event indicating that the event is associated with an entity that is associated with the group of events (see [0006]-[0011] from Ghoula; “generating therefrom a matrix of similarity scores, each similarity score providing an indication of the degree of similarity between the two data records in the pair of the group; a clustering algorithm for receiving as an input the matrix of similarity scores, and creating therefrom clusters of transaction records”. Also see [0041] from Ghoula; “six groups G1-G6 are created, according to the values found in the “process” field, using grouping algorithms 320 … where additional classification fields are used, the groups can be based on more than one field, such as based on “type”, “process” and “activity” fields”).
Regarding to Claim 12, the rejection of Claim 9 is incorporated and further the combination of Ghoula and Subramanian discloses: wherein generating the display content comprises: determining a content type associated with the display content based on the account data; and determining the display content using a content template from a set of content templates that are associated with the content type, wherein the content template is identified based on the information associated with the events included in the group of events (see [0053] from Ghoula; “the members of a cluster can be displayed on a display 190 in a graphical user interface, so that an end user can confirm whether the members are reconciled or not. If data records are determined as being reconciled, automatically or by an end user, they are removed from the dataset. In possible implementations, the method can include a step of prompting an end user to confirm the removal of the data records in a cluster, for example by displaying the clustered data records in a graphical user interface and by detecting an input from the end user, via a keyboard, a mouse or a microphone. In other possible embodiments, the data records can be removed automatically, without prompting a user for confirmation”. Determining a content type like whether with or without a user confirmation, then corresponding content templates that including user confirmation portion or not is determined)
Regarding to Claim 13, the rejection of Claim 12 is incorporated and further the combination of Ghoula and Subramanian discloses: wherein the content template includes one or more modifiable fields, and wherein generating the display content comprises: modifying the one or more modifiable fields to indicate the information associated with the events, wherein the display content includes the content template with the one or more modifiable fields indicating the information associated with the events (see [0053] from Ghoula; “the members of a cluster can be displayed on a display 190 in a graphical user interface, so that an end user can confirm whether the members are reconciled or not. If data records are determined as being reconciled, automatically or by an end user, they are removed from the dataset. In possible implementations, the method can include a step of prompting an end user to confirm the removal of the data records in a cluster, for example by displaying the clustered data records in a graphical user interface and by detecting an input from the end user, via a keyboard, a mouse or a microphone”. The content template for user to confirm the data/member reconciliation includes modifiable fields that allows user to provide input by keyboard, mount or microphone; such modifiable fields are changed or modified according to user’s input to indicate the data or member to be reconciled or not).
Regarding to Claim 14, the rejection of Claim 9 is incorporated and further the combination of Ghoula and Subramanian discloses: determining at least one of a content type or an event type to be indicated via the display content based on user interaction information associated with historical display content provided for the account record (see [0053] from Ghoula; “the members of a cluster can be displayed on a display 190 in a graphical user interface, so that an end user can confirm whether the members are reconciled or not. If data records are determined as being reconciled, automatically or by an end user, they are removed from the dataset. In possible implementations, the method can include a step of prompting an end user to confirm the removal of the data records in a cluster, for example by displaying the clustered data records in a graphical user interface and by detecting an input from the end user, via a keyboard, a mouse or a microphone”. Based on user’s input on historical display content (i.e., the content displayed before user provided inputs), determining final data reconciliation confirmation, i.e., content type).
Regarding to Claim 16, the rejection of Claim 9 is incorporated and further the combination of Ghoula and Subramanian discloses: wherein providing the display content for display comprises: providing, for display in association with the display content, a redeemable offer for an entity that is associated with the display content; obtaining an indication of an acceptance of the redeemable offer; and causing, based on obtaining the indication of the acceptance, the redeemable offer to be applied to the account (see [0053] from Ghoula; “the members of a cluster can be displayed on a display 190 in a graphical user interface, so that an end user can confirm whether the members are reconciled or not. If data records are determined as being reconciled, automatically or by an end user, they are removed from the dataset. In possible implementations, the method can include a step of prompting an end user to confirm the removal of the data records in a cluster, for example by displaying the clustered data records in a graphical user interface and by detecting an input from the end user, via a keyboard, a mouse or a microphone”. In response a redeemable offer related to whether to conform or accept data reconciliation on certain data records).
