# US 2025/0086050 A1 · Data reconciliation for a multi-channel pipeline > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0086050 A1 - Document ID: US20250086050A1 - Status: Published application - Kind: A1 - Application number: 18/466,501 - Filed: 2023-09-13 - Published: 2025-03-13 - Earliest claimed: 2023-09-13 - Assignees: Capital One Drive, LLC - Applicants: Capital One Drive - Inventors: Kirankumar KULKARNI - Web page: https://patentgrep.com/patent/US20250086050A1 ## CPC - G06F 11/0793: {Remedial or corrective actions (recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40)} - G06F 11/2215: {to test error correction or detection circuits} - G06F 16/215: Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors - G06F 16/2365: {Ensuring data consistency and integrity} - G06F 16/254: {Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract In some implementations, a device may obtain, via a source platform, source data that is to be provided to a target platform to be stored in one or more databases included in the target platform, wherein the one or more databases are accessible via a data integration pipeline. The device may provide the source data via the data integration pipeline to cause input data to be provided to the target platform. The device may obtain sets of intermediate data associated with respective channels of multiple channels included in the data integration pipeline. The device may determine, based on a comparison of the source data, the sets of intermediate data, and the input data, data reconciliation information indicating one or more data discrepancy events and channels associated with respective data discrepancy events of the one or more data discrepancy events. The device may perform, based on the data reconciliation information, an action. ## Claims ### Claim 1 A system for data reconciliation for a multi-channel pipeline, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: obtain, via a source platform, source data that is to be provided to a target platform to be stored in one or more databases included in the target platform, wherein the one or more databases are accessible via a function associated with the target platform; provide the source data via the multi-channel pipeline to cause input data to be provided to the function, wherein the source data is an input of the multi-channel pipeline and the input data is an output of the multi-channel pipeline; obtain sets of intermediate data associated with respective channels of multiple channels included in the multi-channel pipeline; compare the source data, the sets of intermediate data, and the input data based on one or more data normalization operations associated with at least one of the sets of intermediate data; identify, based on comparing the source data, the sets of intermediate data, and the input data, data reconciliation information indicating one or more data discrepancy events and channels, of the multiple channels, associated with respective data discrepancy events of the one or more data discrepancy events; and perform, based on the data reconciliation information, an action. ### Claim 2 (depends on 1) The system of claim 1, wherein the one or more processors, to perform the action, are configured to: provide, for display, a report indicating the data reconciliation information. ### Claim 3 (depends on 2) The system of claim 2, wherein the one or processors are further configured to: receive an indication of one or more channels, of the multiple channels, to be associated with the report, wherein the report indicates a subset of the data reconciliation information, and wherein the subset indicates information, from the data reconciliation information, associated with the one or more channels. ### Claim 4 (depends on 1) The system of claim 1, wherein the one or processors are further configured to: obtain, via a user interface, data normalization information indicating one or more mappings between data modelings of two or more channels of the multiple channels, wherein the data normalization information defines how to compare data from the two or more channels. ### Claim 5 (depends on 4) The system of claim 4, wherein the one or more processors, to compare the source data, the sets of intermediate data, and the input data, are configured to: perform, using the sets of intermediate data, the one or more data normalization operations in accordance with the data normalization information. ### Claim 6 (depends on 1) The system of claim 1, wherein the one or more processors, to compare the source data, the sets of intermediate data, and the input data, are configured to: provide, to a machine learning model, first intermediate data associated with a first channel of the multiple channels and second intermediate data associated with a second channel of the multiple channels, wherein the first channel and the second channel are different types of channels; and obtain, via the machine learning model, an indication of whether any data discrepancy events are associated with the first intermediate data and the second intermediate data. ### Claim 7 (depends on 1) The system of claim 1, wherein the one or more processors, to compare the source data, the sets of intermediate data, and the input data, are configured to: identify a data discrepancy event based on comparing the source data to the input data; and compare, based on identifying the data discrepancy event and for each step of the multi-channel pipeline, different set of intermediate data from the sets of intermediate data associated with that step. ### Claim 8 (depends on 1) The system of claim 1, wherein the one or more processors, to compare the source data, the sets of intermediate data, and the input data, are configured to: compare at least one of: a quantity of data elements, one or more fields, or one or more data values. ### Claim 9 A method for data reconciliation for a target platform, comprising: obtaining, by a device and via a source platform, source data that is to