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 in response to the amendment filed on 02/13/2026. Claims 1-20 are presented for examination, with claims 1, 19 and 20 being independent. Claims 1-20 are pending in this action.
The objection to claim 14 is hereby withdrawn.
The rejection set forth under U.S.C. 112(b) in the 09/15/2025 office action is hereby withdrawn.
Claim Interpretation
The following claim terminologies and/or limitations are interpreted in view of the definitions/descriptions (highlighted below for convenience) provided in applicants’ disclosure.
Source-canonical transformation accelerators:
[0044] The source-specific data may be processed by three pipelines in metadata-driven architecture 400. In particular, through the ingestion, processing/staging, and storage layers of metadata-driven architecture 400, regardless of the number T of instances of source-specific data (e.g., tables) that are received, metadata-driven architecture 400 implements 3 pipelines. In some embodiments, source-canonical transformation accelerators may be configured to execute an extract pipeline (e.g., “Pipeline X”), a transformation pipeline (e.g., “Pipeline Y”), and a load pipeline (e.g., “Pipeline Z”) for each stage of an extract-transform-load (ELT) process. In one or more examples, each of the extract pipeline, the transformation pipeline, and the load pipeline may be controlled by the plurality of metadata tables stored in metadata database 144. Implementing the aforementioned process using the plurality of metadata tables provides a technical improvement over existing data processing systems by reducing a number of pipelines used by the ERP to a single instance of each of the extract pipeline, the transformation pipeline, and the load pipeline.
[0045] Transformation logic (e.g., source-canonical transformation accelerators) may be stored in a metadata database 144. The transformation logic may include rules for transforming source-specific data into source-agnostic data. These rules may enable the source-specific data to be transformed from being structured using a data formatting protocol associated with a given data source to being structured using a source-agnostic data formatting protocol.
Transformation database:
[0075] At step 904, a source-canonical transformation accelerator configured to transform the source-specific data into source-agnostic data structured using a source-agnostic data formatting protocol may be accessed from a transformation database. In one or more examples, the source-canonical transformation accelerator may be configured to execute an extract pipeline, a transformation pipeline, and a load pipeline for each stage of an extract-transform-load (ELT) process. Each of the extract pipeline, the transformation pipeline, and the load pipeline may be controlled by the plurality of metadata tables stored in the transformation database.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20, as amended, remain rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 are directed to the abstract idea for generating source-agnostic data for enterprise resource planning.
The claims have been amended, however, still do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1: Claim 1 recites "A method ..."; the claims recite a series of steps and therefore, are processes.
Claim 19 recites "A system"; therefore, the claim is considered as a machine. Claim 20 recites "A non-transitory computer-readable medium ..."; therefore, the claim is a manufacture.
Independent claims 1, 19 and 20 recite limitations of: receiving, from a data source, source-specific data structured using a data formatting protocol associated with the data source; (insignificant extra-solution activity of mere generic gathering/collecting data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d));
selecting , from a transformation database, a source-canonical transformation
accelerator of a plurality of transformation accelerators stored in a transformation database, wherein selection of the source-canonical transformation accelerator is based on the data source associated with the received source-specific data, and wherein the selected source-canonical transformation accelerator is configured to transform the source-specific data, (claims 19 and 20: using the data formatting protocol associated with the data source), into source-agnostic data structured using a source-agnostic data formatting protocol; (insignificant extra-solution activity of mere generic gathering/collecting and transforming data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)); and generating, using the source-canonical transformation accelerator, the source-agnostic data based on the source-specific data; (Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion)).
Examiner notes that the “transformation database” and “selection of source-canonical transformation accelerator” as recited in the amended claims are considered data structure. The claim limitation, “a plurality of transformation accelerators stored in a transformation database, wherein selection of the source-canonical transformation accelerator is based on the data source associated with the received source-specific data,” is not tied any functionalities that are to be performed by a generic computer to specifically configure or reconfigure the computing environment and how to change an existing technology. For instance, the details of how the data received from multiple sources are processed , and/or what is specifically being done to the source-specific data to derive the source-agnostic data, and/or what particular transformations were done to the received data that require specific configuration of the system beyond the recitation of transformation accelerator is found in the claims.
The “canonical transformation accelerator has been defined in par. [0045] of applicants’ disclosure as “transformation logic”, which does not indicate the specificity of configuring of comput8ing environment.
