DETAILED ACTION
This Office Actions is in response to communication filed on 10/03/2024. Claims 1-20 are pending. Claim 1, 15, and 20 are in independent form.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/03/2024 and 1/5/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 15, and 20 are objected to because of the following informalities: “segmenting, by the computer using a trained machine learning (ML) model, the workflow into a plurality of tasks based on the workflow data, the ML model is trained based on training workflow data associated with execution of a plurality of training workflows”. It should be “the trained ML model is trained based on training workflow data associated with execution of a plurality of training workflows”, as recited in previous limitation.
Appropriate correction is required.
Claims 3 and 17 are objected to because of the following informalities: “by the computer using the ML model”. It should be “the trained ML model is trained based on training workflow data associated with execution of a plurality of training workflows”, as recited in previous limitation.
Appropriate correction is required.
Claim 11 is objected to because of the following informalities: “by the computer using the ML model”. It should be “by the computer using the trained ML model”, as recited in previous limitation.
Appropriate correction is required.
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.
Claim 1-20, are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception, an abstract idea, as it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception.
Regarding claims 1, 9, and 15, these claims are within at least one of the four categories of patent eligible subject matter as it is directing to:
Step 1:
Claim 1 is directed to a computer-implemented method and falls within the statutory category of process.
Claim 15 is directed to a computer system and falls within the statutory category of machines.
Claim 20 is directed to a computer program product comprising a computer-readable storage medium and falls within the statutory category of manufacture.
Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes, under Step 1.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
However, the limitations
Claims 1, 15, and 20: “segmenting, ……..., the workflow into a plurality of tasks based on the workflow data,”
as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. 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. For example, a person can perform segmenting/grouping/splitting a workflow into a plurality of task, in their mind, and/or including with the aid of pen and paper. (See MPEP § 2106.04(a)(2)(III)).
Claims 1, 15, and 20: “determining, ……..., execution data associated with execution of each task of the plurality of tasks based on the workflow data,”
as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. 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. For example, a person can perform the determination of an execution data for each task, in their mind, and/or including with the aid of pen and paper. (See MPEP § 2106.04(a)(2)(III)).
Claims 1, 15, and 20: “determining, ………., resource data based on the execution data, wherein the resource data is associated with each of the one or more resources associated with execution of each task of the plurality of tasks”
as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. 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. For example, a person can perform the determination of resource data based on the execution data, in their mind, and/or including with the aid of pen and paper. (See MPEP § 2106.04(a)(2)(III)).
That is, the above limitations in the claims 1, 9, and 15 as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass the human mind carrying out the functions 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 Step 2A Prong 1.
Step 2A Prong 2:
Under Prong 2 Step 2A, this judicial exception is not integrated into a practical application. The claims recite the following additional elements:
Claim 1: “A computer-implemented method, comprising: …... by a computer….; …... by the computer using a trained machine learning (ML) model….;”
Claim 15: “A computer system, comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media, the program instructions executable by the processor set to cause the processor set to; …... using a trained machine learning (ML) model….;”
Claim 20: “A computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a system to cause the system to; …... using a trained machine learning (ML) model….;”
are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception, or Performing a mental process in a computer environment. (See MPEP § 2106.04(a)(2)(III)).
Further, the claims recite the following additional element:
Claims 1, 15, and 20: receiving, ……, workflow data associated with a workflow executable in a virtual environment.
which is the limitation merely a recitation of limit the use of the abstract idea amounts to necessary data transmitting or transferring - Insignificant Extra-Solution Activity - pre-solution and post-solution activity - mere receiving or transmitting data over a network. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of merely receiving or transmitting data over a network for use in a claimed process. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional, which does not integrate a judicial exception into practical application. (See MPEP § 2106.05(d)).
In addition, the claims also recite the following additional elements:
Claims 1, 15, and 20: the ML model is trained based on training workflow data associated with execution of a plurality of training workflows;
which are merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. The above limitations recites only the idea of a solution or outcome i.e., the limitation fails to recite details of how a solution to a problem is accomplished, and only invoke computers or other machinery merely as a tool to perform an existing process or simply adding a general-purpose computer or computer components after the fact to an abstract idea, which does not integrate a judicial exception into a practical application or provide significantly more (see MPEP § 2106.05(f)).
Claims 1, 15, and 20: controlling, ……..., an allocation of at least one of the one or more resources based on the execution data and the resource data,;
which are merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. The above limitation has broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more, and only invoke computers or other machinery merely as a tool to perform an existing process or simply adding a general-purpose computer or computer components after the fact to an abstract idea, which does not integrate a judicial exception into a practical application or provide significantly more (see MPEP § 2106.05(f)).
Claims 1, 15, and 20: wherein the execution data indicates one or more resources for execution of each task of the plurality of tasks; ………. wherein the at least one of the one or more resources is allocated for execution of each task of the plurality of tasks in the virtual environment
which is the limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application. The limitation merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims, and only invoke computers or other machinery merely as a tool to perform an existing process or simply adding a general-purpose computer or computer components after the fact to an abstract idea, which does not integrate a judicial exception into a practical application or provide significantly more (see MPEP § 2106.05(h)).
Claims 1, 15, and 20: and executing, ……..., each task of the plurality of tasks in the virtual environment based on the allocation.
which are merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. The above limitation has broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more, and only invoke computers or other machinery merely as a tool to perform an existing process or simply adding a general-purpose computer or computer components after the fact to an abstract idea, which does not integrate a judicial exception into a practical application or provide significantly more (see MPEP § 2106.05(f)).
Therefore, these additional elements do not integrate the abstract idea into a practical application, and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquiries set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 9, and 15 do not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B:
Under Step 2B, the claims do not include additional elements, alone or in combination, 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 amount to no more than generic computing components and invoking computers and other machinery merely as a tool to perform an existing process, which do not amount to significantly more than the abstract idea. The claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.
The claims recite the following additional elements:
Claims 1, 15, and 20: receiving, ……, workflow data associated with a workflow executable in a virtual environment.
which is the limitation merely a recitation of limit the use of the abstract idea amounts to necessary data transmitting or transferring - Insignificant Extra-Solution Activity - pre-solution and post-solution activity - mere receiving or transmitting data over a network. Extra-solution activity includes both pre-solution and post-solution activity. The courts have recognized the “sending and receiving data” are insignificant extra-solution data gathering activity which do not amount to significantly more than the abstract idea, and are limitations computer functions as well-understood, routine, conventional (WURC) functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. (See TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as “either performing basic computer functions such as sending and receiving data or performing functions ‘known’ in the art.”) and MPEP § 2106.05(d)).
