DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is responsive to the application filed on September 21, 2024.
Thus, claims 1-20 are pending for examination.
Examiner Notes
3. Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
4. 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 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.
Specification
5. The specification is objected to for information provided on page 1, paragraph 0001, where the current status of the Cross Reference to Related Applications should be updated. See MPEP 608.01[R-5] and 37 CFR 1.78.
Double Patenting
6. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
7. Claims 1-20 are rejected on the ground of non-statutory anticipated-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12118338 B1.
The claims of the current application and the claims of the reference patent are compared in the table below.
Current Application
U.S. Patent No. 12118338 B1
1. A system for facilitating updates to data pipelines using modularly-generated
data pipeline portions, the system
comprising: at least one processor; and at least one memory coupled to the at least
one processor and storing instructions that, when executed by the at least one processor, perform operations comprising:
receiving a
selection of a portion of a data pipeline comprising: (i) a set
of nodes each indicating a data pipeline component and (ii) a set of links each linking two or more nodes of the set of nodes;
generating
using the received selection,
a modular-portion of a
data pipeline architecture;
receiving an update to at least one node of the set of nodes indicating a first data pipeline component of the generated
modular-portion of the data pipeline architecture;
determining a set of pre-existing data pipeline architectures that use the generated
modular-portion of the data pipeline; and
in response to determining the set of pre-existing data pipeline architectures that use the generated modular-
portion of the data pipeline, updating at least a subset of the set of pre-existing data pipeline architectures to incorporate the update to the at least one node of the set of nodes such that the at least one node indicates the first data pipeline component.
2. The system of claim 1, wherein the
instructions when executed by the at least one processor further perform operations comprising: retrieving the
modular-portion of the data pipeline architecture;
extracting an identifier corresponding to each node of the set of nodes of the
modular-portion of the data pipeline architecture to determine a respective data pipeline component type; providing the determined data pipeline component type corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to an artificial intelligence model to generate a set of test
cases, wherein the set of test cases comprise
test code to test the
modular-portion of the data pipeline architecture; and providing the set of test cases to the modular-
portion of the data pipeline architecture during a test routine.
3. The system of claim 2, wherein the instructions when executed by the at least one processor further perform operations comprising: receiving a set of test case results in response to providing the set of test cases to the modular-portion of
the data pipeline architecture during the test routine; parsing the set of test case results to identify an anomaly included in the test case results; determining (i) a type of anomaly corresponding to the identified anomaly and (ii) a corresponding data pipeline component of the modular-portion of the data pipeline architecture; and updating the determined corresponding data pipeline component of the modular-portion of the data
pipeline architecture with an updated parameter to resolve the type of anomaly corresponding to the identified anomaly, wherein the updating comprises providing the type of error corresponding to the identifier error and the corresponding data pipeline component of the modular-portion of the data pipeline architecture to a second artificial intelligence model configured to output recommended update parameters.
4. The system of claim 1, wherein the instructions when executed by the at least one processor further perform operations comprising: receiving
a user defined prompt indicating an intended data pipeline output result; providing the user defined prompt indicating the intended data pipeline output result to a large language model (LLM) trained to provide recommended data pipeline architectures based on the intended data pipeline output result, wherein the LLM is communicatively connected to a generative model configured to provide data pipeline component configurations based on historical data-pipeline-architectures; and receiving
the data pipeline, wherein the data pipeline is
generated using a first data pipeline component configuration received from the generative model.
5. The system of claim 1, wherein the instructions when executed by the at least one processor further perform operations comprising: receiving
a second selection of a second
modular-portion of a second data pipeline architecture, wherein the second modular-portion of the second data
pipeline architecture is
pre-generated; linking the second modular-portion of
the second data pipeline architecture to the data pipeline; and
in response to the linking, generating
an updated
version of the data
pipeline using (i) the data pipeline
and (ii) the second
modular-portion of the second data pipeline architecture.
6. The system of claim 1, wherein updating the set of pre-existing data pipeline architectures to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component further comprises: determining the subset of the set of pre-existing data pipeline architectures, wherein the subset of the set of pre-existing data pipeline architectures are associated with an indication to accept updates corresponding to the generated modular-
portion of the data pipeline; and updating the subset of the set of pre-existing data pipeline architectures that are associated with the indication to accept updates to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component, in lieu of other pre-existing data pipeline architectures of the set of pre-existing data pipeline architectures that are associated with an indication to reject updates corresponding to the generated
modular-portion of the data pipeline.
