DETAILED ACTION
Response to Arguments
Applicant's arguments filed February 19, 2026 have been fully considered but they are not persuasive. Claims 1-10 are withdrawn. Claims 11, 12, and 15, 17, and 20 have been amended. Claims 11-22 are pending and presented for examination.
Applicant’s arguments, see page 8, objections to the drawings, filed 02/19/2026, with respect to the drawings have been fully considered and are fully persuasive.
Applicant’s arguments, see page 9, rejections under 35 U.S.C 112(b), filed 02/19/2026, with respect to the rejection under 35 U.S.C. 112(b), have been fully considered and are fully persuasive.
Applicant’s arguments, see page 9, filed 02/19/2026, with respect to the rejection under 35 U.S.C. 103 has been has been fully considered and are not fully persuasive.
Regarding claim 11 and 17, applicant claims Ganeshmani and further in view of Sodhi fails to disclose: wherein at least two of the plurality of resource domains comprise heterogenous resources, wherein the selected set of tasks have been genericized via containers that represent tasks of the selected set of tasks as generic task definitions… selected set of tasks to be executed by the plurality of resource domains.” In particular, “present, from a metadata cache, a plurality of tasks to a user of the system, characteristics of each of the plurality of tasks being represented by associated metadata.” Ganeshmani teaches, column 8, lines 16-25, a workflow creator allowing the users to select tasks for the workflow, a database cache as listed in column 7, line 57, which can store metadata associated with data in a database; see also column 8, lines 23-53). This defines metadata associated with the task itself, allowing users to create new tasks, and is necessary for running tasks.
Further, applicant argues that Ganeshmani fails to teach: define a machine learning workflow comprising the selected set of tasks; public the ML workflow to a distributed ledger; orchestrate execution of the ML workflow by assigning and deploying the ML workflow to a plurality of resource domains.” Ganeshmani teaches, column 6, lines 15-36, and column 8, lines 34-50, execution of one or more tasks, publishing it on a distributed system which can be federated between different systems; see also column 3, lines 14-62; column 18, lines 1-12, workflow information stored in the smart contract, column 10, lines 9-19. This can be done to run a ML workflow, see Sodhi, paragraph 16 – 18, 42-45, 47, 49-51, a ML task that has been standardized, assigning resources from a variety of cloud network locations.
Regarding the argument that Sodhi fails to teach: wherein at least two of the plurality of resource domains comprise heterogenous resources, wherein the selected set of tasks have been genericized via containers…. for the selected set of tasks to be executed by the plurality of resource domains.”. However, Sodhi teaches, paragraph 16 and 0064-0066, resource nodes of a cloud provider hybrid, with different resource combinations. Different cloud providers by nature would have different resource combinations, see claim 8, selecting different resource combinations for a task. Sodhi teaches the ability for a ML task to be separated by different cloud platforms, and it makes no particular claim about the structure of said ML task, (see paragraph 18, 40, 41), thus, it, in combination with Ganeshmani, shows ML tasks being generalized across different mediums, using a smart contact backend for communication.
Regarding the arguments against Todd, Todd teaches using the blockchain to verify the behavior of smart contracts, see fig. 3, column 7, line 52 - column 8, line 19, authorization, identity, and access information using private keys. Thus, it is in a similar field of endeavor and is applicable for this application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ganeshmani et. al (US Patent 10659219) and further in view of Sodhi et. al (US 20230032748 A1).
Regarding claim 11, Ganeshmani recites, a system, comprising: a processor; and a memory including instructions that when executed (column 7, line 1-20, a generic computing device), cause the processor to: present, from a metadata cache, a plurality of tasks to a user of the system, characteristics of each of the plurality of tasks being represented by associated metadata; in accordance with a set of tasks selected by the user from the plurality of tasks (column 8, lines 16-25, a workflow creator allowing the users to select tasks for the workflow, a database cache as listed in column 7, line 57, which can store metadata associated with data in a database; see also column 8, lines 23-53), define a machine learning (ML)workflow comprising the selected set of tasks; publish the ML workflow to a distributed ledger (stored in a database cache as listed in column 7, lines 57, which can store metadata associated with data in a database; see also column 8, lines 23-53) orchestrate execution of the ML workflow by assigning and deploying the ML workflow to a plurality of resource domains (column 3, lines 38-62, a multitude of compute nodes, to complete a multitude of tasks among a workflow, column 8, lines 16-25); execute the ML workflow; and monitor execution of the ML workflow via ML workflow state information published by each of the resource domains to the distributed ledger, the published ML workflow state information allowing for the selected set of tasks to be executed in accordance with the orchestrated execution of the ML workflow (column 6, lines 15-36, and column 8, lines 34-50, execution of one or more tasks, publishing it on a distributed system which can be federated between different systems; see also column 3, lines 14-62; column 18, lines 1-12, workflow information stored in the smart contract, column 10, lines 9-19).