[0045] Transformation logic (e.g., source-canonical transformation accelerators) may be stored in a metadata database 144. The transformation logic may include rules for transforming source-specific data into source-agnostic data. These rules may enable the source-specific data to be transformed from being structured using a data formatting protocol associated with a given data source to being structured using a source-agnostic data formatting protocol.
Step 2A Prong One: The limitations of: generating ...; is a mental step, that, under its broadest
reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting "one or more processors, A non-transitory computer-readable medium", are computer components; nothing in the claim elements preclude the step from practically being performed in a human mind. Note that the limitations are done by the generically recited computer components under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. In particular, the claims recite the additional limitations: receiving accessing ; the limitations are mere generic gathering/collecting and transforming data (see MPEP 2106.05(g)). Further, these additional limitations are recited as being performed by "one or more processors, A non-transitory computer-readable medium" provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations: receiving accessing are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) gathering/collecting and transforming data, Versata Dev. Group Inc.
As explained with respect to Step 2A, Prong Two, the additional elements performing by one or more processors, A non-transitory computer-readable medium; in limitations receiving accessing are at best mere instructions to "apply" the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Since, claims 1, 19 and 20 are directed to abstract ideas; thus, the claims are not patent eligible.
Claims 2-18
The limitations as recited in claims 2-18 are simply describe the concepts for generating source-agnostic data for enterprise resource planning. The claims do not include additional element(s) that is sufficient to amount to significantly more than the judicial exceptions. The claims cannot provide an inventive concept. Therefore, claims 2-7 and 16-18 are directed to abstract ideas and are not patent eligible.
Analysis of the dependent claims are shown below.
Claim 2 recites the limitations, wherein the source-specific data structured comprises first source specific data structured using a first data formatting protocol associated with a first data source; Is a
mental step, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion), the method further comprises: accessing, from the transformation database, a canonical-source transformation accelerator configured to transform the source-agnostic data into second source-specific data structured using a second data formatting protocol associated with a second data source; the limitation is insignificant extra-solution activity of mere generic collecting and analyzing data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)); and generating, using the canonical-source transformation accelerator, the second source- specific data based on the source-agnostic data; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 3 recites the limitations, wherein the source-specific data comprises first source-specific data structured using a first data formatting protocol associated with a first data source, and wherein the source-agnostic data comprises first source-agnostic data; Is a mental step, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion), the method further comprises: receiving, from a second data source, second source-specific data structured using a second data formatting protocol associated with the second data source; the limitation is insignificant extra-solution activity of mere generic gathering/collecting data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d));
accessing, from the transformation database, a second source-canonical transformation
accelerator configured to transform the second source-specific data into second source-agnostic data structured using the source-agnostic data formatting protocol; the limitation is insignificant extra solution activity of mere generic collecting and analyzing data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)); and
generating, using the second source-canonical transformation accelerator, the second source agnostic data based on the second source-specific data; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the
"Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 4 recites the limitation, wherein the data source comprises a first data source of a plurality of data sources, wherein each of the plurality of data sources comprises data structured using a corresponding source-specific data formatting protocol; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 5 recites the limitation, wherein the plurality of data sources comprises at least ten data sources, at least one hundred data sources, or at least one thousand data sources; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 6 recites the limitation, wherein receiving the source-specific data comprises:
receiving an event notification indicating that the source-specific data is available; the limitation
is insignificant extra-solution activity of mere generic collecting data (see MPEP2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)); and
loading the source-specific data into a raw data layer of a cloud-computing service; the
limitation is insignificant extra-solution activity of mere generic gathering data (see MPEP2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)).
Claim 7 recites the limitation, wherein the cloud-computing service comprises a first cloud- computing service of a plurality of cloud-computing services each having a different infrastructure; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 8 recites the limitation: executing a set of metadata classification rules to the source-specific data to identify one or more types of metadata within the source-specific data, wherein the set of metadata classification rules attribute a value to each of the one or more types of metadata based on the source-specific data; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 9 recites the limitations: applying one or more data quality rules to the source-specific data; and storing, based on the one or more data quality rules indicating that the source-specific data is cleansed, the source-specific data as hybrid-based data comprises chunks of columns of data sequentially stored; the limitations are insignificant extra-solution activity of mere generic analyzing and storing data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)).
