DETAILED ACTION
This action is in reply to the Amendments filed on 02/18/2026.
Claims 2-3, 6, 10-11, 14, and 18-19 are cancelled.
Claims 1, 4-5, 7-9, 12-13, 15-17, and 20 are rejected.
Claims 1, 4-5, 7-9, 12-13, 15-17, and 20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 02/18/2026, has been entered. Claims 1, 9, and 17 have been amended.
Objections to the Claims
The Objections to the Claims have been withdrawn pursuant Applicant’s amendments.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/18/2026 has been entered.
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, 4-5, 7-9, 12-13, 15-17, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 17 as representative, the claim recites limitations that fall within the certain methods of organizing human activity grouping of abstract ideas, including:
-a client; and
-a capacity planning (CP) manager, comprising a processor and memory, programmed to:
-obtain a current CP state from the client, wherein:
-the current CP state is sent to the CP manager as part of a message including multiple network packets through one or more network devices that operatively connect the client to the CP manager, and
-the current CP state comprises of:
-first working hours associated with user agents of the client;
-first outage hours of the user agents;
-first shrinkage hours associated with the user agents;
-first reduction in productivity associated with the user agents; and
-first productive hours associated with the user agents;
-in response to obtaining the current CP state:
-select an action based on the current CP state, wherein selecting the action based on the current CP state comprises applying noise and an actor to the current CP state, wherein the actor is an online model;
-provide the action to the client, wherein the client performs the action;
-in response to providing the action:
-obtain a new CP state and a headcount associated with the action;
-calculate a reward based on the headcount and a reward formula;
-store the current CP state, the action, the new CP state, and the reward as a learning set in storage comprising a plurality of learning sets;
-perform a learning update using a portion of the plurality of learning sets to generate an updated actor, an updated critic, an updated target actor, and an updated target critic, wherein performing the learning update comprises:
-randomly sampling the plurality of learning sets to obtain the portion of the plurality of learning sets;
-updating a critic based on the portion of the plurality of learning sets to obtain the updated critic, wherein the critic is an online model, wherein updating the critic comprises adjusting neural network weights and neural network parameters associated with the critic;
-applying the updated critic to the current CP state to generate an action value function;
-generating a gradient of the action value function;
-updating the actor based on the portion of the plurality of learning sets and based on a direction of the gradient to obtain the updated actor, wherein updating the actor comprises adjusting neural network weights and neural network parameters associated with the actor; and
-performing an incremental update of a target actor and a target critic to obtain the updated target actor and the updated target critic;
-select a second action based on a second current CP state using the updated actor; and
-initiate performance of the second action by the client
The above limitations recite the concept of recommending capacity planning actions. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of a client; and obtain a current CP state from the client, wherein: the current CP state comprises of: first working hours associated with user agents of the client; first outage hours of the user agents; first shrinkage hours associated with the user agents; first reduction in productivity associated with the user agents; and first productive hours associated with the user agents; in response to obtaining the current CP state: provide the action to the client, wherein the client performs the action; in response to providing the action: obtain a new CP state and a headcount associated with the action; calculate a reward based on the headcount and a reward formula; perform a learning update using a portion of the plurality of learning sets to generate an updated actor, an updated critic, an updated target actor, and an updated target critic, wherein performing the learning update comprises: applying the updated critic to the current CP state to generate an action value function; generating a gradient of the action value function; performing an incremental update of a target actor and a target critic to obtain the updated target actor and the updated target critic; select a second action based on a second current CP state using the updated actor; and initiate performance of the second action by the client are processes that, under their broadest reasonable interpretation, cover a commercial interaction and managing personal behavior or relationships or interactions between people. For example, “obtain,” “obtaining,” “provide,” “providing,” “obtain,” “calculate,” “perform,” “applying,” “generating,” “performing,” “select,” and “initiate” in the context of this claim encompass advertising, and marketing or sales activities, as well as, social activities, teaching, and following rules or instructions.
