DETAILED ACTION
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 .
Status of the Claims
The Amendment filed on 02/26/2026 has been entered. Claims 1-20 are pending in the instant patent application. Claims 1-2, 6-7, 9-11, 15-16 and 19-20 are amended. This Final Office Action is in response to the claims filed.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
The amendments have further necessitated grounds of rejection under 35 USC 112.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent Claims 1, 10 and 19 recite “automated agent” and “a self-aware automated agent”. It is unclear and indefinite as to whether these automated agents are one in the same or two distinct automated agents. The dependent claims do not cure these deficiencies and appropriate correction is required.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive.
Regarding the Applicant’s assertion that the claims as currently amended do not recite abstract ideas, Examiner respectfully disagrees. The claims as currently presented still recite abstract ideas as noted in the previous Office Action (Mental Processes and Mathematical Concepts). Examiner respectfully reminds Applicant, general purpose computer elements/structure, similar to the claimed invention, used to apply a judicial exception, by use of instruction implemented on a computer, has not been found by the courts to integrate the abstract idea into a practical application; see MPEP 2106.05(f). Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind. Examiner further asserts that the use of a reinforcement learning neural network is merely being used as a tool to carry out the abstract ideas, does not improve the functioning of a computer or the technology and does not 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, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
In addition, as previously stated, MPEP 2106.04(a)(2)(III)(B) discloses, in part, the use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For instance, in CyberSource, the court determined that the step of "constructing a map of credit card numbers" was a limitation that was able to be performed "by writing down a list of credit card transactions made from a particular IP address." In making this determination, the court looked to the specification, which explained that the claimed map was nothing more than a listing of several (e.g., four) credit card transactions. The court concluded that this step was able to be performed mentally with a pen and paper, and therefore, it qualified as a mental process. 654 F.3d at 1372-73, 99 USPQ2d at 1695. See also Flook, 437 U.S. at 586, 198 USPQ at 196 (claimed "computations can be made by pencil and paper calculations"); University of Florida Research Foundation, Inc. v. General Electric Co., 916 F.3d 1363, 1367, 129 USPQ2d 1409, 1411-12 (Fed. Cir. 2019) (relying on specification’s description of the claimed analysis and manipulation of data as being performed mentally "‘using pen and paper methodologies, such as flowsheets and patient charts’"); Symantec, 838 F.3d at 1318, 120 USPQ2d at 1360 (although claimed as computer-implemented, steps of screening messages can be "performed by a human, mentally or with pen and paper"). Also see MPEP 2106.04(a)(2)(III)(C) - Claims can recite a mental process even if they are claimed as being performed on a computer. Additionally, Examiner notes, 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. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015); MPEP 2106.05(f)(2).
In the previous Office Action, it was noted that Claims 2-6, 11-15 and 20 recited the abstract idea grouping of Mathematical Concepts. Applicant has argued that it is not clear how those claims are directed to a Mathematical Concept. Examiner will remind Applicant that the Mathematical Concepts grouping of abstract ideas includes mathematical relationships, mathematical formulas or equations, mathematical calculations, which are presently shown in the noted claims.
Furthermore, the amendments cloud exactly what the inventive concept is. For example, the reinforcement learning neural network cites learning of an impact of task execution, however said results are not implemented or used in the subsequent claim language. Examiner recommends the Applicant incorporate detail regarding the use of the reinforcement learning neural network, it’s use and how it is implemented in the overall functioning of the claimed invention.
Regarding Applicant’s assertion that the claims integrate the judicial exception into a practical application, Examiner respectfully disagrees. The claim’s limitations including “observe or receive from the environment…”, “storing the self-aware current feature data…”, “maintaining a plurality of historical feature data…”, “transmit, by way of said communication interface, a signal for communicating the resource tasks requests…”, “observe or receive from the environment, by way of said communication interface…” and “transmit, by way of said communication interface, a signal for communicating subsequent resource tasks requests…” recite insignificant extra-solution activity to the judicial exception. The use of a reinforcement learning neural network is merely being used as a tool to carry out the abstract ideas, does not improve the functioning of a computer or the technology and does not 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, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Examiner will further note an important consideration to evaluate when determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology. MPEP 2106.04(a) and 2106.05(a) provide a detailed explanation of how to perform this analysis. In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. In analyzing the specification, Examiner maintains that the specification sets forth an improvement, but in a conclusory manner and furthermore the claims do not reflect the disclosed improvement or effectively demonstrate an improvement to existing technology. In addition, (ref: MPEP 2106.04(d)(1)).
Regarding Applicant’s arguments that the claims amount to significantly more than the judicial exception, Examiner respectfully disagrees. In light of the amendments and additional elements, Examiner maintains that in light of Step 2B and evaluating the additional elements individually and in combination, do not amount to significantly more. The claim language as currently presented does not convey any improvements the functioning of the computer or technology.
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.
