DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claims 1-2, 5, and 9 are amended and claim 4 is cancelled. Claims 1-3 and 5-10 are pending.
Response to Arguments
Applicant's arguments filed 12/8/2025 have been fully considered.
Regarding the objection to the drawings, and as noted on page 7 of Applicant’s response, FIG. 1 is amended to overcome the objection, which is withdrawn.
Regarding the objections to claims 2 and 4-6, and as noted on page 8 of Applicant’s response, the amendments to the claims overcome the objections, which are withdrawn.
Regarding the rejection of claim 9 under 112(b), and as noted on page 8 of Applicant’s response, the amendment to claim 9 overcomes the rejection, which is withdrawn.
Regarding the rejections of claims 1-10 under 101, Examiner respectfully disagrees with Applicant’s arguments on pages 8-9 for the following reasons.
Applicant notes on page 8 of the response that claim 1 is amended to include “arranging a first sensor and a second sensor on or in a first vehicle.” On pages 8-9 of the response, Applicant contends that physically arranging a first sensor and a second sensor on a vehicle cannot be performed as mental processes and therefore claim 1 is not directed to a mental process.
Examiner acknowledges that arranging a first and second sensor on/in a vehicle does not fall within any judicial exception and is therefore treated as an “additional element.” However, as explained in the current grounds for rejecting claim 1, this element constitutes high-level data collection having no particularized functional relation to the processing steps falling within the judicial exception and does not otherwise, in combination with the processing steps, improve the functioning of a computer, or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or effectuate a transformation or reduction of a particular article to a different state or thing. Therefore, this element is found to constitute insignificant extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Regarding the finding that some of the elements in claim 1 fall within the mathematical concepts judicial exception as well as the mental processes exception, Applicant contends on page 9 of the response that such characterization is an incorrect application of the standard set forth in MPEP 2106.04(a)(2)(I). Specifically, Applicant asserts that the elements in claim 1 “relates to using determined vehicle dynamics and parameters to predict trajectory, but do not recite any specific mathematical relationship, formulas, or calculations,” and therefore, similar to Subject Matter Eligibility Examples 39 and 48, do not fall within the mathematical concepts exception.
Examiner acknowledges that per MPEP 2106.04(a)(2)(I), to fall within the exception, the element must recite and not merely be based on or merely involve a mathematical concept. Examiner submits that the elements in question including,
“using the first position, the first acceleration, the first velocity and the first yaw rate to determine a first list of points for a prediction of the trajectory,”
“using the second position, the second acceleration, the second velocity and the second yaw rate to determine a second list of points for the prediction of the trajectory,”
“using the parameters to predict the trajectory,”
“using the third position, the third acceleration, the third velocity and the third yaw rate to determine a third list of points for a prediction of the trajectory,” and
“using the fourth position, the fourth acceleration, the fourth velocity and the fourth yaw rate to determine a fourth list of points for the prediction of the trajectory,”
fall within the mathematical relationships sub-category of mathematical concepts because using position, velocity, acceleration, and yaw data for determining the first, second, third, and fourth lists of points for predicting trajectory data conveys mathematical processing governed by physics and trigonometric relations, such that these elements are themselves fundamentally and necessarily characterized by mathematical calculations/relations. As just an example, Applicant’s specification ([0004], [0037]) cites a CYRA motion model as a processing means for generating the “list of points” and CYRA models are characterized by mathematical kinematics relations.
Similarly, the element “using the parameters to predict the trajectory, falls within the mathematical relationships sub-category of mathematical concepts because as explained in Applicant’s specification ([0004], [0039]-[0044]) the parameters used for prediction are curve fitting model/parameters that are fundamentally characterized by mathematical calculations (e.g., Least Squares, quadratic programming), and prediction being performed in accordance with the curve fitting parameters is essentially application of the curve defined by the parameters and therefore constitutes mathematical relationships.