be provided to the target platform to be stored in one or more databases included in the target platform, wherein the one or more databases are accessible via a multi-channel pipeline; providing, by the device, the source data via the multi-channel pipeline to cause input data to be provided to the target platform; obtaining, by the device, sets of intermediate data associated with respective channels of multiple channels included in the multi-channel pipeline; determining, by the device and based on a comparison of the source data, the sets of intermediate data, and the input data, data reconciliation information indicating one or more data discrepancy events and channels, of the multiple channels, associated with respective data discrepancy events of the one or more data discrepancy events; and performing, by the device and based on the data reconciliation information, an action. ### Claim 10 (depends on 9) The method of claim 9, wherein performing the action comprises: providing, for display, a report indicating the data reconciliation information. ### Claim 11 (depends on 9) The method of claim 9, further comprising: obtaining, via a user interface, data normalization information indicating one or more mappings between data modeling of two or more channels of the multiple channels, wherein the data normalization information defines how to compare data from the two or more channels. ### Claim 12 (depends on 9) The method of claim 9, wherein determining the data reconciliation information comprises: providing, to a machine learning model, the source data, the sets of intermediate data, and the input data; and obtaining, via the machine learning model, an output indicating whether any data discrepancy events are detected. ### Claim 13 (depends on 9) The method of claim 9, wherein comparing the source data, the sets of intermediate data, and the input data comprises: identifying a data discrepancy event based on comparing the source data to the input data; and comparing, based on identifying the data discrepancy event and for each step of the multi-channel pipeline, intermediate data from the sets of intermediate data associated with that step. ### Claim 14 (depends on 9) The method of claim 9, wherein the multiple channels include at least one of: one or more application programming interfaces (APIs), one or more data streaming platforms, or one or more data sources. ### Claim 15 (depends on 9) The method of claim 9, wherein performing the action comprises: performing, via a channel that is associated with at least one data discrepancy event of the one or more data discrepancy events, a modification to one or more data processing operations based on the at least one data discrepancy event. ### Claim 16 A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: obtain, via a source platform, source data that is to be provided to a target platform to be stored in one or more databases included in the target platform; provide the source data via a multi-channel pipeline data to cause input data to be provided to the target platform, wherein the source data is an input of the multi-channel pipeline and the input data is an output of the multi-channel pipeline; obtain sets of intermediate data associated with respective channels of multiple channels included in the multi-channel pipeline; compare the source data, the sets of intermediate data, and the input data based on one or more data normalization operations associated with at least one of the sets of intermediate data; identify, based on comparing the source data, the sets of intermediate data, and the input data, data reconciliation information indicating one or more data discrepancy events and channels, of the multiple channels, associated with respective data discrepancy events of the one or more data discrepancy events; and perform, based on the data reconciliation information, an action. ### Claim 17 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the one or more instructions further cause the device to: receive an indication of one or more channels, of the multiple channels, to be associated with a report, and wherein the one or more instructions, that cause the device to perform the action, cause the device to: provide, for display, the report indicating a subset of the data reconciliation information, wherein the subset indicates information, from the data reconciliation information, associated with the one or more channels. ### Claim 18 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the one or more instructions, that cause the device to compare the source data, the sets of intermediate data, and the input data, cause the device to: provide, to a machine learning model, first intermediate data associated with a first channel of the multiple channels and second intermediate data associated with a second channel of the multiple channels, wherein the first channel and the second channel are different types of channels; and obtain, via the machine learning model, an indication of whether any data discrepancy events are associated with the first intermediate data and the second intermediate data. ### Claim 19 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the one or more instructions, that cause the device to compare the source data, the sets of intermediate data, and the input data, cause the device to: identify a data discrepancy event based on comparing the source data to the input data; and compare, based on identifying the data discrepancy event and for each step of the multi-channel pipeline, intermediate data from the sets of intermediate data associated with that step. ### Claim 20 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the one or more instructions, that cause the device to perform the action, cause the device to: perform, via a channel that is associated with at least one data discrepancy event of the one or more data discrepancy events, a modification to one or more data processing operations based on the at least one data discrepancy event.