Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the amendments filed 10/30/2025. Claims 10-25 have been amended, claims 26-27 have been added. Claims 10-27 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/13/2025 was filed before the mailing date of the office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments regarding the 101 rejection have been fully considered but they are not persuasive. Applicant argues that the claims are directed to patentable subject matter based on amendments directed to a computer-implemented driver assistance system which is configured to provide automated driving based on predicted behavior. Examiner respectfully disagrees and notes that the computer-implemented driver assistance system is interpreted as an additional element directed to a generic computer component merely used to implement the claimed abstract ideas (see MPEP 2106.05(f). Examiner also notes that the limitation directed to providing automated driving based on the predicted behavior is interpreted as an additional element directed to the technological environment in which the claimed abstract ideas are performed, and as well-understood, routine, conventional activity in light of the Ashwin-Dayal reference. These additional elements therefore do not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas. The 101 rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
Applicant’s arguments regarding the prior art rejection have been fully considered but they are not persuasive. Applicant argues that the cited portions of the prior art references fail to teach using “at least two hypothesis” for the behavior of the road user. Examiner notes that at least paragraphs [0002] and [0080] of the Gupta reference teach that its turn prediction system can generate “one or more predictions” or hypothesis related to behavior of a road user. The prior art rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 10-27 are rejected under 35 U.S.C. 101. Claims 10-17 and 26-27 are directed to a system and claims 18-25 are directed to a method; therefore, claims 10-27 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). However, claims 10-27 fall within the judicial exception of an abstract idea, specifically the abstract ideas of “Mental Processes” (including observation, evaluation, and opinion) and “Mathematical Concepts (including mathematical calculations and relationships)”.
Claim 10:
Claim 10 is directed to a system; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 10 recites the following abstract ideas:
provide at least two hypothesis for the behavior of the road user (mental step directed to observation, evaluation – a person could provide at least hypotheses for observed or determined behavior of a road user in their mind),
provide a hidden Markov model for each hypothesis of the at least two hypothesis, the hidden Markov model for a respective hypothesis comprising two hidden states, one of said two hidden states representing compliance with the hypothesis by the road user, and the other one of said two hidden states representing non-compliance with the hypothesis by the road user, and comprising possible observations of the hidden Markov model for the respective hypothesis characterizing at least one feature of the road user (Examiner notes that the broadest reasonable interpretation of a hidden Markov model includes a statistical model. Given this interpretation, providing a hidden Markov model is interpreted as a mental step directed to observation, judgement – a person could provide a hidden Markov model for a given hypothesis in their mind, potentially assisted by pen and paper (see MPEP 2106.04(a)(2)(III), wherein the provided model includes two hidden states representing compliance and non-compliance with the hypothesis and possible mental observations characterizing observed or determined features of a road user),
and predict the behavior of the road user as a function of the two hidden states of the hidden Markov model for the at least two hypothesis (mental step directed to observation, judgement – a person could predict the behavior of a road user as a function of observed or determined hidden states of a hidden Markov model for a given hypothesis in their mind, potentially assisted by pen and paper (see MPEP 2106.04(a)(2)(III)).
Claim 10 recites the following additional elements:
a driver assistance system of a vehicle, comprising a computer-implemented device that predicts a behavior of a road user; and wherein the driver assistance system is configured to provide automated driving of the vehicle based on the predicted behavior of the road user. The driver assistance system comprising a device that predicts road user behavior is interpreted as a generic computer component merely used to implement the claimed abstract ideas. Wherein the driver assistance system provides automated driving based on the predicted behavior is interpreted as the technical environment in which the abstract ideas are performed, and as well-understood, routine, conventional activity in light of DE 102019106777 A1 (Ashwin-Dayal et al), page 8 paragraph 10 of which recites “lane markings 16 can from driving assistance systems of vehicles 18 such as B. the vehicle 18, which as shown in the lane 14 drives, for example, can be used to perform automated lateral steering procedures, as is well known in the art”. This additional element does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(h)).
Claim 18 is a method claim and its limitation is included in claim 10. The only difference is that claim 18 requires a method. Therefore, claim 18 is rejected for the same reasons as claim 10.
The independent claims are not patent eligible.
Dependent claims 11-17 and 19-27 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea, as they recite further embellishment of the judicial exception.
Claim 11 recites wherein the at least one feature of the road user is a quantifiable feature of the road user. Examiner notes that this limitation is a further description of the kind of feature characterized by the model in claim 10, which merely links the claimed judicial exceptions in claim 10 to a field of use associated with a road user but does not integrate the claimed judicial exceptions into a practical application or amount to significantly more than the claimed judicial exceptions (see MPEP 2105.06(h)).
