DETAILED ACTION
Status of Claims
Claims 1-12 are currently pending and have been examined in this application. This action is FINAL.
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 Arguments
Applicant’s arguments, see Remarks pg. 9, filed 02/16/2026, with respect to the specification and claim objections and the 35 USC 112(b) rejection have been fully considered and are persuasive. The specification objection, objections to claims 1, 2, and 10, and the 35 USC 112(b) rejection of claim 1, 3, and 10 have been withdrawn.
Applicant's arguments filed 02/16/2026 have been fully considered but they are not persuasive.
Applicant argues:
Regarding the 35 USC 102 rejection, “Independent method claim 1, as amended, includes the feature of "forming data group(s) from the received state variables and the determined commands, each data group comprising the state variables and the commands corresponding to a plurality of successive reception times".
This feature is neither shown nor suggested by Bertram.
Applicant notes that the Office Action, at pages 6 and 7, cites col. 17, lines 46 - 57 of Bertram as teaching the above feature. However, Applicant respectfully submits that Bertram only discloses a definition of a policy linking the state variable of the aircraft at a specific instant, to the state variables of the same aircraft at a further instant, as a function of the reward function. As such, Bertram fails to show or suggest formation of a data group.” (Remarks, pg. 10-11)
Examiner respectfully disagrees.
Regarding point (a), Bertram teaches determining a policy from the value function that defines a plurality of values that relate to transitioning the vehicle from one state space to another. The policy is extracted from the value function and specifies a plurality of actions that are needed to transition state spaces (Bertram, [Col. 17 lines 46-57] “A step (340) may include extracting (e.g., generating) a policy from the value function. The step (340) may occur in tandem with step (330). The policy governs transitions (e.g., actions) from a current state space to a subsequent state spaced based on the future implications of the immediate action. The policy can define a decision to transition from a current state space to a subsequent state space governed by the short-term action that will eventually lead to the “best” (e.g., desirable) long term result (e.g., state space).”). In other words, the policy corresponds to a data group because it contains actions that control the transition from one state to another and issues commands based on the values received from the value function. By teaching a policy of several actions that link the state space values and controls to transfer between state spaces, Bertram teaches forming data groups from the received state variables and the determined commands with each data group containing the state variables and commands corresponding to a plurality of successive receptions times.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3 and 7-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bertram et al. (US 11107001 B1).
Regarding claim 1,
Bertram teaches:
A method for assisting piloting of an aircraft, the method being implemented by an electronic piloting assistance device and comprising:
acquiring a piloting model of the aircraft and a reward function including a piloting constraint;
(Bertram – [Col. 9 lines 58-66] “The reward function 210 includes a plurality of terms associated with subsequent state spaces and end state spaces. The training circuit 202 can use the reward function 210 to control and tune the MDP engine 204. The reward function 210 can be a cost function that applies high values or benefits to desirable (e.g., subjective to the user desired behavior of the PADC system 200) state spaces and maneuvers, and low values or penalties for undesirable state spaces and maneuvers.”)
applying, to the piloting model, a reinforcement learning algorithm from the reward function, comprising:
(Bertram – [Col. 4 lines 37-46] “Training the learning system includes providing the training data as an input to the learning system, the learning system configured to use the reward function to generate a value related to transitioning from each state space in the plurality of input conditions to the end state space, wherein a higher value is indicative of a more desirable state space that has a higher end value at the end state space and wherein a lower value is indicative of a less desirable state space that has a lower end value at the end state space.”)
receiving state variable of the aircraft at reception times;
(Bertram – [Col. 8 lines 30-41] “The state space and transition space definitions 208 can provide context regarding the training data 206. Generally, the state spaces can include a state of a vehicle (e.g., position(s) and orientation(s)) and may include non-vehicle information such as a representation or state of an environment surrounding a vehicle, including behavior of other vehicles or platforms, and commands, instructions, or other communications received from other entities. The transition actions may represent a maneuver between a state space or through a series of entity state spaces over time. For example, the transition actions can represent positions and/or orientations over the course of a maneuver.”)