Similarly, the limitations of a capacity planning (CP) manager, comprising a processor and memory, programmed to: the current CP state is sent to the CP manager as part of a message including multiple network packets through one or more network devices that operatively connect the client to the CP manager, and select an action based on the current CP state, wherein selecting the action based on the current CP state comprises applying noise and an actor to the current CP state, wherein the actor is an online model; store the current CP state, the action, the new CP state, and the reward as a learning set in storage comprising a plurality of learning sets; randomly sampling the plurality of learning sets to obtain the portion of the plurality of learning sets; updating a critic based on the portion of the plurality of learning sets to obtain the updated critic, wherein the critic is an online model, wherein updating the critic comprises adjusting neural network weights and neural network parameters associated with the critic; and updating the actor based on the portion of the plurality of learning sets and based on a direction of the gradient to obtain the updated actor, wherein updating the actor comprises adjusting neural network weights and neural network parameters associated with the actor are processes that, under their broadest reasonable interpretation, cover a commercial interaction and managing personal behavior or relationships or interactions between people. That is, other than reciting that the CP manager comprises a programmed processor and memory, that the message includes multiple network packets through one or more network devices that operatively connect the client to the CP manager, that the current CP state comprises applying noise, the model being an online model, the learning set being stored in storage, that the obtaining is by randomly sampling the plurality of learning sets, that the model is an online model, that the weights are neural network weights, that the parameters are neural network parameters, that the weights are neural network weights, and, that the parameters are neural network parameters, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “processor,” “memory,” “a message including multiple network packets through one or more network devices that operatively connect the client to the CP manager,” “applying noise,” “the actor is an online model,” “storage,” “randomly sampling,” “wherein the critic is an online model,” “neural network weights,” “neural network parameters,” “neural network weights,” and “neural network parameters,” language, “to:” “sent,” “select,” “store,” “obtain,” “updating,” and “updating” in the context of this claim encompasses advertising, and marketing or sales activities, as well as, social activities, teaching, and following rules or instructions.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-a client; and
-a capacity planning (CP) manager, comprising a processor and memory, programmed to:
-obtain a current CP state from the client, wherein:
-the current CP state is sent to the CP manager as part of a message including multiple network packets through one or more network devices that operatively connect the client to the CP manager, and
-the current CP state comprises of:
-first working hours associated with user agents of the client;
-first outage hours of the user agents;
-first shrinkage hours associated with the user agents;
-first reduction in productivity associated with the user agents; and
-first productive hours associated with the user agents;
-in response to obtaining the current CP state:
-select an action based on the current CP state, wherein selecting the action based on the current CP state comprises applying noise and an actor to the current CP state, wherein the actor is an online model;
-provide the action to the client, wherein the client performs the action;
-in response to providing the action:
-obtain a new CP state and a headcount associated with the action;
-calculate a reward based on the headcount and a reward formula;
-store the current CP state, the action, the new CP state, and the reward as a learning set in storage comprising a plurality of learning sets;
-perform a learning update using a portion of the plurality of learning sets to generate an updated actor, an updated critic, an updated target actor, and an updated target critic, wherein performing the learning update comprises:
-randomly sampling the plurality of learning sets to obtain the portion of the plurality of learning sets;
-updating a critic based on the portion of the plurality of learning sets to obtain the updated critic, wherein the critic is an online model, wherein updating the critic comprises adjusting neural network weights and neural network parameters associated with the critic;
-applying the updated critic to the current CP state to generate an action value function;
-generating a gradient of the action value function;
-updating the actor based on the portion of the plurality of learning sets and based on a direction of the gradient to obtain the updated actor, wherein updating the actor comprises adjusting neural network weights and neural network parameters associated with the actor; and
-performing an incremental update of a target actor and a target critic to obtain the updated target actor and the updated target critic;
-select a second action based on a second current CP state using the updated actor; and
-initiate performance of the second action by the client
The additional elements of claim 17 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0019] of Applicant’s specification – “A computing device may be, for example, mobile phones, tablet computers, laptop computers, desktop computers, servers, or cloud resources. The computing device may include one or more processors, memory (e.g., random access memory), and persistent storage (e.g., disk drives, solid state drives, etc.).” Specifically, the additional elements of a processor, a memory, a message including multiple network packets through one or more network devices that operatively connect the client to the CP manager, applying noise, the actor being an online model, storage, randomly sampling, the critic being an online model, neural network weights, and neural network parameters are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of obtaining data, selecting data, providing data, calculating data, storing data, performing an update, updating data, and initiating data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 17, taken individually or as a whole, the additional elements of claim 17 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 1 is a method reciting similar functions as claim 17. Examiner notes that claim 1 recites the additional elements of a message including multiple network packets through one or more network devices that operatively connect the client to the CP manager, applying noise, the actor being an online model, storage, randomly sampling, the critic being an online model, neural network weights, and neural network parameters, claim 1 does not qualify as eligible subject matter for similar reasons as claim 17 indicated above.