Examiner therefore submits that the foregoing elements are not functions that are merely based on or involve a mathematical concept such as in Claim 2 steps (f) and (g) in Example 48, but instead are themselves actions that entail the use of mathematical relations/calculations and therefore are correctly determined as falling within mathematical concepts exception.
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 1-3 and 5-10 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to the abstract idea judicial exception without significantly more.
Representative claim 1 recites (excluding reference numbers):
[a] method of predicting a trajectory for a vehicle, comprising:
arranging a first sensor and a second sensor on or in a first vehicle;
using the first sensor of the first vehicle for capturing first data of a second vehicle;
using the first data of the second vehicle for determining a first position, a first acceleration, a first velocity and a first yaw rate of the second vehicle;
using the first position, the first acceleration, the first velocity and the first yaw rate to determine a first list of points for a prediction of the trajectory;
using the second sensor of the first vehicle for capturing second data of the second vehicle;
using the second data of the second vehicle for determining a second position, a second acceleration, a second velocity and a second yaw rate of the second vehicle;
using the second position, the second acceleration, the second velocity and the second yaw rate to determine a second list of points for the prediction of the trajectory;
using the first list of points and the second list of points to determine parameters for the prediction of the trajectory;
using the parameters to predict the trajectory;
using the first sensor of the first vehicle for capturing third data of a third vehicle;
using the third data for determining a third position, a third acceleration, a third velocity and a third yaw rate of the third vehicle;
using the third position, the third acceleration, the third velocity and the third yaw rate to determine a third list of points for a prediction of the trajectory;
using the second sensor of the first vehicle for capturing fourth data of a fourth vehicle;
using the fourth data for determining a fourth position, a fourth acceleration, a fourth velocity and a fourth yaw rate of the fourth vehicle;
using the fourth position, the fourth acceleration, the fourth velocity and the fourth yaw rate to determine a fourth list of points for the prediction of the trajectory;
using the first list of points, the second list of points, the third list of points and the fourth list of points for determining the one or more parameters for predicting the trajectory; and
determining the one or more parameters for predicting a trajectory of the third vehicle depending on the first list of points, the second list of points, the third list of points and the fourth list of points for prediction of a trajectory of the third vehicle.
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above and the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a method and therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portions of claim 1 fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category (including an observation, evaluation, judgment, opinion) and the mathematical concepts category (mathematical relationships, mathematical formulas or equations, mathematical calculations). MPEP § 2106.04(a)(2).
The recited functions:
“predicting a trajectory for a vehicle,”
“using the first data of the second vehicle for determining a first position, a first acceleration, a first velocity and a first yaw rate of the second vehicle;
using the first position, the first acceleration, the first velocity and the first yaw rate to determine a first list of points for a prediction of the trajectory,”
“using the second data of the second vehicle for determining a second position, a second acceleration, a second velocity and a second yaw rate of the second vehicle;
using the second position, the second acceleration, the second velocity and the second yaw rate to determine a second list of points for the prediction of the trajectory;
using the first list of points and the second list of points to determine parameters for the prediction of the trajectory;
using the parameters to predict the trajectory”
“using the third data for determining a third position, a third acceleration, a third velocity and a third yaw rate of the third vehicle;
using the third position, the third acceleration, the third velocity and the third yaw rate to determine a third list of points for a prediction of the trajectory,”
“using the fourth data for determining a fourth position, a fourth acceleration, a fourth velocity and a fourth yaw rate of the fourth vehicle;
using the fourth position, the fourth acceleration, the fourth velocity and the fourth yaw rate to determine a fourth list of points for the prediction of the trajectory;
using the first list of points, the second list of points, the third list of points and the fourth list of points for determining the one or more parameters for predicting the trajectory; and
determining the one or more parameters for predicting a trajectory of the third vehicle depending on the first list of points, the second list of points, the third list of points and the fourth list of points for prediction of a trajectory of the third vehicle,”
may be performed as mental processes.
