DETAILED ACTION
The Office Action is in response to Amendments filed 1/29/2026.
Claims 1, 15, and 18 are currently amended.
Claim 8 is cancelled.
Claim 21 is a newly added claim.
Claims 1-7 and 9-21 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 and 9-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claim 1 is directed to a method, which is a process (a series of steps or acts), and falls within one of the statutory categories of invention.
Step 2A, Prong One: Claim 1 recites the limitations:
(a) predicting … the at least one machine learning algorithm and the configuration of the one or more workspaces in response to the request
These recited steps, under the broadest reasonable interpretation (BRI), cover performance of the steps in the human mind alone or with the aid of pen and paper. That is, other than reciting:
(1) using one or more machine learning models … wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics; and
(2) wherein the steps of the method are executed by at least one processing device operatively coupled to at least one memory.
Nothing in the claim precludes the steps from practically being performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper. For example, the limitation (a) in the context of the claim encompasses a human predicting a machine learning algorithm and workspace configuration in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper to predict a machine learning algorithm and workspace configuration. See MPEP § 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind alone or with the aid of pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements:
(1) using one or more machine learning models
(2) configuring the one or more workspaces based, at least in part, on the predicted configuration;
(3) wherein the steps of the method are executed by at least one processing device operatively coupled to at least one memory.
The additional elements (1) to (3) amount to no more than mere instructions to apply the judicial exception using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. The use of a computer or other machinery in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
Also, the claim recites the additional element:
(4) wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics;
The additional element (4) fails to meaningfully limit the claim because it does not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. See MPEP § 2106.05(f). The additional element recites only the idea of training the machine learning models with a dataset comprising historical machine learning workspace metrics without details on how this is accomplished. The claim omits any details as to how training the machine learning models with a dataset comprising historical machine learning workspace metrics solves a technical problem, and instead recites only the idea of a solution or outcome. Therefore, the additional element attempts to cover any solution to the identified problem of training the machine learning models with a dataset comprising historical machine learning workspace metrics with no restriction on how the training the machine learning models with a dataset comprising historical workspace metrics is accomplished and no description of the mechanism for accomplishing the training the machine learning models with a dataset comprising workspace metrics, and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it.”
Also, the claim recites the additional elements:
(5) receiving a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed;
The additional element (5) are mere data gathering recited at a high level of generality and thus, are insignificant extra-solution activities. See MPEP § 2106.05(g). Furthermore, all uses of the recited judicial exception require such data gathering/transmitting/outputting, and, as such, the additional elements do not impose any meaningful limits on the claim. The additional elements amount to necessary data gathering/transmitting/outputting. See MPEP § 2106.05(g).
Accordingly, even when viewed in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional elements:
(1) using one or more machine learning models
(2) configuring the one or more workspaces based, at least in part, on the predicted configuration;
(3) wherein the steps of the method are executed by at least one processing device operatively coupled to at least one memory.
The additional elements (1) to (3) amount to no more than mere instructions to apply the judicial exception using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. The use of a computer or other machinery in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
Also, the claim recites the additional element:
(4) wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics;
The additional element (4) does not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. The analysis under Step 2A, Prong Two is carried through to Step 2B. Therefore, the additional element attempts to cover any solution to the identified problem of training the machine learning models with a dataset comprising historical machine learning workspace metrics with no restriction on how the training the machine learning models with a dataset comprising historical machine learning workspace metrics is accomplished and no description of the mechanism for accomplishing the training the machine learning models with a dataset comprising historical machine learning workspace metrics, and does not provide significantly more because this type of recitation is equivalent to the words “apply it.”
Also the claim recites the additional element:
(5) receiving a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed;
The additional element simply appends well-understood, routine, and/or conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to receive a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed. Therefore, the limitations remain insignificant extra-solution activities even upon reconsideration and do not amount to significantly more.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as a combination adds nothing that is not already present when looking at the additional elements taken individually. Even when considered in combination, the additional elements represent mere instructions to apply a judicial exception using generic computer components, only the idea of a solution or outcome, insignificant extra-solution activities, and therefore do not provide an inventive concept. The claim is not patent eligible.
Step 1: Claim 15 is directed to an apparatus, which is a machine, and falls within one of the statutory categories of invention.
Step 2A, Prong One: Claim 15 recites the limitations:
(a) predicting … the at least one machine learning algorithm and the configuration of the one or more workspaces in response to the request
These recited steps, under the broadest reasonable interpretation (BRI), cover performance of the steps in the human mind alone or with the aid of pen and paper. That is, other than reciting:
(1) An apparatus comprising: a processing device operatively coupled to a memory;
(2) using one or more machine learning models … wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics; and configuring the one or more workspaces based, at least in part, on the predicted configuration;
Nothing in the claim precludes the steps from practically being performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper. For example, the limitation (a) in the context of the claim encompasses a human predicting a machine learning algorithm and workspace configuration in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper to predict a machine learning algorithm and workspace configuration. See MPEP § 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind alone or with the aid of pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements:
(1) An apparatus comprising: a processing device operatively coupled to a memory;
(2) configuring the one or more workspaces based, at least in part, on the predicted configuration;
(3) using one or more machine learning models
The additional elements (1) to (3) amount to no more than mere instructions to apply the judicial exception using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. The use of a computer or other machinery in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
Also, the claim recites the additional element:
(4) wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics;
The additional element (4) fails to meaningfully limit the claim because it does not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. See MPEP § 2106.05(f). The additional element recites only the idea of training the machine learning models with a dataset comprising historical machine learning workspace metrics without details on how this is accomplished. The claim omits any details as to how training the machine learning models with a dataset comprising historical machine learning workspace metrics solves a technical problem, and instead recites only the idea of a solution or outcome. Therefore, the additional element attempts to cover any solution to the identified problem of training the machine learning models with a dataset comprising historical machine learning workspace metrics with no restriction on how the training the machine learning models with a dataset comprising historical workspace metrics is accomplished and no description of the mechanism for accomplishing the training the machine learning models with a dataset comprising workspace metrics, and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it.”
The claim recites the additional element:
(5) to receive a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed;
The additional element (5) are mere data gathering recited at a high level of generality and thus, are insignificant extra-solution activities. See MPEP § 2106.05(g). Furthermore, all uses of the recited judicial exception require such data gathering/transmitting/outputting, and, as such, the additional elements do not impose any meaningful limits on the claim. The additional elements amount to necessary data gathering/transmitting/outputting. See MPEP § 2106.05(g).
Accordingly, even when viewed in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional elements:
(1) using one or more machine learning models
(2) configuring the one or more workspaces based, at least in part, on the predicted configuration;
(3) wherein the steps of the method are executed by at least one processing device operatively coupled to at least one memory.