Claims 12-16 recites wherein the possible observations of the hidden Markov model for the respective hypothesis include at least two mutually independent feature groups, a distance of the road user from a center of a traffic lane in which the road user is located, a deviation of an orientation of the road user relative to an orientation of a traffic lane in which the road user is located, an activation of a travel-direction indicator of the road user, and a feature that is characteristic of a yielding behavior of the road user. These observations are interpreted as further descriptions of the kinds of feature characterized by the model in claim 10, which merely link the claimed judicial exceptions in claim 10 to a field of use associated with a road user but do not integrate the claimed judicial exceptions into a practical application or amount to significantly more than the claimed judicial exceptions (see MPEP 2105.06(h)).
Claim 17 recites ascertaining a traffic situation in which the road user is located, and ascertaining the at least one hypothesis for the behavior of the road user as a function of this traffic situation. Ascertaining a traffic situation and ascertaining a hypothesis for behavior of a road user as a function of an ascertained traffic situation are interpreted as mental steps, as a person could ascertain a traffic situation in their mind and ascertain a hypothesis for a behavior of a road user in an ascertained traffic situation in their mind.
Claim 19 is a method claim and its limitation is included in claim 11. Claim 19 is rejected for the same reasons as claim 11.
Claim 20 is a method claim and its limitation is included in claim 12. Claim 20 is rejected for the same reasons as claim 12.
Claim 21 is a method claim and its limitation is included in claim 13. Claim 21 is rejected for the same reasons as claim 13.
Claim 22 is a method claim and its limitation is included in claim 14. Claim 22 is rejected for the same reasons as claim 14.
Claim 23 is a method claim and its limitation is included in claim 15. Claim 23 is rejected for the same reasons as claim 15.
Claim 24 is a method claim and its limitation is included in claim 16. Claim 24 is rejected for the same reasons as claim 16.
Claim 25 is a method claim and its limitation is included in claim 17. Claim 25 is rejected for the same reasons as claim 17.
Claim 26 recites wherein the automated driving comprises automated driving of at least one longitudinal guidance or lateral guidance. Wherein automated driving comprises longitudinal or lateral guidance is interpreted as the technical environment in which the abstract ideas are performed, and as well-understood, routine, conventional activity in light of DE 102019106777 A1 (Ashwin-Dayal et al), page 8 paragraph 10 of which recites “lane markings 16 can from driving assistance systems of vehicles 18 such as B. the vehicle 18, which as shown in the lane 14 drives, for example, can be used to perform automated lateral steering procedures, as is well known in the art”. This additional element does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(h)).
Claim 27 recites wherein the automated driving comprises automated driving. Automated driving is interpreted as the technical environment in which the abstract ideas are performed, and as well-understood, routine, conventional activity in light of DE 102019106777 A1 (Ashwin-Dayal et al), page 8 paragraph 10 of which recites “lane markings 16 can from driving assistance systems of vehicles 18 such as B. the vehicle 18, which as shown in the lane 14 drives, for example, can be used to perform automated lateral steering procedures, as is well known in the art”. This additional element does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(h)).
Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 10-27 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al* (US 20170016734 A1, herein Gupta) in view of Jackson et al (“A two-state mixed hidden Markov model for risky teenage driving behavior”, herein Jackson).
