CTFR 18/158,920 CTFR 99042 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Amendments Claims 1-20 have been amended. Claims 1-20 remain pending in the application. The amendment filed 03/31/2026 is sufficient to overcome the 102 rejections of claims 1-3, 10, 13, 15-18, and 20 over Schrittwieser, the 103 rejections of claims 4-6 and 19 over Schrittwieser in view of Zhernov, the 103 rejections of claims 7-9 over Schrittwieser in view of Zhernov and further in view of Zhang, the 103 rejections of claims 11 and 12 over Schrittwieser in view of Kovács, and the 103 rejections of claim 14 over Schrittwieser in view of Huber. The previous rejections have been withdrawn. Response to Arguments Argument 1 , regarding the 101 rejections, applicant argues that the claims recite limitations that provide an improvement to the field of machine learning including determining state-specific lookahead horizons, applying a current state-action policy to one or more determined state-specific lookahead horizons, and updating the current-state action policy. Applicant argues that these limitations result in improved computational efficiency of reinforcement learning. Examiner respectfully disagrees because the claims remain directed towards mathematical concepts and mental processes, and there are no additional limitations that appear to integrate the abstract ideas into a practical application. Determining state-specific lookahead horizons is a mental process. Computing respective values by applying a current state-action policy to one or more determined state-specific lookahead horizons is a mathematical concept and mental process. Updating the current-state action policy based on computed values is a mental process. The 101 rejections are outlined below. Argument 2 , regarding the prior art rejections, applicant argues that none of the cited references teach determining, for each respective state of the number of states and based on the corresponding respective first value [which indicates an expected total reward to be accumulated by starting from the respective state], state-specific lookahead horizons, applying a current state-action policy to one or more determined state-specific lookahead horizons to compute one or more respective second values, and updating a current-state action policy based on the one or more second values. Applicant argues that Schrittwieser is directed towards predicting outputs similar to what a look ahead search policy may provide but does not actually perform a look ahead search during training. Examiner notes that Borhan et al (Pub. No.: US 20200398859 A1), hereafter Borhan teaches updating a policy based on look ahead or horizon information obtained using machine learning techniques and using this policy to control a system (see Borhan P0045). Applicant also argues that Schrittwieser does not teach state specific lookahead horizons. Examiner notes that Schrittwieser teaches a “look ahead search starting from a root node of the state tree (which corresponds to the hidden state generated at step 202) and continues the look ahead search until a possible future state that satisfies termination criteria is encountered” (see Schrittwieser P0092). Claim Objections 07-29-01 AIA Claim 6 is objected to because of the following informalities: the claim recites “…possible future states reached by by applying current state-action policy…”. The claim should instead recite “possible future states reached by applying current state-action policy” . Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claims recite a method, device, and non-transitory computer-readable medium, each of which are one of the four categories of eligible subject matter. Claims 1, 16, and 20 Step 2A Prong 1 : The claims recite the following limitations: Computing, for each respective state of a number of states in the environment, a respective first value indicating an expected total reward to be accumulated by starting from the respective state ( Mathematical Concept ); determining, for each respective state of the number of states and based on the corresponding respective first value, a respective state-dependent lookahead horizon that indicates a depth of a sequence of state transitions beginning with the respective state ( Mental Process ); computing, for each respective state of one or more respective states in of the number of states, a respective second value by applying a current state-action policy to the corresponding respective state-dependent lookahead horizons ( Mathematical Concept and Mental Process ); and updating the current state-action based on the one or more second values ( Mathematical Concept and Mental Process ). Computing values associated with states in an environment is a mathematical concept because calculating a value associated with states in an environment is a mathematical concept under the broadest reasonable interpretation of the claim language. Determining a lookahead horizon is a mental process because a human mind can practically determine a depth for a lookahead search with the aid of a pencil, paper, and data. Computing values by applying and updating a state-action policy is a mathematical concept and mental process because computing values based on a