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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 2/17/2026, Applicant, on 6/17/2026, amended no claims and added claim 21. Claims 1-3,6-11, 14-19, and 21 are pending in this application and have been rejected below.
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has been withdrawn pursuant to 37 CFR 1.114.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
6/17/2026 has been entered.
Response to Arguments
Applicant’s arguments filed June 17, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed June 17, 2026.
On Pg. 7-8 , regarding the 35 U.S.C. § 101 rejection, Applicant states a specific technical implementation of a computer memory management architecture which includes a memory controller that allocates a particular memory location as a dedicated portion of the RAM, a market data manager that obtains current state data as a feature vector and formats the state data for storage in the dedicated RAM location, appending formatted current state data to an existing state data array at the dedicated RAM location during each training cycle, and transmitting a pointer to the dedicated RAM location to the automated agents, which facilitates asynchronous reading of the state data during each training cycle with reduced overhead and delay. In response,
Applicant’s arguments, with respect to the 101 rejection(s) have been fully considered and are persuasive. Therefore, the 101 rejection has been withdrawn.
On page 8-9, regarding the 35 U.S.C. § 103 rejection, Applicant argues that prior art does not disclose claim limitations. In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Cella and Burhani is now applied for Claims 1, 9 and 17. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
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 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.
Claims 1-3, 6-11 and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Burhani et al., EP3576038A1, [hereinafter Burhani], in view of Cella, US Publication No. 20200098059 A1, [hereinafter Cella] in view of Burhani et al., US Publication No. 2019/361739 A 1, [hereinafter Burhani2].
Regarding Claim 1,
Burhani teaches
A computer-implemented system for training an automated agent, the system comprising: a communication interface; at least one processor; memory in communication with the at least one processor; and software code stored in the memory, which when executed at the at least one processor causes the system to: ((par. [0047]: The matching engine 114 can be a highly performant stock market simulation environment designed to provide rich datasets and ever changing experiences to reinforcement learning networks 110 (e.g. of agents 180) in order to accelerate and improve their learning;”)
instantiate a plurality of automated agents for generating resource task requests for a plurality of resources, each of the automated agents configured to train over a plurality of training cycles; (Burhani (par. [0039]: The platform 100 can train one or more reinforcement learning neural networks 110; par. [0047]: [..] a highly performant stock market simulation environment designed to provide rich datasets and ever changing experiences to reinforcement learning networks 110 (e.g. of agents 180) in order to accelerate and improve their learning; par. [0033]: [..] the automated agent may generate requests for tasks to be performed in relation to securities; par. [0079]: [..] platform 100 instantiates an automated agent 180 that maintains a reinforcement learning neural network[..] The automated agent 180 generates, according to outputs of its reinforcement learning neural network, signals for communicating resource task requests for a given resource;)
for each resource of the plurality of resources, allocate by a memory controller using a memory allocation signal, a particular memory location on random access memory (RAM) as a dedicated portion of the memory device to store all state data for the respective resource; ((par. [0053]: The data storage 120 may be configured to store information associated with or created by the components in memory 108; par. [0040]: The platform 100 can connect to different data sources 160 and databases 170 to receive input data and receive output data for storage; par. [0073]: [..] data storage 120 may store a task completion schedule; par. [0047]: Matching engine 114 is configured to implement a training exchange defined by liquidity, counter parties, market makers and exchange rules. [..] can be a highly performant stock market simulation environment designed to provide rich datasets and ever changing experiences to reinforcement learning networks 110 (e.g. of agents 180) in order to accelerate and improve their learning; see also 160, 170, 120, 122 on Fig. 1 A; dedicated portion follows from storing resource data; it also follows from using database for storing data in a structured manner) “)
receive a request for state data for a particular resource from a subset of the plurality of automated agents; (Burhani (par. [0033]: [..] the automated agent may generate requests for tasks to be performed in relation to securities;)
format, by said market data manager, said current state data for said respective resource for storage in said particular memory location of said RAM (Burhani Par. 75- “In the interest of improving the stability, and efficacy of the reinforcement learning network 110 model training, then platform 100 can normalize the inputs, or state, of the reinforcement learning network 110 model in a number of ways. The platform 100 can implement different processes to normalize the state space. Normalization can transform input data into a range or format that is understandable by the model or reinforcement learning network 110.”);
