DETAILED ACTION
This communication is in response to the Applicant Arguments/Remarks filed 11/17/2025. Claims 1-31 are pending in the application.
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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-31 have been considered.
Applicant's arguments filed 11/17/2025 have been fully considered but they are not persuasive. Regarding the argument on page 9 of the Remarks that neither Chatelain nor Couris teaches: “causing export of the reusable data processing program based at least in part on the set of data transformation steps, the reusable data processing program being applicable to one or more pluralities of records different from the plurality of input records”, examiner respectfully disagrees.
Chatelain teaches at para. 37: facilitate the generation of a universal holistic view of metadata across disparate data sources by integrating various different schemas within which the metadata is transformed to a single universal schema; para. 39: the scan results are parsed for data elements such as codes and scripts, and the scan profiles are captured (e.g. stored) in the UMR; para. 53-55: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for meta data extracted from such new data sources, normalize different metadata schemas from the data sources into a target format and object schema that is common across the universal metadata repository. For example, the metadata ingestion circuitry may provide normalization of metadata through a series of connectors to an ingestible formation, such from a non JSON data to JSON data across the different schemas, reconcile the various disparate formats into a common schema format in the universal metadata repository; para. 73: the business graph serves as a knowledge piece that is exportable and reusable. For example, key aspects of a business graph may reused for differing domains, businesses, industries and the like.
Regarding the argument on page 10 of the Remarks that neither Chatelain nor Couris teaches: “wherein the representation of the set of data transformation steps displays the data transformation steps in a list ordered according to the order specified by the user", examiner respectfully disagrees.
Chatelain teaches at para. 35: all of the workflows are driven by input data. The data sources supply the input data to drive the workflows, and the source metadata characterizes the input data. Thus, the data transformation steps are in order according to the inputs; para. 37: facilitate the generation of a universal holistic view of metadata across disparate data sources by integrating various different schemas within which the metadata is transformed to a single universal schema; para. 53-55: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for meta data extracted from such new data sources, normalize different metadata schemas from the data sources into a target format and object schema that is common across the universal metadata repository. For example, the metadata ingestion circuitry may provide normalization of metadata through a series of connectors to an ingestible formation, such from a non JSON data to JSON data across the different schemas, reconcile the various disparate formats into a common schema format in the universal metadata repository.
Regarding the argument on page 11 of the Remarks that neither Chatelain nor Couris teaches: "wherein the set of data transformation steps rendered in the list interface are ordered according to an order of application of the data transformation steps to the plurality of input records", examiner respectfully disagrees.
Chatelain teaches at para. 16: automatically scans and captures metadata characterizing input data driven into any pre-defined enterprise workflows (ordered transformation steps) from any number and type of data sources; para. 27: provide various reporting using the reporting circuitry based on the data analytics performed. Reporting may be in the form of exportable files, viewable graphs, lists, tables and the like, and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry; para. 35: all of the workflows are driven by input data. The data sources supply the input data to drive the workflows, and the source metadata characterizes the input data. Thus, the data transformation steps are in order according to the inputs; para. 37: facilitate the generation of a universal holistic view of metadata across disparate data sources by integrating various different schemas within which the metadata is transformed to a single universal schema; para. 53-55: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for meta data extracted from such new data sources, normalize different metadata schemas from the data sources into a target format and object schema that is common across the universal metadata repository. For example, the metadata ingestion circuitry may provide normalization of metadata through a series of connectors to an ingestible formation, such from a non JSON data to JSON data across the different schemas, reconcile the various disparate formats into a common schema format in the universal metadata repository).
Regarding the argument on page 13 of the Remarks that neither Chatelain nor Couris teaches: "receiving third user input to change the order of application of the set of data transformation steps," examiner respectfully disagrees.
Chatelain teaches at para. 23, 17: the UMR integrates any desired data profiles and similarity profiles across an entire enterprise platform. The architecture includes a feedback loop that, e.g., enforces business rules, re-scans the data sources, and updates the UMR on any scheduled or directed basis; para. 62, 81: user input regarding identification of additional data sources to add to the catalog may be received (1111), and the operation may return to selecting metadata sources (1102). In addition, the metadata conflict resolution circuitry 724 may map the transactions to the data sources (1112) and map the transactions to the data destinations (1114) as part of creating information for the lineage group structure. The catalog of data sources and the source and destination mapping may be stored in the universal metadata repository.