policy is a mathematical concept under the broadest reasonable interpretation of the claim language, and applying a policy to a lookahead horizon and computed values can practically be performed by a human mind with the aid of a pencil, paper, and data. Accordingly, the claims recite an abstract idea. Step 2A Prong 2 : The judicial exceptions are not integrated into a practical application and the claim does not recite additional elements. The claims are directed towards an abstract idea. Step 2B : The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible. Dependent Claims Claims 2-8, 10, and 17-19 : These claims recite further abstract ideas (mental processes and mathematical concepts) and thus are ineligible. Claims 3-5 and 18-19 : These claims recite mere data gathering which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claims do not provide a practical application or inventive concept and thus are ineligible. Claim 9: This claim recites further generic computing components recited at a high level as a means to apply the judicial exception and as explained above these do not provide a practical application or inventive concept and thus are ineligible. Claims 11-15: These claims further recite elements generally linking the abstract ideas to the field of use of games, autonomous vehicles, and machine learning and as explained above these do not provide a practical application or inventive concept and thus are ineligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-3, 10, 13, 15-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Schrittwieser et al (Pub. No.: US 20230073326 A1), hereafter Schrittwieser in view of Borhan et al (Pub. No.: US 20200398859 A1), hereafter Borhan . Regarding claims 1, 16, and 20 , Schrittwieser teaches a computer-implemented method, device, and non-transitory computer-readable medium, having computer-executable instructions stored thereon, for model training, the computer-executable instructions, when executed by one or more processors, causing the one or more processors to facilitate (embodiments of the subject matter of the specification of the application may be implemented using a method, device, and non-transitory computer-readable medium with computer executable instructions, P0010, P0144) : computing, for each respective state of a number of states in the environment, a respective first value indicating an expected total reward to be accumulated by starting from the respective state (Reinforcement learning system generates plan data 122 for states of the environment. Plan data may include data indicating a respective value for performing a task starting from the current state of the environment. P0030) ; determining, for each respective state of the number of states and based on the corresponding respective first value, a respective state-dependent lookahead horizon that indicates a depth of a sequence of state transitions beginning with the respective state (Look ahead search may be performed for states of the environment including outgoing edges from a first node to a second node representing an action that was performed in response to an observation characterizing the first state and resulted in the environment transitioning into the second state. Plan data 122 includes information for first values needed for the lookahead search. The look ahead search results include relevant values for the second value for each particular state, P0031. “look ahead search starting from a root node of the state tree (which corresponds to the hidden state generated at step 202) and continues the look ahead search until a possible future state that satisfies termination criteria is encountered”, P0092) ; computing, for each respective state of one or more respective states of the number of states, a respective second value by applying a current state-action policy to the corresponding respective state-dependent lookahead horizons (A look ahead search policy is determined for traversing future states of the environment. Look ahead search policy is determined by a predicted policy used for determining outputs representing values of the environment that match the target values determined or otherwise derived from using the given policy, P0079. Node-edge pairs are interpreted as state-action pairs in view of P0032 of Schrittwieser. Multiple iterations of lookahead search are then performed for respective states in the state tree for generating plan data that indicates a respective value to performing the task of the agent performing each of the set of actions in the environment and starting from the current environment state. Lookahead searches include selecting a respective action according to the compiled statistics for a corresponding node-edge pair in the state tree, P0091) . Schrittwieser does not appear to explicitly teach “updating the current state-action policy based on the one or more second values”. Borhan teaches updating the current state-action policy based on the one or more second values (updating a policy based on look ahead or horizon information obtained using machine learning techniques and using this policy to control a system, P0045). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Schrittwieser and Borhan before them, to include Borhan’s specific teaching of updating a policy based on look ahead or horizon information obtained using machine learning techniques in Schrittwieser’s method of planning for agent control using learned hidden states. One would have been motivated to make such a combination of updating a policy based on look ahead or horizon information obtained using machine learning techniques (see Borhan P0045), and using look ahead search to generate value outputs representing values of the environment that match the target values determined or otherwise derived from using the given policy (see Schrittwieser P0079) to improve the policy in an ongoing manner over time (see Borhan P0003). Regarding claims 2 and 17 , Schrittwieser in view of Borhan teaches the limitations of claims 1 and 16 as outlined above. Schrittwieser further teaches determining a target contraction for the current policy, wherein the target contraction indicates a target improvement to be achieved for the current policy (Prediction model generates outputs representing values of the environment that match the target values determined or otherwise derived from using the given policy, P0079) ; wherein the determining, for each respective state of the number of states, the respective state-dependent lookahead horizon further comprises: comparing, for each respective state of the number of states and based on the corresponding respective first value, an improvement to the respective state against the target contraction (Look ahead policy gives rewards based on the reinforcement agent performing different actions, incentivizing improved states by accomplishing goals, P0077-P0079) ; and increasing, based on the improvement to at least one respective state being smaller than the target contraction, a lookahead horizon for the at least one respective state (Look ahead search continues as steps 204-206 are repeated until a criteria is met. Step 204 includes updating reward values, with reward values incentivizing accomplishing goals. P0091-P0093, figure 2, P0077) . Regarding claims 3 and 18 , Schrittwieser in view of Borhan teaches the limitations of claims 2 and 17 as outlined above. Schrittwieser further teaches wherein determining the target contraction for the current policy comprises: obtaining an optimal value function and a target contraction factor (Optimized objective function and target values, P0081, P0128-P0129) ; computing a difference between a value function of the current policy and the optimal value function (Difference between predicted reward and actual reward, difference between predicted value and target value, and difference between predicted policy output and actual action selection policy are all calculated in the objective function, P0129) ; and determining, based on the computed difference and the target contraction factor, the target contraction (the representation model is trained using the optimized objective function to output hidden states relevant to generating future policy outputs based in part on predicted reward values, P0081. The goal/improvement of the agent is dictated by the calculated reward, P0077) . Regarding claim 10 , Schrittwieser in view of Borhan teaches the limitations of claim 1 as outlined above. Schrittwieser further teaches obtaining an approximated value function as an optimal value function (Optimized objective function, P0081, P0129) ; evaluating a difference between the approximated value function and a value function of the current state-action policy to provide evaluation results (Difference between predicted reward and actual reward, difference between predicted value and target value, and difference between predicted policy output and actual action selection policy are all calculated in the objective function, P0129) ; and applying a correction term to the evaluation results, wherein the correction term bounds an error between the approximated value function and an ideal value function, wherein the determining, for each respective state of the number of states and based on the corresponding respective first value, the respective state-dependent lookahead horizon comprises using the evaluation results with the applied correction term (Correction factor is applied to sampled actions to select actions for states during look ahead search, P0119, 0129, P0031) . Regarding claim 13 , Schrittwieser in view of Borhan teaches the limitations of claim 1 as outlined above. Schrittwieser further teaches applying an action to the environment based on the learned state-action policy (Different actions are performed based on an agent following a given look ahead policy, P0079). Regarding claim 15 , Schrittwieser in view of Borhan teaches the limitations of claim 1 as outlined above. Schrittwieser further teaches updating the current state-action policy based on the one or more second values produces an updated state- action policy, and wherein the method further comprises repeating, for a plurality of iterations: the computing, for each respective state of the number of states in the environment, a respective first value, the determining, for each respective state of the number of states and based on the corresponding respective first value, a respective