Claim 9 is a non-transitory computer readable medium reciting similar functions as claim 17. Examiner notes that claim 9 recites the additional elements of a non-transitory computer readable medium, computer readable program code, a computer processor, a message including multiple network packets through one or more network devices that operatively connect the client to the CP manager, applying noise, the actor being an online model, storage, randomly sampling, the critic being an online model, neural network weights, and neural network parameters, however, claim 9 does not qualify as eligible subject matter for similar reasons as claim 17 indicated above.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Therefore, claims 1 and 9 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 4-5, 7-8, 12-13, 15-16, and 20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 4-5, 7-8, 12-13, 15-16, and 20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims4-5, 7-8, 12-13, 15-16, and 20, do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. As such, under prong two of Step 2A, claims 4-5, 7-8, 12-13, 15-16, and 20 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 4-5, 7-8, 12-13, 15-16, and 20 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 9 and 17, dependent claims 4-5, 7-8, 12-13, 15-16, and 20 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. recommending capacity planning actions) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Allowable Subject Matter
In the present application, claims 1, 4-5, 7-9, 12-13, 15-17, and 20 would be allowable if rewritten or amended to overcome the rejections under 35 USC § 101 set forth in this Office action. The following is the Examiner's statement of reasons of allowance:
Regarding 35 U.S.C. §103, upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the applicant’s invention. Claims 1, 4-5, 7-9, 12-13, 15-17, and 20 are allowable over the prior art as follows:
Claims 1, 4-5, 7-9, 12-13, 15-17, and 20 are allowable over 35 U.S.C. §103 as follows:
The most relevant prior art made of record includes McCord et al. (US 2018/0121766 A1), Nahamani et al. (US 2021/0406973 A1), Engelhardt et al. (US 2022/0076799 A1), and Lee et al. (US 2023/0186079 A1). McCord teaches obtaining, by a capacity planning (CP) manager, a current CP state from a client (McCord, see at least: [0041]), wherein: the current CP state is sent to the CP manager as part of a message including multiple network packets through one or more network devices that operatively connect the client to the CP manager (McCord, see at least: [0102]), and the current CP state comprises: first working hours associated with user agents of the client (McCord, see at least: [0093] and [0049]); in response to obtaining the current CP state (McCord, see at least: [0041]): selecting an action based on the current CP state (McCord, see at least: [0041] and [0043]); providing the action to the client, wherein the client performs the action (McCord, see at least: [0055]); in response to providing the action (McCord, see at least: [0055]): obtaining a new CP state and a headcount associated with the action (McCord, see at least: [0055], [0053], and [0079]); calculating a reward based on the headcount and a reward formula (McCord, see at least: [0058-0063], [0056], and [0050]); storing the current CP state, the action, the new CP state, and the reward as a learning set in storage comprising a plurality of learning sets (McCord, see at least: [0043] and [0075]); performing a learning update using a portion of the plurality of learning sets (McCord, see at least: [0054]): selecting a second action based on a second current CP state (McCord, see at least: [0055]); and initiating performance of the second action by the client (McCord, see at least: [0055]).