7. A computer-implemented method for facilitating updates to data pipelines using modularly-generated data pipeline portions, the method comprising:
determining
a portion of a data pipeline comprising (i) a set of nodes each indicating a component and (ii) a
set of links linking the set of nodes; generating a modular-portion of a data pipeline architecture based on the determined portion of the data pipeline;
receiving an update to at least one node of the set of nodes indicating a first
component of the modular-portion of the data pipeline architecture; and
updating at least a subset of a set of pre-existing data pipelines that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes such that the at least one node indicates the first component.
8. The method of claim 7, further comprising: retrieving the modular-portion of the data pipeline architecture;
extracting an identifier corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to determine a respective data pipeline component type; providing the determined data pipeline component type corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to an artificial intelligence model to generate a set of test cases, wherein the set of test cases comprise test code to test the modular-portion of the data pipeline architecture; and providing the set of test cases to the modular-portion of the data pipeline architecture during a test routine.
9. The method of claim 8, further comprising: receiving a set of test case results in response to providing the set of test cases to the modular-portion of the data pipeline architecture during the test routine, wherein the set of test cases are provided to the modular-portion of the data pipeline architecture; parsing the set of test case results to identify an anomaly included in the test case results; determining (i) a type of anomaly corresponding to the identified anomaly and (ii) a corresponding data pipeline component of the modular-portion of the data pipeline architecture; and updating the determined corresponding data pipeline component of the modular-portion of the data pipeline architecture with an updated parameter to resolve the type of anomaly corresponding to the identified anomaly, wherein the updating comprises providing the type of error corresponding to the identifier error and the corresponding data pipeline component of the modular-portion of the data pipeline architecture to a second artificial intelligence model configured to output recommended update parameters.
10. The method of claim 7, further comprising: receiving a user defined prompt
indicating an intended data pipeline output result; providing the user defined prompt indicating the intended data pipeline output result to a large language model (LLM) trained to provide recommended data pipeline architectures based on the intended data pipeline output result, wherein the LLM is communicatively connected to a generative model configured to provide data pipeline component configurations based on historical data-pipeline-architectures; and receiving the data pipeline, wherein the data pipeline is generated using a first data pipeline component configuration received from the generative model.
11. The method of claim 7, further comprising: receiving a second selection of a second modular-portion of a second data pipeline architecture, wherein the second
modular-portion of the second data pipeline architecture is pre-generated;
linking the second modular-portion of the second data pipeline architecture to the data pipeline; and
in response to the linking, generating an updated version of the data pipeline using (i) the data pipeline and (ii) the second modular-portion of the second data pipeline architecture,
wherein the second modular-portion of the second data pipeline architecture is linked to the data pipeline.
12. The method of claim 7, wherein updating at least the subset of the set of pre-existing data pipeline architectures to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component further comprises: determining the subset of the set of pre-existing data pipeline architectures, wherein the subset of the set of pre-existing data pipeline architectures are associated with an indication to accept updates corresponding to the generated modular-portion of the data pipeline; and updating the subset of the set of pre-existing data pipeline architectures that are associated with the indication to accept updates to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component, in lieu of other pre-existing data pipeline architectures of the set of pre-existing data pipeline architectures that are associated with an indication to reject updates corresponding to the generated modular-portion of the data pipeline.
13. The method of claim 7, further comprising: in response to receiving the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture, determining that the update to the at least one node is associated with a second update to a link that links the at least one node to another node of the modular-portion of the data pipeline architecture, wherein the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture is received; and in response to determining that the update to the at least one node is associated with the second update to the link that links the at least one node to the other node of the modular-portion of the data pipeline architecture, updating the subset of the set of pre-existing data pipelines to incorporate the second update to the link that links the at least one node to the other node of the modular-portion of the data pipeline architecture, wherein it is determined that the update to the at least one node is associated with the second update to the link that links the at least one node to the other node of the modular-portion of the data pipeline architecture.
14. The method of claim 7, further comprising: in response to receiving the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture, transmitting a message to each of the set of pre-existing data pipelines that use the generated modular-portion of the data pipeline indicating the update to the at least one node of the set of nodes indicating the first data pipeline component, wherein the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture is received.
15. One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising: receiving a
selection of a portion of a data pipeline comprising (i) a set of nodes each indicating a data pipeline component and (ii) a set of links linking the set of nodes; generating a modular-portion of a data pipeline architecture
based on the received selection;
receiving an update to at least one node of the set of nodes indicating a first data pipeline component of the modular-portion of the data pipeline architecture; and updating at least a subset of a set of pre-existing data pipelines that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes such that the at least one node indicates the first data pipeline component.
16. The media of claim 15, wherein the instructions that, when executed by the one or more processors, further cause operations comprising: retrieving the modular-portion of the data pipeline architecture;
extracting an identifier corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to determine a respective data pipeline component type; providing the determined data pipeline component type corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to an artificial intelligence model to generate a set of test cases, wherein the set of test cases comprise test code to test the modular-portion of the data pipeline architecture; and providing the set of test cases to the modular-portion of the data pipeline architecture during a test routine.