Burhani teaches agent platform and the feature is expounded upon by Cella:
obtain, by a market data manager, current state data for the respective resource, said current state data being a feature vector including feature data for the respective resource (Cella Par. 926- For example, the recurrent neural network may be used to anticipate the state of a market, such as one involving a dynamic process or action, such as a change in state of a resource that is traded in or that enables a marketplace of transactional environment. In embodiments, the recurrent neural network may use internal memory to process a sequence of inputs, such as from other nodes and/or from sensors and other data inputs from or about the transactional environment, of the various types described herein. In embodiments, the recurrent neural network may also be used for pattern recognition, such as for recognizing a machine, component, agent, or other item based on a behavioral signature, a profile, a set of feature vectors (such as in an audio file or image), or the like. In a non-limiting example, a recurrent neural network may recognize a shift in an operational mode of a marketplace or machine by learning to classify the shift from a training data set consisting of a stream of data from one or more data sources of sensors applied to or about one or more resources.) ;
Burhani and Cella are directed to machine resource processing. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon formatting of Burhani, as taught by Cella, by utilizing feature vectorization with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Burhani with the motivation of optimizing capabilities for data collection, storage and processing, automated configuration of inputs, resources and outputs, and learning on a training set of facility outcomes (Cella Par. 12).
Burhani in view pf Cella teach resource allocation and the feature is expounded by Burhani2:
for each of the training cycles for the subset of the plurality of automated agents, store formatted current state data for the particular resource in the particular memory location of said RAM allocated to the particular resource by appending the formatted current state data to an existing state data array for the particular resource during each training cycle at the particular memory location of said RAM allocated to the particular resource, wherein the formatted current state data becomes the newest member of the state data array as the last data instance of the state data array ; (Burhani2- [0067]: Each reinforcement learning network 110 or agent can make its own observations of the market, generate an action, and commit this action against the machine engine 114 changing the state of the market dynamically impacting the simulated market par. [0079]: The matching engine 114 generates output for storage at the database at 214 (e.g. KDBout). The matching engine 114 pushing results out to the database which can allow the result data to be accessed reinforcement learning networks 110 or agents;)
and transmit a pointer to said particular memory location of said RAM for the particular resource to the subset of the automated agents, to facilitate asynchronous reading of the current state data for the particular resource during each training cycle with reduced overhead and delay . (Burhani2 par. [0077]: The matching engine 114 can sample orders from a database (referred to as KDBin 200). These can be historical trades[..];par. [0079]: The matching engine 114 generates output for storage at the database at 214 (e.g. KDBout). The matching engine 114 pushing results out to the database which can allow the result data to be accessed reinforcement learning networks 110 or agents; par. [0067]: Each reinforcement learning network 110 or agent can make its own observations of the market[..];par. [0068]: Market state changes are published out to all agents, which makes a new decision based on the new state observation; asynchronous reading follows from agents independently observing market state changes.)
Burhani, Cella and Burhani2 are directed to resource processing. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Burhani in view of Cella, as taught by Burhani2, by utilizing additional data storage and delivery processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Burhani in view of Cella with the motivation of improving to accelerate and improve their learning (Burhani2 Par. 12).
Regarding Claim 2, Claim 10 and Claim 18,
wherein said current state data for the particular resource comprises the current state data for the particular resource in an environment in which the resource task requests are made. (Burhani Par. 89-90-“ As the automated agents 602 learn during operation, platform 100' obtains update data 606 from one or more of the automated agents 602 reflective of learnings at the automated agents 602. Update data 606 includes data descriptive of an "experience" of an automated agent in generating a task request. Update data 606 may include one or more of: (i) input data to the given automated agent 602 and applied normalizations (ii) a list of possible resource task requests evaluated by the given automated agent with associated probabilities of making each requests, and (iii) one or more rewards for generating a task request.).