However, Ganeshmani fails to teach, wherein at least two of the plurality of resource domains comprise heterogeneous resources, wherein the selected set of tasks have been genericized via containers that represent tasks of the selected set of tasks as common task definitions, and wherein resource characteristics are abstracted, such that the selected set of tasks interface with the plurality of resource domains via the abstracted resource characteristics allowing for the selected set of tasks to be executed by the plurality of resource domains.
Sodhi teaches, wherein at least two of the plurality of resource domains comprise heterogeneous (paragraph 14, 16 and 0064-0066, resource nodes of a cloud provider hybrid, with different resource combinations), wherein the selected set of tasks have been genericized via containers that represent tasks of the selected set of tasks as common task definitions, and wherein resource characteristics are abstracted, such that the selected set of tasks interface with the plurality of resource domains via the abstracted resource characteristics allowing for the selected set of tasks to be executed by the plurality of resource domains (paragraph 14, 16 – 18, 42-45, 47, 49-51, a ML task that has been standardized, assigning resources from a variety of cloud network locations).
It would be obvious before the filing date of this application to one of ordinary skill in the art, to combine the blockchain technology of Ganeshmani with the cloud ML task structure of Sodhi, as known work in one field of endeavor may prompt variations of it for use in a different one, as it would allow the blockchain to be used in a variety of different cloud system for processing a variety of ML tasks.
Regarding claim 12, Ganeshmani recites, the system of claim 11, wherein the associated metadata is stored in the distributed ledger (column 8, lines 34-57, where the data associated with the workflow may be stored on the distributed ledger, and also a metadata cache, column 7, line 57, associated with the data; column 10, lines 9-67; column 13, lines 19-51).
Regarding claim 13, Ganeshmani recites, the system of claim 11, wherein each of the resource domains comprises one or more edge nodes of a network, the one or more edge nodes defining a blockchain network, each of the one or more edge nodes comprising a local copy of the distributed ledger (column 6, lines 15-17; column 3, lines 14-62; column 18, lines 1-12).
Regarding claim 14, Sodhi recites, the system of claim 11, wherein at least two resource domains of the plurality of resource domains comprise disparate resource creating a heterogeneous plurality of resource domains (paragraph 16 – 18, 47, 49, a ML task that has been standardized, assigning resources from a variety of cloud network locations).
It would be obvious before the filing date of this application to one of ordinary skill in the art, to combine the blockchain technology of Ganeshmani with the cloud ML task structure of Sodhi, as known work in one field of endeavor may prompt variations of it for use in a different one, as it would allow the blockchain to be used in a variety of different cloud system for processing a variety of ML tasks.
Regarding claim 15, Sodhi recites, the system of claim 14, wherein each of the plurality of tasks comprises a representative container comprising the common task definitions that define the characteristics of each of the plurality of tasks allowing each of the plurality of tasks to be performed by the heterogeneous plurality of resource domains (paragraph 16 – 18, 47, 49, a ML task that has been standardized, assigning resources from a variety of cloud network locations).
It would be obvious before the filing date of this application to one of ordinary skill in the art, to combine the blockchain technology of Ganeshmani with the cloud ML task structure of Sodhi, as known work in one field of endeavor may prompt variations of it for use in a different one, as it would allow the blockchain to be used in a variety of different cloud system for processing a variety of ML tasks.
Regarding claim 16, Ganeshmani recites, The system of claim 15, wherein the instructions that when executed cause the processor to monitor execution of the ML workflow comprise further instructions that when executed further cause the processor to obtain ML workflow state information from respective agents at the plurality of resource domains via control messaging over the distributed ledger (column 6, lines 15-17, and column 8, lines 34-50, execution of one or more tasks, publishing it on a distributed system which can be federated between different systems; see also column 3, lines 14-62; column 18, lines 1-12, communications link between computers using the blockchain, see Fig. 2, for completion of the workload, column 5, lines 47-column 6, lines 14; col. 10, lines 42-67).
However, Ganeshmani fails to teach, the respective agents facilitating interfacing of the representative containers with respective resources belonging to the plurality of resource domains.
Sodhi teaches, the respective agents facilitating interfacing of the representative containers with respective resources belonging to the plurality of resource domains (paragraph 16 – 18, 42-45, 47, 49-51, a ML task that has been standardized, assigning resources from a variety of cloud network locations).
It would be obvious before the filing date of this application to one of ordinary skill in the art, to combine the blockchain technology of Ganeshmani with the cloud ML task structure of Sodhi, as known work in one field of endeavor may prompt variations of it for use in a different one, as it would allow the blockchain to be used in a variety of different cloud system for processing a variety of ML tasks.