Regarding Claims 1-9, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 1-9 are directed to the abstract idea of training an automated agent.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites instantiate an agent that maintains and configured to generate according to outputs and a policy, signals for communicating resource task requests to be executed in the environment; observe or receive from the environment, input data relating to tasks executed in the environment; generate self-aware training data by: executing a feature extraction process on the input data to generate a self-aware current feature data structure related to a resource of the environment associated with the resource task requests, for a current time step, the self-aware current feature data structure comprising data relating to properties of the tasks executed in the environment associated with the resource, and self-aware environmental data relating to changes to the environment caused at least in part by an execution of the tasks by the agent; storing the self-aware current feature data and maintaining, a plurality of historical feature data structures related to said resource for a plurality of prior time steps with each of the plurality of historical feature data structures corresponding to an input data observed or received at a different time step; computing scaled self-aware feature data using the self-aware current feature data structure and the plurality of historical feature data structures; augmenting a current date structure at a present time with the scaled self-aware feature data, wherein the augmented current state data structures forms the self-aware training data; adjust the policy of said self-aware agent based on outputs; determine, by said self-aware agent, resource task requests determined based on the adjusted policy of said self-aware agent; transmit a signal for communicating the resource tasks requests; observe or receive from the environment, subsequent input data relating to subsequent tasks executed in the environment in response to the signal for communicating the resource tasks requests; and transmit, a signal for communicating subsequent resource task requests, the subsequent resource task requests determined based in part on subsequent self-aware environmental data relating to changes to the environment caused at least in part by an execution of the subsequent tasks.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper. Furthermore, the mere/generic recitation of a reinforcement learning neural network does not take the claim out of the abstract ideas noted. In addition, Claims 2-6 recite Mathematical Concepts due to the mathematical relationships and calculations taking place.
Accordingly, the claim recites an abstract idea and dependent claims 2-9 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a communication interface, at least one processor, data storage elements, memory, reinforcement learning neural network, transmit the self- aware training data to the reinforcement learning neural network thereby training said self-aware automated agent to learn an impact of task execution by said self-aware automated agent on the environment, self-aware automated agent and automated agent. The communication interface, at least one processor, data storage elements, memory, reinforcement learning neural network, transmit the self-aware training data to the reinforcement learning neural network thereby training said self-aware automated agent to learn an impact of task execution by said self-aware automated agent on the environment, self-aware automated agent and automated agent.
Furthermore, the limitations including “observe or receive from the environment…”, “storing the self-aware current feature data…”, “maintaining a plurality of historical feature data…”, “transmit, by way of said communication interface, a signal for communicating the resource tasks requests…”, “observe or receive from the environment, by way of said communication interface…” and “transmit, by way of said communication interface, a signal for communicating subsequent resource tasks requests…” recite insignificant extra-solution activity to the judicial exception.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1 includes various elements that are not directed to the abstract idea under 2A. These elements include a communication interface, data storage elements, at least one processor, memory, reinforcement learning neural network, transmit the self- aware training data to the reinforcement learning neural network thereby training said self-aware automated agent to learn an impact of task execution by said self-aware automated agent on the environment, self-aware automated agent, automated agent and the generic computing elements described in the Applicant's specification in at least Para 00148-00151. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, Claim 1 recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claim 1 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 10-18, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 10-18 are directed to the abstract idea of training an automated agent.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 10, claim 10 recites instantiating an agent that maintains and configured to generate according to outputs and a policy, signals for communicating resource task requests to be executed in the environment; observe or receive from the environment, input data relating to tasks executed in the environment; generate self-aware training data by: executing a feature extraction process on the input data to generate a self-aware current feature data structure related to a resource of the environment associated with the resource task requests, for a current time step, the self-aware current feature data structure comprising data relating to properties of the tasks executed in the environment associated with the resource, and self-aware environmental data relating to changes to the environment caused at least in part by an execution of the tasks by the agent; storing the self-aware current feature data and maintaining, a plurality of historical feature data structures related to said resource for a plurality of prior time steps with each of the plurality of historical feature data structures corresponding to an input data observed or received at a different time step; computing scaled self-aware feature data using the self-aware current feature data structure and the plurality of historical feature data structures; augmenting a current date structure at a present time with the scaled self-aware feature data, wherein the augmented current state data structures forms the self-aware training data; adjusting the policy of said self-aware agent based on outputs; determining, by said self-aware agent, resource task requests determined based on the adjusted policy of said self-aware agent; transmitting a signal for communicating the resource tasks requests; observing or receiving from the environment, subsequent input data relating to subsequent tasks executed in the environment in response to the signal for communicating the resource tasks requests; and transmitting, a signal for communicating subsequent resource task requests, the subsequent resource task requests determined based in part on subsequent self-aware environmental data relating to changes to the environment caused at least in part by an execution of the subsequent tasks.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper. Furthermore, the mere/generic recitation of a reinforcement learning neural network does not take the claim out of the abstract ideas noted. In addition, Claims 11-15 recite Mathematical Concepts due to the mathematical relationships and calculations taking place.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of an automated agent, self-aware automated agent, transmitting the self- aware training data to the reinforcement learning neural network thereby training said self-aware automated agent to learn an impact of task execution by said self-aware automated agent on the environment, a reinforcement learning neural network, data storage elements and a memory. The automated agent, self-aware automated agent, transmitting the self- aware training data to the reinforcement learning neural network thereby training said self-aware automated agent to learn an impact of task execution by said self-aware automated agent on the environment, a reinforcement learning neural network, data storage elements and a memory are merely generic computing devices implemented on a personal computer.