The additional elements (1) to (3) amount to no more than mere instructions to apply the judicial exception using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. The use of a computer or other machinery in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
Also, the claim recites the additional element:
(4) wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics;
The additional element (4) does not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. The analysis under Step 2A, Prong Two is carried through to Step 2B. Therefore, the additional element attempts to cover any solution to the identified problem of training the machine learning models with a dataset comprising historical machine learning workspace metrics with no restriction on how the training the machine learning models with a dataset comprising historical machine learning workspace metrics is accomplished and no description of the mechanism for accomplishing the training the machine learning models with a dataset comprising historical machine learning workspace metrics, and does not provide significantly more because this type of recitation is equivalent to the words “apply it.”
Also the claim recites the additional element:
(5) to receive a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed;
The additional element simply appends well-understood, routine, and/or conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to receive a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed. Therefore, the limitations remain insignificant extra-solution activities even upon reconsideration and do not amount to significantly more.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as a combination adds nothing that is not already present when looking at the additional elements taken individually. Even when considered in combination, the additional elements represent mere instructions to apply a judicial exception using generic computer components, only the idea of a solution or outcome, insignificant extra-solution activities, and therefore do not provide an inventive concept. The claim is not patent eligible.
Step 1: Claim 18 is directed to an article of manufacture comprising a non-transitory processor-readable medium, which is an article of manufacture, and falls within one of the statutory categories of invention.
Step 2A, Prong One: Claim 18 recites the limitations:
(a) predicting … the at least one machine learning algorithm and the configuration of the one or more workspaces in response to the request
These recited steps, under the broadest reasonable interpretation (BRI), cover performance of the steps in the human mind alone or with the aid of pen and paper. That is, other than reciting:
(1) An article of manufacture comprising a non-transitory processor- readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the steps
(2) using one or more machine learning models … wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics; and configuring the one or more workspaces based, at least in part, on the predicted configuration;
Nothing in the claim precludes the steps from practically being performed in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper. For example, the limitation (a) in the context of the claim encompasses a human predicting a machine learning algorithm and workspace configuration in the human mind alone using observation, evaluation, judgment, and opinion or with the aid of pen and paper to predict a machine learning algorithm and workspace configuration. See MPEP § 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind alone or with the aid of pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements:
(1) using one or more machine learning models
(2) configuring the one or more workspaces based, at least in part, on the predicted configuration;
(3) wherein the steps of the method are executed by at least one processing device operatively coupled to at least one memory.
The additional elements (1) to (3) amount to no more than mere instructions to apply the judicial exception using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. The use of a computer or other machinery in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
Also, the claim recites the additional element:
(4) wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics;
The additional element (4) fails to meaningfully limit the claim because it does not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. See MPEP § 2106.05(f). The additional element recites only the idea of training the machine learning models with a dataset comprising historical machine learning workspace metrics without details on how this is accomplished. The claim omits any details as to how training the machine learning models with a dataset comprising historical machine learning workspace metrics solves a technical problem, and instead recites only the idea of a solution or outcome. Therefore, the additional element attempts to cover any solution to the identified problem of training the machine learning models with a dataset comprising historical machine learning workspace metrics with no restriction on how the training the machine learning models with a dataset comprising historical workspace metrics is accomplished and no description of the mechanism for accomplishing the training the machine learning models with a dataset comprising workspace metrics, and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it.”
Also, the claim recites the additional elements:
(5) receiving a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed;
The additional element (5) are mere data gathering recited at a high level of generality and thus, are insignificant extra-solution activities. See MPEP § 2106.05(g). Furthermore, all uses of the recited judicial exception require such data gathering/transmitting/outputting, and, as such, the additional elements do not impose any meaningful limits on the claim. The additional elements amount to necessary data gathering/transmitting/outputting. See MPEP § 2106.05(g).
Accordingly, even when viewed in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional elements:
(1) using one or more machine learning models
(2) configuring the one or more workspaces based, at least in part, on the predicted configuration;
(3) wherein the steps of the method are executed by at least one processing device operatively coupled to at least one memory.
The additional elements (1) to (3) amount to no more than mere instructions to apply the judicial exception using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. The use of a computer or other machinery in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
Also, the claim recites the additional element:
(4) wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics;
The additional element (4) does not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. The analysis under Step 2A, Prong Two is carried through to Step 2B. Therefore, the additional element attempts to cover any solution to the identified problem of training the machine learning models with a dataset comprising historical machine learning workspace metrics with no restriction on how the training the machine learning models with a dataset comprising historical machine learning workspace metrics is accomplished and no description of the mechanism for accomplishing the training the machine learning models with a dataset comprising historical machine learning workspace metrics, and does not provide significantly more because this type of recitation is equivalent to the words “apply it.”
Also the claim recites the additional element:
(5) receiving a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed;
The additional element simply appends well-understood, routine, and/or conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept. MPEP § 2106.05(d)(II) expressly states that the courts have recognized the computer function of receiving or transmitting data over a network, e.g., using the Internet to gather data as a well‐understood, routine, and conventional computer function when it is claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activities. Thus, a person of ordinary skill in the art would readily comprehend that it is well-understood, routine, and conventional in the computing art to receive a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed. Therefore, the limitations remain insignificant extra-solution activities even upon reconsideration and do not amount to significantly more.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as a combination adds nothing that is not already present when looking at the additional elements taken individually. Even when considered in combination, the additional elements represent mere instructions to apply a judicial exception using generic computer components, only the idea of a solution or outcome, insignificant extra-solution activities, and therefore do not provide an inventive concept. The claim is not patent eligible.
Claims 2, 16, and 19 recite the limitation "wherein the predicted configuration identifies at least one of a number of hosting instances and a size of one or more resources for the one or more workspaces" which is a process, under its broadest reasonable interpretation, that can be practically performed by the human mind through observation, evaluation, judgement, and/or opinion with the aid of pen and paper. Thus, the limitation falls under the "Mental Processes" group of abstract ideas. The claims are not patent eligible.
Claim 3 recites the additional element "wherein the hosting instances comprise at least one of a pod, a container and a virtual machine" which is recited at a high level of generality such that it amounts to no more than mere generic computer/computing components to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional element does not integrate the invention into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claim recites an additional element does not amount to significantly more. The claim recites the additional element "wherein the hosting instances comprise at least one of a pod, a container and a virtual machine" which is recited at a high level of generality such that it amounts to no more than mere generic computer/computing components to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claim 4 recites the additional element "wherein the size of the one or more resources comprises at least one of an amount of central processing unit utilization and an amount of memory utilization" which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional element does not integrate the invention into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claim recites an additional element does not amount to significantly more. The claim recites the additional element "wherein the size of the one or more resources comprises at least one of an amount of central processing unit utilization and an amount of memory utilization" which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claims 5, 17, and 20 recite the additional element "wherein configuring the one or more workspaces comprises provisioning the identified number of hosting instances and at least one of the one or more resources at the identified size on at least one device" which is recited at a high level of generality such that it amounts to no more than mere generic computer/computing components to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the claims do not integrate the invention into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claims recite an additional element that does not amount to significantly more. The claims recite the additional element "wherein configuring the one or more workspaces comprises provisioning the identified number of hosting instances and at least one of the one or more resources at the identified size on at least one device" which is recited at a high level of generality such that it amounts to no more than mere generic computer/computing components to apply the abstract idea (See MPEP 2106.05(f)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claims are not patent eligible.