Regarding the argument on page 14 of the Remarks that neither Chatelain nor Couris teaches: "interacting with a data transformation step using the list interface to modify the data transformation step," examiner respectfully disagrees.
Chatelain teaches at para. 45: the architecture modifies or drops the recommended relationships and re-runs the data scanners. When the recommended
relationships are accepted, the architecture save those relationships, and continue to review the data sources into the future for new data relationships; para. 27: Reporting may be in the form of exportable files, viewable graphs, lists, tables and the like,
and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry; para. 36-37: eliminates cumbersome and inexact manual tuning and analysis of the data sources and workflows, in favor of the centralized uniform schema metadata repository architecture stored in a distributed data storage system; para. 50: the frontend layer may include a dashboard for user interaction with the architecture and an administrative user interface to manage and maintain the architecture; para. 68: data profiling may occur when there is a significant change in data from a previous profile time (e.g. value drift, significant data upload, etc.)).
Regarding the argument on page 15 of the Remarks that neither Chatelain nor Couris teaches: "interacting with a data transformation step using the list interface to remove the data transformation step from the set of data transformation steps," examiner respectfully disagrees.
Chatelain teaches at para. 27: reporting may be in the form of exportable files, viewable graphs, lists, tables and the like, and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry; para. 36: create and deliver a holistic view such as an incidence schema in the form of a linked interactive set of GUIs that facilitate interaction within the architecture; para. 45, 50: the frontend layer may include a dashboard for user interaction with the architecture and an administrative user interface to manage and maintain the architecture; para. 73: the business graph may be removed without effecting the underlying infrastructure representation in the technical manifestation (e.g. technical context) of the collected metadata.
Regarding the argument on page 16 of the Remarks that neither Chatelain nor Couris teaches: "wherein the set of data transformation steps includes a filter data transformation step," examiner respectfully disagrees.
Chatelain teaches at para. 56: resolve any duplicated information by identification and deletion of repeated metadata within the universal metadata repository/filtering; para. 85: the event manager 736 may apply predetermined rules and logic that may be triggered on events, conditions, or happenings (1164). The event manager 736 may monitor for a quality threshold (1166). If a quality threshold has not been crossed, processing by the data processing circuitry 708 may be performed, data may be displayed (1170), and data may be stored in the graph store 740 and the document store 742 as appropriate.
Regarding the argument on page 17 of the Remarks that neither Chatelain nor Couris teaches: "wherein causing export of the reusable data processing program includes compiling the set of data transformation steps to form the reusable data processing program," examiner respectfully disagrees.
Chatelain teaches at para. 37: facilitate the generation of a universal holistic view of metadata across disparate data sources by integrating various different schemas within which the metadata is transformed – thus, compiling to a single universal schema; para. 53-55: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for metadata extracted from such new data sources, normalize different metadata schemas from the data sources into a target format and object schema that is common across the universal metadata repository. For example, the metadata ingestion circuitry may provide normalization of metadata through a series of connectors to an ingestible formation, such from a non JSON data to JSON data across the different schemas, reconcile the various disparate formats into a common schema format in the universal metadata repository; para. 73: the business graph serves as a knowledge piece
that is exportable and reusable. For example, key aspects of a business graph may reused for differing domains, businesses, industries and the like. In addition, the business graph may be exportable and reusable by being bootstrapped with existing ontologies to kick start an engagement of the architecture with a different business entity or industry.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chatelain et al. (US 20180144067) in view of Couris et al. (US 2016/0117371).
