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 .
Claim Status
Claims 1, 2, 4, 6-9, 11, 14, and 14 have been amended, and claim 5 has been canceled without prejudice. Claims 1-4 and 6-15 are pending.
Priority
This application is a 371 of application PCT/EP2021/061112 filed 04/28/21 which claims priority from foreign application DE10 2020 111 659.4 filed 04/29/20.
Response to arguments
The 112b issues for claim 11 have been corrected by way of amendment, the rejection of claim 11 has been withdrawn.
The 101 issues for the pending claims have been corrected by way of amendment, the rejection of pending claims under 101 has been withdrawn.
On page 7 of the remarks, applicant asserts (applicant’s direct arguments will be underlined):
Independent Claim 1 and Dependent Claims Thereof Claim 1 is directed to a method and, as amended, recites, among other things, "calculating at least one collision-free region using at least one environment image registered by registering, wherein the at least one collision-free region is at least one region of a surface that is determined to be free of obstacles and passable by the mobile unit in a direction of travel of the mobile unit."
The cited references fail to singly, or in any motivated combination, teach or suggest a method having such features. (Emphasis added)
Applicant then goes to discuss the art of record:
The Office asserts that FIG. 3 and paragraphs [0100]-[0104] of Mennen teach determining a pose of a mobile unit by a comparison of at least one calculated collision-free region with at least one passable region marked on a map. Applicant respectfully disagrees.
Paragraph [0100] of Mennen teaches that, at Step S312 of FIG. 3, a structure matching algorithm is applied to the 2D visually-identified road structure 324 with respect to the target area 326 of the roadmap to attempt to match the visually-identified road structure to corresponding road structure indicated within the target area 326 of the road map. Also, Paragraph [0100] of Mennen teaches that, at Step S314 of FIG. 3, the visually-identified road structure can been merged with the expected road structure indicated on the roadmap.
Nothing has been found, or pointed to, in Mennen which teaches or suggests that the "visually-identified road structure" is determined to be free of obstacles and passable by a mobile unit in a direction of travel of the mobile unit. For example, FIG. 3 fails to mention determining that a visually-identified road structure is free of obstacles.
Moreover, nothing has been found, or pointed to, in Kroepfl that remedies the deficiencies of Mennen identified above.
Accordingly, the cited references, namely, Mennen and Kroepfl fail to singly, or in any motivated combination, teach or suggest every feature of the claimed embodiment. (Emphasis added).
The examiner respectfully disagrees. While there is no explicit recitation of “determining the road structure is free of obstacles”, there is a determination of the "visually-identified road structure" to be free of obstacles and passable by a mobile unit in a direction of travel of the mobile unit by virtue of road detection confidence.
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Looking at Fig. 3 of Mennen, the front images taken from the mobile unit are classified. There are 3 types of classifications: Confident this is a road, Confident this is not road, and Uncertain. For all the pixels labeled as “Confident this is road”, they are determined to be free of obstacles since they are known to be just road. This is further supported by the Uncertain pixels which are overlapping an obstacle (another vehicle). So, Mennen does teach “calculating at least one collision-free region (Area to the right in Fig. 3, #S322a showing only road pixels. Calculation happens by way of classification) using at least one environment image registered by registering (#S322a in an environment image), wherein the at least one collision-free region is at least one region of a surface that is determined to be free of obstacles and passable by the mobile unit in a direction of travel of the mobile unit (The area to the right in #S322a is free of obstacles, and is passable by the mobile unit (passable meaning the mobile unit can use the collision free area to pass the area in front of it) in the direction of travel (in front of it))”. Therefore, Mennen does teach all the claimed limitations of claim 1. The rejection is maintained.