Regarding to Claim 17, Claim 17 is a product claim corresponds to method Claim 9 and is rejected for the same reason set forth in the rejection of Claim 9 above (Note: also see “The non-transitory computer readable medium stores processor-executable instructions for causing a processor to” from [0013] of Ghoula for the claimed “non-transitory computer-readable medium”).
Regarding to Claim 18, Claim 18 is a product claim corresponds to method Claim 10 and is rejected for the same reason set forth in the rejection of Claim 10 above.
Regarding to Claim 19, Claim 19 is a product claim corresponds to method Claim 14 and is rejected for the same reason set forth in the rejection of Claim 14 above.
Regarding to Claim 20, the rejection of Claim 17 is incorporated and further the combination of Ghoula and Subramanian discloses: provide, for display in association with the display content, one or more recommended entities that are determined based on the information associated with the events (see [0053] from Ghoula; “the data records that are members of a cluster can be determined as reconciled automatically, or the members of a cluster can be displayed on a display 190 in a graphical user interface, so that an end user can confirm whether the members are reconciled or not. If data records are determined as being reconciled, automatically or by an end user, they are removed from the dataset. In possible implementations, the method can include a step of prompting an end user to confirm the removal of the data records in a cluster, for example by displaying the clustered data records in a graphical user interface”. Providing suggestion or recommendation on whether members of a cluster are reconciled or not for displaying such suggestion or recommendation at GUI).
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Ghoula et al. (US 20210374164 A1, hereafter Ghoula) in view of Subramanian et al. (US 20170300879 A1, hereafter Subramanian) and further in view of Wulf et al. (US 10489457 B1, hereafter Wulf).
Regarding to Claim 3, the rejection of Claim 1 is incorporated, the combination of Ghoula and Subramanian does not discloses: wherein the one or more processors, to identify the one or more events, are configured to:
obtain, via the machine learning model, confidence scores for respective events from the set of events,
wherein the confidence scores indicate likelihoods that the respective events are the recurring non-subscription-based events; and
determine that the one or more events are the recurring non-subscription-based events based on the one or more events being associated with respective confidence scores, from the confidence scores, that satisfy a threshold.
However, Wulf discloses: wherein the one or more processors, to identify the one or more events, are configured to:
obtain, confidence scores for respective events from the set of events, wherein the confidence scores indicate likelihoods that the respective events are the particular type of events; and determine that the one or more events are the particular type of events based on the one or more events being associated with respective confidence scores, from the confidence scores, that satisfy a threshold (see claim 1; “a second confidence score of the new value, the second confidence score of the new value based on respective contribution scores of respective electronic activities of the subset of electronic activities associated with a respective activity field-value pair corresponding to the new value” and “comparing, by the one more processors, the second confidence score to the first confidence score or to a threshold corresponding to one or more event detection policies, each event detection policy used for determining a respective event type of a plurality of event types, each event type corresponding to one or more of a last name field, a title field, a company name field, a location field, an email address field or a phone number field of a respective node profile; determining, by the one or more processors, based on the second confidence score of the new value exceeding the first confidence score or satisfying the threshold, a first event of a first event type of the plurality of event types based on the field of the first node profile to which the new value corresponds”).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the process of using classifier model 310 to determine and add values of different fields of raw transaction records from the combination of Ghoula and Subramanian by including determining type information of a corresponding event based on comparison between determined confidence score of the corresponding event and a threshold value from Wulf, and thus the combination of Ghoula, Subramanian and Wulf would disclose the missing limitations from the combination of Ghoula and Subramanian, since it would provide a standard to determine whether an event should be belonged to a particular event type (see claim 1 from Wulf).
Regarding to Claim 11, the rejection of Claim 9 is incorporated, the combination of Ghoula and Subramanian does not disclose: wherein the respective event parameters include an event type score, and wherein generating the one or more groups of events comprises:
determining respective event type scores associated with the one or more events,
wherein the respective event type scores indicate a likelihood that the one or more events are associated with an event type, and
wherein the event type is associated with the group of events; and
grouping an event, from the one or more events, into the group of events based on an event type score associated with the event indicating that the event is associated with the event type.