17. The media of claim 16, wherein the instructions that, when executed by the one or more processors, further cause operations comprising: receiving a set of test case results in response to providing the set of test cases to the modular-portion of the data pipeline architecture during the test routine; parsing the set of test case results to identify an anomaly included in the test case results; determining (i) a type of anomaly corresponding to the identified anomaly and (ii) a corresponding data pipeline component of the modular-portion of the data pipeline architecture; and updating the determined corresponding data pipeline component of the modular-portion of the data pipeline architecture with an updated parameter to resolve the type of anomaly corresponding to the identified anomaly, wherein the updating comprises providing the type of error corresponding to the identified error and the corresponding data pipeline component of the modular-portion of the data pipeline architecture to a second artificial intelligence model configured to output recommended update parameters.
18. The media of claim 15, wherein the instructions that, when executed by the one or more processors, further cause operations comprising: receiving a user defined prompt indicating an intended data pipeline output result; providing the user defined prompt indicating the intended data pipeline output result to a large language model (LLM) trained to provide recommended
data pipeline architectures based on the intended data pipeline output result, wherein the LLM is communicatively connected to a generative model configured to provide data pipeline component configurations based on historical data-pipeline-architectures; and receiving the data pipeline,
wherein the data pipeline is generated using a first data pipeline component configuration received from the generative model.
19. The media of claim 15, wherein the instructions that, when executed by the one or more processors, further cause operations comprising: receiving a second
selection of a second modular-portion of a second data pipeline architecture, wherein the second modular-portion of the second data pipeline architecture is pre-generated;
linking the second modular-portion of the second data pipeline architecture to the data pipeline; and in response to the linking, generating
an updated version of the data pipeline using (i) the data pipeline and (ii) the second modular-portion of the second data pipeline architecture.
20. The media of claim 15, wherein updating at least the subset of the set of pre-existing data pipeline architectures to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component further comprises: determining the subset of the set of pre-existing data pipeline architectures, wherein the subset of the set of pre-existing data pipeline architectures are associated with an indication to accept updates corresponding to the generated modular-portion of the data pipeline; and updating the subset of the set of pre-existing data pipeline architectures that are associated with the indication to accept updates to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component, in lieu of other pre-existing data pipeline architectures of the set of pre-existing data pipeline architectures that are associated with an indication to reject updates corresponding to the generated modular-portion of the data pipeline.
1. A system for facilitating updates to data pipelines using modularly-generated platform-agnostic data pipeline portions, the system comprising: at least one processor; and at least one memory coupled to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations comprising:
receiving, via a graphical user interface (GUI) within a web-browsing application, a user selection of a portion of a platform-agnostic data pipeline comprising: (i) a set of nodes each indicating a data pipeline component and (ii) a set of links each linking two or more nodes of the set of nodes;
generating via a transformation
component and using the user selection, a platform-agnostic modular-portion of a data pipeline architecture;
storing the generated platform-agnostic modular-portion of the data pipeline architecture in a remote database configured to store platform-agnostic modular-portions of data pipeline architectures;
receiving an update to at least one node of the set of nodes indicating a first data pipeline
component of the generated platform-agnostic modular-portion of the data pipeline
architecture;
determining a set of pre-existing data pipeline architectures that use the generated
platform-agnostic modular-portion of the data pipeline; and
in response to determining the set of pre-existing data pipeline architectures that use the generated platform-agnostic modular-portion of the data pipeline, updating at least a subset of the set of pre-existing data
pipeline architectures to incorporate the update to the at least one node of the set of nodes
indicating the first data pipeline component.
2. The system of claim 1, wherein the instructions when executed by the at least one processor further perform operations comprising: retrieving the platform-agnostic modular-portion of the data pipeline architecture from the remote database; extracting an identifier corresponding to each node of the set of nodes of the platform-agnostic modular-portion of the data pipeline architecture to determine a respective data pipeline component type; providing the determined data pipeline component type corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to an artificial intelligence model to generate a set of test cases, wherein the set of test cases comprise test code to test the platform-agnostic modular-portion of the data pipeline architecture; and providing the set of test cases to the platform-agnostic modular-portion of the data pipeline architecture during a test routine.