for each reception time, modifying the model from an evaluation of the reward function from the state variable(s) received at the reception time; and
(Bertram – [Col. 7 line 63 – Col. Line 19] “Referring further to FIG. 2A, the training circuit 202 includes a learning system that includes an MDP engine 204. As discussed above, the MDP engine 204 can generate one or more MDP algorithms that define a desired response to received input conditions, such as a response maneuver that should be performed given the input conditions in order to reach an intended end space/and or increase a value of an end space reached based on the performed response maneuver. The MDP engine 204 is configured to receive a plurality of training data 206, receive a plurality of state spaces and transition spaces 208, and execute a reward function 210 to generate an MDP learning system 216 and/or policies.”)
for each reception time, determining piloting commands from the modified piloting model and the state variables received at that time;
(Bertram – [Col. 10 lines 21-54] “The MDP engine 204 is configured to receive the training data 206, the state space and transition space definitions 208, and the reward function 210, and generate a value function and converge on the optimal policy. The value function and the policy can be strongly correlated, as the value function measures how valuable an action and subsequent state are, and the policy drives the transition from a current state through subsequent states to a desired end state. For example, the MDP engine 204 can extract the policy from the value function. During run-time, the control circuit 220 can use the MDP engine 204 to work backward from high reward end states and derive value of an action from how much reward can be expected from taking the action generating the value function. The MDP engine 204 can examine the current state and use the value function to calculate which action is most valuable. If the MDP engine 204 is programmed to always take the most valuable action, then the policy will make decisions (e.g., maneuvers) to maximize the end state reward.”)
forming data group(s) from the received state variables and the determined commands, each data group comprising the state variables and the commands corresponding to a plurality of successive reception times;
(Bertram – [Col. 17 lines 46-57] “A step (340) may include extracting (e.g., generating) a policy from the value function. The step (340) may occur in tandem with step (330). The policy governs transitions (e.g., actions) from a current state space to a subsequent state spaced based on the future implications of the immediate action. The policy can define a decision to transition from a current state space to a subsequent state space governed by the short-term action that will eventually lead to the “best” (e.g., desirable) long term result (e.g., state space). Beneficially, the MDP engine 204 can be tunable, such as to balance the importance for achieving an immediate reward versus achieving a long-term reward using a discount factor.”)
for each data group, assigning at least one aircraft state to the state variables and at least one piloting action to the commands, to generate a piloting rule comprising the at least one state and the at least one piloting action; and
(Bertram – [Col. 11 lines 15-30] “The generated value function and policy form an MDP learning system 216, which can be outputted by the training circuit 202 for use by the control circuit 220 to determine, at run-time, response maneuvers that should be performed. In some embodiments, once an MDP learning system 216 (or, in some embodiments, the MDP engine 204) is trained, the application of the MDP learning system 216 is deterministic with respect to the state and transition space that is defined for the problem (e.g., input), such that the same action will always be taken from the same state space. Thus, with an appropriately defined state space, the MDP learning system 216 will then behave predictably. In some embodiments, the MDP learning system 216 includes a generated implicit set of commands that include the sequence of commands, their parameterization, and the context for which each of the commands would be appropriate.”)
transmitting the at least one piloting rule to a display device for display to a pilot of the aircraft.
(Bertram – [Col. 5 lines 30-51] “According to various exemplary embodiments of the inventive concepts disclosed herein, at least one of the flight displays 20 may be configured to display a visual representation of a widget generated according to the systems and methods of the inventive concepts disclosed herein.”)
Regarding claim 2,
Bertram teaches the limitations of claim 1.