Claim 10 recites the limitation, wherein each chunk of columns comprises values for each of the one or more types of metadata; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 14 recites the limitation, wherein generating the source-agnostic data comprises:
generating, using the source-canonical transformation accelerator, the source-agnostic data
based on the hybrid-based data; Is a process, that, under its broadest reasonable interpretation,
covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 12 recites the limitation: applying one or more data analytics solutions to the source-agnostic data, wherein the one or more data analytics solutions are executing using a selected cloud-computing service used for the enterprise resource planning; the limitations are insignificant extra-solution activity of mere generic analyzing data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)).
Claim 13 recites the limitation: generating an interface for providing the one or more data analytics solutions to a client device; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 14 recites the limitations, wherein the one or more data analytics solutions comprise one or more first data analytics solutions, and the selected cloud-computing service comprises a first cloud computing service of a plurality of cloud-computing services; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion), the method further comprises: applying one or more second data analytics solutions to the source-agnostic data, wherein the one or more second data analytics solutions are executing using a second cloud-computing service used for ERP; the limitations are insignificant extra-solution activity of mere generic analyzing data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)).
Claim 15 recites the limitation, wherein the transformation database comprises a plurality of metadata tables storing one or more data schemas, one or more transformation rules, one or more control parameters, and error-handling logic; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 16 recites the limitation, wherein the source-canonical transformation accelerator is configured to execute an extract pipeline, a transformation pipeline, and a load pipeline for each stage of an extract transform- load (ELT) process, wherein each of the extract pipeline, the transformation pipeline, and the load pipeline are controlled by the plurality of metadata tables stored in the transformation database; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 17 recites the limitation, wherein the plurality of metadata tables reduce a number of pipelines used by the ERP to a single instance of each of the extract pipeline, the transformation pipeline, and the load pipeline; Is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claim 18 recites the limitations:
generating the source-canonical transformation accelerator, comprising:
identifying one or more types of metadata stored within sample source-specific data
associated with the data source; and creating one or more rules for parsing the source-specific data based on the one or more types of metadata, wherein the source-canonical transformation accelerator stores the one or more rules; are processes, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, and 18-20 under 35 U.S.C. 103 as being unpatentable over US PG-PUB 20210248146 A1 issued to Fry, Date Published 2021-08-12, hereinafter “Fry” in view of U S Patent 9,158,827 issued to Vu et al. (“Vu”).
With respect to claim 1, Fry teaches a method for generating source-agnostic data for enterprise resource planning (ERP), the method being implemented by one or more processors of a computing system, the method comprising:
receiving, (Fig 4, step 404) from a data source (Fry, [0111], “ a data stream is received from many data sources”), source-specific data structured using a data formatting protocol associated with the data source:
selecting a source-canonical transformation accelerator (In Fig 4, step 410, any one or more of the multiple canonical data formats are retrieved) wherein selection of the source-canonical transformation accelerator is based on the data source associated with the received source-specific data (The data transformed to the pre-determined format is received and stored at 408 in the form of multiple canonical data formats provided by the compilable data model”), and wherein the selected source-canonical transformation accelerator is configured to transform the source-specific data into source- agnostic data (Fry, Fig 4, [0111], “At 406, the data is transformed using a compilable data model to a pre-determined format that is agnostic to the variety of data types”) structured using a source-agnostic data formatting protocol; and
generating, using the source-canonical transformation accelerator, the source-agnostic data (“At 406, the data is transformed using a compilable data model to a pre-determined format that is agnostic to the variety of data types such as consumption pattern”)
based on the source-specific data.
Problem solved by Fry: [0004] Difficulties abound in this field, particularly when data is sourced from a multiplicity of incompatible devices and over a multiplicity of incompatible communications channels. It would, in such cases, be desirable to virtualise data sources to enable any application to retrieve and manipulate data without requiring technical information about the data such as how the data is formatted, where it is located, how it is delivered across a network, and how it can be consumed by an application, such as a data analysis tool, to produce usable information.
With respect to claim 1, Fry does not explicitly teach a transformation database.
With respect to claim 1, Vu, in a prior art reference in the same field of endeavor, teaches a metadata repository, (see Vu, Fig. 1, 118 and 119), equivalent to the claimed “transformation database,” that can be used for, [Vu, 0039] , “scanning and metadata retrieval module 108 may be configured to scan at least one of a data source and a data target, locate metadata and extract it to a metadata repository.” The Metadata repository 118 and transformation rules store 119 (in Vu) can be consolidated into a single electronic data store. See Vu, [0051].