Claim Interpretation
Many of the dependent claims contain the phrasing and/or. Any of the claims reciting and/or will be considered a list of alternatives. While the scope of A and/or B and/or C could be read as A and B and C, under the broadest reasonable interpretation, that claims may be read as A or B or C requiring only one of the limitations need to be taught to meet the standard for prior art rejection. For clarity, the limitation being met of the alternative list will be underlined for all prior art rejections.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“an electronic storage unit, in which a map is stored having at least one passable region,” in claims 13-14.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6, and 8-15 are rejected under 35 U.S.C. 102 (a)(2) as being unpatentable over Mennen et al. (US 20230054914 A1 Hereinafter “Mennen”).
Regarding claim 1, Mennen teaches a method for determining a pose of a mobile unit having at least one sensor device ([0044]: “an image input configured to receive captured images from an image capture device of an autonomous vehicle”) and a map, in which at least one passable region is marked ([0073]: “Predetermined road map data refers to data or a road map or maps that have been created in advance, of the kind currently used in GPS-based navigation units (such as smartphones or “satnavs”) and the like, or the kind used by many autonomous driving systems commonly called HD Maps which provide cm accurate detailed information about road and lane boundaries as well as other detailed driving information such as sign and traffic light location”. The passable region is the road and lane boundaries), the method comprising:
calculating at least one collision-free region (Fig. 3: Area to the right in #S322a showing only road pixels. Calculation happens by way of classification) using at least one environment image registered by registering (#S322a in an environment image), wherein the at least one collision-free region is at least one region of a surface that is determined to be free of obstacles and passable by the mobile unit in a direction of travel of the mobile unit (Fig. 3: The area to the right in #S322a is free of obstacles, and is passable by the mobile unit (passable meaning the mobile unit can use the area to pass) in the direction of travel (in front of it)); and
determining the pose of the mobile unit by a comparison of the at least one calculated collision-free region with the at least one passable region marked on the map (Fig. 3, [0100-104]: “At Step S312, a structure matching algorithm is applied to the 2D visually-identified road structure 324 with respect to the target area 326 of the roadmap to attempt to match the visually-identified road structure to corresponding road structure indicated within the target area 326 of the road map”. Thus, comparison allows the position of the car to be determined. This can include the lateral position, longitudinal position, or the orientation of the vehicle),
wherein the calculated at least one collision-free region and the at least one passable region marked on the map is projected onto the local ground level of the mobile unit ([0064]: “In the described examples, 3D imaging is used to capture spatial depth information for pixels of the images, to allow the visually detected road structure to be projected into the plane of a 2D road map, which in turn allows the visually detected road structure to be compared with corresponding road structure on the 2D map”. The “visually detected road structures” come from calculating the collision free regions, and by projecting them onto the 2D map, they are projected onto the ground level of the mobile unit. The passable region is any region which the mobile unit can pass, which coincide with the collision-free regions).
Regarding claim 2, Mennen teaches the method according to claim 1, wherein the pose of the mobile unit encompasses position indications and/or angle of orientation indications and/or the at least one passable region corresponds to a two- dimensional point set or a three-dimensional point set in a map coordinates system (Fig. 3, [0100-104]: “At Step S312, a structure matching algorithm is applied to the 2D visually-identified road structure 324 with respect to the target area 326 of the roadmap to attempt to match the visually-identified road structure to corresponding road structure indicated within the target area 326 of the road map”. Thus, comparison allows the position of the car to be determined. This can include the lateral position, longitudinal position, or the orientation of the vehicle).
Regarding claim 3, Mennen teaches the method according to claim 1, wherein the map is a semantic map and/or it comprises at least one known landmark with map position indications ([0073]: “Predetermined road map data refers to data or a road map or maps that have been created in advance, of the kind currently used in GPS-based navigation units (such as smartphones or “satnavs”) and the like, or the kind used by many autonomous driving systems commonly called HD Maps which provide cm accurate detailed information about road and lane boundaries as well as other detailed driving information such as sign and traffic light location”. The passable region is the road and lane boundaries).