3. The system of claim 2, wherein the instructions when executed by the at least one processor further perform operations comprising: receiving a set of test case results in response to providing the set of test cases to the platform-agnostic modular-portion of the data pipeline architecture during the test routine; parsing the set of test case results to identify an anomaly included in the test case results; determining (i) a type of anomaly corresponding to the identified anomaly and (ii) a corresponding data pipeline component of the modular-portion of the data pipeline architecture; and updating the determined corresponding data pipeline component of the platform-agnostic modular-portion of the data pipeline architecture with an updated parameter to resolve the type of anomaly corresponding to the identified anomaly, wherein the updating comprises providing the type of error corresponding to the identifier error and the corresponding data pipeline component of the platform-agnostic modular-portion of the data pipeline architecture to a second artificial intelligence model configured to output recommended update parameters.
4. The system of claim 1, wherein the instructions when executed by the at least one processor further perform operations comprising: receiving, via the GUI, within the web-browsing application, a user defined prompt indicating an intended data pipeline output result; providing the user defined prompt indicating the intended data pipeline output result to a large language model (LLM) trained to provide recommended data pipeline architectures based on the intended data pipeline output result, wherein the LLM is communicatively connected to a generative model configured to provide data pipeline component configurations based on historical data-pipeline-architectures; and receiving, via the GUI, the platform-agnostic data pipeline, wherein the platform-agnostic data pipeline is generated using a first data pipeline component configuration received from the generative model.
5. The system of claim 1, wherein the instructions when executed by the at least one processor further perform operations comprising: receiving, via the GUI within the web-browsing application, a second user selection of a second platform-agnostic modular-portion of a second data pipeline architecture, wherein the second platform-agnostic modular-portion of the second data pipeline architecture is (i) stored in the remote database and (ii) is pre-generated; linking the second platform-agnostic modular-portion of the second data pipeline architecture to the platform-agnostic data pipeline; and
in response to the linking, generating, via the transformation component, an updated version of the platform-agnostic data
pipeline using (i) the platform-agnostic data pipeline and (ii) the second platform-agnostic modular-portion of the second data pipeline architecture.
6. The system of claim 1, wherein updating the set of pre-existing data pipeline architectures to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component further comprises: determining the subset of the set of pre-existing data pipeline architectures, wherein the subset of the set of pre-existing data pipeline architectures are associated with an indication to accept updates corresponding to the generated platform-agnostic modular-portion of the data pipeline; and updating the subset of the set of pre-existing data pipeline architectures that are associated with the indication to accept updates to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component, in lieu of other pre-existing data pipeline architectures of the set of pre-existing data pipeline architectures that are associated with an indication to reject updates corresponding to the generated platform-agnostic modular-portion of the data pipeline.
7. A computer-implemented method for facilitating updates to data pipelines using modularly-generated data pipeline portions, the method comprising:
receiving, via a graphical user interface (GUI), a user selection of a portion of a data pipeline comprising (i) a set of nodes each indicating a data pipeline component and (ii) a set of links linking the set of nodes; generating a modular-portion of a data pipeline architecture, via a transformation component, based on the user selection;
storing the modular-portion of the data pipeline architecture in a remote database;
receiving an update to at least one node of the set of nodes indicating a first data pipeline component of the modular-portion of the data pipeline architecture; and
updating at least a subset of a set of pre-existing data pipelines that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component.
8. The method of claim 7, further comprising: retrieving the modular-portion of the data pipeline architecture from the remote database; extracting an identifier corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to determine a respective data pipeline component type; providing the determined data pipeline component type corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to an artificial intelligence model to generate a set of test cases, wherein the set of test cases comprise test code to test the modular-portion of the data pipeline architecture; and providing the set of test cases to the modular-portion of the data pipeline architecture during a test routine.
9. The method of claim 8, further comprising: receiving a set of test case results in response to providing the set of test cases to the modular-portion of the data pipeline architecture during the test routine, wherein the set of test cases are provided to the modular-portion of the data pipeline architecture; parsing the set of test case results to identify an anomaly included in the test case results; determining (i) a type of anomaly corresponding to the identified anomaly and (ii) a corresponding data pipeline component of the modular-portion of the data pipeline architecture; and updating the determined corresponding data pipeline component of the modular-portion of the data pipeline architecture with an updated parameter to resolve the type of anomaly corresponding to the identified anomaly, wherein the updating comprises providing the type of error corresponding to the identifier error and the corresponding data pipeline component of the modular-portion of the data pipeline architecture to a second artificial intelligence model configured to output recommended update parameters.
10. The method of claim 7, further comprising: receiving, via the GUI, a user defined prompt indicating an intended data pipeline output result; providing the user defined prompt indicating the intended data pipeline output result to a large language model (LLM) trained to provide recommended data pipeline architectures based on the intended data pipeline output result, wherein the LLM is communicatively connected to a generative model configured to provide data pipeline component configurations based on historical data-pipeline-architectures; and receiving, via the GUI, the data pipeline, wherein the data pipeline is generated using a first data pipeline component configuration received from the generative model.