It would have been obvious to one of ordinary skill in the art before filing of this application to incorporate the metadata repository of Vu in the system of Fry because Vu recognizes the deficiencies of current system (such as “Existing data mapping applications lack standardization, collaboration, versioning, traceability, impact analysis, management visibility, auditability and programmatic control,” and provides a solution. See Vu, col. 1 lines 22-25). The person of ordinary skill would be motivated to incorporate the new Mapping Manager of Vu along with and the metadata repository because “the Mapping Manager application is an enterprise grade data mapping application which enables collaboration and reuse of many data integration components by leveraging metadata and enterprise transformations, and improves the data mapping process by creating automation, versioning, traceability, and impact analysis via its web-enabled portal” and further because the “Mapping Manager brings standardization, collaboration, versioning, traceability, impact analysis, management visibility, and programmatic control where previous solutions had none.” (see Vu, col. 2, lines 1-16).
Additional relevant teachings of Vu are provided below.
With respect claim 1, Vu teaches,
Claim 1 (Currently Amended) A method for generating source-agnostic data for enterprise resource planning (ERP, the method being implemented by one or more processors of a computing system, the method comprising:
receiving, (Fig. 2, 202-204; [0056] “At an operation 202, information may be received relating to a data integration project and including data source information and data target information”…. “At an operation 204, at least one of a data source and a data target may be scanned…” ) from a data source, source-specific data structured using a data formatting protocol associated with the data source;
selecting (Fig. 206, [0058], “At an operation 206, transformation rules are assigned to the data integration project based on user selections from the transformation rules store…”
a source-canonical transformation accelerator of a plurality of transformation accelerators stored in a transformation database (Fig. 1, 118, “Metadata Store”), wherein selection of the source-canonical transformation accelerator is based on the data source associated with the received source-specific data, and wherein the selected source-canonical transformation accelerator is configured to transform the source-specific data into source- agnostic data structured using a source-agnostic data formatting protocol (Fig. 2, step 214, “[0062] “At an operation 214 an ETL job may be generated for any of at least two ETL software tools based on the generated data mappings. Operation 214 may be performed by an Agnostic ETL job generator module that is the same as or similar to Agnostic ETL job generator module 114…” ; and
generating, using the source-canonical transformation accelerator, the source-agnostic data based on the source-specific data (Fig. 2, 214-216; [0068] “At an operation 314 an ETL job may be generated for an ETL software tools different from the one the ETL job was imported from”).
With respect to claim 2 (the method of claim 1, wherein the source-specific data structured comprises first source specific data structured using a first data formatting protocol associated with a first data source, the method further comprises: accessing, from the transformation database, a canonical-source transformation accelerator configured to transform the source-agnostic data into second source-specific data structured using a second data formatting protocol associated with a second data source; and generating, using the canonical-source transformation accelerator, the second source- specific data based on the source-agnostic data), see Vu, Fig. 2, step 314 - “At an operation 314 an ETL job may be generated for an ETL software tools different from the one the ETL job was imported from), and Fry (Fig. 4, [0111]) teaches, “step 406, the data is transformed using a compilable data model to a pre-determined format that is agnostic to the variety of data types such as consumption pattern, rate or shape of the data. The data transformed to the pre-determined format is received and stored at 408 in the form of multiple canonical data formats provided by the compilable data model. The data at 408 is now stored in a neutral format that can in practice be communicated with any number of tools.”
With respect to claims 3 and 4, The method of claim 1, wherein the source-specific data comprises first source- specific data structured using a first data formatting protocol associated with a first data source, and wherein the source-agnostic data comprises first source-agnostic data, the method further comprises: receiving, from a second data source, second source-specific data structured using a second data formatting protocol associated with the second data source; accessing, from the transformation database, a second source-canonical transformation accelerator configured to transform the second source-specific data into second source-agnostic data structured using the source-agnostic data formatting protocol; and generating, using the second source-canonical transformation accelerator, the second source-agnostic data based on the second source-specific data, and a method of claim 1, wherein the data source comprises a first data source of a plurality of data sources, wherein each of the plurality of data sources comprises data structured using a corresponding source-specific data formatting protocol,
see Fry, Fig 4, [0111], (a data stream is received from many data sources in a variety data types having differing specific data rates, data patterns, data formats and data shapes as described in relation to the data stream input 102. At 406, the data is transformed using a compilable data model to a pre-determined format ), see also Vu,
Fig. 3, and [0065] – “FIG. 3 illustrates a method for converting an ETL job from one ETL software tool to another… “.