As per claims 1, 24-26, Chatelain teaches
a method for developing a reusable data processing program, and the method
including: accessing a plurality of input records (fig. 5: request access; para. 46, 58-59: perform data quality metric analysis and data lineage development and analysis; para. 73: the business graph serves as a knowledge piece that is exportable and reusable. For example, key aspects of a business graph may reused for differing domains, businesses, industries and the like. In addition, the business graph may be exportable and reusable by being bootstrapped with existing ontologies to kick start an engagement of the architecture with a different business entity or industry);
rendering a representation of the plurality of input records in one or more user interfaces (para. 16: automatically scans and captures metadata characterizing input data driven into any pre-defined enterprise workflows from any number and type of data sources; para. 27-28: the architecture provides various reporting using the reporting circuitry based on the data analytics performed. Reporting may be in the form of
exportable files, viewable graphs, lists, tables and the like, and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry. The user interface circuitry 126 may include hardware displays such as liquid crystal displays (LCD), Light Emitting Diode (LED) displays or any other form of image
rendering hardware);
receiving a set of one or more data transformation steps; applying the set of data transformation steps to the plurality of input records to obtain a plurality of transformed records (para. 37, 53: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for meta data extracted from such
new data sources; para. 87: data transformation, normalization and enrichment may be based on rules, machine learning, logic, artificial intelligence, modeling and other computer based analysis and functionality);
rendering a representation of the plurality of transformed records in the one or more user interfaces (para. 16: automatically scans and captures metadata characterizing input data driven into any pre-defined enterprise workflows from any number and type of data sources; para. 27-28: the architecture provides various reporting using the reporting circuitry based on the data analytics performed. Reporting may be in the form of exportable files, viewable graphs, lists, tables and the like, and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry. The user interface circuitry 126 may include hardware displays such as liquid crystal displays (LCD), Light Emitting Diode (LED) displays or any other form of image rendering hardware);
receiving first user input as the user manipulates the representation of the plurality of transformed records using the one or more user interfaces, the first user input including one or more data transformation steps (para. 16-18: automatically scans and captures metadata characterizing input data driven into any pre-defined enterprise workflows from any number and type of data sources, provides
a single logical user interface view of input data to the workflows, including business and technical data in the form of, for example, a graph schema);
for each data transformation step of the one or more data transformation steps of the first user input: adding the data transformation step to the set of data transformation steps to update the set of data transformation steps (para. 81: user input regarding identification of additional data sources to add to the catalog may be received, and the operation may return to selecting metadata sources; para. 87: perform computer based selection of data sources of interest from a myriad of workflows that might be of interest. Data is ingested from each of the data source(s) that align with a workflow. Data transformation, normalization and enrichment may be based on rules, machine learning, logic, artificial intelligence, modeling and other computer based analysis and functionality),
updating the plurality of transformed records, including applying the updated set of data transformation steps to the plurality of input records to obtain an updated
plurality of transformed records (para. 30: creating or updating a data lineage structure for the input data; para. 63: integrate all data profiles and similarity profiles across the entire platform. This approach also includes a feedback loop that enforces the business rules and re-runs the scans automatically to update the universal metadata repository. The architecture may apply the rules defined on specific columns or fields, re-run the profiles and similarity scans to update the data quality metrics as per the newly applied rules),
rendering a representation of the updated plurality of transformed records in the one or more user interfaces (para. 27: provide various reporting using the reporting circuitry 124 based on the data analytics performed. Reporting may be in the form of exportable files, viewable graphs, lists, tables and the like, and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry; para. 92);
receiving second user input causing export of the reusable data processing program, said exported program being based at least in part on the updated set of data transformation steps (para. 73: the business context graph contains concepts, rules, reports, etc. that are used to understand and make business decisions. The business graph serves as a knowledge piece that is exportable and reusable; para. 81: user input regarding identification of additional data sources to add to the catalog may be received, and the operation may return to selecting metadata sources; para. 39: the scan results are parsed for data elements such as codes and scripts, and the scan profiles are captured (e.g. stored) in the UMR; para. 53-55: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for meta data extracted from such new data sources, normalize different metadata schemas from the data sources into a target format and object schema that is common across the universal metadata repository. For example, the metadata ingestion circuitry may provide normalization of metadata through a series of connectors to an ingestible formation, such from a non JSON data to JSON data across the different schemas, reconcile the various disparate formats into a common schema format in the universal metadata repository),
the reusable data processing program being applicable to one or more pluralities of records different from the plurality of input records (para. 39: if there are
more, different, or new data quality rules to apply, the architecture re-runs the profile scans using the same or different mappers to continue to test the data quality; para. 73: key aspects of a business graph may reused for differing domains, businesses, industries and the like).
Even if Chatelain does not explicitly teach transformed records.