11. The method of claim 7, further comprising: receiving, via the GUI, a second user selection of a second modular-portion of a second data pipeline architecture, wherein the second modular-portion of the second data pipeline architecture is (i) stored in the remote database and (ii) is pre-generated; linking the second modular-portion of the second data pipeline architecture to the data pipeline; and in response to the linking, generating, via the transformation component, an updated version of the data pipeline using (i) the data pipeline and (ii) the second modular-portion of the second data pipeline architecture, wherein the second modular-portion of the second data pipeline architecture is linked to the data pipeline.
12. The method of claim 7, wherein updating at least the subset of the set of pre-existing data pipeline architectures to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component further comprises: determining the subset of the set of pre-existing data pipeline architectures, wherein the subset of the set of pre-existing data pipeline architectures are associated with an indication to accept updates corresponding to the generated modular-portion of the data pipeline; and updating the subset of the set of pre-existing data pipeline architectures that are associated with the indication to accept updates to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component, in lieu of other pre-existing data pipeline architectures of the set of pre-existing data pipeline architectures that are associated with an indication to reject updates corresponding to the generated modular-portion of the data pipeline.
13. The method of claim 7, further comprising: in response to receiving the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture, determining that the update to the at least one node is associated with a second update to a link that links the at least one node to another node of the modular-portion of the data pipeline architecture, wherein the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture is received; and in response to determining that the update to the at least one node is associated with the second update to the link that links the at least one node to the other node of the modular-portion of the data pipeline architecture, updating the subset of the set of pre-existing data pipelines to incorporate the second update to the link that links the at least one node to the other node of the modular-portion of the data pipeline architecture, wherein it is determined that the update to the at least one node is associated with the second update to the link that links the at least one node to the other node of the modular-portion of the data pipeline architecture.
14. The method of claim 7, further comprising: in response to receiving the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture, transmitting a message to each of the set of pre-existing data pipelines that use the generated modular-portion of the data pipeline indicating the update to the at least one node of the set of nodes indicating the first data pipeline component, wherein the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture is received.
15. One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising: receiving, via a graphical user interface (GUI), a user selection of a portion of a data pipeline comprising (i) a set of nodes each indicating a data pipeline component and (ii) a set of links
linking the set of nodes; generating a modular-portion of a data pipeline architecture, via a transformation component, based on the user selection;
storing the modular-portion of the data pipeline architecture in a remote database; receiving an update to at least one node of the set of nodes indicating a first data pipeline component of the modular-portion of the data pipeline architecture; and updating at least a subset of a set of pre-existing data pipelines
that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component.
16. The media of claim 15, wherein the instructions that, when executed by the one or more processors, further cause operations comprising: retrieving the modular-portion of the data pipeline architecture from the remote database; extracting an identifier corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to determine a respective data pipeline component type; providing the determined data pipeline component type corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to an artificial intelligence model to generate a set of test cases, wherein the set of test cases comprise test code to test the modular-portion of the data pipeline architecture; and providing the set of test cases to the modular-portion of the data pipeline architecture during a test routine.
17. The media of claim 16, wherein the instructions that, when executed by the one or more processors, further cause operations comprising: receiving a set of test case results in response to providing the set of test cases to the modular-portion of the data pipeline architecture during the test routine; parsing the set of test case results to identify an anomaly included in the test case results; determining (i) a type of anomaly corresponding to the identified anomaly and (ii) a corresponding data pipeline component of the modular-portion of the data pipeline architecture; and updating the determined corresponding data pipeline component of the modular-portion of the data pipeline architecture with an updated parameter to resolve the type of anomaly corresponding to the identified anomaly, wherein the updating comprises providing the type of error corresponding to the identified error and the corresponding data pipeline component of the modular-portion of the data pipeline architecture to a second artificial intelligence model configured to output recommended update parameters.
18. The media of claim 15, wherein the instructions that, when executed by the one or more processors, further cause operations comprising: receiving, via the GUI, a user defined prompt indicating an intended data pipeline output result; providing the user defined prompt indicating the intended data pipeline output result to a large language model (LLM) trained to provide recommended data pipeline architectures based on the intended data pipeline output result, wherein the LLM is communicatively connected to a generative model configured to provide data pipeline component configurations based on historical data-pipeline-architectures; and receiving, via the GUI, the data pipeline, wherein the data pipeline is generated using a first data pipeline component configuration received from the generative model.
19. The media of claim 15, wherein the instructions that, when executed by the one or more processors, further cause operations comprising: receiving, via the GUI, a second user selection of a second modular-portion of a second data pipeline architecture, wherein the second modular-portion of the second data pipeline architecture is (i) stored in the remote database and (ii) is pre-generated; linking the second modular-portion of the second data pipeline architecture to the data pipeline; and in response to the linking,
generating, via the transformation
component, an updated version of the data pipeline using (i) the data pipeline and (ii) the second modular-portion of the second data pipeline architecture.