Claims 6 and 21 discloses the additional element "wherein configuring the one or more workspaces comprises loading one or more libraries into the one or more workspaces to enable the at least one machine learning algorithm" which is directed to the insignificant extra solution activity of mere data gathering (See MPEP 2106.05(g)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits upon practicing the abstract idea.
The claims recite additional elements that does not amount to significantly more. The claims recite the additional element "wherein configuring the one or more workspaces comprises loading one or more libraries into the one or more workspaces to enable the at least one machine learning algorithm" which has been determined to be a well-known, routine, and/or conventional activity of receiving or transmitting data over a network (See MPEP 2106.05(d)(II)). Accordingly, the additional elements recited in the claims cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claim 7 recites the additional element "wherein the one or more workspaces correspond to one or more host devices." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional element does not integrate the invention into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claim recites an additional element does not amount to significantly more. The claim recites the additional element "wherein the one or more workspaces correspond to one or more host devices." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional elements recited in the claim cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claim 9 recites the additional element "wherein the historical machine learning workspace metrics comprise one or more of machine learning type, domain type, training dataset size, feature dimension size, a number of users and usage type for respective ones of a plurality of workspaces." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional element does not integrate the invention into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claim recites an additional element does not amount to significantly more. The claim recites the additional element "wherein the historical machine learning workspace metrics comprise one or more of machine learning type, domain type, training dataset size, feature dimension size, a number of users and usage type for respective ones of a plurality of workspaces." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional elements recited in the claim cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claim 10 recites the additional element "wherein the historical machine learning workspace metrics further comprise an amount of central processing unit utilization, an amount of memory utilization and an amount of input/output utilization for the respective ones of the plurality of workspaces." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional element does not integrate the invention into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claim recites an additional element does not amount to significantly more. The claim recites the additional element "wherein the historical machine learning workspace metrics further comprise an amount of central processing unit utilization, an amount of memory utilization and an amount of input/output utilization for the respective ones of the plurality of workspaces." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional elements recited in the claim cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claim 11 recites the additional element "further comprising creating from the dataset one or more independent variable datasets and one or more dependent variable datasets." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional element does not integrate the invention into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claim recites an additional element does not amount to significantly more. The claim recites the additional element "further comprising creating from the dataset one or more independent variable datasets and one or more dependent variable datasets." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional elements recited in the claim cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claim 12 recites the additional element "wherein the one or more dependent variable datasets correspond to at least one of machine learning algorithm type, a number of containers, central processing unit utilization and memory utilization for respective ones of a plurality of workspaces." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional element does not integrate the invention into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claim recites an additional element does not amount to significantly more. The claim recites the additional element "wherein the one or more dependent variable datasets correspond to at least one of machine learning algorithm type, a number of containers, central processing unit utilization and memory utilization for respective ones of a plurality of workspaces." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional elements recited in the claim cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claim 13 recites the additional element "wherein the one or more machine learning models comprise a multiple output classification and regression machine learning algorithm." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional element does not integrate the invention into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claim recites an additional element does not amount to significantly more. The claim recites the additional element "wherein the one or more machine learning models comprise a multiple output classification and regression machine learning algorithm." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional elements recited in the claim cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claim 14 recites the additional element "wherein outputs of the multiple output classification and regression machine learning algorithm comprise a type of the at least one machine learning algorithm, a number of containers, a memory size and a number of central processing unit core units for the one or more workspaces." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional element does not integrate the invention into a practical application because it does not impose any meaningful limits upon practicing the abstract idea.
The claim recites an additional element does not amount to significantly more. The claim recites the additional element "wherein outputs of the multiple output classification and regression machine learning algorithm comprise a type of the at least one machine learning algorithm, a number of containers, a memory size and a number of central processing unit core units for the one or more workspaces." which is directed to a field of use and technological environment (See MPEP 2106.05(h)). Accordingly, the additional elements recited in the claim cannot provide an inventive concept nor amount to significantly more. Thus, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 7, 9, 15, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240220831 A1 hereinafter "Wyman" in view of US 20200184380 A1 hereinafter "Thomas" and further in view of US 20230315397 A1 hereinafter "Prasad Tanniru".
With regards to claim 1, Wyman teaches
A method comprising:
receiving a request to predict at least one machine learning algorithm to perform one or more tasks and
to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed; (Wyman [0022], “Such functionality can be provided using an AI manager, such as a management service, that can determine the requirements, capabilities, and limitations of AI-related components, such as those of various AI models, engines, and accelerators, as well as the hardware (e.g., graphics processing units (GPUs), central processing units (CPUs), parallel processing units (PPUs), or data processing units (DPUs)) that run or make up the AI-related resources. An AI manager can receive a request from an application (or other requestor) for use of an AI model for inferencing, and can utilize information for the request and the requested model, along with information about currently available resources, to determine a selection and configuration of resources that is not only appropriate for serving the request, but that can also be optimized for factors such as throughput, resource utilization, and inference latency. An AI manager can ensure compatibility of resources and configuration, and can enforce access control to models and data. Where possible, the service can attempt to reuse one or more models and share one or more hardware accelerators to optimize utilization, and avoid the delay introduced by spinning up new resources. When resources used to execute, operate, or implement one or more instances of applications or other software constructs such as models are no longer being used, those resources—or even the software instances (e.g., models)—can be reclaimed to minimize the presence of underutilized resources”)
predicting, using one or more machine learning models, [the at least one machine learning algorithm] and the configuration of the one or more workspaces in response to the request; (Wyman [0025], "In this example, a client device 102 may submit a request that involves use of a specific artificial intelligence (AI) model [using one or more machine learning models]. While AI models are used as a primary use case, it should be understood that there may be other AI or machine learning (ML)-related technologies requested and used as well, as may relate to artificial neural networks (ANNs), deep neural networks (DNNs), AI algorithms, and the like, and that recitation of an AI model in various examples is for simplicity of explanation and is not intended as a limitation on scope of various embodiments unless otherwise specifically stated. In this example, a request for access or use of such an AI model can be directed from interface layer 112 to an AI manager [in response to the request] 114, which may take the form of a system, service, application, component, device, or process, machine learning model, among other such options, which can manage resources, configuration, and other aspects to support one or more AI-related operations. An AI manager 114 can analyze information in the request to determine an appropriate AI model 118 to select from, for example, a trusted model repository 116. The AI manager 114 can also analyze existing hardware resources, such as available types of AI engines 120, 122 that can support tasks such as inferencing or classification using a selected model 118. The AI manager 114 may work with a resource manager 124 to determine available capacity of various hardware or software resources, such as to determine whether there is an AI engine 120 of an appropriate type and configuration for the selected AI model 118, or whether a new AI engine should be allocated for such usage, among other such options. Once an AI engine 120 (along with any AI acceleration or other resources/aspects, as discussed in more detail elsewhere herein) of an appropriate type, capacity, and configuration is selected for use, communication can be sent back to the initiating client device 102, which can then communicate with the allocated AI engine to perform tasks such as to provide input data and receive inferences generated using the AI engine [predict ... the configuration of the one or more workspaces].") and
configuring the one or more workspaces based, at least in part, on the predicted configuration; (Wyman [0033], “Such an approach can enable a client 202, or other entity or source, to request AI functionality to be performed without having to have knowledge about how that AI functionality is to be provided, or specify any resources to be used. This AI functionality can be exposed as a service, where a call or request can be made to an API or other interface that specifies at least a type of inferencing or task to be performed, and an AI manager or other relevant service (or system or process, etc.) can manage the resources needed to provide the appropriate AI functionality. In some embodiments, the AI manager itself may use AI (e.g., machine learning using a neural network trained for a given resource environment) to determine aspects such as an optimal selection and configuration of resources currently available, or able to be made available, in a given environment. The neural network can take as input information relating to a task to be performed, as well as capability or capacity data for various models, engines, and accelerators, and can attempt to optimize a potential deployment.”)