In addition, the claims also recite the following additional elements:
Claims 1, 15, and 20: the ML model is trained based on training workflow data associated with execution of a plurality of training workflows;
which are merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. The above limitations recites only the idea of a solution or outcome i.e., the limitation fails to recite details of how a solution to a problem is accomplished, and only invoke computers or other machinery merely as a tool to perform an existing process or simply adding a general-purpose computer or computer components after the fact to an abstract idea, which does not integrate a judicial exception into a practical application or provide significantly more. The courts have found the additional elements to be mere instructions to apply an exception, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the training, which was equivalent to the words “apply it”, and are limitations computer functions as well-understood, routine, conventional (WURC) functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. (See Intellectual Ventures I LLC v. Capital One Bank (USA), 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims “so result focused, so functional, as to effectively cover any solution to an identified problem”))) and MPEP § 2106.05(f)).
Claims 1, 15, and 20: controlling, ……..., an allocation of at least one of the one or more resources based on the execution data and the resource data;
which are merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. The above limitation having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more, and only invoke computers or other machinery merely as a tool to perform an existing process or simply adding a general-purpose computer or computer components after the fact to an abstract idea, which does not integrate a judicial exception into a practical application or provide significantly more. The courts have found the additional elements to be mere instructions to apply an exception, because the additional limitations generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words “apply it” to the judicial exception, and are limitations computer functions as well-understood, routine, conventional (WURC) functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See Internet Patents Corporation v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (The recitation of maintaining the state of data in an online form without restriction on how the state is maintained and with no description of the mechanism for maintaining the state describes “the effect or result dissociated from any method by which maintaining the state is accomplished” and does not provide a meaningful limitation because it merely states that the abstract idea should be applied to achieve a desired result), and MPEP § 2106.05(f)).
Claims 1, 15, and 20: wherein the execution data indicates one or more resources for execution of each task of the plurality of tasks; ………. wherein the at least one of the one or more resources is allocated for execution of each task of the plurality of tasks in the virtual environment.
which is the limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application. The courts have explained that although the limitation did limit the use of the abstract idea, the limitation merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims, and are limitations computer functions as well-understood, routine, conventional (WURC) functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. (See Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016) (838 F.3d at 1259, 120 USPQ2d at 1204) and MPEP § 2106.05(h)).
Claims 1, 15, and 20: and executing, ……..., each task of the plurality of tasks in the virtual environment based on the allocation.
which are merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. The above limitation having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more, and only invoke computers or other machinery merely as a tool to perform an existing process or simply adding a general-purpose computer or computer components after the fact to an abstract idea, which does not integrate a judicial exception into a practical application or provide significantly more. The courts have found the additional elements to be mere instructions to apply an exception, because the additional limitations generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words “apply it” to the judicial exception, and are limitations computer functions as well-understood, routine, conventional (WURC) functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See Internet Patents Corporation v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (The recitation of maintaining the state of data in an online form without restriction on how the state is maintained and with no description of the mechanism for maintaining the state describes “the effect or result dissociated from any method by which maintaining the state is accomplished” and does not provide a meaningful limitation because it merely states that the abstract idea should be applied to achieve a desired result), and MPEP § 2106.05(f)).
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception.
Having concluded analysis within the provided framework, claims 1, 15, and 20 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Apply the same analysis to the dependent claims 2-14, and 16-19, according to the above analysis, are mental processes, groupings of abstract ideas. Thus, the claims are ineligible.
Regarding claims 2 and 16, the claims have limitations to further indicate the wherein the execution data comprises at least one of dependency data associated with each task of the plurality of tasks, complexity data for execution of each task of the plurality of tasks, technical requirement data for execution of each task of the plurality of tasks, or security requirement data for execution of each task of the plurality of tasks. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. Further, the claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claims do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 3 and 17, the claims further include the limitation of “predicting, ……., input data for execution of each task of the plurality of tasks; predicting, ……., output data for execution of each task of the plurality of tasks; that fall with the “Mental Processes” grouping of abstract ideas, which is analyzed under Prong 1 Step 2A. The limitation: “determining, ……., the dependency data associated with each task of the plurality of tasks based on the input data and the output data;” that fall with the “Mental Processes” grouping of abstract ideas, which is analyzed under Prong 1 Step 2A. The limitation: “and generating, ……., order data based on the dependency data, wherein the order data is generated for execution of the plurality of tasks based on the dependency data.” that fall with the “Mental Processes” grouping of abstract ideas, which is analyzed under Prong 1 Step 2A. The limitation “by the computer using the ML model” and “by the computer” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. Further, the claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claims do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 4 and 18, the claims have limitations to further indicate the receiving, by the computer, user information associated with each user of a plurality of users of the virtual environment. The limitations are the additional element analyzed under Prong 2 Step 2A, which are merely a recitation of limiting the use of the abstract idea to insignificant extra-solution data gathering activity or are no more than mere instructions to implement an abstract idea or other exception on a computer. In addition, the claims have limitations to further indicate the and allocating, by the computer, the one or more resources to one or more user devices associated with a set of users of the plurality of users based on the user information associated with each user of the set of users. The limitation is the additional element analyzed under Prong 2 Step 2A, which merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. In addition, the claims have limitations to further indicate the wherein the user information comprises skills data of each user of the plurality of users and user device data of each user of the plurality of users, ……….., wherein the one or more resources are allocated to the one or more user devices in the virtual environment for execution of a task of the plurality of tasks. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. Further, the claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claims do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 5 and 19 the claims further include the limitation of “determining, ……., service data associated with a plurality of service resources executable in the virtual environment;” that fall with the “Mental Processes” grouping of abstract ideas, which is analyzed under Prong 1 Step 2A. The limitation “by the computer; by the computer using the trained ML model” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. In addition, the claims have limitations to further indicate the wherein the plurality of service resources comprises the one or more resources associated with execution of each task of the plurality of tasks. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. In addition, the claims have limitations to further indicate the segmenting, ……, the workflow into the plurality of tasks based on the service data, that fall with the “Mental Processes” grouping of abstract ideas, which is analyzed under Prong 1 Step 2A. Further, the claims do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claims also fail both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claims do not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 6, the claim further includes the limitation of “determining, by the computer, first execution data associated with the first task, ……., determining, by the computer, second execution data associated with the second task, ……., determining, by the computer, resource data associated with each of the one or more first resources and the one or more second resources” that fall with the “Mental Processes” grouping of abstract ideas, which is analyzed under Prong 1 Step 2A. The limitation “by the computer” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. In addition, the claims have limitations to further indicate the wherein the plurality of tasks comprises a first task and a second task, such that the dependency data indicates a dependency relationship between the first task and the second task; ……..; wherein the first execution data indicates one or more first resources of the one or more resources for execution of the first task, and wherein the execution data comprises the first execution data; ……..; wherein the second execution data indicates one or more second resources of the one or more resources for execution of the second task, and wherein the execution data comprises the second execution data; ……. wherein the one or more first resources are allocated for execution of the first task and the one or more second resources are allocated for execution of the second task. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. Further, the claims have limitations controlling, by the computer, an allocation of at least one of the one or more first resources or the one or more second resources based on the dependency relationship. The limitation is the additional element analyzed under Prong 2 Step 2A, which merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. Further, the claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claim also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 7, the claim has limitation to further indicate the wherein the dependency relationship indicates a dependency of the first task on the second task, ……..., wherein the one or more second resources are allocated for execution of the second task in the virtual environment. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. In addition, the claim has limitations to further indicate the controlling, by the computer, the allocation of the one or more first resources for execution of the first task in the virtual environment; ……. Validating, by the computer, the completion status of the first task; and controlling, by the computer, the allocation of the one or more second resources based on the validation. The limitation is the additional element analyzed under Prong 2 Step 2A, which merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. The limitation “by the computer” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. In addition, the claim has limitations to further indicate the receiving, by the computer from at least one of the one or more first resources, a completion status of the first task. The limitations are the additional element analyzed under Prong 2 Step 2A, which are merely a recitation of limiting the use of the abstract idea to insignificant extra-solution data gathering activity or are no more than mere instructions to implement an abstract idea or other exception on a computer. Further, the claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claim also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 8, the claim further includes the limitation of “determining, by the computer, resource data associated with the third resource,” that fall with the “Mental Processes” grouping of abstract ideas, which is analyzed under Prong 1 Step 2A. The limitation “by the computer” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. Further, the claims have limitations wherein the resource data indicates one or more attributes of the third resource; ……. wherein each of the one or more first resources for execution of the first task and the one or more second resources for execution of the second task comprises a third resource. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. In addition, the claim has limitations to further indicate the “executing, by the computer, at least one of the first task or the second task using the third resource based on the one or more attributes.” The limitation is the additional element analyzed under Prong 2 Step 2A, which merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. Further, the claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claim also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 9, the claim further includes the limitation of “determining, by the computer, an unavailability of the third resource based on the one or more attributes, wherein the unavailability of the third resource corresponds to a failure in execution of at least one of the first task or the second task;” that fall with the “Mental Processes” grouping of abstract ideas, which is analyzed under Prong 1 Step 2A. The limitation “by the computer” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. Further, the claims have limitations performing, by the computer, at least one of scaling the third resource or triggering a notification for the third resource based on the determination of the unavailability of the third resource. The limitation is the additional element analyzed under Prong 2 Step 2A, which merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. Further, the claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claim also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 10, the claim further includes the limitation of “determining, by the computer, the unavailability of the third resource based on the one or more attributes for execution of at least one of the first task or the second task;” that fall with the “Mental Processes” grouping of abstract ideas, which is analyzed under Prong 1 Step 2A. The limitation “by the computer” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. Further, the claims have limitations initiating, by the computer, an updated instance of the third resource based on the determination, ……., and allocating, by the computer, the updated instance of the third resource for execution of at least one of the first task or the second task. The limitation is the additional element analyzed under Prong 2 Step 2A, which merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. Further, the claims have limitations wherein the updated instance is initiated for execution of at least one of the first task or the second task. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. Further, the claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claim also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claims 11, the claim further includes the limitation of “analyzing, by the computer using the ML model, each task of the plurality of tasks based on the workflow data; determining, by the computer using the ML model, one or more tasks of the plurality of tasks based on the analysis;” that fall with the “Mental Processes” grouping of abstract ideas, which is analyzed under Prong 1 Step 2A. The limitation “by the computer using the ML model; …… by the computer” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. Further, the claims have limitations wherein the one or more tasks are to be automated. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. Further, the claims have limitations automating, by the computer, the one or more tasks of the plurality of tasks based on the corresponding execution data. The limitation is the additional element analyzed under Prong 2 Step 2A, which merely recites "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. Further, the claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claim also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 12, the claim has limitation to further indicate the wherein the one or more resources associated with execution of each task of the plurality of tasks are cloud resources. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. Further, the claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claim also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 13, the claim has limitation to further indicate the wherein the virtual environment corresponds to a metaverse environment. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. Further, the claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claim also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
Regarding claim 14, the claim has limitation to further indicate the wherein the one or more resources comprise at least a digital twin, an Artificial Intelligence (AI) model, one or more security resources, one or more analytics resources, and one or more load balancing resources. The limitation is the additional element analyzed under Prong 2 Step 2A, which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application, and the limitation merely confines the use of the abstract idea to a particular technological environment, and thus fails to add an inventive concept to the claims. Further, the claim does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, the claim also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fail Step 2B as not amounting to significantly more. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
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 1, 2, 5, 12-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over SEIFOORY et al. US Pub. No. US 20260044768 A1 (hereafter SEIFOORY), in further view of BOUDIA et al. US Pub. No. US 20250060978 A1 (hereafter BOUDIA).
Regarding claim 1, SEIFOORY teaches the invention substantially as claimed: A computer-implemented method, comprising: receiving, by a computer, workflow data associated with a workflow executable in an environment; (e.g. FIG. 4, FIG. 5 and [0107]: “FIG. 4 illustrates a method for resource allocation in a hybrid distributed computational environment 400, in accordance with an embodiment of the disclosure. As shown in block 402, a task, task execution parameters, and information associated with computational resources may be received.” and FIG. 1A and [0043]: “A reference throughout this disclosure to a CPU and/or GPU should be understood to apply to both physical or virtual respective processing units.”) the citation discloses the receiving of task execution parameters/workflow data, and a task/workflow. At FIG.1A and [0043] discloses the computing environment of the reference could be virtual, since the device 102 sends task to server 106 for processing.
segmenting, by the computer using a trained machine learning (ML) model, the workflow into a plurality of tasks based on the workflow data, (e.g. FIG. 4, FIG. 5, and [0116]: “As shown in block 404, the task may be partitioned into a plurality of sub-tasks based on the task, the task execution parameters, and information associated with the computational resources. In this regard, the task partitioning can be performed using default partitioning parameters, user-defined partitioning parameters, or rule-based partitioning parameters. Partitioning parameters refer to the criteria or guidelines used to divide a task into smaller, manageable sub-tasks,” and [0118]: “Once trained, the machine learning model may be used to determine the task partitioning parameters to partition the task into various sub-tasks. Once determined, the task partitioning parameters may be transmitted to the task partitioning circuitry (as described in connection with FIGS. 3A and 3B) to partition the task into multiple sub-tasks.”) the citation discloses the trained machine learning model partitions/segmenting, the task into a plurality of sub-tasks, using the task execution parameters.
the ML model is trained based on training workflow data associated with execution of a plurality of training workflows; (e.g. [0118]: “As described herein in FIGS. 3A and 3B, the machine learning model may be trained using a using a plurality of known tasks, a plurality of known sub-tasks for each known task, known task execution parameters for each known task, and known information associated with the computational resources at a time of execution of the known task. Through repeated cycles of experimentation, testing, and tuning, the machine learning model may learn to identify optimal partitioning strategies by adjusting its parameters based on feedback from previous performances. Such an iterative training process may refine the machine learning model's ability to predict the most optimal way to divide tasks.”)