Couris teaches said limitations in fig. 5: item 520 as described in para. 40: selection of a data source results in presentation of preview panel 510 alongside
the workspace panel 420. In a first portion 520 of the preview panel 510, at least a subset of data, here, in a tabular form is presented. This provides a user with a general idea of the data included in the selected data source as well as the effect of changes. Second portion 530 of the preview panel 510 is a toolbar or ribbon including graphical representations of a set of transformation operations. Upon selection, code for the
transformation operation can be automatically generated and the first portion 520 can be updated to reflect application of the operation.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chatelain to include the job authoring with data preview of transformed records of Couris in order for quick previews of relating records to be updated to reflect the operation and backend code generated that implements the operation after a transformation operation is selected – See Couris, para. 3.
As per claims 2-3, Chatelain teaches
wherein the set of data transformation steps includes a plurality of data transformation steps; wherein the plurality of data transformation steps is applied sequentially according to an order specified by the user (para. 53, 87: perform computer based selection of data sources of interest from a myriad of workflows that might be of interest. Data is ingested from each of the data source(s) that align with a workflow/ thus, sequentially. Data transformation, normalization and enrichment may be based on rules, machine learning, logic, artificial intelligence, modeling and other computer based analysis and functionality).
As per claim 4, Chatelain teaches
rendering a representation of the set of data transformation steps in the user interface during development of the reusable data processing program (para. 58-59: the metadata analytics circuitry includes a descriptive module and a predictive module to perform data quality metric analysis and data lineage development and analysis; para. 73: the business graph serves as a knowledge piece that is exportable and reusable. For example, key aspects of a business graph may reused for differing domains, businesses, industries and the like. In addition, the business graph may be exportable and reusable by being bootstrapped with existing ontologies to kick start an engagement of the architecture with a different business entity or industry).
As per claim 5, Chatelain teaches
wherein the representation of the set of data transformation steps displays the data transformation steps in a list ordered according to the order specified by the user (fig. 2: display circuitry, machine interface 210; para. 37: facilitate the generation of a universal holistic view of metadata across disparate data sources by integrating various different schemas within which the metadata is transformed to a single universal schema; para. 53-55: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for meta data extracted from such new data sources, normalize different metadata schemas from the data sources into a target format and object schema that is common across the universal metadata repository. For example, the metadata ingestion circuitry may provide normalization of metadata through a series of connectors to an ingestible formation, such from a non JSON data to JSON data across the different schemas, reconcile the various disparate formats into a common schema format in the universal metadata repository).
As per claim 6, Chatelain teaches
wherein the representation of the set of data transformation steps includes a dataflow graph representation of the data transformation steps (para. 16-18, 65: the universal metadata selection circuitry may also map transaction source data sources and transaction destinations within the dataflow layer; para. 34: the data sources provides any type of input data to any number and type of enterprise workflows defined within the enterprise. As just a few examples, the enterprise workflows include: human resources (HR) workflows that govern HR procedures such as hiring, reviews, and firing; banking workflows that create new client accounts, approve loans, or issue mortgages).
As per claim 7, Chatelain teaches
receiving third user input causing removal of one or more data transformation steps from the set of data transformation steps (para. 56: deletion of repeated metadata within the universal metadata repository once the metadata from the different
data sources has been normalized and duplication can be recognized; para. 73: the business graph may be removed without effecting the underlying infrastructure representation in the technical manifestation (e.g. technical context) of the collected metadata).
As per claim 8, Chatelain teaches
receiving third user input causing modification of one or more data transformation steps from the set of data transformation steps (para. 45: the architecture modifies or drops the recommended relationships and re-runs the data scanners. When the recommended relationships are accepted, the architecture save those relationships, and continue to review the data sources into the future for new data relationships; para. 81: identify and select data sources to be sources of metadata and provide transaction flow data; para. 87: perform computer-based selection of data sources of interest from a myriad of workflows that might be of interest. Data is ingested from each of the data source(s) that align with a workflow).
As per claims 9-10, Chatelain teaches
wherein the user interface includes a tabular interface and the representation of the plurality of transformed records is rendered in the tabular interface; wherein the user interface further includes a list interface where the set of data transformation steps is rendered as a list in the list interface (para. 27: provide various reporting using the reporting circuitry 124 based on the data analytics performed. Reporting may be in the form of exportable files, viewable graphs, lists, tables and the like, and/or generation of databases or other data repositories.
Reporting may be via the user interface circuitry; para. 92).