wherein the steps of the method are executed by at least one processing device operatively coupled to at least one memory. (Wyman [0216], “In at least one embodiment, a process such as those processes described herein (or variations and/or combinations thereof) is performed under control of one or more computer systems configured with executable instructions and is implemented as code (e.g., executable instructions, one or more computer programs or one or more applications) executing collectively on one or more processors, by hardware or combinations thereof. In at least one embodiment, code is stored on a computer-readable storage medium, for example, in form of a computer program comprising a plurality of instructions executable by one or more processors. In at least one embodiment, a computer-readable storage medium is a non-transitory computer-readable storage medium that excludes transitory signals (e.g., a propagating transient electric or electromagnetic transmission)”)
Wyman teaches predicting the configuration of the one or more workspaces in response to the request but does not teach: predicting, using one or more machine learning models, the at least one machine learning algorithm [and the configuration of the one or more workspaces] in response to the request, wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics;
However, in an analogous art Thomas teaches predicting, using one or more machine learning models, the at least one machine learning algorithm [and the configuration of the one or more workspaces] in response to the request, […] (Thomas [0038], "Thus, the invention includes a technique of automating a machine- learning process for generating optimal NN models based on customer-provided data within a client server environment [using one or more machine learning models] according user-provided instructions by analyzing the dataset to identify a best suited ML algorithm among many (e.g., from a database of ML algorithms). The method includes automating a machine-learning process based on receiving a customer dataset for analyzing and choosing the most optimum ML algorithm for the data by running few sample algorithms on the dataset to observe and analyze the test results for predicting the next best suited algorithm for the dataset [predicting... the at least one machine learning algorithm].")
Therefore, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have incorporated the teachings of Thomas into
the teachings of Wyman. This combination of teachings would have resulted in a method to
predict and configure a workspace that can execute the requested machine learning task, as in
Wyman, wherein the machine learning task is selected by a machine learning model in view of
user requests and requirements, as in Thomas. One of ordinary skill in the art would have been
motivated to combine these teachings for the purpose of deciding a machine learning algorithm
that is best suited for the customer data and target hardware (Thomas [0023-24]).
The combination of Wyman and Thomas teaches predicting, using one or more machine learning models, the at least one machine learning algorithm and the configuration of the one or more workspaces in response to the request, but does not teach wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics;
However, in an analogous art Prasad Tanniru teaches wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics (Prasad Tanniru [0082], "As shown by reference number 205, a machine learning model may be trained using a set of observations. The set of observations may be obtained from training data (e.g., historical data), such as data gathered during one or more processes described herein. In some implementations, the machine learning system may receive the set of observations (e.g., as input) from the user device 340, as described elsewhere herein.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Prasad Tanniru into the teachings of Wyman in view of Thomas. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas, and further training the machine learning model using prior executional workspace metrics, as in Prasad Tanniru. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of serverless environment-based building that is optimized for cost and resource consumption using execution blueprints with flexible and scaled deployments (Prasad Tanniru [0024]).
With regards to claim 7, the rejection of claim 1 is incorporated.
Wyman further teaches wherein the one or more workspaces correspond to one or
more host devices (Wyman [0021], "Disclosed embodiments may be comprised in a variety of
different systems such as automotive systems (e.g., a control system for an autonomous or semiautonomous machine, a perception system for an autonomous or semi-autonomous machine), systems implemented using a robot, aerial systems, medial systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using an edge device, systems incorporating one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least partially in a data center, systems for performing conversational AI operations, systems for performing light transport simulation, systems for performing collaborative content creation for 3D assets, systems implemented at least partially using cloud computing resources, and/or other types of systems.")
With regards to claim 9, the rejection of claim 1 is incorporated.
The combination of Wyman and Thomas does not teach: wherein the historical machine learning workspace metrics comprise one or more of machine learning type, domain type, training dataset size, feature dimension size, a number of users and usage type for respective ones of a plurality of workspaces.
However, in an analogous art Prasad Tanniru teaches wherein the historical machine learning workspace metrics comprise one or more of machine learning type, domain type, training dataset size, feature dimension size, a number of users and usage type for respective ones of a plurality of workspaces (Prasad Tanniru [0084], "For example, the feature set may include one or more of the following features: technology stack IDs, tool IDs, selected technology stack compositions (e.g., tools included in technology stacks), contextual information (e.g., time instances, application types, cloud service providers, cloud resource IDs, task IDs, task definitions, application parameters [and usage type for respective ones of a plurality of workspaces], and/or memory consumption metrics [feature dimension size].")
(Prasad Tanniru [0087-89], "In some implementations, the machine learning model may be trained on a set of observations that do not include a target variable. This may be referred to as an unsupervised learning model. In this case, the machine learning model may learn patterns from the set of observations without labeling or supervision, and may provide output that indicates such patterns, such as by using clustering and/or association to identify related groups of items within the set of observations [domain type] As shown by reference number 230, the machine learning system may apply the trained machine learning model 225 to a new observation, such as by receiving a new observation and inputting the new observation to the trained machine learning model 225. As shown, the new observation may include a first feature of feature 1 data X, a second feature of feature 2 data Y, a third feature of feature 3 data Z, and so on, as an example. The machine learning system may apply the trained machine learning model 225 to the new observation to generate an output (e.g., a result). The type of output may depend on the type of machine learning model and/or the type of machine learning task being performed [comprise one or more of machine learning type]. For example, the output may include a predicted value of a target variable, such as when supervised learning is employed. Additionally, or alternatively, the output may include information that identifies a cluster to which the new observation belongs and/or information that indicates a degree of similarity between the new observation and one or more other observations, such as when unsupervised learning is employed.") (Prasad Tanniru [0095], "The machine learning system enables recognition and/or identification of tens, hundreds, thousands, or millions of features and/or feature values for tens, hundreds, thousands, or millions of observations [training dataset size], thereby increasing accuracy and consistency and reducing delay associated with recommending technology stacks, detecting anomalies and/or optimizing memory consumption relative to requiring computing resources to be allocated for tens, hundreds, or thousands of operators [a number of users] for manually recommending technology stacks, detecting anomalies and/or optimizing memory consumption using the features or feature values.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Prasad Tanniru into the teachings of Wyman in view of Thomas. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas, and further training the machine learning model using prior executional workspace metrics, as in Prasad Tanniru. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of serverless environment-based building that is optimized for cost and resource consumption using execution blueprints with flexible and scaled deployments (Prasad Tanniru [0024]).