determining, by the computer using the trained ML model, execution data associated with execution of each task of the plurality of tasks based on the workflow data, wherein the execution data indicates one or more resources for execution of each task of the plurality of tasks; (FIG. 5 and [0119]: “As shown in block 504, in some embodiments, the machine learning model is used to determine the computational resource for each sub-task based on the task, the task execution parameters, the information associated with the computational resources, and a computational resource type associated with the computational resource.” and [0120]: “As shown in block 506, the machine learning model may be used to determine an execution sequence for the execution of the sub-tasks using corresponding computational resources. In specific embodiments, the machine learning model may analyze dependencies between sub-tasks, the availability and capabilities of computational resources, and the task execution parameters to establish an optimal order for executing the sub-tasks. In this regard, the machine learning model may sequence the sub-tasks to ensure that those with interdependencies are executed in the correct order, timing the availability of computational resources to maintain continuous engagement, and prioritizing sub-tasks based on their execution parameters, such as priority levels and deadlines. Upon determining the execution sequence, the machine learning model may transmit the sequence to the processor (as described in connection with FIGS. 3A and 3B) to execute the sub-tasks using corresponding computational resources concurrently.”) The citation discloses at [0120] the determination of execution sequence/execution data, of the sub-tasks, using corresponding computational resources, and since the using computational resources is determined based on the task execution parameters/ workflow data, it would imply that the execution sequence also determined based on the task execution parameters. Because system is based on the execution sequence to assign the sub-tasks to the corresponding computational resource, it would imply that the execution sequence is associated with the computational resources for executing the sub-tasks.
determining, by the computer, resource data based on the execution data, wherein the resource data is associated with each of the one or more resources associated with execution of each task of the plurality of tasks; ([0006]: “determine an execution sequence for execution of the quantum sub-circuits using corresponding computational resources; and initiate the execution of the quantum sub-circuits according to the execution sequence.” And [0120]: “Upon determining the execution sequence, the machine learning model may transmit the sequence to the processor (as described in connection with FIGS. 3A and 3B) to execute the sub-tasks using corresponding computational resources concurrently.”) The citation discloses system using the execution sequence to send the sub-tasks to the corresponding resources, so it would imply that the execution sequence would indicate the computational resource/resource data, for executing the sub-tasks.
controlling, by the computer, an allocation of at least one of the one or more resources based on the execution data and the resource data, wherein the at least one of the one or more resources is allocated for execution of each task of the plurality of tasks in the environment; ([0090] Once the task is partitioned, the resource allocation circuitry 124 may allocate each sub-task to an appropriate computational resource. In this regard, the resource allocation circuitry 124 may be configured to transmit control signals to the processor 112, directing the processor 112 to assign each sub-task to a specific computational resource (e.g., a computational resource 110 shown in FIG. 1A).” and FIG. 4 and [0117]: “As shown in block 406, upon partitioning the task into sub-tasks, each sub-task is allocated to a classical computational resource or a quantum computational resource. As described herein in FIGS. 3A and 3B, various allocation patterns may be employed. In one embodiment, each sub-task may be assigned to a single computational resource, ensuring dedicated execution. Alternatively, a single sub-task may be distributed across multiple computational resources, allowing parallel execution and leveraging the combined computational power of these resources.”) the citation discloses after partitioning the tasks into plurality sub-task, each sub-task is sent to the appropriate computational resource.
and executing, by the computer, each task of the plurality of tasks in the environment based on the allocation. (e.g. [0070]: “After the sub-tasks 108 are allocated to the respective computational resources, the system 106 may manage the execution of each sub-task. Monitoring execution of each sub-task may include monitoring the progress, managing data flow, and ensuring that the sub-tasks are executed in a coordinated manner. The system 106 may handle any dependencies among sub-tasks, such that the sub-tasks are processed in the correct sequence. Upon completion of all sub-tasks 108, the system 106 may collect the results from the computational resources 110. The results may then be aggregated and processed to produce the final output for the task 104.”)
SEIFOORY does not teach an environment as a virtual environment
However, BOUDIA teaches a virtual environment; (e.g. [0013]: “The publishing platforms 104 may be based on any present or future electronic media servers that publish and/or provide content for the client devices 108. In particular, the publishing platforms 104 may comprise augmented reality and/or virtual reality platforms (and/or mixed reality platforms) which may provide augmented reality and/or virtual reality environments, such as “metaverse” environments.”) The citation discloses the virtual reality platforms/virtual environment.
Both SEIFOORY and BOUDIA are in the same field of endeavor, and both references disclose the concept relating to computing resource optimization, and specifically to a device, system and method for implementing computing resource optimization, and therefore, are combinable/modifiable.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add a virtual environment, as taught in BOUDIA’s invention into SEIFOORY’s invention because the addition features would provide an isolated and flexible execution space, wherein each resources can be allocated to each task without directly affecting other tasks, therefore improving the resource utilization and more efficient execution of tasks.
Regarding claim 2, SEIFOORY, in view of BOUDIA, discloses the computer-implemented method of claim 1, and SEIFOORY further teaches wherein the execution data comprises at least one of dependency data associated with each task of the plurality of tasks, [0120]: “As shown in block 506, the machine learning model may be used to determine an execution sequence for the execution of the sub-tasks using corresponding computational resources. In specific embodiments, the machine learning model may analyze dependencies between sub-tasks, the availability and capabilities of computational resources, and the task execution parameters to establish an optimal order for executing the sub-tasks. In this regard, the machine learning model may sequence the sub-tasks to ensure that those with interdependencies are executed in the correct order, timing the availability of computational resources to maintain continuous engagement, and prioritizing sub-tasks based on their execution parameters, such as priority levels and deadlines.”) the citation discloses the dependencies between sub-tasks / dependency data associated with each task
complexity data for execution of each task of the plurality of tasks,
technical requirement data for execution of each task of the plurality of tasks, [0120]: “As shown in block 506, the machine learning model may be used to determine an execution sequence for the execution of the sub-tasks using corresponding computational resources. In specific embodiments, the machine learning model may analyze dependencies between sub-tasks, the availability and capabilities of computational resources, and the task execution parameters to establish an optimal order for executing the sub-tasks. In this regard, the machine learning model may sequence the sub-tasks to ensure that those with interdependencies are executed in the correct order, timing the availability of computational resources to maintain continuous engagement, and prioritizing sub-tasks based on their execution parameters, such as priority levels and deadlines.”) the citation discloses the availability and capabilities of computational resources/ technical requirement data.
or security requirement data for execution of each task of the plurality of tasks.