Regarding claim 4, Mennen teaches the method according to claim 1, wherein the at least one collision-free region corresponds to a two-dimensional point set or a three-dimensional point set in a camera coordinates system and/or the at least one collision-free region is calculated using at least two sequentially registered environment images and/or the at least one collision-free region is calculated using of a monocular environment image sequence and/or the at least one collision-free region is calculated by way of semantic segmentation and/or the at least one collision-free region is calculated by way of a machine learning method, especially by means of a trained neural net (Fig. 3, [0071]: “The road detection component 102 performs road structure detection, based on what is referred to in the art as machine vision. When given a visual input in the form of one or more captured images, the road detection component 102 can determine real-world structure, such as road or lane structure, e.g. which part of the image is road surface, which part of the image makes up lanes on the road, etc. This can be implemented with machine learning, e.g. using convolutional neural networks, which have been trained based on large numbers of annotated street scene images”. Fig. 3 (S322a) shows the road to the right as a collision free region).
Regarding claim 6, Mennen teaches the method according to claim 1, wherein at least one hypothesis for the pose of the mobile unit is calculated by way of a localization method and a plausibility check is performed during the comparison of the at least one hypothesis ([0066]: “1) Visual road shape detection and road shape from a map are compared. There are various forms the comparison can take, which can be used individually or in combination. Specific techniques are described by way of example below with reference to step S312 in FIG. 3. [0067] 2) The above comparison allows the vehicle to be positioned on the map. In this respect, it is noted that it is the position and orientation of the vehicle on the map that is estimated, which is not necessarily the vehicle's global position in the world. [0068] 3) Multiple such estimates are made over time”. These estimates act as hypothesis, which are determined by the localization method of the vehicle in relativity to the detected roads. The plausibility of these hypothesis is checked by associating an error with the estimate [0024]: “The method may comprise a step of determining an error estimate for the determined location of the vehicle on the road map, based on the matching of the visually identified road structure with the corresponding road structure of the road map”).
Regarding claim 8, Mennen teaches the method according to claim 6, wherein during the plausibility check a partial region of the at least one collision-free region that is unambiguously matched up with the at least one passable region marked on the map is determined using the at least one hypothesis and/or the at least one hypothesis is plausible if a ratio between a size of the partial region of the at least one collision-free region which can be matched up with the at least one passable region using the at least one hypothesis and its complement exceeds a critical value (Fig. 7, [0126]: “The filter 702 fuses (combines) the received location estimates 704a-708a, based on their respective error indications 704b-708b, to provide the overall location estimate 218, which respects the indicated errors in the individual estimates 704a-708a, the overall location estimate 218 being an overall estimate of the location of the vehicle 100 on the map”. The overall estimate and plausibility is determined with the assistance from multiple estimates, so during the plausibility check of the overall location estimate, the collision free region and the passable region is unambiguously matched with the assistance of at least one hypothesis (estimate)).
Regarding claim 9, Mennen teaches the method according to claim 6, wherein the pose of the mobile unit determined by the comparison corresponds to a plausible hypothesis and/or a pose of the mobile unit is initially estimated by way of a further sensor device and the pose of the mobile unit as determined by the comparison corresponds to an initially estimated pose of the mobile unit or a plausible hypothesis and/or multiple plausible hypotheses are determined by way of the plausibility check and the comparison involves a weighting of the multiple plausible hypotheses and the pose of the mobile unit as determined by the comparison corresponds to the largest weight ([0135]: “As a consequence, there will be certain locations within the real-world space 800 at which it is possible to conclude there is road with total confidence assuming the map is accurate. This is because, although the vehicle 100 might be at one of a range of locations on the map 804 (the vehicle location error range, as defined by the error in the location estimate), there are certain locations relative to the vehicle 100 that are either definitely road or definitely not road irrespective of where the vehicle is actually located within the vehicle location error range”. If the vehicle was within the “vehicle location error range” it would be seen as plausible since the error is within the desired level bounds).