*this document was included in the IDS dated 10/24/2022
Regarding claim 10, Gupta teaches a driver assistance system of a vehicle, comprising: a computer-implemented device that predicts a behavior of a road user (para. [0002] recites “a system for turn predictions may include a navigation component, a communication component, a modeling component, and a prediction component” (i.e., a driver assistance system), the device being operatively configured to:
provide at least two hypothesis for the behavior of the road user (para. [0002] recites “The modeling component may build a model including the operating environment, the first vehicle, and one or more of the other vehicles based on the environment layout information and the additional environment layout information. The model may be indicative of an intent of a driver of one of the one or more other vehicles. The prediction component may generate one or more predictions based on the model”. Para. [0076] recites “vehicle information or other observations, such as activation of turn signals, drift, etc. may be used to connect a model, such as a DBN model to a hidden state of a driver's intention directly, thus facilitating determination of driver intention by the modeling component 130 or a corresponding prediction by the prediction component 140” (i.e., providing one or more predictions, or hypotheses, of a road user’s behavior)),
provide a hidden Markov model for each hypothesis of the at least two hypothesis, comprising possible observations of the hidden Markov model for the respective hypothesis characterizing at least one feature of the road user (para. [0002] recites “The prediction component may generate one or more predictions based on the model”. Para. [0008] recites “The modeling component may build the model based on a Hidden Markov Model (HMM), a Support Vector Machine (SVM), a Dynamic Bayesian Network (DBN), or a combination thereof”. Para. [0076] recites “vehicle information or other observations, such as activation of turn signals, drift, etc. may be used to connect a model, such as a DBN model to a hidden state of a driver's intention directly, thus facilitating determination of driver intention by the modeling component 130 or a corresponding prediction by the prediction component 140” (i.e., a hidden Markov model can be used to connect environmental observations to one or more hypotheses, or intentions, of a road user’s behavior)),
and predict the behavior of the road user as a function of the [two] hidden states of the hidden Markov model for the at least two hypothesis (para. [0076] recites “vehicle information or other observations, such as activation of turn signals, drift, etc. may be used to connect a model, such as a DBN model to a hidden state of a driver's intention directly, thus facilitating determination of driver intention by the modeling component 130 or a corresponding prediction by the prediction component 140”. Para. [0080] recites “a model or predictive model may be used by the prediction component 140 to generate one or more predictions of driver intentions for drivers of other vehicles within the operating environment, such as at an intersection. Accordingly, these predictions may be used for turn predictions associated with other vehicles in a customizable or generalized fashion. The prediction component 140 may generate one or more predictions for one or more other vehicles in the operating environment based on one or more corresponding models. Thus, the prediction component 140 may generate one or more of the predictions based on a Hidden Markov Model (HMM), a Support Vector Machine (SVM), or a Dynamic Bayesian Network (DBN)” (i.e., predicting the behavior of a road user for one or more hypotheses, such as whether a vehicle will turn or not turn, using the hidden states of a hidden Markov model));
wherein the driver assistance system is configured to provide automated driving of the vehicle based on the predicted behavior of the road user (para. [0090] recites “The assist component 160 may determine one or more assist actions based on one or more of the predictions”. Para. [0091] recites “the assist component 160 may enable one or more autonomous features, such as an advanced driver assisted system (ADAS), other autonomous driving systems, intersection movement assist (IMA), left turn assist (LTA), etc., to cause a vehicle to make a tum or mitigate a collision at an intersection (e.g., using environment layout information and/or associated vehicle information of one or more other vehicles” (i.e., providing automated driving instructions to a vehicle based on a predicted behavior)).
However, while Gupta teaches use of hidden Markov model to model compliance or non-compliance with a predicted behavior of a road user (see at least para. [0008] and para. [0080]), Gupta does not explicitly teach a hidden Markov model for a respective hypothesis comprising two hidden states, one of said two hidden states representing compliance with the hypothesis by the road user, and the other one of said two hidden states representing non-compliance with the hypothesis by the road user.
Jackson teaches a hidden Markov model for a respective hypothesis comprising two hidden states, one of said two hidden states representing compliance with the hypothesis by the road user, and the other one of said two hidden states representing non-compliance with the hypothesis by the road user (section 1 para. 1 recites “Crash or near crash outcomes are our binary outcome of interest, while excessive g-force events are our proxy for risky driving. Our approach here characterizes the joint distribution of crash/near crash and kinematic outcomes using a mixed hidden Markov model where both outcomes contribute to the calculation of the hidden state probabilities”. Section 2.1 recites “Let bi = (bi1, bi2,. . ., bini) be an unobserved binary random vector whose elements follow a two-state Markov chain (state “0” represents a good driving state and state “1” represents a poor driving state) with unknown transition probabilities p01, p10 and initial probability distribution r0. We model the crash/near crash outcome Yij, where i represents an individual and j the month since licensure, using the logistic regression model shown in (EQ1)” (i.e., a hidden Markov model comprising two hidden states representing compliance with a hypothesis, or that a road user’s behavior represents good driving, and non-compliance, or that a road user’s behavior represents poor driving)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by adapting the hidden Markov model from Gupta to use two hidden states from the hidden Markov model taught by Jackson. Jackson and Gupta are both directed to use of hidden Markov models to represent driver behaviors and intentions; as such, one of ordinary skill would understand how to modify the hidden Markov model from Gupta to make use of the two hidden states in the hidden Markov model from Jackson.