Claim 15 is directed to an apparatus corresponding to the method limitations as disclosed in claim 1. Thus, claim 15 is rejected for the same reasons set forth in claim 1.
Claim 18 is directed to an article of manufacture corresponding to the method limitations as disclosed in claim 1. Thus, claim 18 is rejected for the same reasons set forth in claim 1.
Claims 2-5, 10, 16-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wyman in view of Thomas in view of Prasad Tanniru as applied to claims 1, 15, and 18 above, and further in view of US 20250037006 Al hereinafter "Mahadik".
With regards to claim 2, the rejection of claim 1 is incorporated.
Wyman further teaches wherein the predicted configuration identifies at least one of a number of hosting instances [and a size of one or more resources for the one or more workspaces.] (Wyman [0040], "An AI manager can also actively allocate additional "instances"
of an AI model to vertically scale inference throughput. Understanding accelerator capacity and
abilities can help to make better decisions, such as to actively deploy additional AI engines on
demand to meet capacity requirements, as well as to ensure AI engines, AI models, and
accelerators are aligned for best performance. An Al manager may also effectively provide for
load balancing by deploying additional instances of an AI model to additional AI engines and
redirecting queries for optimal latency and throughput. An AI manager can perform other tasks
as well, such as to proactively reclaim resources as AI model utilization decreases, such as by
reducing a number of instances to meet reduced demand. Such an approach can help to guard
against underutilized, unavailable resources, and can help to reduce a probability of resource
exhaustion.")
The combination of Wyman, Thomas, and Prasad Tanniru teaches wherein the predicted configuration identifies at least one of a number of hosting instances but does not teach and a size of one or more resources for the one or more workspaces.
However, in an analogous art Mahadik teaches […] and a size of one or more resources for the one or more workspaces. (Mahadik [0031], "In various embodiments, the prediction model 126 is trained using the benchmark dataset 124 to predict various system performance metrics of the computing instance 128 when executing the workload 112. For example, the prediction model 126 predicts the epoch training time, epoch training cost, average processor utilization, average memory utilization, or other metrics. In various embodiments, the prediction model 126 includes a regression model, a transformer, a neural network, or other any other machine learning model capable of predicting system performance metrics. In one example, during inferencing, the prediction model 126 takes as an input the computing instance 128 (e.g., a set of possible configurations of the computing instance) and the workload 112 (e.g., number of layers, number of activations, floating point operations, model parameters, batch size, etc.) and outputs the epoch training time (c) and average GPU utilization (u.sub.G) for the workload 112 (w.sub.T) on the set of possible computing instances.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Mahadik into the teachings of Wyman in view of Thomas and further in view of Prasad Tanniru. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas, and further training the machine learning model using prior executional workspace metrics, as in Prasad Tanniru, and the predicted workspace specifically defines resource size or utilization, as in Mahadik. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of generating recommendation for any combination of workloads to optimize the use of any attributes therein (Mahadik [0018]).
With regards to claim 3, the rejection of claim 2 is incorporated.
Wyman further teaches wherein the hosting instances comprise at least one of a pod, a container and a virtual machine (Wyman [0113], "In at least one embodiment, tasks of data processing pipeline may be encapsulated in a container(s) that each represents a discrete, fully functional instantiation of an application and virtualized computing environment that is able to reference machine learning models. In at least one embodiment, containers or applications may be published into a private (e.g., limited access) area of a container registry (described in more detail herein), and trained or deployed models may be stored in model registry 1324 and associated with one or more applications. In at least one embodiment, images of applications (e.g., container images) may be available in a container registry, and once selected by a user from a container registry for deployment in a pipeline, an image may be used to generate a container for an instantiation of an application for use by a user's system.") [Examiner's Note: A pod can be described as a plurality of "container(s)"]
With regards to claim 4, the rejection of claim 2 is incorporated.
The combination of Wyman, Thomas, and Prasad Tanniru does not teach wherein the size of the one or more resources comprises at least one of an amount of central processing unit utilization and an amount of memory utilization.
However, in an analogous art Mahadik teaches wherein the size of the one or more resources comprises at least one of an amount of central processing unit utilization and an amount of memory utilization. (Mahadik [0031], "In various embodiments, the prediction model 126 is trained using the benchmark dataset 124 to predict various system performance metrics of the computing instance 128 when executing the workload 112. For example, the prediction model 126 predicts the epoch training time, epoch training cost, average processor utilization, average memory utilization, or other metrics.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Mahadik into the teachings of Wyman in view of Thomas and further in view of Prasad Tanniru. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas, and further training the machine learning model using prior executional workspace metrics, as in Prasad Tanniru, and the predicted workspace specifically defines resource size or utilization, as in Mahadik. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of generating recommendation for any combination of workloads to optimize the use of any attributes therein (Mahadik [0018]).
With regards to claim 5, the rejection of claim 2 is incorporated.
Wyman further teaches configuring the one or more workspaces comprises provisioning the identified number of hosting instances and at least one of the one or more resources at the identified size on at least one device. (Wyman [0028], "There may also be a number of different applications that may each use various portions of that hardware at different times. An AI manager 114 can attempt to optimize usage of these resources to provide for maximum availability and utilization, with minimum downtime due to changing deployments. For example, various applications may be allowed to share certain resources (e.g., GPUs) and
deployed instances of models at different times. An AI model might be deployed that can
translate English language text into German language text, and this task might need to be
performed for a number (e.g., fifteen) of different instances of this application [configuring the
one or more workspaces comprises provisioning the identified number of hosting instances]. An
AI manager 114 can maintain a deployment of that model on a specific instance of hardware
over time so that this model can be reused by the instances of this application as necessary. In
some instances, an AI manager might maintain two or three of these models that can be shared
among the application instances, as appropriate, where there is sufficient capacity and demand
for multiple models to be concurrently deployed. These various application instances may only
submit requests every few seconds, for example, and translation for a task may only last for a
fraction of a second, such that an ability to reuse a deployment for multiple instances of the
application can help to improve utilization of the hardware (e.g., GPUs) as well as to reduce a
total number of GPUs that might otherwise be needed to support these 15 instances, without
having to significantly expand capacity or reduce throughput. An AI manager can determine to
keep a pool of available resources loaded and ready for use, or can instead determine to launch at
least some types of resources as needed, as may depend upon factors such as network load,
resource utilization, and task frequency [and at least one of the one or more resources at the
identified size on at least one device]. In some instances a cluster of resources may be allocated
and maintained available for sharing or usage by multiple different applications or users, for
example. At least some amount of resource health monitoring can be performed, to determine
factors such as utilization for different hardware resources. There may be one or more utilization
thresholds used to determine when to add or remove resource capacity, such as to deploy another hardware resource if an instance is at or above 90% utilization, or to remove or reclaim a
resource that is at or below 10% utilization.”)