20. The media of claim 15, wherein updating at least the subset of the set of pre-existing data pipeline architectures to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component further comprises: determining the subset of the set of pre-existing data pipeline architectures, wherein the subset of the set of pre-existing data pipeline architectures are associated with an indication to accept updates corresponding to the generated modular-portion of the data pipeline; and updating the subset of the set of pre-existing data pipeline architectures that are associated with the indication to accept updates to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component, in lieu of other pre-existing data pipeline architectures of the set of pre-existing data pipeline architectures that are associated with an indication to reject updates corresponding to the generated modular-portion of the data pipeline.
Based on the comparison of the above table, indicates that, although claims 1-20 of U.S. Patent No. `338 are not identical, but they are not patentably distinct from claims 1-20 of the current examined application since the claims 1-20 of the U.S. Patent No. ‘338 are read on claims 1-20 of the current examined application.
Accordingly, claims 1-20 of the current examined application are not patentably distinct from claims 1-20 of the U.S. Patent Application No. ‘338 and as such are unpatentable for anticipated-type double patenting.
Claim Rejections - 35 USC § 101
8. 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.
9. Claims 1, 4-7, 10-15, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1 recites:
A system for facilitating updates to data pipelines using modularly-generated data pipeline portions, the system comprising:
at least one processor; and at least one memory coupled to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations comprising:
[a] receiving a selection of a portion of a data pipeline comprising: (i) a set of nodes each indicating a data pipeline component and (ii) a set of links each linking two or more nodes of the set of nodes;
[b] generating using the received selection, a modular-portion of a data pipeline architecture;
[c] receiving an update to at least one node of the set of nodes indicating a first data pipeline component of the generated modular-portion of the data pipeline architecture;
[d] determining a set of pre-existing data pipeline architectures that use the generated modular-portion of the data pipeline; and
[e] in response to determining the set of pre-existing data pipeline architectures that use the generated modular-portion of the data pipeline, updating at least a subset of the set of pre-existing data pipeline architectures to incorporate the update to the at least one node of the set of nodes such that the at least one node indicates the first data pipeline component.
Step 2A – prong 1:
The claim recites the limitation of:
[b] generating using the received selection, a modular-portion of a data pipeline architecture;
[d] determining a set of pre-existing data pipeline architectures that use the generated modular-portion of the data pipeline; and
[e] in response to determining the set of pre-existing data pipeline architectures that use the generated modular-portion of the data pipeline, updating at least a subset of the set of pre-existing data pipeline architectures to incorporate the update to the at least one node of the set of nodes such that the at least one node indicates the first data pipeline component.
These limitations of steps [b], [d], and [e] as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the function through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1.
Step 2A – Prong 2:
Under Prong 2, this judicial exception is not integrated into a practical application. The claims recite the following additional elements of “A system for facilitating updates to data pipelines using modularly-generated data pipeline portions, the system comprising: at least one processor; and at least one memory coupled to the at least one processor and storing instructions that, when executed by the at least one processor” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components, and the additional elements of step [a] “receiving a selection of a portion of a data pipeline comprising: (i) a set of nodes each indicating a data pipeline component and (ii) a set of links each linking two or more nodes of the set of nodes” and step [c] “receiving an update to at least one node of the set of nodes indicating a first data pipeline component of the generated modular-portion of the data pipeline architecture” do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering data. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “A system for facilitating updates to data pipelines using modularly-generated data pipeline portions, the system comprising: at least one processor; and at least one memory coupled to the at least one processor and storing instructions that, when executed by the at least one processor” amount to no more than mere instructions, or generic computer/computer components to carry out the exception, and for the limitations of step [a] “receiving a selection of a portion of a data pipeline comprising: (i) a set of nodes each indicating a data pipeline component and (ii) a set of links each linking two or more nodes of the set of nodes” and step [c] “receiving an update to at least one node of the set of nodes indicating a first data pipeline component of the generated modular-portion of the data pipeline architecture” the courts have identified mere data gathering is well-understood, routine and conventional activity. See MPEP 2106.05(d). Accordingly, the claim is not patent eligible under 35 USC 101.
Independent claim 7 recites:
A computer-implemented method for facilitating updates to data pipelines using modularly-generated data pipeline portions, the method comprising:
[A] determining a portion of a data pipeline comprising (i) a set of nodes each indicating a component and (ii) a set of links linking the set of nodes;
[B] generating a modular-portion of a data pipeline architecture based on the determined portion of the data pipeline;
[C] receiving an update to at least one node of the set of nodes indicating a first component of the modular-portion of the data pipeline architecture; and
[D] updating at least a subset of a set of pre-existing data pipelines that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes such that the at least one node indicates the first component.