However, Wulf discloses: determining respective event type scores associated with the one or more events, wherein the respective event type scores indicate a likelihood that the one or more events are associated with an event type, and wherein the event type is associated with the group of events; and grouping an event, from the one or more events, into the group of events based on an event type score associated with the event indicating that the event is associated with the event type (see claim 1; “a second confidence score of the new value, the second confidence score of the new value based on respective contribution scores of respective electronic activities of the subset of electronic activities associated with a respective activity field-value pair corresponding to the new value” and “comparing, by the one more processors, the second confidence score to the first confidence score or to a threshold corresponding to one or more event detection policies, each event detection policy used for determining a respective event type of a plurality of event types, each event type corresponding to one or more of a last name field, a title field, a company name field, a location field, an email address field or a phone number field of a respective node profile; determining, by the one or more processors, based on the second confidence score of the new value exceeding the first confidence score or satisfying the threshold, a first event of a first event type of the plurality of event types based on the field of the first node profile to which the new value corresponds”. Note: identifying or determining an event is actually belonged to an event type can be considered as grouping such event into the group of events associated with the event type).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the process of using classifier model 310 to determine and add values of different fields of raw transaction records from the combination of Ghoula and Subramanian by including determining type information of a corresponding event based on comparison between determined confidence score of the corresponding event and a threshold value from Wulf, and thus the combination of Ghoula, Subramanian and Wulf would disclose the missing limitations from the combination of Ghoula and Subramanian, since it would provide a standard to determine whether an event should be belonged to a particular event type (see claim 1 from Wulf).
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Ghoula et al. (US 20210374164 A1, hereafter Ghoula) in view of Subramanian et al. (US 20170300879 A1, hereafter Subramanian) and further in view of Eliyahu et al. (US 20230351383 A1, hereafter Eliyahu).
Regarding to Claim 5, the rejection of Claim 1 is incorporated, the combination of Ghoula and Subramanian does not disclose: wherein the one or more processors, to generate the display content, are configured to:
determine a ridicule level to be associated with the display content based on the account data; and
determine the display content based on the ridicule level.
However, Eliyahu discloses: determine a ridicule level to be associated with the display content based on the account data; and determine the display content based on the ridicule level (see [0046] and [0059]; “detecting an original amount being entered (e.g., via a user interface) for a current transaction, selectively performing one or more actions based on whether the original amount is classified as normal or anomalous, generating, for the original amount, at least one of a type abnormality score, an industry abnormality score, a category abnormality score, and one or more interaction scores based on said abnormality scores, annotating a current transaction based on determining whether a final amount stored is different than an original amount entered, and/or selectively providing a current transaction to one or more appropriate components for further processing” and “The interface 110 may be one or more input/output (I/O) interfaces for receiving input data, such as a transaction amount entered by a user. The interface 110 may also be used to present information to a user, such as a notification that a transaction amount may be inaccurate, a request to enter a replacement transaction amount, or the like”. Based on the abnormality scores, i.e., claimed ridicule level, of data transactions, determining the displayed content related to the notification and request discussed at [0059]. Also see “implementations of the subject matter described in this disclosure may provide one or more benefits such as identifying incorrectly entered amounts as soon as possible (e.g., immediately) and as accurately as possible … reducing system errors, reducing reconciliation errors” from [0047], “the final amount stored for a given transaction may be obtained after the given transaction is reconciled with a particular (e.g., external) source” from [0067]).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the data reconciliation system on account transactions from the combination of Ghoula and Subramanian by including notification about transaction being inaccurate and request to correct such inaccurate to use from Eliyahu, and thus the combination of Ghoula, Subramanian and Eliyahu would disclose the missing limitations from the combination of Ghoula and Subramanian, since it would provide a method to provide benefits of “identifying incorrectly entered amounts as soon as possible (e.g., immediately) and as accurately as possible … reducing system errors, reducing reconciliation errors, reducing user time and effort, reducing system processing and memory resources, increasing user satisfaction and retention” (see [0047] from Eliyahu).