As per claim 11, Chatelain teaches
wherein the set of data transformation steps rendered in the list interface are ordered according to an order of application of the data transformation steps to the plurality of input records (para. 16: automatically scans and captures metadata characterizing input data driven into any pre-defined enterprise workflows (ordered transformation steps) from any number and type of data sources; para. 27: provide various reporting using the reporting circuitry based on the data analytics performed. Reporting may be in the form of exportable files, viewable graphs, lists, tables and the like, and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry; para. 35: all of the workflows are driven by input data. The data sources supply the input data to drive the workflows, and the source metadata characterizes the input data. Thus, the data transformation steps are in order according to the inputs; para. 37: facilitate the generation of a universal holistic view of metadata across disparate data sources by integrating various different schemas within which the metadata is transformed to a single universal schema; para. 53-55: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for meta data extracted from such new data sources, normalize different metadata schemas from the data sources into a target format and object schema that is common across the universal metadata repository. For example, the metadata ingestion circuitry may provide normalization of metadata through a series of connectors to an ingestible formation, such from a non JSON data to JSON data across the different schemas, reconcile the various disparate formats into a common schema format in the universal metadata repository).
As per claim 12, Chatelain teaches
receiving third user input to change the order of application of the set of data transformation steps (para. 17: the UMR integrates any desired data profiles and similarity profiles across an entire enterprise platform. The architecture includes a feedback loop that, e.g., enforces business rules, re-scans the data sources, and updates the UMR on any scheduled or directed basis; para. 62, 81: user input regarding identification of additional data sources to add to the catalog may be received (1111), and the operation may return to selecting metadata sources (1102). In addition, the metadata conflict resolution circuitry 724 may map the transactions to the data sources (1112) and map the transactions to the data destinations (1114) as part of creating information for the lineage group structure. The catalog of data sources and the source and destination mapping may be stored in the universal metadata repository; para. 53-55: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for meta data extracted from such new data sources, normalize different metadata schemas from the data sources into a target format and object schema that is common across the universal metadata repository).
As per claim 13, Chatelain teaches
interacting with a data transformation step using the list interface to modify the data transformation step (para. 45: the architecture modifies or drops the recommended relationships and re-runs the data scanners. When the recommended
relationships are accepted, the architecture save those relationships, and continue to review the data sources into the future for new data relationships; para. 27: Reporting may be in the form of exportable files, viewable graphs, lists, tables and the like,
and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry; para. 36, 50: the frontend layer may include a dashboard for user interaction with the architecture and an administrative user interface to manage and maintain the architecture; para. 68: data profiling may occur when there is a significant change in data from a previous profile time (e.g. value drift, significant data upload, etc.)).
As per claim 14, Chatelain teaches
interacting with a data transformation step using the list interface to remove the data transformation step from the set of data transformation steps (para. 27: reporting may be in the form of exportable files, viewable graphs, lists, tables and the like, and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry; para. 36: create and deliver a holistic view such as an incidence schema in the form of a linked interactive set of GUIs that facilitate interaction within the architecture; para. 45, 50: the frontend layer may include a dashboard for user interaction with the architecture and an administrative user interface to manage and maintain the architecture; para. 73: the business graph may be removed without effecting the underlying infrastructure representation in the technical manifestation (e.g. technical context) of the collected metadata).
As per claim 15, Chatelain teaches
wherein the set of data transformation steps includes one or more of a filter data transformation step, an add field data transformation step, and a choose fields data transformation step (para. 56: resolve any duplicated information by identification and deletion of repeated metadata within the universal metadata repository/filtering; para. 85: the event manager 736 may apply predetermined rules and logic that may be triggered on events, conditions, or happenings (1164). The event manager 736 may monitor for a quality threshold (1166). If a quality threshold has not been crossed, processing by the data processing circuitry 708 may be performed, data may be displayed (1170), and data may be stored in the graph store 740 and the document store 742 as appropriate; para. 17, 35: input data includes, as just a few examples: database tables, columns, and fields; keyboard and mouse input; documents; graphs; metrics; para. 61: add the newly extracted metadata to the universal metadata repository; para. 81: user input regarding identification of additional data sources to add to the catalog may be received, and the operation may return to selecting metadata sources).
As per claim 16, Chatelain teaches
wherein the set of data transformation steps includes a filter data transformation step (para. 53, 56: perform metadata object matching (thus, filtering out unmatched fields) and conflict resolution among data from different data sources; para. 87: data
transformation, normalization and enrichment may be based on rules, machine learning, logic, artificial intelligence, modeling and other computer based analysis and functionality).