Bertram further teaches:
wherein, during said applying, for each reception time, a value of the piloting constraint is calculated during evaluation of the reward function,
(Bertram – [Col. 4 lines 30-46] “Training the learning system includes providing the training data as an input to the learning system, the learning system configured to use the reward function to generate a value related to transitioning from each state space in the plurality of input conditions to the end state space, wherein a higher value is indicative of a more desirable state space that has a higher end value at the end state space and wherein a lower value is indicative of a less desirable state space that has a lower end value at the end state space.”)
during said forming, each data group further comprises the values of the piloting constraint associated with the state variables and the commands, and
(Bertram – [Col. 9 line 58 – Col. 10 line 7] “The reward function 210 includes a plurality of terms associated with subsequent state spaces and end state spaces. The training circuit 202 can use the reward function 210 to control and tune the MDP engine 204. The reward function 210 can be a cost function that applies high values or benefits to desirable (e.g., subjective to the user desired behavior of the PADC system 200) state spaces and maneuvers, and low values or penalties for undesirable state spaces and maneuvers. During training, the training circuit 202 uses the reward function 210 to calculate how much reward is obtained from a given state. For example, the training circuit 202 can use the reward function 210 to determine a high reward for having an aircraft wing in correct position for formation flight, but penalties for maneuvers to reach that end state space that have too high of a roll, cause an undesirable G force to the airframe, or result in too much altitude loss.”)
during said assigning, for the or each group, at least one effect on the constraint is assigned to the values of the piloting constraint of the group, each rule further comprising the at least one effect on the constraint.
(Bertram – [Col. 17 lines 35-45] “A step (330) may include generating a value function. The value function includes a plurality of values that are related to the reward function and defined desirable and undesirable maneuvers and state spaces. The value is related to transitioning from each state space in the plurality of input conditions to an end state space, responsive to the long-term reward achieved at the end state space. Generally, a higher value is indicative of a more desirable state space that has a higher end value at the end state space and a lower value is indicative of a less desirable state space that has a lower end value at the end state space.”)
Regarding claim 3,
Bertram teaches the limitations of claim 2.
Bertram further teaches:
wherein said assigning a plurality of rules is formed, the method further comprising between said assigning and said applying, identifying principal rule(s) from among the plurality of piloting rules generated by application of a variable frequency analysis algorithm, and
(Bertram – [Col. 7 lines 6-25] “In some embodiments, the control circuit 220 executes the MDP engine 204 using a reward function that may be at least partially user-generated, and that can be configured to generate higher values of the rewards for actions that lead to desirable state spaces (e.g., contains all of the information necessary to predict the effects of an action and to determine if it is a goal state) and maneuvers, and lower values of rewards for actions that lead to less and/or undesirable state spaces and maneuvers. In some embodiments, the MDPs are structured such that the value function and policy is learned by the machine learning engine 224.”)
during said transmitting, only the principal rule(s) being are transmitted.
(Bertram – [Col. 7 line 63 – Col. 8 line 4] “Referring further to FIG. 2A, the training circuit 202 includes a learning system that includes an MDP engine 204. As discussed above, the MDP engine 204 can generate one or more MDP algorithms that define a desired response to received input conditions, such as a response maneuver that should be performed given the input conditions in order to reach an intended end space/and or increase a value of an end space reached based on the performed response maneuver.”)
Regarding claim 7,
Bertram teaches the limitations of claim 1.
Bertram further teaches:
wherein during said acquiring, a preliminary reward function is acquired, the method further comprising, between said acquiring and said applying, training a model, comprising:
applying a preliminary reinforcement learning algorithm to the piloting model from the preliminary reward function; and
modifying the model from an evaluation of the preliminary reward function.
(Betram – [Col. 11 lines 31-53] “In those embodiments, each state space and action combination is passed through the regression analysis and various algorithms are generated. Due to the convergence of the MDP engine 204, the various algorithms converge to a best-fit approximated value function that may be as close to the real value function for a given state space without the robust memory requirement of storing all state space and action combinations. Beneficially, the machine learning engine 224 excels at fitting incoming data to arbitrary, non-linear functions and is therefore well-suited to generating and approximating the value function and policy generated by the MDP engine 204.”)