McCord is deficient in a number of ways. As written, the claims require that the current CP state comprises: first outage hours of the user agents; first shrinkage hours associated with the user agents; first reduction in productivity associated with the user agents; and first productive hours associated with the user agents; wherein selecting the action based on the current CP state comprises applying noise and an actor to the current CP state, wherein the actor is an online model; performing a learning update to generate an updated actor, an updated critic, an updated target actor, and an updated target critic, wherein performing the learning update comprises: randomly sampling the plurality of learning sets to obtain the portion of the plurality of learning sets; updating a critic based on the portion of the plurality of learning sets to obtain the updated critic, wherein the critic is an online model, wherein updating the critic comprises adjusting neural network weights and neural network parameters associated with the critic; applying the updated critic to the current CP state to generate an action value function; generating a gradient of the action value function; updating the actor based on the portion of the plurality of learning sets and based on a direction of the gradient to obtain the updated actor, wherein updating the actor comprises adjusting neural network weights and neural network parameters associated with the actor; and performing an incremental update of a target actor and a target critic to obtain the updated target actor and the updated target critic; and selecting a second action based on a second current CP state using the updated actor.
Regarding Nahamani, Nahamani teaches that the current CP state comprises: first outage hours of the user agents (Nahamani, see at least: [0052]); first shrinkage hours associated with the user agents (Nahamani, see at least: [0052]); first reduction in productivity associated with the user agents (Nahamani, see at least: [0052]); and first productive hours associated with the user agents (Nahamani, see at least: [0052]).
Though disclosing these features, Nahamani does not disclose or render obvious the features discussed above.
Regarding Engelhardt, Engelhardt teaches wherein selecting the action based on the current CP state comprises applying noise and an actor to the current CP state, wherein the actor is an online model (Engelhardt, see at least: [0144]); performing a learning update to generate an updated actor, an updated critic, an updated target actor, and an updated target critic (Engelhardt, see at least: [0130]), wherein performing the learning update comprises: randomly sampling the plurality of learning sets to obtain the portion of the plurality of learning sets (Engelhardt, see at least: [0146]); updating a critic based on the portion of the plurality of learning sets to obtain the updated critic (Engelhardt, see at least: [0146] and [0145]), wherein the critic is an online model, wherein updating the critic comprises adjusting neural network weights and neural network parameters associated with the critic (Engelhardt, see at least: [0146], [0147], [0126] and [0151]); applying the updated critic to the current CP state to generate an action value function (Engelhardt, see at least: [0126]); generating a gradient of the action value function (Engelhardt, see at least: [0146], [0147] and Fig. 9B); updating the actor based on the portion of the plurality of learning sets and to obtain the updated actor, wherein updating the actor comprises adjusting neural network weights and neural network parameters associated with the actor (Engelhardt, see at least: [0146] and [0147]); performing an incremental update of a target actor and a target critic to obtain the updated target actor and the updated target critic (Engelhardt, see at least: [0147]); and selecting a second action based on a second current CP state using the updated actor (Engelhardt, see at least: [0130]).
Though disclosing these features, Engelhardt does not disclose or render obvious the features discussed above.
Regarding Lee, Lee teaches updating the actor based on the portion of the plurality of learning sets and based on a direction of the gradient to obtain the updated actor (Lee, see at least: [0060]).
Though disclosing these features, Lee does not disclose or render obvious the features discussed above.
Ultimately, the particular combination of limitations as claimed, is not anticipated nor rendered obvious in view of McCord, Nahamani, Engelhardt, and Lee, and the totality of the prior art. While certain references may disclose more general concepts and parts of the claim, the prior art available does not specifically disclose the particular combination of these limitations.
McCord, Nahamani, Engelhardt, and Lee, however, do not teach or suggest, alone or in combination the claimed invention. Examiner emphasizes that the prior art/additional art would only be combined and deemed obvious based on knowledge gleaned from the applicant’s disclosure. Such a reconstruction is improper (i.e. hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Cited NPL reference U (cited 03/17/2026 on PTO-892) teaches utilizing reinforcement learning to balance ride sharing during rush hour, but does not teach or suggest the recited limitations.