Regarding claim 10, Mennen teaches the method according to claim 1, wherein at least one hypothesis is determined for the pose of the mobile unit and during the comparison a minimization of a cost function is performed, where the cost function indicates the distance between the at least one calculated collision-free region and the passable region marked on the map in a common coordinates system, and the pose of the mobile unit corresponds to the minimum of the cost function ([0105]: “The overall error can be captured in a cost function, which can for example be a summation of individual errors between corresponding pixels of the two images. These individual error between two pixels can be defined in any suitable way, e.g. as the mean square error (MSE) etc. The cost function can be optimized using any suitable optimization algorithm, such as gradient descent etc. To begin with, the assumed location is the approximate vehicle location 214, which is gradually refined through the performance of the optimization algorithm, until that algorithm completes. Although not reflected in the graphical illustrations on the right hand side of FIG. 3, in this context the target area 326 is an area corresponding to the field of view of the image capture device 202 at the assumed vehicle location and orientation on the map, which can be matched to the road structure detected within the actual field of view as projected into the plane of the road map. Changing the assumed location/orientation of the vehicle in turn changes the assumed location/orientation of the field of view, gradually bringing it closer to the actual field of view as the cost function is optimized”. The optimized cost function is the “minimum of the cost function”).
Regarding claim 11, Mennen teaches the method according to claim 10, wherein the at least one characteristic distance is determined by way of shortest distances between a respective three-dimensional point of the at least one collision-free region and the surface defined on the map by the at least one passable region ([0105]: “The overall error can be captured in a cost function, which can for example be a summation of individual errors between corresponding pixels of the two images. These individual error between two pixels can be defined in any suitable way, e.g. as the mean square error (MSE) etc. The cost function can be optimized using any suitable optimization algorithm, such as gradient descent etc. To begin with, the assumed location is the approximate vehicle location 214, which is gradually refined through the performance of the optimization algorithm, until that algorithm completes. Although not reflected in the graphical illustrations on the right hand side of FIG. 3, in this context the target area 326 is an area corresponding to the field of view of the image capture device 202 at the assumed vehicle location and orientation on the map, which can be matched to the road structure detected within the actual field of view as projected into the plane of the road map. Changing the assumed location/orientation of the vehicle in turn changes the assumed location/orientation of the field of view, gradually bringing it closer to the actual field of view as the cost function is optimized”. The optimized cost function would result in a characteristic distance being determines as the shortest distance between the 3D point of the collision free region and the marked passable area on the map since the 3D coordinates are projected onto the 2D region [0064]: “In the described examples, 3D imaging is used to capture spatial depth information for pixels of the images, to allow the visually detected road structure to be projected into the plane of a 2D road map, which in turn allows the visually detected road structure to be compared with corresponding road structure on the 2D map”)
Regarding claim 12, Mennen teaches the method according to claim 10, wherein an updating of the pose of the mobile unit is done by minimizing a cost function ([0105]: “The overall error can be captured in a cost function, which can for example be a summation of individual errors between corresponding pixels of the two images. These individual error between two pixels can be defined in any suitable way, e.g. as the mean square error (MSE) etc. The cost function can be optimized using any suitable optimization algorithm, such as gradient descent etc. To begin with, the assumed location is the approximate vehicle location 214, which is gradually refined through the performance of the optimization algorithm, until that algorithm completes. Although not reflected in the graphical illustrations on the right hand side of FIG. 3, in this context the target area 326 is an area corresponding to the field of view of the image capture device 202 at the assumed vehicle location and orientation on the map, which can be matched to the road structure detected within the actual field of view as projected into the plane of the road map. Changing the assumed location/orientation of the vehicle in turn changes the assumed location/orientation of the field of view, gradually bringing it closer to the actual field of view as the cost function is optimized”. The optimized cost function is the cost function minimized).