Regarding claim 8,
Bertram teaches the limitations of claim 1.
Bertram further teaches:
wherein the state variables are selected from among the group consisting of: an aircraft roll angle, an aircraft pitch angle, an aircraft yaw angle, an aircraft speed, an aircraft acceleration, a wind speed on contact with the aircraft, a wind orientation relative to the aircraft, and an aircraft position.
(Bertram – [Col. 8 lines 42-59] “The state space can include at least one of a position, a velocity, an acceleration, or an orientation, for example, the state space can include a position and orientation of an airborne platform. Additionally, the state space can include at least one of absolute information or relative information, for example, the entity state can indicate that the vehicle is located at absolute GPS coordinates.”)
Regarding claim 9,
Bertram teaches the limitations of claim 1.
Bertram further teaches:
A computer program product comprising software instructions which, when implemented by a computer, implement a method according claim 1.
(Bertram – [Col. 14 line 55 – Col. 15 line 12] “The processing circuit 248 (e.g., control circuit, processing electronics) can include a processor 230 and memory 252… The memory 252 is one or more devices (e.g., RAM, ROM, flash memory, hard disk storage) for storing data and computer code for completing and facilitating the various user or client processes, layers, and modules described in the present disclosure.”)
Regarding claim 10,
Claim 10 recites a device comprising substantially the same limitation as claim 1 above, therefore it is rejected for the same reasons.
Regarding claim 11,
Bertram teaches the limitations of claim 10.
Bertram further teaches:
An aircraft flight assistance system comprising:
an electronic piloting assistance device according to claim 10; and
a display device configured to receive at least one piloting rule from said piloting assistance device and to display the rule to the pilot of the aircraft.
(Bertram – [Col. 5 lines 30-51] “One or more of the flight displays 20 may be configured to function as, for example, a primary flight display (PFD) used to display altitude, airspeed, vertical speed, and navigation and traffic collision avoidance system (TCAS) advisories…According to various exemplary embodiments of the inventive concepts disclosed herein, at least one of the flight displays 20 may be configured to display a visual representation of a widget generated according to the systems and methods of the inventive concepts disclosed herein.”)
Claim Rejections - 35 USC § 103
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 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Bertram et al. (US 11107001 B1) in view of Candido et al. (US 20210124352 A1).
Regarding claim 4,
Bertram teaches the limitations of claim 2.
Bertram does not explicitly disclose the following limitation, however, Candido teaches:
wherein said identifying further comprises comparing the effect(s) on the constraint of each principal rule with a predetermined threshold to obtain at least one filtered rule, each filtered rule being a respective principal rule comprising at least one effect on the constraint greater than or equal to the threshold.
(Candido – [0054] “Thresholds may be predetermined to categorize learned flight policies into various levels of performance (e.g., high, medium, or low performing) based on how a learned flight policy scores according to the reward function (i.e., how high of a reward produced by said learned flight policy). In some examples, the reward score may comprise a value. In other examples, where certain types of reinforcement learning is implemented (e.g., distributional Q-learning), a reward score may comprise a probability distribution or a distribution of rewards. In an example, learning module 506 may be configured to provide medium and high performing learned flight policies to policy server 508 for storage and further use by simulation module 502 to run simulations, and also to provide high performing learned flight policies to operation-ready policies server 510 for use in operational navigation systems, according to methods described below. In another example, learning module 506 may be configured to provide medium and high performing learned flight policies to policy server 508, and to provide a separate category of highest performing learned flight policies to operations policies server 510 for use in operational navigation systems. Learning module 506 may discard (i.e., delete) low-performing learned flight policies.”)
Bertram and Candido are both considered to be analogous to the claimed invention because they are both in the same field of using reinforcement learning to for pilot assistance. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Bertram with Candido to include filtering rules by a performance threshold in order to optimize flights for a variety of aerial vehicles and across a variety of data sets (Candido, para. [0005]).