Regarding claim 5, SEIFOORY, in view of BOUDIA, discloses the computer-implemented method of claim 1, and SEIFOORY further teaches further comprising: determining, by the computer, service data associated with a plurality of service resources executable in the virtual environment, wherein the plurality of service resources comprises the one or more resources associated with execution of each task of the plurality of tasks; (e.g. [0084]: “The information associated with the computational resources may provide insight into the current state and capabilities of the available resources, for informed decision-making for optimal task allocation. Such information may include resource availability, processing power, memory, utilization rate, execution time estimates, cost metrics, temperature and thermal limits, failure rate, resource health, load balancing policies, and/or the like associated with each computational resource, including classical computational resources and quantum computational resources. By leveraging information about the computational resources, the task partitioning circuitry 120 may optimize the allocation of available resources for efficient task execution.” And [0115]: “Information associated with computational resources, as described herein, may provide insight into the current state and capabilities of the available resources. Such information may include resource availability, processing power, memory, utilization rate, execution time estimates, cost metrics, temperature and thermal limits, failure rate, resource health, load balancing policies, and/or the like. Resource availability may indicate which computational resources are currently available for task allocation. As such, resource availability may include real-time data on the status of CPUs, GPUs, and QPUs, helping to avoid resource contention and ensuring efficient utilization.”) the citation discloses the resource availability/service data, that associated with each computational resource for task execution.
and segmenting, by the computer using the trained ML model, the workflow into the plurality of tasks based on the service data. (e.g. [0116]: “As shown in block 404, the task may be partitioned into a plurality of sub-tasks based on the task, the task execution parameters, and information associated with the computational resources. In this regard, the task partitioning can be performed using default partitioning parameters, user-defined partitioning parameters, or rule-based partitioning parameters. Partitioning parameters refer to the criteria or guidelines used to divide a task into smaller, manageable sub-tasks, such that the partitioned sub-tasks are efficiently executable within the constraints and requirements of the computational environment.”) the citation discloses the partitioning of task into a plurality of sub-tasks based on the information associated with the computational resources, which comprises the resource availability/service data.
Regarding claim 12, SEIFOORY, in view of BOUDIA, discloses the computer-implemented method of claim 1, and SEIFOORY further teaches wherein the one or more resources associated with execution of each task of the plurality of tasks are cloud resources. (e.g. [0079]: “The use of the term “processing circuitry” may be understood to include a single core processor, a multi-core processor, multiple processors internal to the apparatus, and/or remote or “cloud” processors.”)
Regarding claim 13, SEIFOORY, in view of BOUDIA, discloses the computer-implemented method of claim 1, and BOUDIA further teaches wherein the virtual environment corresponds to a metaverse environment. (e.g. [0058] In a particular example, a publisher platform 104 may provide content according to a metaverse environment, and virtual geographic locations and/or regions of such a metaverse environment may be defined by a respective electronic map 110.”)
Regarding claim 14, SEIFOORY, in view of BOUDIA, discloses the computer-implemented method of claim 1, and SEIFOORY further teaches wherein the one or more resources comprise at least a digital twin,
an Artificial Intelligence (AI) model,
one or more security resources,
one or more analytics resources, (e.g. [0061]: “The system 106 may be implemented in a number of different forms. For example, the system 106 may be implemented as a standard server, or multiple times in a group of such servers.”) The citation discloses the system 106 could be a group of such servers/one or more analytics resources.
and one or more load balancing resources.
Regarding claim 15, it is a computer system claim having similar limitations cited in claim 1, so it is also rejected under the same rational.
Regarding claim 16, it is a computer system claim having similar limitations cited in claim 2, so it is also rejected under the same rational.
Regarding claim 19, it is a computer system claim having similar limitations cited in claim 5, so it is also rejected under the same rational.
Regarding claim 20, it is a computer program product comprising a computer-readable storage medium claim having similar limitations cited in claim 1, so it is also rejected under the same rational.
Claims 3, 6, 8, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over SEIFOORY and BOUDIA, in further view of MISHRA et al. US Pub. No. US 20240393956 A1 (hereafter MISHRA).
Regarding claim 3, SEIFOORY, in view of BOUDIA, discloses the computer-implemented method of claim 2, but fails to teach further comprising: predicting, by the computer using the ML model, input data for execution of each task of the plurality of tasks; predicting, by the computer using the ML model, output data for execution of each task of the plurality of tasks; determining, by the computer using the ML model, the dependency data associated with each task of the plurality of tasks based on the input data and the output data; and generating, by the computer, order data based on the dependency data, wherein the order data is generated for execution of the plurality of tasks based on the dependency data.
However, MISHRA teaches predicting, by the computer …….. , input data for execution of each task of the plurality of tasks; predicting, by the computer ……. , output data for execution of each task of the plurality of tasks; (e.g. FIG. 2, 3, FIG. 7, and [0058]: “The cloud orchestrator circuitry 104 processes the input application to identify the input data to each function and the output data of each function. The cloud orchestrator circuitry 104 determines the data dependencies between the functions based on the input data and output data of each function. Determining the data dependencies includes determining the input data used by each function, and from what other function that input data is output from. The functions and data dependencies are used to generate the data graph (e.g., the data graph 300 of FIG. 3).”) The citation discloses the concept of generating the input data and output data of each application/task.
determining, by the computer ……., the dependency data associated with each task of the plurality of tasks. (e.g. FIG. 2, 3, FIG. 7, and [0058]: “The cloud orchestrator circuitry 104 processes the input application to identify the input data to each function and the output data of each function. The cloud orchestrator circuitry 104 determines the data dependencies between the functions based on the input data and output data of each function. Determining the data dependencies includes determining the input data used by each function, and from what other function that input data is output from. The functions and data dependencies are used to generate the data graph (e.g., the data graph 300 of FIG. 3).”) The citation discloses the concept of generating dependencies data between the application/task based on the input data and output data of each function.
and generating, by the computer, order data based on the dependency data, wherein the order data is generated for execution of the plurality of tasks based on the dependency data. (e.g. FIG. 2, 3, FIG. 7, and [0058]: “The functions and data dependencies are used to generate the data graph (e.g., the data graph 300 of FIG. 3).” and [0060]: “At 730 of the method 700, processing resources are allocated based on the task graph, the data graph, and the function popularity heap parameter. For example, the fabric manager circuitry 106 of FIG. 1 receives the task graph, the data graph, and the function popularity heap parameter from the cloud orchestrator circuitry and allocates the programmable IC devices 120 within the processing nodes 110.”) the citation discloses the data graph/order data, for allocation tasks to the corresponding resources for execution.
The teaching of MISHRA does not teach the system comprises the ML model. However, the ML model is taught by SEIFOORY, as explained in claim 1.