As per claim 17, Chatelain teaches
wherein causing export of the reusable data processing program includes compiling the set of data transformation steps to form the reusable data processing program (para. 37: facilitate the generation of a universal holistic view of metadata across disparate data sources by integrating various different schemas within which the metadata is transformed – thus, compiling to a single universal schema; para. 53-55: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for metadata extracted from such new data sources, normalize different metadata schemas from the data sources into a target format and object schema that is common across the universal metadata repository. For example, the metadata ingestion circuitry may provide normalization of metadata through a series of connectors to an ingestible formation, such from a non JSON data to JSON data across the different schemas, reconcile the various disparate formats into a common schema format in the universal metadata repository; para. 73: the business graph serves as a knowledge piece
that is exportable and reusable. For example, key aspects of a business graph may reused for differing domains, businesses, industries and the like. In addition, the business graph may be exportable and reusable by being bootstrapped with existing ontologies to kick start an engagement of the architecture with a different business entity or industry).
As per claim 18, Chatelain teaches
wherein causing export of the reusable data processing program includes forming a dataflow graph representation of the set of data transformation steps to form the reusable data processing program (para. 41: use a review tool to confirm the relationships to verify the dataflow; para. 73: the business graph serves as a knowledge piece that is exportable and reusable. For example, key aspects of a business graph may reused for differing domains, businesses, industries and the like);
As per claim 19, Chatelain teaches
computing a data profile for the plurality of transformed records and rendering a representation of the data profile in the user interface (fig. 3: apply data quality rules and re-run profile scans to test data quality; para. 37: facilitate the generation of a universal holistic view of metadata across disparate data sources by integrating various different schemas within which the metadata is transformed to a single universal
schema; para. 53: perform exploration of existing data sources resulting in simplified onboarding of data sources. Onboarding of data sources may include cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for meta data extracted from such new data sources).
As per claim 20, Chatelain teaches
wherein the second user input is received upon determining that a data profile for the plurality of transformed records is in accordance with a predetermined data profile (para. 39: if there are more, different, or new data quality rules to apply, the
architecture re-runs the profile scans using the same or different mappers to continue to test the data quality; para. 53: cataloging and indexing trusted data sources, as well as onboarding new data sources and configure transformation rules for meta data extracted from such new data sources; fig. 3: apply data quality rules and re-run profile scans to test data quality; fig. 6, item 606; para. 48: the architecture 110 executes data
profilers or data mappers to run profile scans on the data sources to obtain a profile of respective input data in the form of a dataset of source metadata, such as technical
metadata objects).
As per claim 21, Chatelain teaches
wherein the predetermined data profile or predetermined profile rule specifies an allowable range for some characteristics of the data profile (para. 23-25, 36: allow operators to set configuration and preference parameters for the overall operation of the architecture; para. 68: data profiling may occur when there is a significant change in data from a previous profile time (e.g. value drift, significant data upload, etc.) Occurrence of a significant change may be based on a predetermined set point or threshold which is compared at the respective data source to previously received metadata parameters).
Chatelain does not teach an allowable range.
Couris teaches said limitation at para. 31.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chatelain to include the job authoring with data preview of transformed records of Couris in order to effectively compute relating values and measures can be presented to the users.
As per claim 22, Chatelain teaches
computing a data quality for the plurality of transformed records and rendering a representation of the data quality in the user interface (para. 16, 27: provide various reporting using the reporting circuitry based on the data analytics performed. Reporting may be in the form of exportable files, viewable graphs, lists, tables and the like, and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry; para. 36: performs the data analyses, including determining data quality metrics, and building and maintaining the data lineage structure. The machine interface creates and delivers a holistic view such as an incidence schema in the form of a linked interactive set of GUIs that facilitate interaction within the).
As per claim 23, Chatelain teaches
wherein the data quality includes at least one of counts of valid values, invalid values, NULL values, distinct values, unique values, and/or maximum and minimum values (fig. 3: apply data quality rules and re-run profile scans to test data quality, perform data quality; para. 18-20: data quality metrics, data lineage and reporting; receives the source metadata, and on behalf of any given enterprise running any pre-defined workflows, analyzes and processes the source metadata, builds and maintains a UMR, determines data quality metrics, scores data quality metrics,
builds and maintains data lineage, performs data lineage scoring, sends feedback to the data sources and provides a holistic view of the UMR in a graphical user interface; para. 25, 88).