Regarding claim 12,
The combination of Bertram and Candido teaches the limitations of claim 4.
Candido teaches:
wherein during said transmitting, only the filtered rule or rules are supplied for display to the pilot of the aircraft.
(Candido – [0049] “Computing device 301 may further include a display 306, a network interface 308, an input device 310, and/or an output module 312. Display 306 may be any display device by means of which computing device 301 may output and/or display data.” [0054] “In another example, learning module 506 may be configured to provide medium and high performing learned flight policies to policy server 508, and to provide a separate category of highest performing learned flight policies to operations policies server 510 for use in operational navigation systems. Learning module 506 may discard (i.e., delete) low-performing learned flight policies.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Bertram with Candido to include filtering rules by a performance threshold in order to optimize flights for a variety of aerial vehicles and across a variety of data sets (Candido, para. [0005]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Bertram et al. (US 11107001 B1) in view of Ogawa et al. (US 20210269171 A1).
Regarding claim 5,
Bertram teaches the limitations of claim 2.
Bertram further teaches:
assigning, to the piloting constraint, a short-term effect on the piloting constraint, from the first difference and a piloting constraint mapping table; and
(Bertram – [Col. 8 lines 4-25] “The MDP engine 204 can generate the policy to define transitions (e.g., actions) from a current state space to a subsequent state spaced based on the future implications of the immediate action. As such, the MDP engine 204 can generate the policy to represent a decision to transition from a current state space to a subsequent state space based on a short-term action (e.g., a single maneuver) that will eventually lead to an optimal (e.g., desirable, high value) long term result (e.g., state space reached by performing a plurality of maneuvers). Beneficially, the MDP engine 204 can be tunable to balance the importance of achieving an immediate reward versus achieving a long-term reward. For example, the MDP engine 204 can tune immediate versus long-term rewards by modifying a value of a discount factor used to discount rewards as a function of time.”)
assigning, to the piloting constraint values, a long-term effect on the piloting constraint from the second difference and the piloting constraint mapping table.
(Bertram – [Col. 17 lines 35-45] “A step (330) may include generating a value function. The value function includes a plurality of values that are related to the reward function and defined desirable and undesirable maneuvers and state spaces. The value is related to transitioning from each state space in the plurality of input conditions to an end state space, responsive to the long-term reward achieved at the end state space. Generally, a higher value is indicative of a more desirable state space that has a higher end value at the end state space and a lower value is indicative of a less desirable state space that has a lower end value at the end state space.”)
Bertram does not explicitly disclose the following limitations, however, Ogawa teaches:
wherein said assigning comprises for each data group:
calculating a first difference between the value of the piloting constraint at the last reception time from among the reception times of the state variables of the data group, and the value of the piloting constraint at the first reception time from among the reception times of the state variables of the data group; calculating a second difference between the value of the piloting constraint at a reception time subsequent to the last reception time from among the reception times of the state variables of the data group, and the value of the piloting constraint at the first reception time from among the reception times of the state variables of the data group;
(Ogawa - [0076] “In the step S220, the threshold value determining unit 74 determines whether the current value of the difference Z is equal to or greater than the first threshold value K1 and the last value of the difference Z is less than the first threshold value K1. If the last value of the difference Z is less than the first threshold value K1 and the current value of the difference Z is equal to or greater than the first threshold value K1, this means that the time-varying difference Z reaches the first threshold value K1 while increasing. In other words, the determination to be made in the step S220 is designed to detect whether the difference Z has increased to reach the first threshold value K1. To make this determination, the threshold value determining unit 74 first obtains the current and last values of the difference Z calculated by the difference calculating unit 72. The threshold value determining unit 74 compares the current value of the difference Z against the first threshold value K1 and also compares the last value of the difference Z against the first threshold value K1.”)