Regarding Claim 3, Claim 11 and Claim 19,
wherein the updated state data for the particular resource further comprises historical state data for the particular resource in the environment in which the resource task requests are made. (Burhani Par. 89-90-“ Platform 100' processes update data 606 to update master model 600 according to the experience of the automated agent 602 providing the update data 606. Consequently, automated agents 602 instantiated thereafter will have benefit of the learnings reflected in update data 606. Platform 100' may also sends model changes 608 to the other automated agents 602 so that these pre-existing automated agents 602 will also have benefit of the learnings reflected in update data 606..”;Par. 133).
Regarding Claim 4, Claim 12 and Claim 20- Cancelled
Regarding Claim 5 and Claim 13, - Cancelled
Regarding Claim 6 and Claim 14,
store updated state data for each of the plurality of resources ….. ; (Burhani Par. 89-92-“ In some embodiments, platform 100' obtains update data 606 after each time step. In other embodiments, platform 100' obtains update data 606 after a predefined number of time steps, e.g., 2, 5, 10, etc. In some embodiments, platform 100' updates master model 600 upon each receipt update data 606. In other embodiments, platform 100' updates master model 600 upon reaching a predefined number of receipts of update data 606, which may all be from one automated agents 602 or from a plurality of automated agents 602.; In one example, platform 100' instantiates a first automated agent 602 and a second automated agent 602, each from master model 600. Platform 100' obtains update data 606 from the first automated agents 602. Platform 100' modifies master model 600 in response to the update data 606 and then applies a corresponding modification to the second automated agent 602. Of course, the roles of the automated agents 602 could be reversed in another example such that platform 100' obtains update data 606 from the second automated agent 602 and applies a corresponding modification to the first automated agent 602.”)
Burhani in view of Cella teach resource allocation and the feature is expounded upon by Burhani2:
in the respective particular memory location of the RAM allocated to the respective resource par. [0079]: The matching engine 114 generates output for storage at the database at 214 (e.g. KDBout). The matching engine 114 pushing results out to the database which can allow the result data to be accessed reinforcement learning networks 110 or agents; par. [0067]: Each reinforcement learning network 110 or agent can make its own observations of the market[..];par. [0068]: Market state changes are published out to all agents, which makes a new decision based on the new state observation; asynchronous reading follows from agents independently observing market state changes.)
Burhani, Cella and Burhani2 are directed to resource processing. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Burhani in view of Cella, as taught by Burhani2, by utilizing additional data storage and delivery processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Burhani in view of Cella with the motivation of improving to accelerate and improve their learning (Barhani2 Par. 12).
Regarding Claim 7 and Claim 15,
wherein the current state data for the particular resource includes a market price of the particular resource. (Burhani Par. 97-98-“ Pricing Features: In some embodiments, input normalization may involve the training engine 118 computing pricing features. In some embodiments, pricing features for input normalization may involve price comparison features, passive price features, gap features, and aggressive price features.”)
Regarding Claim 8 and Claim 16,
wherein the environment includes at least one trading venue. (Burhani Par. 94-“ In some embodiments, the plurality of automated agents 602 may be distributed geographically, e.g., with certain of the automated agent 602 placed for geographic proximity to certain trading venues.”)