Regarding claim 13, the content of claim 13 is similar to the content of claim 1, with the additional teachings of an electronic storage unit and an electronic evaluation and control unit. Mennen also discloses this information ([0080]: “For a software implementation, the functions in question are implemented by one or more processors of the autonomous vehicle 100 (not shown), which can be general-purpose processing units such as CPUs and/or special purpose processing units such as GPUs”. [0081]: “Machine-readable instructions held in memory of the autonomous vehicle 100 cause those functions to be implemented when executed on the one or more processors”). Therefore, claim 13 is rejected for the same reasons of anticipation as claim 1, along with the additional teachings above.
Regarding claim 14, Mennen teaches the system according to claim 13, wherein the system and/or the mobile unit and/or the electronic evaluation and control unit comprises at least one further sensor device, while the at least one further sensor device involves a satellite location system and/or a mobile communication system ([0117]: “At step S313, the location/orientation estimate as described from the matching is combined with one or more corresponding location/orientation estimates from one or more additional sources of location/orientation information, such as satellite positioning (GPS or similar) and/or odometry”).
Regarding claim 15, the content of claim 15 is similar to the content of claim 1, with the additional teachings of a computer program product, comprising a computer program, containing software for carrying out a method for determining a pose of the mobile unit when the computer program is running in a computer unit. Mennen also discloses this information ([0054]: “Another aspect of the invention provides a computer program comprising executable instructions stored on a non-transitory computer-readable storage medium and configured, when executed, to implement any of the method or system functionality disclosed herein”). Therefore, claim 15 is rejected for the same reasons of anticipation as claim 1, along with the additional teachings above.
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 7 is rejected under 35 U.S.C. 103 as being unpatentable over Mennen et al. (US 20230054914 A1 Hereinafter “Mennen”) in view of Kroepfl et al. (US 11698272 B2 Hereinafter “Kroepfl”).
Regarding claim 7, Mennen teaches the method according to claim 6, wherein the localization method is a and/or the at least one hypothesis for the pose of the mobile unit is determined by matching up at least one landmark detected in the at least one environment image with at least one known landmark marked on the map and/or multiple hypotheses are determined for the pose of the mobile unit by way of a landmark-based localization method ([0074]: “One such application is localization, where road structure identified by the trained road detection component 102 can be used to more accurately pinpoint the vehicle's location on a road map (structure-based localization)”).
Mennen does not expressly disclose using a landmark based localization method,
However, Kroepfl teaches using a landmark based localization method (Col. 22, lines 29-35: “As such, a pose from a trajectory of a first section may be known, and the pose of the trajectory from the second section may be sampled with respect to the first section using localization to determine—once the alignment between landmarks is achieved—the relative poses between the two”).
At the time the invention was made, it would have been obvious to one of ordinary skill in the art to modify Mennen’s localization method to include Kroepfl’s localization method because such a modification is the result of applying a known technique to a known device ready for improvement to yield predictable results. More specifically, Kroepfl’s localization method permits localization of the vehicles position by leveraging landmarks found in images and in known maps. This known benefit in Kroepfl is applicable to Mennen’s localization method as they both share characteristics and capabilities, namely, they are directed to localization for autonomous driving. Mennen’s maps also contain information on landmarks contemplating localization using them if needed ([0073]: “HD Maps which provide cm accurate detailed information about road and lane boundaries as well as other detailed driving information such as sign and traffic light location). Therefore, it would have been recognized that modifying Mennen’s localization method to include Kroepfl’s localization method would have yielded predictable results because (i) the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate Kroepfl’s localization method in localization of autonomous driving and (ii) the benefits of such a combination would have been recognized by those of ordinary skill in the art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Liu et al. (US 11474204 B2) teaches localization of points using map data and acquired data.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEFANO A DARDANO whose telephone number is (703)756-4543. The examiner can normally be reached Monday - Friday 11:00 - 7:00.
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, Greg Morse can be reached at (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEFANO ANTHONY DARDANO/Examiner, Art Unit 2663
/MICHAEL HORABIK/Supervisory Patent Examiner, Art Unit 2675
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