Predicting a trajectory for a vehicle may be performed via mental processes (e.g., judgment). Using first data of a second vehicle for determining a first position, a first acceleration, a first velocity and a first yaw rate of the second vehicle, using second data of a second vehicle for determining a second position, a second acceleration, a second velocity and a second yaw rate of the second vehicle, using third data for determining a third position, a third acceleration, a third velocity and a third yaw rate of a third vehicle, and using fourth data for determining a fourth position, a fourth acceleration, a fourth velocity and a fourth yaw rate of a fourth vehicle, may be performed via mental processes (e.g., evaluation of first data captured by first sensor and analysis, possibly via pen-and-paper analysis, to ascertain resultant kinematic values).
Using the first position, the first acceleration, the first velocity and the first yaw rate to determine a first list of points for a prediction of the trajectory; using the second position, the second acceleration, the second velocity and the second yaw rate to determine a second list of points for the prediction of the trajectory; using the third position, the third acceleration, the third velocity and the third yaw rate to determine a third list of points for predicting trajectory; and using the fourth position, the fourth acceleration, the fourth velocity and the fourth yaw rate to determine a fourth list of points for predicting trajectory may be performed via mental processes (e.g., analyzing each of first and second position, acceleration, velocity and yaw rate to determine such as judgment or manually-aided (via pen-and-paper) computation of one or more data points that itself characterizes trajectory or from which trajectory may be computed).
Using the first list of points and the second list of points to determine parameters for the prediction of the trajectory; using the first list of points, the second list of points, the third list of points and the fourth list of points for determining the one or more parameters for predicting trajectory may be performed via mental processes (e.g., evaluation of first and second list and evaluation of first, second, third and fourth list of points and judgment to ascertain corresponding parameters (e.g., model parameters) for predicting trajectory.
Using the parameters to predict the trajectory may be performed via mental processes (e.g., evaluation of parameter data and judgement to determine trajectory).
Determining the one or more parameters for predicting a trajectory of the third vehicle in dependence on the first, second, third, and fourth list of points may be performed via mental processes, potentially aided by pen-and-paper, (e.g., evaluation of first, second, third and fourth list of points and judgment to ascertain corresponding parameters such as curve-fitting model parameters).
The recited functions,
“using the first position, the first acceleration, the first velocity and the first yaw rate to determine a first list of points for a prediction of the trajectory,”
“using the second position, the second acceleration, the second velocity and the second yaw rate to determine a second list of points for the prediction of the trajectory,”
“using the parameters to predict the trajectory,”
“using the third position, the third acceleration, the third velocity and the third yaw rate to determine a third list of points for a prediction of the trajectory,” and
“using the fourth position, the fourth acceleration, the fourth velocity and the fourth yaw rate to determine a fourth list of points for the prediction of the trajectory,”
are determined by the Examiner as falling within the mathematical relationships sub-category of mathematical concepts (MPEP 2106.04(a)(2)) because using position, velocity, acceleration, and yaw data for determining the first, second, third, and fourth lists of points for predicting trajectory data entails mathematical processes governed by physics and trigonometric relations that are fundamentally characterized by mathematical calculations/relations and therefore constitutes mathematical relationships. For example, Applicant’s specification ([0004], [0037]) cites a CYRA motion model as a processing means for generating the “list of points” and CYRA models are characterized by mathematical kinematics relations.
The recited function, “using the parameters to predict the trajectory, is determined by the Examiner as falling within the mathematical relationships sub-category of mathematical concepts (MPEP 2106.04(a)(2)) because as explained in Applicant’s specification ([0004], [0039]-[0044]) the parameters used for prediction are curve fitting model/parameters that are fundamentally characterized by mathematical calculations (e.g., Least Squares, quadratic programming), and prediction using the parameters is performed in accordance with the curve fitting parameters is essentially application of the curve defined by the parameters and therefore constitutes mathematical relationships.