Regarding Claim 9,
Burhani teaches
A computer-implemented method for training an automated agent, the method comprising: (Burhani (par. [0047]: The matching engine 114 can be a highly performant stock market simulation environment designed to provide rich datasets and ever changing experiences to reinforcement learning networks 110 (e.g. of agents 180) in order to accelerate and improve their learning”)
instantiating a plurality of automated agents for generating resource task requests for a plurality of resources, each of the automated agents configured to train over a plurality of training cycles; (Burhani (par. [0039]: The platform 100 can train one or more reinforcement learning neural networks 110; par. [0047]: [..] a highly performant stock market simulation environment designed to provide rich datasets and ever changing experiences to reinforcement learning networks 110 (e.g. of agents 180) in order to accelerate and improve their learning; par. [0033]: [..] the automated agent may generate requests for tasks to be performed in relation to securities; par. [0079]: [..] platform 100 instantiates an automated agent 180 that maintains a reinforcement learning neural network[..] The automated agent 180 generates, according to outputs of its reinforcement learning neural network, signals for communicating resource task requests for a given resource;)
formatting, by said market data manager, said current state data for said respective resource for storage in said particular memory location of said RAM; (Burhani Par. 75- “In the interest of improving the stability, and efficacy of the reinforcement learning network 110 model training, then platform 100 can normalize the inputs, or state, of the reinforcement learning network 110 model in a number of ways. The platform 100 can implement different processes to normalize the state space. Normalization can transform input data into a range or format that is understandable by the model or reinforcement learning network 110.”);
receiving a request for state data for a particular resource from a subset of the plurality of automated agents (Burhani par. [0033]: [..] the automated agent may generate requests for tasks to be performed in relation to securities;)
Burhani teaches agent platform and the feature is expounded upon by Cella:
obtain, by a market data manager, current state data for the respective resource, said current state data being a feature vector including feature data for the respective resource (Cella Par. 926- For example, the recurrent neural network may be used to anticipate the state of a market, such as one involving a dynamic process or action, such as a change in state of a resource that is traded in or that enables a marketplace of transactional environment. In embodiments, the recurrent neural network may use internal memory to process a sequence of inputs, such as from other nodes and/or from sensors and other data inputs from or about the transactional environment, of the various types described herein. In embodiments, the recurrent neural network may also be used for pattern recognition, such as for recognizing a machine, component, agent, or other item based on a behavioral signature, a profile, a set of feature vectors (such as in an audio file or image), or the like. In a non-limiting example, a recurrent neural network may recognize a shift in an operational mode of a marketplace or machine by learning to classify the shift from a training data set consisting of a stream of data from one or more data sources of sensors applied to or about one or more resources.) ;
Burhani and Cella are directed to machine resource processing. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon formatting of Burhani, as taught by Cella, by utilizing feature vectorization with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Burhani with the motivation of optimizing capabilities for data collection, storage and processing, automated configuration of inputs, resources and outputs, and learning on a training set of facility outcomes (Cella Par. 12).
Burhani in view pf Cella teach resource allocation and the feature is expounded by Burhani2:
for each of the training cycles for the subset of the plurality of automated agents, storing said formatted current state data for the particular resource in said particular memory location of said RAM allocated to the particular resource by appending the formatted current state data to an existing state data array for the particular resource during each training cycle at the particular memory location of said RAM allocated to the particular resource, wherein the formatted current state data becomes the newest member of the state data array as the last data instance of the state data array;; (Burhani2- [0067]: Each reinforcement learning network 110 or agent can make its own observations of the market, generate an action, and commit this action against the machine engine 114 changing the state of the market dynamically impacting the simulated market par. [0079]: The matching engine 114 generates output for storage at the database at 214 (e.g. KDBout). The matching engine 114 pushing results out to the database which can allow the result data to be accessed reinforcement learning networks 110 or agents;)
and transmitting a pointer to said particular memory location of the RAM allocated to the particular resource to the subset of the automated agents, to facilitate asynchronous reading of the current state data for the particular resource during each training cycle with reduced overhead and delay. (Burhani2 par. [0077]: The matching engine 114 can sample orders from a database (referred to as KDBin 200). These can be historical trades[..];par. [0079]: The matching engine 114 generates output for storage at the database at 214 (e.g. KDBout). The matching engine 114 pushing results out to the database which can allow the result data to be accessed reinforcement learning networks 110 or agents; par. [0067]: Each reinforcement learning network 110 or agent can make its own observations of the market[..];par. [0068]: Market state changes are published out to all agents, which makes a new decision based on the new state observation; asynchronous reading follows from agents independently observing market state changes.)