Step 2A – prong 1:
The claim recites the limitation of:
[A] determining a portion of a data pipeline comprising (i) a set of nodes each indicating a component and (ii) a set of links linking the set of nodes;
[B] generating a modular-portion of a data pipeline architecture based on the determined portion of the data pipeline; and
[D] updating at least a subset of a set of pre-existing data pipelines that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes such that the at least one node indicates the first component.
These limitations of steps [A], [B], and [D] as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the function through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1.
Step 2A – Prong 2:
Under Prong 2, this judicial exception is not integrated into a practical application. The claims recite the following additional elements of step [C] “receiving an update to at least one node of the set of nodes indicating a first component of the modular-portion of the data pipeline architecture” do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering data. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of step [C] “receiving an update to at least one node of the set of nodes indicating a first component of the modular-portion of the data pipeline architecture” the courts have identified mere data gathering is well-understood, routine and conventional activity. See MPEP 2106.05(d).
Accordingly, the claim is not patent eligible under 35 USC 101.
Independent claim 15 recites:
One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising:
[I] receiving a selection of a portion of a data pipeline comprising (i) a set of nodes each indicating a data pipeline component and (ii) a set of links linking the set of nodes;
[II] generating a modular-portion of a data pipeline architecture based on the received selection;
[III] receiving an update to at least one node of the set of nodes indicating a first data pipeline component of the modular-portion of the data pipeline architecture; and
[IV] updating at least a subset of a set of pre-existing data pipelines that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes such that the at least one node indicates the first data pipeline component.
Step 2A – prong 1:
The claim recites the limitation of:
[II] generating a modular-portion of a data pipeline architecture based on the received selection; and
[IV] updating at least a subset of a set of pre-existing data pipelines that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes such that the at least one node indicates the first data pipeline component.
These limitations of steps [II] and [IV] as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the function through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1.
Step 2A – Prong 2:
Under Prong 2, this judicial exception is not integrated into a practical application. The claims recite the following additional elements of “One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components, and the additional elements of step [I] “receiving a selection of a portion of a data pipeline comprising (i) a set of nodes each indicating a data pipeline component and (ii) a set of links linking the set of nodes” and step [III] “receiving an update to at least one node of the set of nodes indicating a first data pipeline component of the modular-portion of the data pipeline architecture” do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering data. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising” amount to no more than mere instructions, or generic computer/computer components to carry out the exception, and for the limitations of [I] “receiving a selection of a portion of a data pipeline comprising (i) a set of nodes each indicating a data pipeline component and (ii) a set of links linking the set of nodes” and step [III] “receiving an update to at least one node of the set of nodes indicating a first data pipeline component of the modular-portion of the data pipeline architecture” the courts have identified mere data gathering is well-understood, routine and conventional activity. See MPEP 2106.05(d). Accordingly, the claim is not patent eligible under 35 USC 101.
Regarding to claims 4, 10, and 18, do not recite any mental process, however, the additional elements of “providing the user defined prompt indicating the intended data pipeline output result to a large language model (LLM) trained to provide recommended data pipeline architectures based on the intended data pipeline output result, wherein the LLM is communicatively connected to a generative model configured to provide data pipeline component configurations based on historical data-pipeline-architectures” and “ wherein the data pipeline is generated using a first data pipeline component configuration received from the generative model” merely the use of a computer/instructions running on the computer to carry out the judicial exception, which is neither a practical application under prong 2, nor an inventive concept under step 2B. Furthermore, the limitations of “receiving a user defined prompt indicating an intended data pipeline output result” and “receiving the data pipeline” are data gathering merely insignificant extra solution activity under prong 2. Under step 2B, as explained above, the courts have identified data gathering is well-understood, routine and conventional activity. See MPEP 2106.05(d).
Regarding to claims 5 and 19, the limitations “linking the second modular-portion of the second data pipeline architecture to the data pipeline” and “in response to the linking, generating an updated version of the data pipeline using (i) the data pipeline and (ii) the second modular-portion of the second data pipeline architecture” recite further mental process. Furthermore, the additional limitation of “receiving a second selection of a second modular-portion of a second data pipeline architecture, wherein the second modular-portion of the second data pipeline architecture is pre-generated” are data gathering merely insignificant extra solution activity under prong 2. Under step 2B, as explained above, the courts have identified data gathering is well-understood, routine and conventional activity. See MPEP 2106.05(d).