With respect to claim 5, the method of claim 4, wherein the plurality of data sources comprises at least ten data sources, at least one hundred data sources, or at least one thousand data sources, neither Fry nor Vu limits the number and count of data sources and targets. It would have been obvious to one of ordinary skill in the art before filing of this application, to extend the number of sources to one thousand for versatility.
Claims 6-17 are rejected over US PG-PUB 20210248146 A1 issued to Fry, Date Published 2021-08-12, hereinafter “Fry” in view of U S Patent 9,158,827 issued to Vu et al. (“Vu”), and further in view of US 2023/0244475 (hereinafter Holowaty).
For claims 6-17, the rationale used for claim 1 is applied, and in addition,
Regarding claim 6, Holowaty further discloses, wherein receiving the source-specific data comprises: receiving an event notification indicating that the source-specific data is available; and loading the source-specific data into a raw data layer of a cloud-computing service (e.g. AZURE DATA FACTORY™ is Azure's cloud ETL service for scale-out serverless data integration and data transformation. ETL stands for extract, transform, and load and is a way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake. The data lake is a place to store structured and unstructured data, as well as a method for organizing large volumes of highly diverse data from diverse sources 10. The data lake manager 57 may organize the data that is received from the ingest interface layer 51. For example, the data may be organized into layers designated as Bronze/Silver/Gold. Bronze layer data can be raw ingestion data, Silver layer data can be the filtered and cleaned data, and Gold layer data can be business-level aggregates , Holowaty: [0020], [0041]).
Regarding claim 7, Holowaty further discloses, wherein the cloud-computing service comprises a first cloud- computing service of a plurality of cloud-computing services each having a different infrastructure (e.g. this disclosure includes a detailed description on cloud computing. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes, Holowaty: [0115], [0126]- [0130]).
Regarding claim 8, Holowaty further discloses:
executing a set of metadata classification rules to the source-specific data to identify one or more types of metadata within the source-specific data, wherein the set of metadata classification rules attribute a value to each of the one or more types of metadata based on train module. The machine learning method can include decision tree learning, association rule learning, artificial neural networks, deep learning, inductive logic programming, support vector machines, clustering analysis, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, genetic algorithms, rule-based machine learning, learning classifier systems, and combinations thereof, Holowaty: [0047]).
Regarding claim 9, Holowaty further discloses: applying one or more data quality rules to the source-specific data (e.g. machine learning tools may also be integrated into the preparation and train module. The command and control configuration interface 200 includes
an operation order field 204 for ordering the operations, e.g., ordering the operations higher and lower relative to the other operations in the sequence, Holowaty: [0047], [0073] and Fig. 4); and storing, based on the one or more data quality rules indicating that the source-specific data is cleansed, the source-specific data as hybrid-based data comprises chunks of columns of data sequentially stored (e.g. Patterns and relationships can be identified in information extracted from a number of input sources [as source specific data] including devices, sensors, clickstreams, social media feeds, and applications. These patterns can be used to trigger actions
and initiate workflows such as creating alerts [as quality rules indicating], feeding information to a reporting tool, or storing transformed data for later use, Holowaty: [0044]).
Regarding claim 10, Holowaty further discloses, wherein each chunk of columns comprises values for each of the one or more types of metadata (see Holowaty: Search field, in Fig. 4).
Regarding claim 11, Holowaty further discloses, wherein generating the source-agnostic data comprises: generating, using the source-canonical transformation accelerator, the source-agnostic data based on the hybrid-based data (e.g. performing operations can then be performed in the order specified in the configuration [as source-specific data], e.g. by the auto ETL accelerator 70 [as the source-canonical transformation accelerator] to generate a finite set of a finite set of Azure Data Factory (ADF) [as the source-agnostic data]. Data may be an Excel spreadsheet, or a collection of cloud-based and on premises hybrid data warehouses, Holowaty: [0055], [0080]-[0083] and [0086])).
Regarding claim 12, Holowaty further discloses: applying one or more data analytics solutions to the source-agnostic data, wherein the one or more data analytics solutions are executing using a selected cloud-computing service used for the enterprise resource planning (e.g. the existing Azure data factor (ADF) creates a data pipeline for each dataset that an analytical solution uses. The Auto-ETL accelerator 50 employs the most common operations that a pipeline can use, and then drives those operations using a configuration that was selected by the user, e.g. the sources. The sources can include enterprise systems. Examples
of enterprise systems can include systems for enterprise resource planning, Holowaty: [0029], [0062]).