With regards to claim 10, the rejection of claim 9 is incorporated.
The combination of Wyman, Thomas, and Prasad Tanniru does not teach: wherein the historical machine learning workspace metrics further comprise an amount of central processing unit utilization, an amount of memory utilization and an amount of input/output utilization for the respective ones of the plurality of workspaces.
However, in an analogous art Mahadik teaches wherein the historical machine learning workspace metrics further comprise an amount of central processing unit utilization, an amount of memory utilization and an amount of input/output utilization for the respective ones of the plurality of workspaces. (Mahadik [0037], "In various embodiments, the prediction model 226 includes a trained regression model to output metrics for a particular input workload W and computing instances Ij 213. As described above, for example, the prediction model 226 performs 3 tasks (e.g., includes three models): system performance metrics prediction, epoch training time c prediction (e.g., which can then be multiplied by the available per-hour computing instance usage costs), and average processor utilization ug. In various embodiments, workload and computing instance data 202 is obtained and used to generate a training data set 234 used to train the prediction model 226. For example, the computing instance data 202 includes hardware metrics such as GPU power usage, GPU core temperature, GPU performance, resource efficiency, storage availability, core temperature, memory bandwidth, cache usage, power usage, memory utilization, processor utilization, and time-series-based utilization values. For example, as described below in connection with FIG. 3, a profiler or other application extracts or otherwise obtains metrics to be included in the training dataset 234.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Mahadik into the teachings of Wyman in view of Thomas and further in view of Prasad Tanniru. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas, and further training the machine learning model using prior executional workspace metrics, as in Prasad Tanniru, and the predicted workspace specifically defines resource size or utilization, as in Mahadik. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of generating recommendation for any combination of workloads to optimize the use of any attributes therein (Mahadik [0018]).
Claims 16-17 are directed to an apparatus corresponding to the method limitations as disclosed in claims 2 and 5 respectively. Thus, claims 16-17 are rejected for the same reasons set forth in claims 2 and 5.
Claims 19-20 are directed to an article of manufacture corresponding to the method limitations as disclosed in claims 2 and 5 respectively. Thus, claims 19-20 are rejected for the same reasons set forth in claims 2 and 5.
Claims 6 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Wyman in view of Thomas in view of Prasad Tanniru as applied to claims 1 and 18 above, and further in view of US 20230034658 Al hereinafter "Jha".
With regards to claim 6, the rejection of claim 1 is incorporated.
The combination of Wyman, Thomas, and Prasad Tanniru does not teach wherein configuring the one or more workspaces comprises loading one or more libraries into the one or more workspaces to enable the at least one machine learning algorithm.
However, in an analogous art Jha teaches wherein configuring the one or more
workspaces comprises loading one or more libraries into the one or more workspaces to
enable the at least one machine learning algorithm. (Jha [0063], "In some embodiments, a
step 208 formulates unpacking script 54 comprising a specification for reconstructing individual
ML packages from aggregate package 50. An exemplary script 54 comprises a set of commands
(e.g., command lines or a portable executable file) for de-compressing aggregate package 50,
copying various RPA libraries to specific storage locations, and writing a set of metadata such as
a path indicator indicating the respective storage locations, among others. In some embodiments,
script 54 comprises commands for building and/or compiling a content of reconstructed ML
packages to produce a set of executable objects. In another exemplary embodiment, script 54
may comprise a set of commands for building individual container images corresponding to each
individual ML package 45a-c (using the example illustrated in FIG. 7). The commands may
explicitly indicate which libraries to include in each individual container image. In a further step
210, provisioning server 28 may transmit aggregate package 50 and unpacking script 54 to
intermediate host 26 over communication network 13.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Jha into the teachings of Wyman in view of Thomas and further in view of Prasad Tanniru. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas, and further training the machine learning model using prior executional workspace metrics, as in Prasad Tanniru, and configuring the container instances by loading in machine learning libraries, as in Jha. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of facilitating the provision of machine learning based software by bundling multiple library packages upon request based on desired or available ML, hardware, and software resources (Jha [0056]).
Claim 21 is directed to an article of manufacture corresponding to the method limitations as disclosed in claim 6. Thus, claim 21 is rejected for the same reasons set forth in claim 6.
Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Wyman in view of Thomas in view of Prasad Tanniru as applied to claim 1 above, and further in view of US 20240135161 A1 hereinafter "Mysore Jayaram".
With regards to claim 11, the rejection of claim 1 is incorporated.
The combination of Wyman, Thomas, and Prasad Tanniru does not teach creating from the dataset one or more independent variable datasets and one or more dependent variable datasets.
However, in an analogous art Mysore Jayaram teaches creating from the dataset one or more independent variable datasets and one or more dependent variable datasets. (Mysore Jayaram [0050], "The multi-output classification and regression machine learning model uses one or more independent variables (e.g., inputted factors) to predict multiple dependent variable outputs (e.g., type and quantity of different resources). The outputs are dependent on the input(s) and may be dependent on each other. For example, memory utilization may be dependent upon the CPU utilization and vice versa. In another example, resource quantity may be dependent on resource type and vice versa. The outputs are not necessarily independent of each other and may require a model that predicts outputs together or each output contingent upon other outputs.").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Mysore Jayaram into the teachings of Wyman in view of Thomas and further in view of Prasad Tanniru. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas, and further training the machine learning model using prior executional workspace metrics, as in Prasad Tanniru, and tracking independent and dependent variables for an implementation of a dataset usable in training the machine learning model, as in Mysore Jayaram. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of training a machine learning model to dynamically predict resources using a dataset that comprises historical information regarding the user (Mysore Jayaram [0005]).
With regards to claim 12, the rejection of claim 11 is incorporated.
Wyman does not teach wherein the one or more dependent variable datasets correspond to at least one of machine learning algorithm type, a number of containers, central processing unit utilization and memory utilization for respective ones of a plurality of workspaces.
However, in an analogous art Thomas teaches wherein the one or more dependent variable datasets correspond to at least one of machine learning algorithm type, […] (Thomas [0027], "The ML algorithm database (or in the alternative a term that could be used would be a 'registry', or 'repository') [wherein the one or more dependent variable datasets]. 150 allows the invention to plug-in one or many algorithm(s) as needed. Each algorithm can be designed to satisfy a different set of type of data and budget and boundary (e.g., memory size, etc.) requirements. In other words, the database 150 can continuously be expanded to include any new state of the art ML algorithms newly designed [correspond to at least one of machine learning algorithm type]. Thereby, the invention is continuously operating at a state of the art level. The ML algorithms uniquely generate, manipulate, or select NN models to be served to the user as-is or partially or completely retrained, given any previously created NN model. As such, the invention is very dynamic to changes in technology.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Thomas into the teachings of Wyman. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of deciding a machine learning algorithm that is best suited for the customer data and target hardware (Thomas [0023-24]).