Burhani, Cella and Burhani2 are directed to resource processing. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Burhani in view of Cella, as taught by Burhani2, by utilizing additional data storage and delivery processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Burhani in view of Cella with the motivation of improving to accelerate and improve their learning (Burhani2 Par. 12).
Regarding Claim 17,
Burhani teaches
A non-transitory computer-readable storage medium storing instructions which when executed adapt at least one computing device to: (Burhani (par. [0047]: The matching engine 114 can be a highly performant stock market simulation environment designed to provide rich datasets and ever changing experiences to reinforcement learning networks 110 (e.g. of agents 180) in order to accelerate and improve their learning”)
instantiate a plurality of automated agents for generating resource task requests for a plurality of resources, each of the automated agents configured to train over a plurality of training cycles; (Burhani (par. [0039]: The platform 100 can train one or more reinforcement learning neural networks 110; par. [0047]: [..] a highly performant stock market simulation environment designed to provide rich datasets and ever changing experiences to reinforcement learning networks 110 (e.g. of agents 180) in order to accelerate and improve their learning; par. [0033]: [..] the automated agent may generate requests for tasks to be performed in relation to securities; par. [0079]: [..] platform 100 instantiates an automated agent 180 that maintains a reinforcement learning neural network[..] The automated agent 180 generates, according to outputs of its reinforcement learning neural network, signals for communicating resource task requests for a given resource;)
format, by said market data manager, said current state data for said respective resource for storage in said particular memory location of said RAM; (Burhani Par. 75- “In the interest of improving the stability, and efficacy of the reinforcement learning network 110 model training, then platform 100 can normalize the inputs, or state, of the reinforcement learning network 110 model in a number of ways. The platform 100 can implement different processes to normalize the state space. Normalization can transform input data into a range or format that is understandable by the model or reinforcement learning network 110.”);
receive a request for state data for a particular resource from a subset of the plurality of automated agents (Burhani par. [0033]: [..] the automated agent may generate requests for tasks to be performed in relation to securities;)
Burhani teaches agent platform and the feature is expounded upon by Cella:
obtain, by a market data manager, current state data for the respective resource, said current state data being a feature vector including feature data for the respective resource (Cella Par. 926- For example, the recurrent neural network may be used to anticipate the state of a market, such as one involving a dynamic process or action, such as a change in state of a resource that is traded in or that enables a marketplace of transactional environment. In embodiments, the recurrent neural network may use internal memory to process a sequence of inputs, such as from other nodes and/or from sensors and other data inputs from or about the transactional environment, of the various types described herein. In embodiments, the recurrent neural network may also be used for pattern recognition, such as for recognizing a machine, component, agent, or other item based on a behavioral signature, a profile, a set of feature vectors (such as in an audio file or image), or the like. In a non-limiting example, a recurrent neural network may recognize a shift in an operational mode of a marketplace or machine by learning to classify the shift from a training data set consisting of a stream of data from one or more data sources of sensors applied to or about one or more resources.) ;
Burhani and Cella are directed to machine resource processing. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon formatting of Burhani, as taught by Cella, by utilizing feature vectorization with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Burhani with the motivation of optimizing capabilities for data collection, storage and processing, automated configuration of inputs, resources and outputs, and learning on a training set of facility outcomes (Cella Par. 12).