Both SEIFOORY and MISHRA are in the same field of endeavor, and both references disclose the concept relating to allocating resources for processing computing tasks, and therefore, are combinable/modifiable.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the further comprising: predicting, by the computer using the ML model, input data for execution of each task of the plurality of tasks; predicting, by the computer using the ML model, output data for execution of each task of the plurality of tasks; determining, by the computer using the ML model, the dependency data associated with each task of the plurality of tasks based on the input data and the output data; and generating, by the computer, order data based on the dependency data, wherein the order data is generated for execution of the plurality of tasks based on the dependency data, as taught in MISHRA’s invention into SEIFOORY and BOUDIA’s invention because the newly added limitation would enable the system to generate an appropriate execution order and coordinate resource allocation based on the task dependencies, thereby reducing unnecessary resource usage, avoiding execution conflicts, and improving the efficiency of the whole system. (MISHRA-[0023])
Regarding claim 6, SEIFOORY, in view of BOUDIA, discloses the computer-implemented method of claim 2, and SEIFOORY further teach and wherein the method further comprises: determining, by the computer, first execution data associated with the first task, wherein the first execution data indicates one or more first resources of the one or more resources for execution of the first task, and wherein the execution data comprises the first execution data; determining, by the computer, second execution data associated with the second task, wherein the second execution data indicates one or more second resources of the one or more resources for execution of the second task, and wherein the execution data comprises the second execution data; (e.g. [0044]: “For example, if it is known that a first task has a peak computational demand at a first time and a second task has a peak demand at a second, different time, the two tasks may be scheduled in a staggered fashion where the respective peak times do not overlap (or minimally overlap) with each other.” and FIG. 5 and [0119]: “As shown in block 504, in some embodiments, the machine learning model is used to determine the computational resource for each sub-task based on the task, the task execution parameters, the information associated with the computational resources, and a computational resource type associated with the computational resource.” and [0120]: “As shown in block 506, the machine learning model may be used to determine an execution sequence for the execution of the sub-tasks using corresponding computational resources. In specific embodiments, the machine learning model may analyze dependencies between sub-tasks, the availability and capabilities of computational resources, and the task execution parameters to establish an optimal order for executing the sub-tasks. In this regard, the machine learning model may sequence the sub-tasks to ensure that those with interdependencies are executed in the correct order, timing the availability of computational resources to maintain continuous engagement, and prioritizing sub-tasks based on their execution parameters, such as priority levels and deadlines. Upon determining the execution sequence, the machine learning model may transmit the sequence to the processor (as described in connection with FIGS. 3A and 3B) to execute the sub-tasks using corresponding computational resources concurrently.”) The citation discloses at [0120] the determination of execution sequence/execution data, of the sub-tasks, using corresponding computational resources, and since there are more than one sub-task, the first task and the second task are within the plurality of sub-tasks, along with the first and second execution sequence/execution data, are within the execution sequence.
determining, by the computer, resource data associated with each of the one or more first resources and the one or more second resources; ([0006]: “determine an execution sequence for execution of the quantum sub-circuits using corresponding computational resources; and initiate the execution of the quantum sub-circuits according to the execution sequence.” And [0120]: “Upon determining the execution sequence, the machine learning model may transmit the sequence to the processor (as described in connection with FIGS. 3A and 3B) to execute the sub-tasks using corresponding computational resources concurrently.”) The citation discloses system using the execution sequence to send the sub-tasks to the corresponding resources, so it would imply that the execution sequence would indicate the computational resource/resource data, for executing the sub-tasks. Because there are more than one sub-task, the first task and the second task are within the plurality of sub-tasks, along with the first and second resource, are within the computational resource that corresponding to each of the sub-task.
and controlling, by the computer, an allocation of at least one of the one or more first resources or the one or more second resources based on the dependency relationship, wherein the one or more first resources are allocated for execution of the first task and the one or more second resources are allocated for execution of the second task. (e.g. [0090] Once the task is partitioned, the resource allocation circuitry 124 may allocate each sub-task to an appropriate computational resource. In this regard, the resource allocation circuitry 124 may be configured to transmit control signals to the processor 112, directing the processor 112 to assign each sub-task to a specific computational resource (e.g., a computational resource 110 shown in FIG. 1A).” and FIG. 4 and [0117]: “As shown in block 406, upon partitioning the task into sub-tasks, each sub-task is allocated to a classical computational resource or a quantum computational resource. As described herein in FIGS. 3A and 3B, various allocation patterns may be employed. In one embodiment, each sub-task may be assigned to a single computational resource, ensuring dedicated execution. Alternatively, a single sub-task may be distributed across multiple computational resources, allowing parallel execution and leveraging the combined computational power of these resources.”) the citation discloses after partitioning the tasks into plurality sub-task, each sub-task is sent to the appropriate computational resource.
SEIFOORY, in view of BOUDIA fails to teach wherein the plurality of tasks comprises a first task and a second task, such that the dependency data indicates a dependency relationship between the first task and the second task,
However, MISHRA teaches wherein the plurality of tasks comprises a first task and a second task, such that the dependency data indicates a dependency relationship between the first task and the second task, (e.g. FIG. 2, 3, FIG. 7, and [0058]: “The cloud orchestrator circuitry 104 processes the input application to identify the input data to each function and the output data of each function. The cloud orchestrator circuitry 104 determines the data dependencies between the functions based on the input data and output data of each function. Determining the data dependencies includes determining the input data used by each function, and from what other function that input data is output from. The functions and data dependencies are used to generate the data graph (e.g., the data graph 300 of FIG. 3).”) The citation discloses the concept of generating the input data and output data of each application/task.
Regarding claim 8, SEIFOORY, in view of BOUDIA and MISHRA, discloses the computer-implemented method of claim 6, and SEIFOORY further teach wherein each of the one or more first resources for execution of the first task and the one or more second resources for execution of the second task comprises a third resource, (e.g. [0018]: “wherein the computational resources comprise at least one of a classical computational resource or a quantum computational resource, wherein the classical computational resource comprises a central processing unit (CPU) or a graphics processing unit (GPU), and wherein the quantum computational resource comprises a quantum processing unit (QPU); and partition the task into sub-tasks based on the task execution parameters and the information associated with computational resources; and a resource allocation circuitry operatively coupled to the task partitioning circuitry and configured to: allocate each sub-task to the classical computational resource or the quantum computational resource.”) the citation discloses multiple different type of resources of the system, so the third resource within the multiple different type of resources of the system.
and wherein the method further comprises: determining, by the computer, resource data associated with the third resource, wherein the resource data indicates one or more attributes of the third resource; ([0006]: “determine an execution sequence for execution of the quantum sub-circuits using corresponding computational resources; and initiate the execution of the quantum sub-circuits according to the execution sequence.” And [0120]: “Upon determining the execution sequence, the machine learning model may transmit the sequence to the processor (as described in connection with FIGS. 3A and 3B) to execute the sub-tasks using corresponding computational resources concurrently.” and [0018]: “wherein the computational resources comprise at least one of a classical computational resource or a quantum computational resource, wherein the classical computational resource comprises a central processing unit (CPU) or a graphics processing unit (GPU), and wherein the quantum computational resource comprises a quantum processing unit (QPU); and partition the task into sub-tasks based on the task execution parameters and the information associated with computational resources; and a resource allocation circuitry operatively coupled to the task partitioning circuitry and configured to: allocate each sub-task to the classical computational resource or the quantum computational resource.”) The citation discloses system using the execution sequence to send the sub-tasks to the corresponding resources, so it would imply that the execution sequence would indicate the computational resource/resource data, for executing the sub-tasks. Because there are more than one sub-task, the first task and the second task are within the plurality of sub-tasks. In addition, there are different type of resources of the computational resource, the third resource within the different type of resources.
and executing, by the computer, at least one of the first task or the second task using the third resource based on the one or more attributes. (e.g. [0070]: “After the sub-tasks 108 are allocated to the respective computational resources, the system 106 may manage the execution of each sub-task. Monitoring execution of each sub-task may include monitoring the progress, managing data flow, and ensuring that the sub-tasks are executed in a coordinated manner. The system 106 may handle any dependencies among sub-tasks, such that the sub-tasks are processed in the correct sequence. Upon completion of all sub-tasks 108, the system 106 may collect the results from the computational resources 110. The results may then be aggregated and processed to produce the final output for the task 104.”)