Chatelain does not teach minimum, maximum range.
Couris teaches said limitation at para. 31.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chatelain to include the job authoring with data preview of transformed records of Couris in order to effectively compute relating values and measures can be presented to the users.
Claim 27 claims the same subject matters as of claims 1, 24-26 and therefore rejected based on the same ground of rejection.
The preamble of claim 1 “A method for developing a reusable data processing program including a set of data transformation steps by displaying a set of records and iteratively enabling a user to select one or more data transformation steps, iteratively applying the data transformation steps to the records, and iteratively displaying the transformed records, the method including:” contains the steps of rendering a representation/display of the plurality of input records in one or more user interfaces; receiving/select a set of one or more data transformation steps.
As per claim 28, Chatelain teaches
determining that a data profile for the plurality of transformed records satisfies a predetermined rule and causing export of the reusable data processing program based on that determination (para. 39: if there are more, different, or new data quality rules to apply, the architecture re-runs the profile scans using the same or different mappers to continue to test the data quality; para. 63: integrate all data profiles and similarity profiles across the entire platform. This approach also includes a feedback loop that enforces the business rules and re-runs the scans automatically to update the universal metadata repository. The architecture may apply the rules defined on specific columns or fields, re-run the profiles and similarity scans to update the data quality metrics as per the newly applied rules; para. 73: the business graph serves as a knowledge piece that is exportable and reusable. For example, key aspects of a business graph may reused for differing domains, businesses, industries and the like).
As per claim 29, Chatelain teaches
wherein computing a data profile for the plurality of transformed records includes dynamically updating profile data for each transformation applied and rendering the updated profile data together with the transformed records in the user interface (para. 17: the UMR/universal metadata repository integrates any desired data profiles and similarity profiles across an entire enterprise platform. The architecture
includes a feedback loop that, e.g., enforces business rules, re-scans the data sources, and updates the UMR on any scheduled or directed basis; para. 27: provide various reporting using the reporting circuitry based on the data analytics performed. Reporting may be in the form of exportable files, viewable graphs, lists, tables and the like, and/or generation of databases or other data repositories. Reporting may be via the user interface circuitry; para. 53, 68: data profiling may occur when there is a significant change in data from a previous profile time (e.g. value drift, significant data upload, etc.))
As per claim 30, Chatelain teaches
wherein causing export of the reusable data processing program includes generating a dataflow graph representation comprising vertices corresponding to the data transformation steps and links representing record flows between the vertices (para. 41: configure lineage scanners, or mappers, to proceed with proposed relationships based on the metadata, attributes and related information, and use a review tool to confirm the relationships to verify the dataflow; para. 73-76: the graph store itself is comprised of a technical context and a business context which are two graphs that are loosely connected. The technical graph contains all information and relationships with regard to data, infrastructure, and transactions that occur on the data. The business graph serves as a knowledge piece that is exportable and reusable. For example, key aspects of a business graph may reused for differing domains, businesses, industries and the like).
Allowable Subject Matter
Claim 31 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Specification, para. 50 discloses “Note the pencil icon 593 beside the title of the Density field, indicating that the field is added by the user.” See fig. 5D, icon 593.
Chatelain teaches at para. 27, 35: all of the workflows are driven by input data. The data sources supply the input data to drive the workflows, and the source metadata characterizes the input data. Example input data includes, as just a few examples: database tables, columns, and fields; para. 63; fig. 3, items 312, 224.)
Stanfill et al. (US 20060294150) teaches at para.68-72: data flow graph; fig. 16: pencil icon. However, it does not teach “the user created column includes an icon indicating the column as user created”. The cited arts do not teach claim 31.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Burke et al. (US 20180103038) further teaches at para. 142: In the shown interface, the table 1214 displays a list of merchants (i.e., requesting computers) that are each associated with the acquirer, where each row of the table corresponds to a merchant and each cell of the row corresponds to an attribute of the merchant.”
Spicer et al. (US 20100057618) teaches in fig. 5c: an interactive user interface; col. 12: this interactivity can be real-time display of data as it is changing or-being customized. The result can be an improved workflow that can create, evaluate, and modify risk adjusted investment portfolios-in seconds-and at a fraction of the cost.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LINH BLACK/Examiner, Art Unit 2163 3/9/2026
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163