state-dependent lookahead horizon, the computing, for each respective state of one or more respective states of the number of states, a respective second value by applying a current state-action policy to the corresponding respective state-dependent lookahead horizon, and the updating the current state-action policy based on the one or more second values to produce an updated state-action policy, and wherein, during each iteration of the plurality of iterations, the current state-action policy is the updated state-action policy from a prior iteration (At the beginning of each iteration, the agent may update the policy to facilitate more action exploration, P0056. Multiple iterations are performed to generate plan data that indicates a respective value to performing the task of the agent performing each of the set of actions in the environment and starting from the current environment state, P0091. Node-edge pairs are interpreted as state-action pairs in view of P0032 of Schrittwieser. Lookahead searches include selecting a respective action according to the compiled statistics for a corresponding node-edge pair in the state tree, P0091) . 07-21-aia AIA Claim s 4-6 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Schrittwieser in view of Borhan and further in view of Zhernov et al (Pub. No.: US 20240267532 A1), hereafter Zhernov. Note that the examiner finds that the subject matter of claims 4, 5, and 19 do not appear to be supported by the provisional application and thus are afforded only the later effective filing date of 1/24/2023 . Regarding claims 4 and 19 , Schrittwieser in view of Borhan teaches the limitations of claims 1 and 16 as outlined above. Schrittwieser in view of Borhan does not appear to explicitly teach “obtaining an optimal value function and a set of quantiles, wherein each quantile of the set of quantiles is associated with a predefined lookahead horizon and indicates a predefined computation budget for the predefined lookahead horizon, wherein the determining, for each respective state of the number of possible states, the respective state-dependent further comprises: determining, for each respective state of the number of possible states and based on the corresponding respective first value, a quantile among the set of quantiles; and determining, for each respective state of the number of possible states and based on the corresponding quantile determined for the respective state, the respective state-dependent lookahead horizon”. Zhernov teaches obtaining an optimal value function and a set of quantiles, wherein each quantile of the set of quantiles is associated with a predefined lookahead horizon and indicates a predefined computation budget for the predefined lookahead horizon, wherein the determining, for each respective state of the number of possible states, the respective state-dependent further comprises: determining, for each respective state of the number of possible states and based on the corresponding respective first value, a quantile among the set of quantiles (For each state, quantile regression loss is calculated. Quantile regression loss is the calculation determining which quantile values associated with a state belongs to. P0131) ; and determining, for each respective state of the number of possible states and based on the corresponding quantile determined for the respective state, the respective state-dependent lookahead horizon (Quantization parameters determine the number of subsequent actions searched during look ahead search, with quantization parameters begin determined by the quantile of each state, P0131). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Schrittwieser, Borhan, and Zhernov before them, to include Zhernov’s specific teaching of quantile regression loss and quantization parameters being calculated for each state during look ahead search in Schrittwieser’s method of planning for agent control using learned hidden states. One would have been motivated to make such a combination of quantile regression loss and quantization parameters being calculated for each state during look ahead search (see Zhernov P0131), and using look ahead search to generate value outputs representing values of the environment that match the target values determined or otherwise derived from using the given policy (see Schrittwieser P0079) to improve efficiency of the training process (see Zhernov P0010). Regarding claim 5 , Schrittwieser in view of Borhan and further in view of Zhernov teaches the limitations of claim 4 as outlined above. Zhernov further teaches wherein a replay buffer stores a plurality of states of the number of states, and wherein the determining, for each respective state of the number of possible states and based on the corresponding respective first value, the quantile among the set of quantiles associated is based, at least on part, on the plurality of states stored in the replay buffer (Batch of states is stored in a replay buffer. For each state stored in the replay buffer, a number of quantization parameters (QP) decisions are sampled, P0131). Regarding claim 6 , Schrittwieser in view of Borhan and further in view of Zhernov teaches the limitations of claim 5 as outlined above. Schrittwieser further teaches