Regarding claim 17, it is a computer system claim having similar limitations cited in claim 3, so it is also rejected under the same rational.
Claims 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over SEIFOORY and BOUDIA, in further view of BHATIA et al. US Pub. No. US 20210264293 A1 (hereafter BHATIA).
Regarding claim 4, SEIFOORY, in view of BOUDIA, discloses the computer-implemented method of claim 2, but fails to teach further comprising: receiving, by the computer, user information associated with each user of a plurality of users of the virtual environment, wherein the user information comprises skills data of each user of the plurality of users and user device data of each user of the plurality of users; and allocating, by the computer, the one or more resources to one or more user devices associated with a set of users of the plurality of users based on the user information associated with each user of the set of users, wherein the one or more resources are allocated to the one or more user devices in the virtual environment for execution of a task of the plurality of tasks.
However, BHATIA teaches further comprising: receiving, by the computer, user information associated with each user of a plurality of users of the virtual environment, wherein the user information comprises skills data of each user of the plurality of users and user device data of each user of the plurality of users (e.g. [0072]: “As will be described below, the CREM 306 may be configured to access a database 312 that stores data related to skill set of each user received from one or more administrator computing devices 309(1)-309(n) and data related to availability information of each user received from one or more scheduling systems (not shown); receive data associated with each of a plurality work items received from one or more source systems, e.g., client devices 308(1)-308(n) via one or more communication networks 310; dynamically allocate, from the received data, both a score value to assign a priority to each work item and a bit-mapped skill value for each work item regardless of the source system from which each work item is being received or work type of each work item; query the database to identify one or more users having availability data and associated skill set data correlated with the score value associated with the assigned priority for each work item and the bit-mapped skill value for each work item; and automatically assign a work item to a user computing device (e.g., one or more CREDs) utilized by a user based on identifying that the user's skill set data matches the score value associated with the assigned priority for the work item and the bit-mapped skill value for the work item, and that the user's availability data matches the availability information received from the database 312 via the one or more communication networks 310.”) The citation discloses the concept of receiving user information for each of a plurality of users, including skill-set data associated with each user, and then identifies a user computing device utilized by the respective user for assigning task.
and allocating, by the computer, the one or more resources to one or more user devices associated with a set of users of the plurality of users based on the user information associated with each user of the set of users, wherein the one or more resources are allocated to the one or more user devices in the virtual environment for execution of a task of the plurality of tasks. (e.g. [0072]: “As will be described below, the CREM 306 may be configured to access a database 312 that stores data related to skill set of each user received from one or more administrator computing devices 309(1)-309(n) and data related to availability information of each user received from one or more scheduling systems (not shown); receive data associated with each of a plurality work items received from one or more source systems, e.g., client devices 308(1)-308(n) via one or more communication networks 310; dynamically allocate, from the received data, both a score value to assign a priority to each work item and a bit-mapped skill value for each work item regardless of the source system from which each work item is being received or work type of each work item; query the database to identify one or more users having availability data and associated skill set data correlated with the score value associated with the assigned priority for each work item and the bit-mapped skill value for each work item; and automatically assign a work item to a user computing device (e.g., one or more CREDs) utilized by a user based on identifying that the user's skill set data matches the score value associated with the assigned priority for the work item and the bit-mapped skill value for the work item, and that the user's availability data matches the availability information received from the database 312 via the one or more communication networks 310.”) The citation discloses the automatically assigning a work item/task, to a user computing device based on user’s skill and availability, in which corresponding to allocating a resource to a user device based on information associated with the user.
Both SEIFOORY and BHATIA are in the same field of endeavor, and both references disclose the concept relating to allocating tasks for processing by computing resources, and therefore, are combinable/modifiable.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the further comprising: receiving, by the computer, user information associated with each user of a plurality of users of the virtual environment, wherein the user information comprises skills data of each user of the plurality of users and user device data of each user of the plurality of users; and allocating, by the computer, the one or more resources to one or more user devices associated with a set of users of the plurality of users based on the user information associated with each user of the set of users, wherein the one or more resources are allocated to the one or more user devices in the virtual environment for execution of a task of the plurality of tasks, as taught in BHATIA’s invention into SEIFOORY and BOUDIA’s invention because the newly added limitation would enable the system to more efficiently coordinate resource allocation within the virtual environments based on user capabilities and device characteristics, thereby improving resource utilization and faster and more efficiency processing complex tasks. (BHATIA-[0119])
Regarding claim 18, it is a computer system claim having similar limitations cited in claim 4, so it is also rejected under the same rational.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over SEIFOORY, BOUDIA and MISHRA, in further view of Alluboyina et al. US Pub. No. US 20190220205 A1 (hereafter Alluboyina).
Regarding claim 11, SEIFOORY, in view of BOUDIA and MISHRA, discloses the computer-implemented method of claim 6, but fails to teach wherein the dependency relationship indicates a dependency of the first task on the second task, and wherein the method further comprises: controlling, by the computer, the allocation of the one or more first resources for execution of the first task in the virtual environment; receiving, by the computer from at least one of the one or more first resources, a completion status of the first task; validating, by the computer, the completion status of the first task; and controlling, by the computer, the allocation of the one or more second resources based on the validation, wherein the one or more second resources are allocated for execution of the second task in the virtual environment.