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art as combining various references from the totality of evidence to reach the combination of features as claimed would be a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
It is thereby asserted by Examiner that, in light of the above and further deliberation over all of the evidence at hand, that the claims are allowable as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that the claims are directed to a method/process and thus are directed to statutory categories of invention under Step 1 (Remarks, page 11).
As indicated in the current and prior 101 rejections, Examiner agrees that all the claims are directed to one of the four statutory categories under step 1.
Applicant further argues that the claims are not directed to a judicial exception as the claims do not recite an abstract idea, they merely involve an abstract idea. As a practical matter, the human mind is not equipped to perform this method. See MPEP § 2106.04(a)(2)(III)(A). The method, as a whole, cannot be performed by a human mind without the use of a computer. See MPEP § 2106.04(a)(2)(11). Subsequently, because the claims, as amended, cannot be performed in the human mind or by a human using pen and paper, the amended claims fail to recite a mental process (i.e., an abstract idea) or certain methods of organizing human activity, and the analysis under the Mayo/ Alice Test should end with a conclusion that the claims are directed to patent eligible subject matter, and the independent claims should be considered patent eligible. (Remarks, pages 11-13).
Examiner respectfully disagrees. Applicant’s argument is moot as Examiner did not indicate that the limitations fall into the mental processes grouping.
Applicant further argues that the term "certain" qualifies the "certain methods of organizing human activity" grouping as a reminder of several important points. See MPEP § 2106.04(a)(2)(11). First, not all methods of organizing human activity are abstract ideas (e.g., "a defined set of steps for combining particular ingredients to create a drug formulation" is not a certain "method of organizing human activity"). Id Applicant asserts that the amended claims provide a novel mechanism to fine-tune and adjust models (i.e., the actor and the critic) to assist in capacity planning and are not directed to a certain method of organizing human activity (Remarks, page 14).
Examiner respectfully disagrees. The recited limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a) as they cover a commercial interaction and managing personal behavior or relationships or interactions between people in that they encompass advertising, and marketing or sales activities, as well as, social activities, teaching, and following rules or instructions. Accordingly, the amended claims recite an abstract idea.
Applicant further argues that the independent claims, as amended, integrate all concepts therein into the practical applications. As to amended independent claims 1, 9, and 17, the concepts therein are integrated into a practical application of a method for leveraging artificial intelligence to perform capacity planning using reinforcement learning. Thus, like the patent at issue in Enfish, the concepts in the claims are integrated into a practical application. Further, the practical application is one that cannot be performed in the human mind and is necessarily only able to be performed in a computing environment. See MPEP § 2106.06(b ). Applicant respectfully asserts that such actions are clearly a practical application into which the concepts in amended independent claims 1, 9, and 17 are integrated and are not "no more than adding insignificantly extra-solution activity to the judicial exception". As such, the claims are not "directed" to an abstract idea, and the rejection (Remarks, page 15).
Examiner respectfully disagrees. In Enfish the storing of tabular data is specifically directed to a self-referential table. Thus, the claims were “directed to a specific improvement to the way computers operate,” rather than utilizing a computer as a means for implementing an abstract idea. Id at 1336. Unlike Enfish, merely utilizing artificial intelligence and reinforcement learning to improve capacity planning improves the abstract idea rather than improving the machine learning technology itself. The recited the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. Additionally, Examiner did not indicate that any of the limitations are insignificantly extra-solution activity, rather, the claims are ineligible because they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) and do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Accordingly, the recited claims are not integrated into a practical application and are ineligible.
Applicant further argues that the claims include significantly more than that which the Examiner contends is abstract as the Examiner has failed to consider the additional elements as a whole. As such, Step 2B of the Mayo/Alice Test can be answered with a "yes", and the rejection should be withdrawn. See MPEP § 2106(I)(B) (Remarks, page 16).
Examiner respectfully disagrees. Examiner has considered the additional elements both individually and as a whole. Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Accordingly, the recited claims do not amount to significantly more than an abstract idea and are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-He et al. (US 2022/0164657 A1) teaches training a policy neural network and the value neural network using deep reinforcement learning.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689