expanding, for each respective state of the one or more respective states of the number of states, a respective tree to a depth of the corresponding respective state-dependent lookahead horizon, wherein the respective tree represents possible sequences of transitions from the corresponding state (Look ahead search may be a tree search where the edges in the tree represent state transitions, P0031) , wherein a root node of the respective tree is associated with the respective state (Look ahead search begins from the root node, P0092) , and other nodes of the tree represent possible future states reached by applying current state-action policy, (Look ahead search policy may dictate the search, P0079) ; and computing, for each respective state of the one or more respective states of the number of states, the respective second value based on searching the corresponding expanded respective tree (Values associated with each state in the environment are determined based on the look ahead search, P0079) . 07-21-aia AIA Claim s 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Schrittwieser in view of Borhan and Zhernov and further in view of Zhang et al (Pub. No.: US 20200302248 A1), hereafter Zhang . Regarding claim 7 , Schrittwieser in view of Borhan and Zhernov teaches the limitations of claim 6 as outlined above. Schrittwieser in view of Borhan and Zhernov does not appear to explicitly teach “wherein, for each respective tree, each respective depth is associated with a per-depth Q-network comprising one or more shared layers and one or more depth-specific layers, wherein the one or more depth-specific layers of each per-depth Q-network are stored in a buffer, and wherein the searching the corresponding expanded respective tree utilizes depth-specific layers stored in the buffer”. Zhang teaches wherein, for each respective tree, each respective depth is associated with a per-depth Q-network comprising one or more shared layers and one or more depth-specific layers, wherein the one or more depth-specific layers of each per-depth Q-network are stored in a buffer, and wherein the searching the corresponding expanded respective tree utilizes depth-specific layers stored in the buffer (policy selection network of the tree-based reinforcement learning is double deep Q-network. Q-values are determined based on the time of actions and states after each exploration of the tree, meaning q-values within the network are dependent upon depth of the tree searched, P0056). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Schrittwieser, Borhan, Zhernov, and Zhang before them, to include Zhang’s specific teaching of determining q-values based on states after each exploration of a tree and depth of a tree search in Schrittwieser’s method of planning for agent control using learned hidden states. One would have been motivated to make such a combination of determining q-values based on states after each exploration of a tree and depth of a tree search (see Zhang P0056), and using look ahead search to generate value outputs representing values of the environment that match the target values determined or otherwise derived from using the given policy (see Schrittwieser P0079). Regarding claim 8 , Schrittwieser in view of Borhan and Zhernov and further in view of Zhang teaches the limitations of claim 7 as outlined above. Zhang further teaches wherein each respective per-depth Q-network comprises one or more Q-functions, and wherein Q-function is associated with a state-action pair comprising a state in the respective depth of the respective tree and a respective action (Each state-action pair has a corresponding q-value determined by a q-function, P0056). Regarding claim 9 , Schrittwieser in view of Borhan and Zhernov and further in view of Zhang teaches the limitations of claim 8 as outlined above. Schrittwieser further teaches wherein the computing, for each respective state of the one or more respective states of the number of states, the respective second value based on searching the corresponding expanded respective tree comprises evaluating Q-functions comprised in a respective per-depth Q-network in a batch by a parallel processor (look-ahead or tree searching may be performed using parallel processing, P0158-P0159) . 07-21-aia AIA Claim s 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Schrittwieser in view of Borhan and further in view of Kovács et al (Pub. No.: US 20190197402 A1), hereafter Kovács . Regarding claim 11 , Schrittwieser in view of Borhan teaches the limitations of claim 1 as outlined above. Schrittwieser in view of Borhan does not appear to explicitly teach “wherein the environment is a game and the agent plans moves in the game against an opponent, wherein the number of states is associated with possible moves that the agent can select”. Kovács teaches wherein the environment is a game and the agent plans moves in the game against an opponent, wherein the number of states is associated with possible moves that the agent can select (Environment may be a game with non-playable characters being operated by an AI agent with possible actions of the agent being determined by states in the game, P0126, P0245, P0255. An example of a non-playable character includes a sentry patrolling a building and chasing an intruder, with the intruder being interpreted as an opponent, P0035). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Schrittwieser, Borhan, and Kovács before them, to include Kovács’s specific teaching of an AI agent controlling non-playable characters in a game with possible actions of the agent being determined by states in the game in Schrittwieser’s method of planning for agent control using learned hidden states. One would have been motivated to make such a combination of an AI agent controlling non-playable characters in a game with possible actions of the agent being determined by states in the game (see Kovács P0126, P0245, P0255), and using a reinforcement learning system to control an agent in an environment based on the state of the environment (see Schrittwieser P0028). Regarding claim 12 , Schrittwieser in view of Borhan teaches the limitations of claim 1 as outlined above. Schrittwieser in view of Borhan does not appear to explicitly teach “wherein the agent is implemented in an autonomous vehicle, wherein the agent plans a route for the autonomous vehicle, wherein the number of states is associated with sensed states of roads around the autonomous vehicle”. Kovács teaches wherein the agent is implemented in an autonomous vehicle, wherein the agent plans a route for the autonomous vehicle, wherein the number of states is associated with sensed states of roads around the autonomous vehicle (AI agent is implemented in an autonomous vehicle, where the agent determines a route for the vehicle, P0084, P0089, fig 7). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Schrittwieser, Borhan, and Kovács before them, to include Kovács’s specific teaching of an AI agent determining routes of an autonomous vehicle in Schrittwieser’s method of planning for agent control using learned hidden states. One would have been motivated to make such a combination of an AI agent determining routes of an autonomous vehicle (see Kovács P0084, P0089, fig 7), and using a reinforcement learning system to control an agent in an environment based on the state of the environment (see Schrittwieser P0028) . 07-21-aia AIA Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Schrittwieser in view of Borhan and further in view of Huber et al (Pub. No.: US 20230168649 A1), hereafter Huber . Regarding claim 14 , Schrittwieser in view of Borhan teaches the limitations of claim 1 as outlined above. Schrittwieser in view of Borhan does not appear to explicitly teach “wherein the computing, for each respective state of the number of states in the environment, the respective first value is based on a value function of a first state-action policy”. Huber teaches wherein the computing, for each respective state of the number of states in the environment, the respective first value is based on a value function of a first state-action policy (The control policy determined using a reinforcement learning model initially has random state transition probabilities, P0213). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Schrittwieser, Borhan, and Huber before them, to include Huber’s specific teaching of a control policy determined using a reinforcement learning model being randomly initialized in Schrittwieser’s method of planning for agent control using learned hidden states. One would have been motivated to make such a combination of a control policy determined using a reinforcement learning model being randomly initialized (see Huber P0213), and using a reinforcement learning system to control an agent in an environment based on the state of the environment (see Schrittwieser P0028). Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISHAN MOUNDI whose telephone number is (703)756-1547. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /I.M./Examiner, Art Unit 2141 /MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141 Application/Control Number: 18/158,920 Page 2 Art Unit: 2141 Application/Control Number: 18/158,920 Page 3 Art Unit: 2141 Application/Control Number: 18/158,920 Page 4 Art Unit: 2141 Application/Control Number: 18/158,920 Page 5 Art Unit: 2141 Application/Control Number: 18/158,920 Page 6 Art Unit: 2141 Application/Control Number: 18/158,920 Page 7 Art Unit: 2141 Application/Control Number: 18/158,920 Page 8 Art Unit: 2141 Application/Control Number: 18/158,920 Page 9 Art Unit: 2141 Application/Control Number: 18/158,920 Page 10 Art Unit: 2141 Application/Control Number: 18/158,920 Page 11 Art Unit: 2141 Application/Control Number: 18/158,920 Page 12 Art Unit: 2141 Application/Control Number: 18/158,920 Page 13 Art Unit: 2141 Application/Control Number: 18/158,920 Page 14 Art Unit: 2141 Application/Control Number: 18/158,920 Page 15 Art Unit: 2141 Application/Control Number: 18/158,920 Page 16 Art Unit: 2141 Application/Control Number: 18/158,920 Page 17 Art Unit: 2141 Application/Control Number: 18/158,920 Page 18 Art Unit: 2141 Application/Control Number: 18/158,920 Page 19 Art Unit: 2141 Application/Control Number: 18/158,920 Page 20 Art Unit: 2141 Application/Control Number: 18/158,920 Page 21 Art Unit: 2141 Application/Control Number: 18/158,920 Page 22 Art Unit: 2141
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