Regarding claim 13, Holowaty further discloses: generating an interface for providing the one or more data analytics solutions to a client device (e.g. command and control-configuration interface 200, Holowaty: Fig. 4).
Regarding claim 14, Holowaty further discloses, wherein the one or more data analytics solutions comprise one or more first data analytics solutions, and the selected cloud-computing service comprises a first cloud-computing service of a plurality of cloud-computing services (e.g. The command and control configuration interface 200 may be a graphic user interface that a user may employ to select the order of the common operations 20. In one embodiment, the command and control configuration interface 200 includes a configuration entry field 201.
The configuration entry field 201 includes a list of operations 202. The list of operations 202 includes the common operations 20, Holowaty: Fig. 4 and [0073]), the method further comprises: applying one or more second data analytics solutions to the source-agnostic data, wherein the one or more second data analytics solutions are executing using a second cloud-computing service used for ERP (e.g. the existing Azure data factor (ADF) creates a data pipeline
for each dataset that an analytical solution uses. The Auto-ETL accelerator 50 employs the most common operations that a pipeline can use, and then drives those operations using a configuration that was selected by the user, e.g. the sources. The sources can include enterprise systems. Examples of enterprise systems can include systems for enterprise resource planning, Holowaty: [0029], [0062]).
Regarding claim 15, Holowaty further discloses, wherein the transformation database comprises a plurality of metadata tables storing one or more data schemas, one or more transformation rules, one or more control parameters, and error-handling logic (see Holowaty: Fig. 5).
Regarding claim 16, Holowaty further discloses, wherein the source-canonical transformation accelerator is configured to execute an extract pipeline, a transformation pipeline, and a load pipeline for each stage of an extract-transform-load (ELT) process, wherein each of the extract pipeline, the transformation pipeline, and the load pipeline are controlled by the plurality of metadata tables stored in the transformation database (e.g. the data that has been collected from the sources 10 and introduced to the data platform 100 through the
ingest interface layer 51 and stored in the storage 55 can be processed using the preparation and train module layer 58, which includes ELT/ETL and artificial intelligence, Holowaty: [0042]-[0043] and Fig. 2).
Regarding claim 17, Holowaty further discloses, wherein the plurality of metadata tables reduce a number of pipelines used by the ERP to a single instance of each of the extract pipeline, the transformation pipeline, and the load pipeline (e.g. FIG. 5 is a screen shot of a command and control batch status dashboard 300. The auto ETL accelerator 70 can capture batch level audit trails to track status of the data processing pipelines, which can be displayed on the command and control batch status dashboard 300. The auto ETL accelerator 70 operates in performing the operations by reducing the number of pipelines for delivering data, Holowaty: [0079]-[0081] – and Fig. 5).
Regarding claim 18, Holowaty further discloses: generating the source-canonical transformation accelerator (e.g. The auto ETL accelerator 70, Holowaty: Fig. 2), comprising:
identifying one or more types of metadata stored within sample source-specific data
associated with the data source (e.g. The preparation and train layer 58 includes streaming analytic engines 59. Generally, streaming analytics is useful for the types of data sources.
Patterns and relationships can be identified in information extracted from a number of input sources. These patterns can be used to trigger actions and initiate workflows such as creating
alerts, feeding information to a reporting tool, or storing transformed data for later use, Holowaty: [0044]); and creating one or more rules for parsing the source-specific data based on the one or more types of metadata, wherein the source-canonical transformation accelerator stores the one or more rules (e.g. machine learning tools may also be integrated
into the preparation and train module 58. "Machine learning" is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on systems
learning from data, identifying patterns and make decisions with minimal human intervention. Machine learning employs statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data. The preparation and train layer 58 is in communication with the Auto Extraction, Transformation, Loading (ETL) accelerator 70 and pipeline 50, Holowaty: [0047]-[0049]).
Claim 19 recites A system comprising steps are similar to claim 1. Therefore, claim 19 is rejected by the same reasons as discussed in claim 1.
Claim 20 recites A non-transitory computer-readable medium storing computer program instructions (e.g. a computer readable storage medium having computer readable program code embodied, Holowaty: [0110]) that, when executed by one or more processors (e.g. The program instructions are executable by a processor, Holowaty: [0110) of a computing system, effectuate operations comprising steps are similar to claim 1. Therefore, claim 20 is rejected by the same reasons as discussed in claim 1.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20, as amended, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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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/HOSAIN T ALAM/Supervisory Patent Examiner, Art Unit 2132
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