Regarding claim 17, Ganeshmani recites, a system, comprising: a processor; and a memory including instructions that when executed (column 7, line 1-20, a generic computing device), cause the processor to: obtain a federated machine learning (ML) workflow from a distributed ledger (column 8, lines 16-25, a workflow creator allowing the users to select tasks for the workflow), the federated ML workflow comprising a plurality of tasks whose characteristics are represented by metadata associated with each of the plurality of tasks, the metadata also being stored in the distributed ledger (stored in a database cache as listed in column 7, lines 57, which can store metadata associated with data in a database; see also column 8, lines 23-53) orchestrate federated execution of one or more tasks of the plurality of tasks; execute the one or more tasks of the plurality of tasks across a plurality of resource domains (column 3, lines 38-62, a multitude of compute nodes, to complete a multitude of tasks among a workflow, column 8, lines 16-25); and publish a current state of the federated ML workflow corresponding to a current operational condition of the federated ML workflow from the system's perspective to the distributed ledger, the publishing of the current state of the federated ML workflow allowing for the one or more tasks of the plurality of tasks to be performed in accordance with the orchestration of the federated execution (column 6, lines 15-17, and column 8, lines 34-50, execution of one or more tasks, publishing it on a distributed system which can be federated between different systems; see also column 3, lines 14-62; column 18, lines 1-12, workflow information stored in the smart contract, column 10, lines 9-19).
However, Ganeshmani fails to teach, wherein at least two of the plurality of resource domains comprise heterogeneous resources, wherein the one or more tasks of the plurality of tasks have been genericized via containers that represent the one or more tasks of the plurality of tasks as common task definitions, and wherein resource characteristics are abstracted, such that the one or more tasks of the plurality of tasks interface with the plurality of resource domains via the abstracted resource characteristics.
Sodhi teaches, wherein at least two of the plurality of resource domains comprise heterogeneous resources (paragraph 41, 51 resource nodes of a cluster with different resource combinations), wherein the one or more tasks of the plurality of tasks have been genericized via containers that represent the one or more tasks of the plurality of tasks as common task definitions, and wherein resource characteristics are abstracted, such that the one or more tasks of the plurality of tasks interface with the plurality of resource domains via the abstracted resource characteristics (paragraph 16 – 18, 42-45, 47, 49-51, a ML task that has been standardized, assigning resources from a variety of cloud network locations).
It would be obvious before the filing date of this application to one of ordinary skill in the art, to combine the blockchain technology of Ganeshmani with the cloud ML task structure of Sodhi, as known work in one field of endeavor may prompt variations of it for use in a different one, as it would allow the blockchain to be used in a variety of different cloud system for processing a variety of ML tasks.
Regarding claim 18, Ganeshmani recites, the system of claim 17, comprising a resource domain operative in a network comprising a plurality of resource domains facilitating the federated execution of the one or more tasks of the plurality of tasks (column 6, lines 15-17, and column 8, lines 34-50, execution of one or more tasks, publishing it on a distributed system which can be federated between different systems; see also column 3, lines 14-62; column 18, lines 1-12).
Regarding claim 19, Ganeshmani recites, the system of claim 18, wherein the resource domain comprises one or more nodes of a blockchain network, each of the one or more nodes including a local version of the distributed ledger (column 6, lines 15-17; column 3, lines 14-62; column 18, lines 1-12).
Regarding claim 20, Ganeshmani recites, the system of claim 19, wherein the memory includes instructions that when executed, further cause the processor to perform at least one of the following: update the local version of the distributed ledger or obtain an updated version of the distributed ledger associated with at least one other resource domain of the plurality of resource domains (column 13, lines 61-67, where the system receives data about the status of the workflow, which is stored in the blockchain which contains the ledger).
Claim(s) 21 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ganeshmani et. al (US Patent 10659219) and further in view of Sodhi et. al (US 20230032748 A1), as applied to claim 11 - 13 and 17 - 20 above, and further in view of US 11025626 B1 (Todd et. al).
Regarding claim 21, Ganeshmani teaches, The system of claim 11.
However, Ganeshmani does not teach, wherein the resource characteristics comprise resource identity, resource access, and resource authorization (Fig. 3, column 7, line 52 - column 8, line 19, authorization, identity, and access information using private keys).
Todd teaches, wherein the resource characteristics comprise resource identity, resource access, and resource authorization (Fig. 3, column 7, line 52 - column 8, line 19, authorization, identity, and access information using private keys).
It would be obvious before the filing date of this application to combine the blockchain technology of Ganeshmani with the smart contract structure of Todd, as it would allow better security of the transaction items within the blockchain.
Regarding claim 22, Ganeshmani teaches, The system of claim 17.
However, Ganeshmani does not teach, wherein the resource characteristics comprise resource identity, resource access, and resource authorization (Fig. 3, column 7, line 52 - column 8, line 19, authorization, identity, and access information using private keys).
Todd teaches, wherein the resource characteristics comprise resource identity, resource access, and resource authorization (Fig. 3, column 7, line 52 - column 8, line 19, authorization, identity, and access information using private keys).
It would be obvious before the filing date of this application to combine the blockchain technology of Ganeshmani with the smart contract structure of Todd, as it would allow better security of the transaction items within the blockchain.
Conclusion
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/C.M.B./ Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/ Supervisory Patent Examiner, Art Unit 2199