The combination of Wyman, Thomas, Prasad Tanniru teaches wherein the one or more dependent variable datasets correspond to at least one of machine learning algorithm type but does not teach wherein the one or more dependent variable datasets correspond to [at least one of machine learning algorithm type,] a number of containers, central processing unit utilization and memory utilization for respective ones of a plurality of workspaces.
However, in an analogous art Mysore Jayaram teaches wherein the one or more dependent variable datasets correspond to […] a number of containers, central processing unit utilization and memory utilization for respective ones of a plurality of workspaces. (Mysore Jayaram [0044], "In order to build and update the historical infrastructure and utilization repository 122, the monitoring, collection and logging layer 121 of the data collection engine 120 extracts and collects parameters corresponding to resource infrastructure and resource utilization of various components of existing or previously deployed computing environments (e.g., existing or previously deployed private cloud environments) [wherein the one or more dependent variable datasets correspond to at least one of]. The parameters may be collected from the compute host devices 103, storage systems 105 and network systems 107 and/or from applications used for monitoring component metrics. The parameters comprise, for example, virtual instance types (e.g., VM, container, pod, etc.), virtual instance identifiers (e.g., VM ID, container ID, pod ID, etc.) [a number of containers], compute quantity, compute size (e.g., number of CPU cores (milli cores)), memory size (e.g., size of RAM), storage size (e.g., ephemeral storage size), time period (e.g., one or more timestamps identifying when (e.g., date, time) certain parameters were collected, CPU utilization, memory utilization, storage utilization [central processing unit utilization and memory utilization for respective ones of a plurality of workspaces], network input-output (e.g., average network IO amounts), block input-output (e.g., average block IO amounts), server type, server quantity (number of each server type), storage system type, storage system quantity, network system type and network system quantity. The server, storage system and network system types, if, for example, associated with particular enterprise, may specify the product name/enterprise name for the server, storage system or network system and/or one or more specifications of the server, storage system or network system. The server, storage system and network system quantities specify, for example, the numbers of each server, storage system or network system type. Other collected parameters can include, for example, throughput, IO operations per second (IOPS), latency and/or user/customer information associated with particular resources or groups of resources.") [Examiner's Note: A dependent variable dataset corresponds to an output parameter that is logged or collected from historical use]
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Mysore Jayaram into the teachings of Wyman in view of Thomas and further in view of Prasad Tanniru. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas, and further training the machine learning model using prior executional workspace metrics, as in Prasad Tanniru, and tracking independent and dependent variables for an implementation of a dataset usable in training the machine learning model, as in Mysore Jayaram. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of training a machine learning model to dynamically predict resources using a dataset that comprises historical information regarding the user (Mysore Jayaram [0005]).
With regards to claim 13, the rejection of claim 1 is incorporated.
The combination of Wyman, Thomas, and Prasad Tanniru does not teach: wherein the one or more machine learning models comprise a multiple output classification and regression machine learning algorithm.
However, in an analogous art Mysore Jayaram teaches wherein the one or more machine learning models comprise a multiple output classification and regression machine learning algorithm. (Mysore Jayaram [0052], "Illustrative embodiments may use different approaches and algorithms to achieve multi-target regression and classification. Some algorithms have built-in support for multiple outputs. In some embodiments, algorithms that do not have built-in support for multi-target regression and classification use a wrapper to achieve multioutput support. The embodiments utilize, for example, linear regression, k-nearest neighbor (KNN) regression and/or random forest regression algorithms, which natively support multitarget predictions. Some embodiments utilize, for example, support vector machine (SVM) regression or gradient boosting regression algorithms that do not natively support multi-target predictions. In this case, these algorithms are used in conjunction with a wrapper function (e.g., MultiOutputRegressor/MultiOutputRegressor) available from a multi-output package of an ScikitLearn library). Instances of the unsupported algorithms are input to the wrapper function to create a model that is capable of predicting multiple output values.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Mysore Jayaram into the teachings of Wyman in view of Thomas and further in view of Prasad Tanniru. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas, and further training the machine learning model using prior executional workspace metrics, as in Prasad Tanniru, and regressively tracking and using machine learning algorithm outputs in training the machine learning model, as in Mysore Jayaram. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of training a machine learning model to dynamically predict resources using a dataset that comprises historical information regarding the user (Mysore Jayaram [0005]).
With regards to claim 14, the rejection of claim 13 is incorporated.
Wyman further teaches wherein outputs of the multiple output classification and
regression machine learning algorithm comprise a type of the at least one machine learning algorithm, [a number of containers, a memory size and a number of central processing unit
core units for the one or more workspaces.] (Wyman [0103], "Inference and/or training logic
715 are used to perform inferencing and/or training operations associated with one or more
embodiments. Details regarding inference and/or training logic 715 are provided below in
conjunction with FIGS. 7a and/or 7b. In at least one embodiment portions or all of inference
and/or training logic 715 may be incorporated into processor 1200. For example, in at least one
embodiment, training and/or inferencing techniques described herein may use one or more of
ALUs embodied in graphics processor 1512, graphics core(s) 1202A-1202N, or other
components in FIG. 12. Moreover, in at least one embodiment, inferencing and/or training
operations described herein may be done using logic other than logic illustrated in FIG. 7A or
7B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory
and/or registers (shown or not shown) that configure ALUs of graphics processor 1200 to
perform one or more machine learning algorithms, neural network architectures, use cases, or
training techniques described herein.")
The combination of Wyman, Thomas, and Prasad Tanniru does not teach wherein outputs of the multiple output classification and regression machine learning algorithm comprise [a type of the at least one machine learning algorithm,] a number of containers, a memory size and a number of central processing unit core unit for the one or more workspaces.