Regarding claim 11, the combination of Gupta and Jackson teaches the driver assistance system according to claim 10, wherein: the at least one feature of the road user is a quantifiable feature of the road user (Gupta para. [0022] recites “vehicle information, such as vehicle information associated with environment layout information (e.g., associated vehicle information), may include movement information, acceleration, velocity, angle of motion, steering angle, bearing, heading, orientation, position, location, pitch, roll, yaw, angle of incline, etc. of an associated vehicle or a log or history thereof. The current position, current location, past positions, or past locations associated with another vehicle may include a lane position or a lane location for the corresponding vehicle” (i.e., an observable features include quantifiable features such as acceleration)).
Regarding claim 12, the combination of Gupta and Jackson teaches the driver assistance system according to claim 10, wherein: the possible observations of the hidden Markov model for the respective hypothesis characterize at least two mutually independent feature groups (Gupta para. [0022] recites “vehicle information, such as vehicle information associated with environment layout information (e.g., associated vehicle information), may include movement information, acceleration, velocity, angle of motion, steering angle, bearing, heading, orientation, position, location, pitch, roll, yaw, angle of incline, etc. of an associated vehicle or a log or history thereof. The current position, current location, past positions, or past locations associated with another vehicle may include a lane position or a lane location for the corresponding vehicle” (i.e., recorded sensor observations can include mutually independent features such as environmental features and vehicle positioning features)).
Regarding claim 13, the combination of Gupta and Jackson teaches the driver assistance system according to claim 10, wherein: the possible observations of the hidden Markov model for the respective hypothesis include, by way of a feature, a distance of the road user from a center of a traffic lane in which the road user is located (Gupta para. [0022] recites “vehicle information, such as vehicle information associated with environment layout information (e.g., associated vehicle information), may include movement information, acceleration, velocity, angle of motion, steering angle, bearing, heading, orientation, position, location, pitch, roll, yaw, angle of incline, etc. of an associated vehicle or a log or history thereof. The current position, current location, past positions, or past locations associated with another vehicle may include a lane position or a lane location for the corresponding vehicle”. Gupta para. [0033] recites “the sensor component 110 may be used to estimate a distance to a target area, such as a distance to an intersection, distance to a center-line of a lane, etc.” (i.e., an observed feature related to the distance of a road user to a center of a traffic lane)).
Regarding claim 14, the combination of Gupta and Jackson teaches the driver assistance system according to claim 10, wherein: the possible observations of the hidden Markov model for the respective hypothesis include, by way of a feature, a deviation of an orientation of the road user relative to an orientation of a traffic lane in which the road user is located (Gupta para. [0052] recites “the model may include one or more vehicles, such as a first vehicle or current vehicle and a second vehicle or other vehicle in the surrounding operating environment. Here, the modeling component 130 may identify one or more potential travel paths or intended travel paths for one or more of the vehicles which are specific to a modeled intersection. Driver intention (e.g., left turn, right turn, straight, U-turn, etc.) may be predicted for other vehicles or other traffic participants of an intersection” (i.e., an observed feature related to a turn, or a deviation of a road user from a given lane)).
Regarding claim 15, the combination of Gupta and Jackson teaches the driver assistance system according to claim 10, wherein: the possible observations of the hidden Markov model for the respective hypothesis include, by way of a feature, an activation of a travel-direction indicator of the road user (Gupta para. [0031] recites “if the sensor component 110 detects that a driver of another vehicle has his or her left turn signal on, the modeling component 130 may build a model which is indicative of an inference that the other vehicle has a high likelihood of turning left” (i.e., an observed feature related to a turn signal, direction indicator of a road user)).
Regarding claim 16, the combination of Gupta and Jackson teaches the driver assistance system according to claim 10, wherein: the possible observations of the hidden Markov model for the respective hypothesis include a feature that is characteristic of a yielding behavior of the road user (Gupta para. [0086] recites “The navigation component 120 may further determine a travel path for a vehicle based on one or more of the predictions for one or more of the other vehicles indicative of motion of respective other vehicles”. Gupta para. [0091] recites “the assist component 160 may enable one or more autonomous features, such as an advanced driver assisted system (ADAS), other autonomous driving systems, intersection movement assist (IMA), left turn assist (LTA), etc., to cause a vehicle to make a turn or mitigate a collision at an intersection” (i.e., an observed feature related to avoiding a collision, or yielding to an approaching vehicle)).