Step 2A, Prong Two of the analysis entails determining whether the claim includes additional elements that integrate the recited judicial exception into a practical application. “A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception” (MPEP § 2106.04(d)).
MPEP § 2106.04(d) sets forth considerations to be applied in Step 2A, Prong Two for determining whether or not a claim integrates a judicial exception into a practical application. Based on the individual and collective limitations of claim 1 and applying a broadest reasonable interpretation, the most applicable of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” including “arranging a first sensor and a second sensor on or in a first vehicle,” “using the first sensor of the first vehicle for capturing first data of a second vehicle” and “using the second sensor of the first vehicle for capturing second data of the second vehicle,” “using the first sensor of the first vehicle for capturing third data of a third vehicle,” and “using the second sensor of the first vehicle for capturing fourth data of a fourth vehicle,” in any combination appear to integrate the abstract idea in a manner that technologically improves any aspect of a device or system, such as a computer, that may be used to implement the highlighted step or a device for implementing the highlighted step such as a signal processing device or a generic computer. Instead, the arranging of two sensors in/on a vehicle and use of the sensors for collecting the first, second, third, and fourth data is recited broadly in a manner having no particularized relation to the processing steps falling within the judicial exception, and therefore constitute extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements using a “first sensor” and a “second sensor” that are arranged in/on a vehicle to capture vehicle-related data are configured do not represent any form of a particularized system/method but instead represent high-level data collection as a necessary prerequisite to the processing steps for implementing vehicle trajectory prediction.
Regarding a transformation or reduction of a particular article to a different state or thing, claim 1 does not include any such transformation or reduction. Instead, claim 1 as a whole entails capturing/collecting prerequisite sensor input information (i.e., first and second information for second, third, and fourth vehicles), applying processing techniques to the information to determining kinematic and corresponding trajectory-related data, with the additional elements of collecting first and second data of the second vehicle, collecting third data of the third vehicle, and collecting fourth data of the fourth vehicle failing to provide a meaningful integration of the abstract idea (determination of kinematic information and corresponding trajectory-related data) in an application that transforms an article to a different state. Instead, the additional elements represent extra-solution activity that does not integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 1 does not include additional elements that integrate the recited abstract idea into a practical application.
Therefore, claim 1 is directed to a judicial exception and requires further analysis under Step 2B.
Regarding Step 2B, and as explained in the Step 2A Prong Two analysis, the additional elements constitute insignificant extra solution and therefore do not result in the claim as a whole amounting to significantly more than the judicial exception. Furthermore, the additional elements in claim 1 appears to be generic and well understood as evidenced by the disclosures of Laddah (US 2022/0035376 A1) and Lee (US 2018/0267544 A1), each of which teach substantially similar vehicle trajectory data collection structures in which first and second sensors in a first vehicle are used to collect trajectory data for external objects (e.g., other vehicles) (Laddah: FIG. 1 sensors 114 within vehicle computing system 112 of vehicle 102; Lee: FIG. 1 vehicle 100 including sensor system 120 comprising multiple sensors).
Therefore, the additional elements are insufficient for resulting in the claim as a whole amounting to significantly more than the judicial exception.
Independent claim 1 is therefore not patent eligible.
Claims 2-3 and 5-10, depending from claim 1, provide additional features/steps that are part of an expanded algorithm that includes the abstract idea of claim 1 (Step 2A, Prong One). None of dependent claims 2-3 and 5-10 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for substantially similar reasons as discussed with regards to claim 1.