Regarding to claim 11, the limitations “linking the second modular-portion of the second data pipeline architecture to the data pipeline” and “in response to the linking, generating an updated version of the data pipeline using (i) the data pipeline and (ii) the second modular-portion of the second data pipeline architecture, wherein the data pipeline is generated using a first data pipeline component configuration received from the generative model” recite further mental process. Furthermore, the additional limitation of “receiving a second selection of a second modular-portion of a second data pipeline architecture, wherein the second modular-portion of the second data pipeline architecture is pre-generated” are data gathering merely insignificant extra solution activity under prong 2. Under step 2B, as explained above, the courts have identified data gathering is well-understood, routine and conventional activity. See MPEP 2106.05(d).
Regarding to claims 6, 12, and 20, the limitations “wherein updating the set of pre-existing data pipeline architectures to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component further comprises: determining the subset of the set of pre-existing data pipeline architectures, wherein the subset of the set of pre-existing data pipeline architectures are associated with an indication to accept updates corresponding to the generated modular-portion of the data pipeline” and “updating the subset of the set of pre-existing data pipeline architectures that are associated with the indication to accept updates to incorporate the update to the at least one node of the set of nodes indicating the first data pipeline component, in lieu of other pre-existing data pipeline architectures of the set of pre-existing data pipeline architectures that are associated with an indication to reject updates corresponding to the generated modular-portion of the data pipeline” recite further mental process.
Regarding to claim 13, the limitations “in response to receiving the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture, determining that the update to the at least one node is associated with a second update to a link that links the at least one node to another node of the modular-portion of the data pipeline architecture, wherein the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture is received” and “in response to determining that the update to the at least one node is associated with the second update to the link that links the at least one node to the other node of the modular-portion of the data pipeline architecture, updating the subset of the set of pre-existing data pipelines to incorporate the second update to the link that links the at least one node to the other node of the modular-portion of the data pipeline architecture, wherein it is determined that the update to the at least one node is associated with the second update to the link that links the at least one node to the other node of the modular-portion of the data pipeline architecture” recite further mental process.
Regarding to claim 14, the additional limitation of “further comprising: in response to receiving the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture, transmitting a message to each of the set of pre-existing data pipelines that use the generated modular-portion of the data pipeline indicating the update to the at least one node of the set of nodes indicating the first data pipeline component, wherein the update to the at least one node of the set of nodes indicating the first data pipeline component of the modular-portion of the data pipeline architecture is received” are merely insignificant extra solution activity under prong 2. Under step 2B, as explained above, the courts have identified transmitting data/information is well-understood, routine and conventional activity. See MPEP 2106.05(d).
Allowable Subject Matter
10. Claims 2-3, 8-9, and 16-17 are objected to as being dependent upon rejected base claims 1, 7, and 15 respectively, but would be allowable if rewritten or amended to overcome the non-statutory double patenting rejections set forth in this office action as well as including all of the limitations of the base claim and any intervening claims.
11. The following is an Examiner’s statement of reasons for allowance:
Seif et al. (US 20190384577 A1) disclosed the pipeline engine may customize the selected operator node by at least adding, to the selected operator node, an additional configuration parameter having the first key and a second value included in the request node to the graph and generate a second file associated with the customized operator node for storing the customizations applied to the selected operator node to generate the customized operator node, in which the second file is in JSON format. See at least Fig. 4.
Rowlands et al. (US 20240095029 A1) disclosed managing and deploying applications and modular code of catalog of pipelines for enterprise via a user may employ the user interface to create and modify pipelines including verifying the modified pipeline within the storing integrated graph by traversing the graph and updating the pipeline graph. See at least Fig. 4.
Walker et al. (US 12174732 B2) disclosed regression testing on deployment pipelines via a regression suite configured to run a plurality of test applications on the deployment pipeline to test the deployment pipeline with respect to the software change within generating of user cases.
The prior arts of record or made of record, taken alone or in combination do not disclose and/or suggest, and/or motivation to combine, at least “…retrieving the modular-portion of the data pipeline architecture; extracting an identifier corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to determine a respective data pipeline component type; providing the determined data pipeline component type corresponding to each node of the set of nodes of the modular-portion of the data pipeline architecture to an artificial intelligence model to generate a set of test cases, wherein the set of test cases comprise test code to test the modular-portion of the data pipeline architecture; and providing the set of test cases to the modular-portion of the data pipeline architecture during a test routine...”
Conclusion
12. The prior art made of record and not relied upon (cited on 892 form) is considered pertinent to application disclosure.
Chandrasekharan et al. (US-11676072-B1) discloses interface for incorporating user feedback into training of clustering model.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Marina Lee whose telephone number is (571)270-1648. The examiner can normally be reached Monday to Friday (8 am to 4:00 pm ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung S. Sough can be reached at (571)-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARINA LEE/Primary Examiner, Art Unit 2192
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