Regarding to Claim 6, the rejection of Claim 5 is incorporated and further the combination of Ghoula, Subramanian and Eliyahu discloses: wherein the one or more processors, to generate the display content, are configured to: provide, to a large language model, the ridicule level and the information associated with the events included in the group (see [0046] and [0094] from Eliyahu; “generating, for the original amount, at least one of a type abnormality score, an industry abnormality score, a category abnormality score, and one or more interaction scores based on said abnormality scores” and “the prediction engine 162 generates the category interaction abnormality score based on a corresponding portion of the anomaly scoring algorithm”. Also see [0121] from Eliyahu; “the anomaly scoring algorithm is generated using a machine learning process, and the predicted likelihood is based on the overall abnormality score generated based on a combination of at least one of the type abnormality score, the industry abnormality score, the user category abnormality score, the category interaction abnormality score, the industry interaction abnormality score, the type interaction abnormality score”); and obtain, via the large language model, the display content (see [0059] from Eliyahu; “The interface 110 may also be used to present information to a user, such as a notification that a transaction amount may be inaccurate, a request to enter a replacement transaction amount, or the like”).
Claims 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ghoula et al. (US 20210374164 A1, hereafter Ghoula) in view of Subramanian et al. (US 20170300879 A1, hereafter Subramanian) and further in view of Loganathan et al. (US 20230040705 A1, hereafter Loganathan).
Regarding to Claim 8, the rejection of Claim 7 is incorporated, the combination of Ghoula and Subramanian does not disclose: wherein the trigger category includes at least one of: a travel category, or a new event type category.
However, Loganathan discloses: detect, based on the event data, that the one or more events are associated with a trigger category and wherein the trigger category includes at least one of: a travel category, or a new event type category (see [0026]; “categorize a given transaction … The transactions may be broken up into any number of categories …. For example, the expenses over a given time period (such as a week, month, or year) may be grouped by category to provide an overall picture of the debtor's expenses. This may include categorizing expenses as fixed recurring expenses (e.g., car payment, phone bill, mortgage/rent payment, and the like), variable recurring expenses (e.g., utility payments, groceries, etc.), and/or other expenses (e.g., one-time purchases, entertainment costs, dining, travel, etc.)”.)
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the different particular process types of raw transaction records from the combination of Ghoula and Subramanian by including different particular expense categories of transactions including both of fixed recurring category and at least travel category from Loganathan, and thus the combination of Ghoula, Subramanian and Loganathan would disclose the missing limitations from the combination of Ghoula and Subramanian, since it would provide additional examples of the expense categories for monetary transactions (see [0026] from Loganathan).
Regarding to Claim 15, Claim 15 is a method claim corresponds to system Claim 8 and is rejected for the same reason set forth in the rejection of Claim 8 above.
Response to Arguments
Applicant’s arguments, filed 7/21/2026, with respect to rejections of claims 1-20 under 35 U.S.C. 102 or 35 U.S.C. 103 have been full considered. New grounds of rejections are made based on the new added amendments from the independent claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stepanova (US 20210157564 A1) discloses: attributes for a subscription product feature may include subscription payment date, renewal date, subscription length, payment amount, etc (see [0020]).
Thompson (US 20180365786 A1) discloses: a customer may have a subscription that incurs an automatic periodic payment to maintain the subscription (see [0025]).
Fanelli (US 9092753 B1) discloses: The subscription detail includes account number, billing period and the next renew date and allows the user to select or deselect auto renewal of the subscription (see lines 49-52 of col. 24).
Pattabiraman et al. (US 20100131393 A1) discloses: Renew next period field 531 indicates whether the bundle, associated with a particular subscriber, is to be automatically renewed when the current period ends (e.g., at the renewal date field) (see [0060]).
Morikawa et al. (US 5943405 A) discloses: A flag (0: non-subscription, 1: subscription) is stored in each region for identifying whether or not the subscriber subscribes each telecommunication service (see lines 11-29 of col. 9).
Chai et al. (US 20180278754 A1) discloses: whether a certain event specifically belongs to a non-subscribed event or a subscribed event is determined according to an operation policy of the operator and positioning of the event by the operator (see [0183]).
Burns (US 20220222736 A1) discloses: check the subscription data retrieved at step, including current renewal date, subscription status, and subscription price associated with each unique subscription ID. Based on this subscription data, the rebilling module 123 may determine if billing action is required for the subscription (see [0022]).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHI CHEN whose telephone number is (571)272-0805. The examiner can normally be reached on M-F from 9:30AM to 5:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, April Y Blair can be reached on 571-270-1014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Zhi Chen/
Patent Examiner, AU2196
/APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196
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