In claim 2, “using the first data captured (202) by the first sensor in a predetermined first period of time for determining a first list of positions of the second vehicle (102) in the first period of time” and “using the second data captured (202) by the second sensor in the predetermined first period of time for determining a second list of positions of the second vehicle (102) in the first period of time” fall within the mental processes exception because “determining a first list of positions of the second vehicle (102) in the first period of time” and “determining a second list of positions of the second vehicles (102) in the first period of time” may be performed via mental processes (e.g., evaluation and judgement such as may be aided by pen-and-paper) with the characterization that the data is captures in “a predetermined period of time” also part of the mental processes evaluation (e.g., selecting, by evaluation/judgment regarding which time interval may be most relevant, which input data to consider).
Claims 3 and 9 further characterize the time windows of the “first period” (claim 3) and the “second period” (claim 9), which similar to claim 2 is entailed within and is an extension of the underlying element (trajectory prediction) that constitutes the judicial exception in terms of the selectivity of a particular time interval for the first and second periods being a mental processes function (e.g., evaluation/judgement in selecting a suitable time interval for the first and second periods).
Claim 5 does not recite any significant further “additional elements” and the recited functions are fundamentally analogous/similar to the elements in claim 1 found to fall within the mental processes and/or mathematical concepts exceptions and therefore fall within the mental processes and/or mathematical concepts exceptions for substantially the same reasons as set forth in the grounds for rejecting claim 1.
Claims 6 and 10 further recite structural components in terms of the types of sensors (claims 6 and 10) and a processor for processing the sensor data to execute the recited method (claim 10). The sensor types (radar, LiDAR, and camera) constitute insignificant extra solution activity as being a part of routine, conventional (Step 2B) as well as high-level data collection (Step 2A Prong 2) (each of Laddah and Lee teaches using radar, LiDAR, and/or camera as the sensor(s) (Laddah: [0068], Lee FIG. 1). Using a processor to implement the processing functions entails routine, conventional data processing activity and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Each of claims 7 and 8 recite functions that fall within the mathematical relations sub-category of the mathematical concepts exception because least squares method and quadratic programming are fundamentally characterized by mathematical calculations/relations.
Dependent claims 2-3 and 5-10 therefore also constitute ineligible subject matter under 101.
Subject Matter Patentably Distinct Over the Prior Arts
Claims 1-3 and 5-10 are found to be patentably distinct over the prior arts for the following reasons.
Regarding claim 1, the most pertinent prior arts are represented by Laddah (US 2022/0035376 A1) and Lee (US 2018/0267544 A1), which as set forth in the grounds for rejecting claim 1 in the Non-Final Office Action dated 9/8/2025 teach (excluding reference numbers),
“[a] method of predicting a trajectory for a vehicle, comprising:”
arranging a first sensor and a second sensor on or in a first vehicle;
“using the first sensor of the first vehicle for capturing first data of a second vehicle;
using the first data of the second vehicle for determining a first position, a first acceleration, a first velocity and a first yaw rate of the second vehicle;
using the first position, the first acceleration, the first velocity and the first yaw rate to determine a first list of points for a prediction of the trajectory;
using the second sensor of the first vehicle for capturing second data of the second vehicle;
using the second data of the second vehicle for determining a second position, a second acceleration, a second velocity and a second yaw rate of the second vehicle;
using the second position, the second acceleration, the second velocity and the second yaw rate to determine a second list of points for the prediction of the trajectory;
using the first list of points and the second list of points to determine parameters for the prediction of the trajectory;
using the parameters to predict the trajectory.”