Regarding claim 17, the combination of Gupta and Jackson teaches the driver assistance system according to claim 10, wherein the device is further operatively configured to: ascertain a traffic situation in which the road user is located (Gupta para. [0026] recites “A system for turn predictions may gather real-time vehicle information from other vehicles, such as a second vehicle, which have travelled or passed through a target area, such as an intersection, shortly before (e.g., during a five second window) a first vehicle arriving at the intersection or target area” (i.e., ascertaining a situation of a given vehicle related to other nearby vehicles, or a traffic situation)), and ascertain the at least one hypothesis for the behavior of the road user as a function of this traffic situation (Gupta para. [0076] recites “vehicle information or other observations, such as activation of turn signals, drift, etc. may be used to connect a model, such as a DBN model to a hidden state of a driver's intention directly, thus facilitating determination of driver intention by the modeling component 130 or a corresponding prediction by the prediction component 140”. Para. [0080] recites “a model or predictive model may be used by the prediction component 140 to generate one or more predictions of driver intentions for drivers of other vehicles within the operating environment, such as at an intersection. Accordingly, these predictions may be used for turn predictions associated with other vehicles in a customizable or generalized fashion. The prediction component 140 may generate one or more predictions for one or more other vehicles in the operating environment based on one or more corresponding models. Thus, the prediction component 140 may generate one or more of the predictions based on a Hidden Markov Model (HMM), a Support Vector Machine (SVM), or a Dynamic Bayesian Network (DBN)” (i.e., determining, or ascertaining a hypothesis for a road user, such as whether a vehicle will turn or not turn, based on the environmental information related to a traffic situation of a given road user)).
Claim 18 is a method claim and its limitation is included in claim 10. The only difference is that claim 18 requires a method. Therefore, claim 18 is rejected for the same reasons as claim 10.
Claim 19 is a method claim and its limitation is included in claim 11. Claim 19 is rejected for the same reasons as claim 11.
Claim 20 is a method claim and its limitation is included in claim 12. Claim 20 is rejected for the same reasons as claim 12.
Claim 21 is a method claim and its limitation is included in claim 13. Claim 21 is rejected for the same reasons as claim 13.
Claim 22 is a method claim and its limitation is included in claim 14. Claim 22 is rejected for the same reasons as claim 14.
Claim 23 is a method claim and its limitation is included in claim 15. Claim 23 is rejected for the same reasons as claim 15.
Claim 24 is a method claim and its limitation is included in claim 16. Claim 24 is rejected for the same reasons as claim 16.
Claim 25 is a method claim and its limitation is included in claim 17. Claim 25 is rejected for the same reasons as claim 17.
Regarding claim 26, the combination of Gupta and Jackson teaches the driver assistance system according to claim 10, wherein the automated driving comprises automated driving of at least one longitudinal guidance or lateral guidance (Gupta para. [0085] recites “the navigation component 120 may determine a current position and/or an intended travel path or route for a first vehicle or a current vehicle based on a navigation unit, GPS, an entered destination, vehicle information for the first vehicle, such as vehicle heading, bearing, direction, activation of a turn signal, steering angle, etc. The navigation component 120 may determine the current position of the current vehicle at a lane level of granularity or such that a lane level position or location is determined” (i.e., automated driving can include navigation based on at least GPS guidance, which one of ordinary skill would recognize includes at least longitudinal information)).
Regarding claim 27, the combination of Gupta and Jackson teaches the driver assistance system according to claim 10, wherein the automated driving comprises automated driving (Gupta para. [0090] recites “The assist component 160 may determine one or more assist actions based on one or more of the predictions”. Gupta para. [0091] recites “the assist component 160 may enable one or more autonomous features, such as an advanced driver assisted system (ADAS), other autonomous driving systems, intersection movement assist (IMA), left turn assist (LTA), etc., to cause a vehicle to make a tum or mitigate a collision at an intersection (e.g., using environment layout information and/or associated vehicle information of one or more other vehicles” (i.e., providing automated driving instructions to a vehicle based on a predicted behavior)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 9511767 B1 (Okumura et al) teaches an automated driving system which can identify a transition between vehicle states, the vehicle states being associated with a planned action for the autonomous vehicle and send a command to one or more vehicle systems to control the autonomous vehicle to execute the planned action according to the transition.
US 11403949 B2 (Imanishi et al) teaches a system for predicting vehicle behavior to reduce the number of sensors and the amount of data needed for vehicle control systems.
US 20210339762 A1 (Yee) teaches a method for determining the trajectory of an automated-driving vehicle based on the latitude and longitude information from a GPS receiver.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/L.M.F./ Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147