Furthermore, the limitations including “observing or receiving from the environment…”, “storing the self-aware current feature data…”, “maintaining a plurality of historical feature data…”, “transmitting, by way of said communication interface, a signal for communicating the resource tasks requests…”, “observing or receiving from the environment, by way of said communication interface…” and “transmitting, by way of said communication interface, a signal for communicating subsequent resource tasks requests…” recite insignificant extra-solution activity to the judicial exception.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 10 includes various elements that are not directed to the abstract idea under 2A. These elements include an automated agent, self-aware automated agent, transmitting the self- aware training data to the reinforcement learning neural network thereby training said self-aware automated agent to learn an impact of task execution by said self-aware automated agent on the environment, data storage elements, a reinforcement learning neural network, a memory and the generic computing elements described in the Applicant's specification in at least Para 00148-00151. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, Claim 10 recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claim 10 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 19-20, they are directed to a non-transitory computer readable storage medium, however the claims are directed to a judicial exception without significantly more. Claims 19-20 are directed to the abstract idea of training an automated agent.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 19, claim 19 recites instantiate an agent that maintains and configured to generate according to outputs and a policy, signals for communicating resource task requests to be executed in the environment; observe or receive from the environment, input data relating to tasks executed in the environment; generate self-aware training data by: executing a feature extraction process on the input data to generate a self-aware current feature data structure related to a resource of the environment associated with the resource task requests, for a current time step, the self-aware current feature data structure comprising data relating to properties of the tasks executed in the environment associated with the resource, and self-aware environmental data relating to changes to the environment caused at least in part by an execution of the tasks by the agent; storing the self-aware current feature data and maintaining, a plurality of historical feature data structures related to said resource for a plurality of prior time steps with each of the plurality of historical feature data structures corresponding to an input data observed or received at a different time step; computing scaled self-aware feature data using the self-aware current feature data structure and the plurality of historical feature data structures; augmenting a current date structure at a present time with the scaled self-aware feature data, wherein the augmented current state data structures forms the self-aware training data; adjust the policy of said self-aware agent based on outputs; determine, by said self-aware agent, resource task requests determined based on the adjusted policy of said self-aware agent; transmit a signal for communicating the resource tasks requests; observe or receive from the environment, subsequent input data relating to subsequent tasks executed in the environment in response to the signal for communicating the resource tasks requests; and transmit, a signal for communicating subsequent resource task requests, the subsequent resource task requests determined based in part on subsequent self-aware environmental data relating to changes to the environment caused at least in part by an execution of the subsequent tasks.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper. Furthermore, the mere/generic recitation of a reinforcement learning neural network does not take the claim out of the abstract ideas noted. In addition, Claim 20 recites Mathematical Concepts due to the mathematical relationships and calculations taking place.
Accordingly, the claim recites an abstract idea and dependent claim 20 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of an automated agent, self-aware automated agent, data storage elements, a reinforcement learning neural network, transmit the self- aware training data to the reinforcement learning neural network thereby training said self-aware automated agent to learn an impact of task execution by said self-aware automated agent on the environment, and a memory. The automated agent, self-aware automated agent, data storage elements, a reinforcement learning neural network, transmit the self- aware training data to the reinforcement learning neural network thereby training said self-aware automated agent to learn an impact of task execution by said self-aware automated agent on the environment, and a memory are merely generic computing devices implemented on a personal computer.
Furthermore, the limitations including “observing or receiving from the environment…”, “storing the self-aware current feature data…”, “maintaining a plurality of historical feature data…”, “transmitting, by way of said communication interface, a signal for communicating the resource tasks requests…”, “observing or receiving from the environment, by way of said communication interface…” and “transmitting, by way of said communication interface, a signal for communicating subsequent resource tasks requests…” recite insignificant extra-solution activity to the judicial exception.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 19 includes various elements that are not directed to the abstract idea under 2A. These elements include an automated agent, self-aware automated agent, transmit the self- aware training data to the reinforcement learning neural network thereby training said self-aware automated agent to learn an impact of task execution by said self-aware automated agent on the environment, a reinforcement learning neural network, data storage elements, a memory and the generic computing elements described in the Applicant's specification in at least Para 00148-00151. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, Claim 19 recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claim 19 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Conclusion
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.
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/T.E.S./Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625