Burhani in view pf Cella teach resource allocation and the feature is expounded by Burhani2:
for each of the training cycles for the subset of the plurality of automated agents, storing said formatted current state data for the particular resource in said particular memory location of said RAM allocated to the particular resource by appending the formatted current state data to an existing state data array for the particular resource during each training cycle at the particular memory location of said RAM allocated to the particular resource, wherein the formatted current state data becomes the newest member of the state data array as the last data instance of the state data array;; (Burhani2- [0067]: Each reinforcement learning network 110 or agent can make its own observations of the market, generate an action, and commit this action against the machine engine 114 changing the state of the market dynamically impacting the simulated market par. [0079]: The matching engine 114 generates output for storage at the database at 214 (e.g. KDBout). The matching engine 114 pushing results out to the database which can allow the result data to be accessed reinforcement learning networks 110 or agents;)
and transmit a pointer to said particular memory location of the RAM allocated to the particular resource to the subset of the automated agents, to facilitate asynchronous reading of the current state data for the particular resource during each training cycle with reduced overhead and delay. (Burhani2 par. [0077]: The matching engine 114 can sample orders from a database (referred to as KDBin 200). These can be historical trades[..];par. [0079]: The matching engine 114 generates output for storage at the database at 214 (e.g. KDBout). The matching engine 114 pushing results out to the database which can allow the result data to be accessed reinforcement learning networks 110 or agents; par. [0067]: Each reinforcement learning network 110 or agent can make its own observations of the market[..];par. [0068]: Market state changes are published out to all agents, which makes a new decision based on the new state observation; asynchronous reading follows from agents independently observing market state changes.)
Burhani, Cella and Burhani2 are directed to resource processing. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Burhani in view of Cella, as taught by Burhani2, by utilizing additional data storage and delivery processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Burhani in view of Cella with the motivation of improving to accelerate and improve their learning (Burhani2 Par. 12).
Claim 21 are rejected under 35 U.S.C. 103 as being unpatentable over Burhani et al., EP3576038A1, [hereinafter Burhani], in view of Cella, US Publication No. 20200098059 A1, [hereinafter Cella] in further view of Burhani et al., US Publication No. 2019/361739 A 1, [hereinafter Burhani2], and in further view of Burhani et al., US Publication No. 2019/361739A1, [hereinafter Burhani3] .
Burhani in view of Cella in further view of Burhani2 teach storing memory resources and the feature is expounded upon by Burhani3:
wherein each automated agent of the subset of automated agents, upon receiving the pointer, stores the pointer in a local memory and associates the pointer with the particular resource, such that the memory controller does not retransmit the pointer for the particular resource to the same automated agent throughout the plurality of training cycles, and wherein each automated agent of the subset uses the stored pointer to retrieve updated state data for the particular resource from the particular memory location of said RAM during subsequent training cycles (Burhani 3- Par. 106- In some embodiments, communicating the executed task data to the reinforcement learning agent includes sending a message to the reinforcement learning agent. In some embodiments, communicating the executed task data to the reinforcement learning agent includes updating a list of matched data processing tasks which can be polled, transmitted, broadcasted, pushed, pulled, or otherwise may available to the reinforcement learning agent(s). In some embodiments, the executed task data can be communicated to reinforcement learning agent(s) which were not one of the two agents associated with the pair of matched data processing tasks.”) .
Burhani, Cella, Burhani2 and Burhani3are directed to resource processing. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Burhani in view of Cella in further view of Burhani2, as taught by Burhani3, by utilizing additional data storage and delivery processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Burhani in view of Cella in further view of Burhani2 with the motivation of providing ever changing experiences to reinforcement learning networks 110 in order to accelerate and improve their learning (Burhani3 Par. 44).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20220383100 A1 to Zhu et al.- Abstract- A first neural network can be trained to approximate a state-action value function to estimate an expected cumulative return for an agent to perform an action in a given state, the agent being an autonomous reinforcement learning agent running on the processor. A second neural network can be trained to generate a simulated experience, the second network trained to predict a simulated state at a next time step after performing a given action, the second neural network being trained using real experience in a real environment. The first neural network is trained based on the simulated experience and a real experience from a real environment. A selected action selected by the second neural network given a current state of the real environment can be performed. The agent can explore an action space by uniformly sampling an action from all possible remaining action-state space combinations and performing the sampled action.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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