However, in an analogous art Mysore Jayaram teaches wherein outputs of the multiple output classification and regression machine learning algorithm comprise [a type of the at least one machine learning algorithm,] a number of containers, a memory size and a number of central processing unit core unit for the one or more workspaces. (Mysore Jayaram [0053], "As noted herein, historical infrastructure and utilization data is used for training the multi-target classification and regression models. FIG. 4 depicts example training data in an illustrative embodiment. As can be seen in the table 400, the training data identifies user/customer information, and the following data associated with each user/customer ("Cust."): virtual instance types ("Instance Type") (e.g., VM, container (Cont.), Mixed (combination of different virtual instances)), compute quantity, compute size (e.g., number of CPU cores (millicores)), storage size (e.g., ephemeral storage size (MiB)), average CPU utilization (Avg. CPU utilization (%)), average memory utilization (Avg. memory utilization (%)), average storage utilization (Avg. storage utilization (%)), average network input-output (e.g., Avg. network IO (KiB)) and average block input-output (e.g., Avg. block IO (MiB)). Compute quantity indicates how many compute engines (including VMs, containers, mixed) a user/customer has. For example, if a user/customer has 9 VMs, the value of compute quantity will be 9 and the instance type will be VM.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Mysore Jayaram into the teachings of Wyman in view of Thomas and further in view of Prasad Tanniru. This combination of teachings would have resulted in a method to predict and configure a workspace that can execute the requested machine learning task, as in Wyman, wherein the machine learning task is selected by a machine learning model in view of user requests and requirements, as in Thomas, and further training the machine learning model using prior executional workspace metrics, as in Prasad Tanniru, and regressively tracking and using machine learning algorithm outputs in training the machine learning model, as in Mysore Jayaram. One of ordinary skill in the art would have been motivated to combine these teachings for the purpose of training a machine learning model to dynamically predict resources using a dataset that comprises historical information regarding the user (Mysore Jayaram [0005]).
Response to Arguments
Applicant Argues:
This is believed to be an incorrect interpretation of claim 1, particularly in view of the recent decision in Ex Parte Desjardins et al., No. 2024-000567 (PTAB Appeals Review Panel, September 26, 2025), which states that "[c]ategorically excluding Al innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology," and further states that improvements to how a machine learning model itself operates, including training of a machine learning model, represent improvements to computer functionality.
Accordingly, even if one assumes for purposes of argument only that claim 1 could somehow be construed as reciting an abstract idea, such claims are not directed to an abstract idea for reasons similar to those set forth in the above-cited Ex Parte Desjardins decision, as claim 1 clearly integrates any such abstract idea into a practical application that provides improvements in computer technology.
The July 2024 Guidance Update from the USPTO expressly states as follows regarding mental processes, with emphasis supplied and citations omitted:
USPTO guidance defines the "mental processes" abstract idea grouping as concepts performed in the human mind and explains that claims recite a mental process when they contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions. In contrast, USPTO guidance explains that claims do not recite a mental process when they contain limitations that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations.
The mental processes grouping is not without limits, and as such, claim limitations that only encompass Al in a way that cannot practically be performed in the human mind do not fall within this grouping.
In view of the above portions of the July 2024 Updated Guidance, Applicant submits that claim 1 cannot reasonably be said to be directed to mental processes as alleged, at least in part because the claim explicitly incorporates AI-related recitations that cannot practically be performed in the human mind.
Examiner’s Response:
Examiner Respectfully disagrees. With respect to the applicant’s assertion “claims are not directed to an abstract idea for reasons similar to those set forth in the above-cited Ex Parte Desjardins decision, as claim 1 clearly integrates any such abstract idea into a practical application that provides improvements in computer technology” examiner asserts that the Ex Parte Desjardins decision describes that a new Artificial Intelligence Model cannot be abstract idea because of its improvement over the current art as a computing component that can continuously learn. However, claim 1 recites the limitation of “predicting” is an abstract mental process that can be performed in the human mind with the additional element of “using one or more machine learning models” and “wherein the one or more machine learning models are trained with a dataset comprising historical machine learning workspace metrics;”. The additional elements are recited at a high level of generality such that it amounts to no more than mere generic computer/computing components to apply the abstract idea. In contrast to the Ex Parte Desjardins decision the invention merely uses a generic and trained machine learning model to apply a prediction of a configuration more efficiently rather than providing an improvement in the functioning of machine learning technology itself.
Applicant Argues:
Independent claim 1 is directed to an Al invention that provides a particular solution to an important problem in the technological field of machine learning, namely, the problem of selecting which machine learning algorithm to use for a given task, along with a corresponding configuration of various resources that will be needed for execution of the predicted machine learning algorithm. Accordingly, even if one assumes for purposes of argument only that the claim could somehow be construed as reciting an abstract idea, it clearly integrates any such abstract idea into a practical application that provides an improvement in computer technology. For the reasons stated above, the rejection with respect to claims 1-20 under 35 U.S.C. 101 are proper and are thus maintained.
Examiner’s Response:
Examiner respectfully disagrees. With regards to the applicant’s assertion that “selecting which machine learning algorithm to use for a given task, along with a corresponding configuration of various resources that will be needed for execution of the predicted machine learning algorithm” the Examiner asserts that while the claim could provide an improvement to the existing technology the claims, under its broadest reasonable interpretation, still cover a prediction process that can be practically performed by the human mind with the aid of pen and paper. Merely reciting the use of one or more trained machine learning models with no further description on how it contributes to configuring the workspace does not provide sufficient detail into further patentable subject matter. For the reasons stated above, the rejection with respect to claims 1-20 under 35 U.S.C. 101 are proper and are thus maintained.
Applicant’s Argues:
However, nowhere does the Office Action explain how Thomas predicts the at least one machine learning algorithm and the configuration of the one or more workspaces in response to the request using one or more machine learning models, as claimed. The Office Action simply states that Thomas generates NN models but is silent as to how those NN models are used to predict the at least one machine learning algorithm and the configuration of the one or more workspaces in response to the request.
Examiner’s Response:
Examiner Respectfully disagrees. Thomas teaches the technique of “automating a machine-learning process for generating optimal NN models”. For one of ordinary skill in the art, using a machine learning process describes the process of using a continuously learning and trained model for the purpose of generating neural network models. Thomas further teaches the process of using the machine learning process (a continuously trained machine learning model) that can make predictions by selecting a machine learning algorithm based on prior training processes that best suits a customer’s requirements. Wyman is used to generate any predictive responses as a result of a customer’s request. However, Thomas also could teach to predict the at least one machine learning algorithm…in response to the request (Thomas [0037], “The customer of the system initiates the overall ‘optimization’ job by invoking clearly defined REST APIs with relevant JSON-formatted payload data. Those REST API can be hidden behind a clean and simple user-interface (UI) (e.g., as shown in FIG. 2).”) wherein the request is triggered by a customer API invocation/initiation. For the reasons stated above, the rejection with respect to claims 1-20 under 35 U.S.C. 103 are proper and are thus maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rao (US 12518181 B2) teaches a request is received for a machine learning model recommendation that specifies parameters associated with a desired machine learning model (e.g., schema, etc.). Thereafter, a machine learning-based discovery model, recommends at least one machine learning model (to reuse) based on the parameters specified in the request. Next, data characterizing the recommended at least one machine learning model is provided (e.g., loaded into memory, displayed in a graphical user interface, transmitted to a remote computing system, and/or stored in physical persistence, etc.). In some variations, a graphical user interface can be rendered that allows a client application to select one of the recommended machine learning models and, further optionally, to activate the selected machine learning models.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRAVIS VIET TRAN whose telephone number is (571)272-3720. The examiner can normally be reached Monday-Friday 8:30AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wei Mui can be reached at 571-272-3708. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.V.T./Examiner, Art Unit 2191 /WEI Y MUI/Supervisory Patent Examiner, Art Unit 2191
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