However, Alluboyina teaches wherein the dependency relationship indicates a dependency of the first task on the second task, and wherein the method further comprises: controlling, by the computer, the allocation of the one or more first resources for execution of the first task in the virtual environment; (e.g. FIG. 28 and [0233]: “A child job 2802, 2804 may also have various phases that are performed in order as part of completion of the child job 2802, 2804. In the illustrated embodiment, these tasks include a validate phase 2808a, prepare phase 2808b, a do phase 2808c, a finalize phase 2808d, and possibly an undo phase 2808e. The phases 2808a-2808e may be constrained to be executed in the order listed above in some embodiments. These phases may perform the following general functions” and [0234]-[0235]) The citation discloses the process of preparing and allocating resources for the processing of jobs.
receiving, by the computer from at least one of the one or more first resources, a completion status of the first task; validating, by the computer, the completion status of the first task; (e.g. [0237]: “The finalize phase 2808d includes verifying that the job was completed successfully at the do phase 2808c. For example, one or more checks specified by a developer may be executed at step 2808d. Step 2808d may include sending, by the target node, a notification that the task corresponding to the job was completed successfully and receiving such a notification by the orchestration layer 1300. For example, a child job 2802, 2804 may initiate a synchronous channel with a component created according to the child job 2802, 2804 on the target node and evaluate its function. For a job with child jobs, the finalize phase 2808c may include verifying the successful completion of each of these child jobs.”) The citation discloses the process of sending and receiving notification regarding the complete of jobs.
and controlling, by the computer, the allocation of the one or more second resources based on the validation, wherein the one or more second resources are allocated for execution of the second task in the virtual environment. (e.g. [0232]: “Each child job 2802-2804 may include various attributes stored in a data structure representing the child jobs 2802, 2804 or otherwise associated with the child jobs 2802, 2804. For example, a child job 2802, 2804 may have a dependency 2806 with respect to another task. In particular, a first child jobs 2802, 2804 may be constrained by a developer to executed only after a second child job 2802, 2804 has completed.”) The citation discloses the concept where the first child job must be complete before the execution of the second child job. Therefore, the first child jobs can’t start the various phases described at [0233] until the system receive and validate the completion of the second child job.
Both SEIFOORY and Alluboyina are in the same field of endeavor, and both references disclose the concept relating to allocating tasks for processing by computing resources, and therefore, are combinable/modifiable.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the wherein the dependency relationship indicates a dependency of the first task on the second task, and wherein the method further comprises: controlling, by the computer, the allocation of the one or more first resources for execution of the first task in the virtual environment; receiving, by the computer from at least one of the one or more first resources, a completion status of the first task; validating, by the computer, the completion status of the first task; and controlling, by the computer, the allocation of the one or more second resources based on the validation, wherein the one or more second resources are allocated for execution of the second task in the virtual environment, as taught in Alluboyina’s invention into SEIFOORY and BOUDIA, and MISHRA’s invention because the newly added limitation would enable the system to ensure that resources for task are allocated only after the task on which it depends has successfully completed, which helps to improve the reliability and efficiency of task execution and improve resource allocation.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over SEIFOORY and BOUDIA, in further view of Rando et al. US Pub. No. US 20240411591 A1 (hereafter Rando).
Regarding claim 11, SEIFOORY, in view of BOUDIA, discloses the computer-implemented method of claim 2, but fails to teach further comprising: analyzing, by the computer using the ML model, each task of the plurality of tasks based on the workflow data; determining, by the computer using the ML model, one or more tasks of the plurality of tasks based on the analysis, wherein the one or more tasks are to be automated; and automating, by the computer, the one or more tasks of the plurality of tasks based on the corresponding execution data.
However, Rando teaches further comprising: analyzing, by the computer using the ML model, each task of the plurality of tasks based on the workflow data; (e.g. FIG. 14 and [0132]: “With continued reference to FIG. 14, method 1400 contains a step 1410 of categorizing, using a data organizer operating on at least one server, a plurality of tasks to one or more task categories. In some embodiments, method 1400 may further include distributing, using the data organizer, each of the one or more automatable tasks to one task executing machine-learning model of the plurality of task executing machine-learning models as a function of the one or more task categories.”) the citation discloses the categorizing/analyzing of a plurality of tasks, based on an execution time length.
determining, by the computer using the ML model, one or more tasks of the plurality of tasks based on the analysis, wherein the one or more tasks are to be automated; (e.g. FIG. 14 and [0133]: “With continued reference to FIG. 14, method 1400 contains a step 1415 of identifying, using a task identification module operating on at least one server, one or more automatable tasks for each one of the one or more task categories using a task identifying machine-learning model trained with task identification training data, wherein the task identification training data comprises exemplary tasks correlated to exemplary automatable tasks.”) the citation discloses the identifying / determining the automatable tasks.
and automating, by the computer, the one or more tasks of the plurality of tasks based on the corresponding execution data. (e.g. FIG. 14 and [0134]: “With continued reference to FIG. 14, method 1400 contains a step 1420 of executing, using a task execution module operating on at least one server, one or more automatable tasks as a function of one or more task categories, wherein the plurality of task execution machine-learning models is configured to automate the execution of the one or more automatable tasks. In some embodiments, each one of the one or more automatable tasks may include at least a portion of each task of the plurality of tasks that can be automatically executed.”) The citation discloses the executing of the automatable tasks.
Both SEIFOORY and Rando are in the same field of endeavor, and both references disclose the concept relating to allocating tasks for processing by computing resources, and therefore, are combinable/modifiable.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the further comprising: analyzing, by the computer using the ML model, each task of the plurality of tasks based on the workflow data; determining, by the computer using the ML model, one or more tasks of the plurality of tasks based on the analysis, wherein the one or more tasks are to be automated; and automating, by the computer, the one or more tasks of the plurality of tasks based on the corresponding execution data, as taught in Rando’s invention into SEIFOORY and BOUDIA’s invention because the newly added limitation would enable the system to automatically identify and execute tasks that are suitable fore automated execution, which helps to improve the efficiency of task execution, reduce manual effort and processing time.
Allowable Subject Matter
Claims 9-10 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20260017115 A1: describes a way to predict how much computing power a distributed system needs for a particular workflow. The system receives a workflow and a target performance level the user wants to meet. It then creates many test cases that combine different resource allocations with different sample data and runs the workflow on those test cases and measures performance. Using those results, it trains a machine learning model to learn what resource allocation is needed for a given kind of input data. When new data arrives, the model can recommend the resources to allocate before processing starts.
US 8887163 B2: describes a way to schedule work items, or “tasks,” by looking at which tasks must happen before others and which computing resources are available. The system builds a task graph or dependency model so it can tell which tasks are ready to run and which must wait. It is designed to run tasks in parallel, when possible, rather than forcing everything to happen one at a time. The system can also adjust task order at runtime if task results change what should happen next.
Examiner has cited particular columns/paragraphs/sections and line numbers in the references applied and not relied upon to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested by the applicant in preparing responses, to 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.
When responding to the Office action, applicant is advised to clearly point out the patentable novelty the claims present in view of the state of the art disclosed by the reference(s) cited or the objections made. A showing of how the amendments avoid such references or objections must also be present. See 37 C.F.R. 1.111(c).
When responding to this Office action, applicant is advised to provide the line and page numbers in the application and/or reference(s) cited to assist in locating the appropriate paragraphs
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUAN M NGUYEN whose telephone number is (703)756-1599. The examiner can normally be reached Monday-Friday: 9:30am - 5:30PM ET.
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/TUAN M NGUYEN/Examiner, Art Unit 2198
/PIERRE VITAL/Supervisory Patent Examiner, Art Unit 2198