Regarding the elements added by amendment to claim 1 (elements of now-cancelled dependent claim 4), the combination of Laddah and Lee teaches
“using the first sensor of the first vehicle (101) (Laddah: FIG. 1 any one of sensors 114 within vehicle computing system 112 of vehicle 102, [0057]) for capturing third data of a third vehicle (103) (Laddah: FIG. 1 sensor data 116, [0068] sensors 114 configured to generate sensor data 116 of another object (i.e., a different object) among the one or more objects that may include a vehicle);
using the third data for determining (204) a third position, a third acceleration, a third velocity and a third yaw rate of the third vehicle (103) (Laddah: FIG. 1 sensor data 116 input to autonomy computing system 120 that includes joint perception/prediction system 123, [0073] joint perception/prediction system 123 processes sensor data 116 to identify and determine object state (perception data 130), which may include location/position, velocity, acceleration, and yaw rate. As combined with Lee in the grounds for rejecting claim 1, the data from a particular sensor (e.g., third data) is used to determine position, acceleration, velocity, and yaw rate of target object.);
using the third position, the third acceleration, the third velocity and the third yaw rate (Laddah: state/perception data 130 derived from one of the sensors) with the vehicle model (Laddah: FIG. 1 prediction system 126 configured to process state/perception data 130 (may be for vehicle object) to generate prediction data 132 (processing of vehicle-based perception data into prediction data entails modeling); [0074]) for determining a third list of points for a prediction of the trajectory (108) (Laddah: [0074] prediction system 126 uses perception data 130 (list of points) to predict moving paths that may entail predicted trajectory of each object);
using the second sensor of the first vehicle (101) (Laddah: FIG. 1 any of the other sensors 114 within vehicle computing system 112 of vehicle 102, [0057]), for capturing fourth data of a fourth vehicle (104) (Laddah: FIG. 1 sensor data 116, [0068] sensors 114 configured to generate sensor data 116 of one or more objects that may include a vehicle (each of the sensors inherently captures data specific to itself such that a first of the sensors captures first data of the vehicle and a second sensor captures second data of the vehicle);
using the fourth data for determining a fourth position, a fourth acceleration, a fourth velocity and a fourth yaw rate of the fourth vehicle (102) (Laddah: FIG. 1 sensor data 116 input to autonomy computing system 120 that includes joint perception/prediction system 123, [0073] joint perception/prediction system 123 processes sensor data 116 to identify and determine object state (perception data 130), which may include location/position, velocity, acceleration, and yaw rate. As combined with Lee in the grounds for rejecting claim 1, the data from a particular sensor (e.g., fourth data) is used to determine position, acceleration, velocity, and yaw rate of target object.);
using the fourth position, the fourth acceleration, the fourth velocity and the fourth yaw rate (Laddah: state/perception data 130 derived from one of the sensors) with the vehicle model (Laddah: FIG. 1 prediction system 126 configured to process state/perception data 130 (may be for vehicle object) to generate prediction data 132 (processing of vehicle-based perception data into prediction data entails modeling); [0074]) for determining a fourth list of points for the prediction of the trajectory (108) of the second vehicle (102) (Laddah: [0074] prediction system 126 uses perception data 130 (list of points) to predict moving paths that may entail predicted trajectory of each object).
The combination of Laddah and Lee does not appear to teach obtaining and using trajectory tracking data from a first additional target vehicle (third vehicle) using a same one of the pair of sensors in the host vehicle (first vehicle) used to track trajectory of the first target vehicle (second vehicle), and further obtaining and using trajectory tracking data from a second additional target vehicle (fourth vehicle) using the other sensor of the pair of sensors in the host vehicle in which the trajectory data is for the trajectory of the first target vehicle (second vehicle), and further combining these respective sets trajectory to determine trajectory parameters of the first target vehicle, such that the prior arts do not fairly teach or suggest,
“using the first list of points, the second list of points, the third list of points and the fourth list of points for determining the one or more parameters for predicting the trajectory (108) of the second vehicle (102); and
determining the one or more parameters for predicting the trajectory of the third vehicles (103) depending on the first list of points, the second list of points, the third list of points and the fourth list of points with a model for the prediction of the trajectory of the third vehicle (103),” in combination with the other limitations of claim 1.
Claims 2-3 and 5-10 depend from claim 1 and are patentably distinct over the prior arts for the same reasons.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW W BACA whose telephone number is (571)272-2507. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW W. BACA/ Examiner, Art Unit 2857
/ANDREW SCHECHTER/ Supervisory Patent Examiner, Art Unit 2857