Ogawa is considered to be analogous to the claimed invention because it is in the same field of monitoring aircraft states. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Bertram with Ogawa to include calculating a current difference and a last difference of values in order to insure that moving surfaces are set to appropriate values a compensate for any discrepancies in the values (Ogawa, para. [0004]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Bertram et al. (US 11107001 B1) in view of Costas (US 20190005828 A1).
Regarding claim 6,
Bertram teaches the limitations of claim 1.
Bertram further teaches:
wherein said forming comprises:
classifying the determined piloting commands from among a plurality of predefined classes and via a command mapping table;
(Bertram – [Col. 11 lines 31-38] “The MDP learning system 216 may be operably connected to and/or implemented within the machine learning engine 224 of the control circuit 220. In some embodiments, the MDP learning system 216 includes one or more approximated value functions. While each state space and action combination can be stored in a table in memory, the stored values increase exponentially as more training data 206 is analyzed and more state spaces are assigned values.”)
Bertram does not explicitly teach the following limitations, however, Costas teaches:
for each class, grouping together the piloting commands belonging to the class and determined for the reception times forming the longest possible sequence of consecutive reception times, to form at least one set of grouped commands; and
(Costas – [0014] “According to an embodiment, the aircraft intent is expressed in AIDL format, and the actions comprise navigation and guidance commands expressed as AIDL instructions. The mapping function may comprise a table mapping sensor values with AIDL instructions. The step of determining actions may comprise searching the mapping table for sensor values corresponding to the retrieved onboard sensor data and, if no exact match is found, using an interpolation process to determine the associated AIDL instructions.” [0035] “In an embodiment, the mapping process comprises a lookup in a mapping table searchable by sensor values (such as position, altitude, speed, heading, fuel, time and others) that returns six AIDL instructions (one per AIDL thread) per search. Every row in the mapping table is a state-action vector, wherein the states 222 are the sensor values and the action 224 is the AIDL instruction associated to the state.”)
for each set of grouped commands, forming a respective group comprising the commands of the set and the state variables received at the reception times for which the commands have been determined.
(Costas – [0035] “Using the retrieved onboard sensor data 210, the onboard command and control process 220 obtains real-time aircraft states 222 and applies the mapping function 180 previously determined in the pre-flight training step 100 to determine actions 224 associated to the real-time aircraft states 222. The mapping function 180 must be understood in a broad sense as a mapping process, relationship or a correlation that allows determining actions 224 for given aircraft states 222. In an embodiment, the mapping process comprises a lookup in a mapping table searchable by sensor values (such as position, altitude, speed, heading, fuel, time and others) that returns six AIDL instructions (one per AIDL thread) per search. Every row in the mapping table is a state-action vector, wherein the states 222 are the sensor values and the action 224 is the AIDL instruction associated to the state.”)
Costas is considered to be analogous to the claimed invention because it is in the same field of using reinforcement learning for piloting assistance. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Bertram with Costas to include grouping variables and commands into a table in order to take into account uncertainty present in training data (Costas, para. [0006]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
D’Silva et al. (US 20180061248 A1) discloses an aid device for the piloting of an aircraft, the aircraft including a set of systems implementing operational capabilities of the aircraft, the aircraft performing a flight according to a flight plan, the flight plan including a plurality of waypoints of the aircraft and a plurality of constraints at least at some of these waypoints, the flight plan being associated with a plurality of operational parameters relative to the flight and/or the aircraft and assuming different values at various waypoints of the flight plan.
Fujishima et al. (US 20200285202 A1) discloses acquiring an optimal action parameter by reinforcement learning, for each of a plurality of actions. In a case where it is not necessary to learn an optimal value of an action parameter for all actions, the parameter calculation unit of an action may be prepared only for a necessary action
THIS ACTION IS MADE FINAL. 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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/M.G.H./Examiner, Art Unit 3668
/JAMES J LEE/Supervisory Patent Examiner, Art Unit 3668
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