DETAILED ACTION
Election/Restrictions
Applicant’s election without traverse of group II comprising Claims 24-43 in the reply filed on 08/25/2026 is acknowledged. Claims 1-23 are canceled. Claims 24-43 are pending, of which claim 29-32 are amended, claim 33-43 are newly added.
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 Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 24-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zust et al. (US 2007/0053057) (Zust) in view of Stegmann (US 2013/0213945, IDS).
Regarding claim 24, Zust teaches a method comprising
capturing a first image of a first cross-section of a sample. Zust teaches “removal of a layer of the specimen is performed with the cutting edge of the knife,” and that “[a]s a result, a respective new cut surface is produced.” Zust further teaches that “acquisition of an image of the new cut surface is accomplished with the camera” (par [0015]). The new cut surface corresponds to the claimed first cross-section of the sample because the cut surface is exposed after a layer of the sample/specimen is removed.
Zust further teaches cutting the sample because Zust teaches that “removal of a layer of the specimen is performed with the cutting edge of the knife” and that “the specimen holder together with the specimen is moved along, proceeding from a starting position, beneath the knife” (par [0015]). Zust also teaches that a knife “comprises a cutting edge 6a with which the layers are successively removed from specimen 3a” (par [0023]). Thus, Zust teaches cutting the sample.
Zust further teaches capturing a second image of a second cross-section of the sample. Zust teaches that after the first image is acquired, “[t]he knife holder is then raised and the specimen holder is moved into the starting position, so that a further cut surface can once again be produced” and that the method is performed until “a sufficient number of images of different layers of the specimen have been collected in the computer” (par [0015]). Zust also teaches that a computer synchronizes the imaging procedure and stores “successive images of the many just-produced cut surfaces of the specimen” for image processing (par [0012]). Therefore, Zust teaches capturing images of successive cross-sections of the sample.
Zust does not expressly teach identifying a first target area in the first image, determining a first set of cutting parameters based on the first target area, identifying a second target area in the second image, and determining a second set of cutting parameters based on the second target area.
Stegmann teaches identifying a target area in an image and determining cutting parameters based on the target area. Stegmann teaches that an object is “first visually examined and an image of the object is made,” that “object portions that are of interest” can be identified, and that “the object zone to be prepared is delineated” (par [0019]). Stegmann further teaches that the object zone is delineated by superimposing a boundary demarcation, such as a rectangle, on the image of the object using operating software (par [0019]). The identified object portions of interest and delineated object zone correspond to the claimed target area.
Stegmann further teaches determining cutting parameters based on the target area. Stegmann teaches that a second boundary demarcation is added, and that the area within the second boundary demarcation minus the area within the first boundary demarcation defines the zone where material is to be removed (par [0020]). Stegmann further teaches that “[t]he working path of the laser beam can be defined with the software program, namely by determining the movement pattern by which the laser beam is to be guided over the object surface to be machined, i.e. for example in parallel rows or in a circular path” (par [0020]). The working path and movement pattern of the laser beam correspond to cutting parameters because they define how the cutting/laser-machining operation is performed based on the identified target area.
Stegmann also teaches cutting the sample with the determined cutting parameters because Stegmann teaches that “the sample volume that is to be cleared away is removed by laser-machining along the defined laser-machining path” and that “a volume of material . . . is ablated using light pulses” (par [0022]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method of Zust to include the image-based target-area identification and cutting-parameter/path determination taught by Stegmann. One of ordinary skill in the art would have been motivated to make the combination because Zust teaches synchronized cutting, imaging, and image processing of successive cut surfaces of a specimen (par [0012], [0015]), and Stegmann teaches using an image to identify an object portion of interest and to determine a laser-machining path so that a desired sample structure is prepared from the object (par [0019]-[0022]). The combination would have involved applying a known image-guided cutting/path-planning technique to the similar sample-preparation and imaging method of Zust to improve automated preparation of desired sample regions in a predictable way.
Accordingly, Zust in view of Stegmann teaches or fairly suggests capturing a first image of a first cross-section of a sample, identifying a first target area in the first image, determining a first set of cutting parameters based on the first target area, cutting the sample with the first set of cutting parameters, capturing a second image of a second cross-section of the sample, identifying a second target area in the second image, determining a second set of cutting parameters based on the second target area, and cutting the sample with the second set of cutting parameters.
Regarding claim 25, Zust in view of Stegmann teaches the method of claim 24 as discussed above. Zust further teaches collecting images of different layers of the specimen because Zust teaches that the method is performed until “a sufficient number of images of different layers of the specimen have been collected in the computer” so that the computer can produce a three-dimensional depiction of the specimen (par [0015]). Stegmann teaches determining a target volume because Stegmann teaches that, for tomography samples, “the target volume first is set free within a larger material space” (par [0007]). Stegmann further teaches that, to avoid obscuration and redeposition effects, “the area around the target volume is cleared away with a wide reach” and that the method can produce samples of different shapes (par [0036]).
Stegmann also teaches image-based determination of the target region because Stegmann teaches that the object is visually examined and an image of the object is made, object portions of interest can be identified, and the object zone to be prepared is delineated on the image using a boundary demarcation (par [0019]). Therefore, in view of Zust’s teaching of collecting images of different layers to produce a three-dimensional depiction and Stegmann’s teaching of identifying an object zone/target volume from image information, it would have been obvious to determine a target volume based on the first image and the second image.
Regarding claim 26, Zust in view of Stegmann teaches the method of claim 24 as discussed above. Stegmann further teaches that the first set of cutting parameters includes a variable sized scoring pattern because Stegmann teaches defining a working path of a laser beam with a software program by determining the movement pattern by which the laser beam is guided over the object surface to be machined, such as “in parallel rows or in a circular path” (par [0020]). Stegmann further teaches that the size of the boundary demarcation “can be freely selected, depending on how much material is to be removed” (par [0020]). The movement pattern of the laser beam, including parallel rows, corresponds to a scoring pattern because it defines the pattern of cuts or ablations made on the sample surface, and the freely selected size of the boundary demarcation corresponds to the variable size of the scoring pattern.
Regarding claim 27, Zust in view of Stegmann teaches the method of claim 24 as discussed above. Stegmann teaches creating a target tissue fragment and waste fragments because Stegmann teaches a target volume and surrounding material to be removed. In particular, Stegmann teaches that, for tomography samples, “the target volume first is set free within a larger material space” (par [0007]). Stegmann further teaches that “the area around the target volume is cleared away with a wide reach” and that, to arrive at a slab-shaped sample, “one starts by clearing away the material around the contours of a rectangular block” until the sample block is set free (par [0036]). Stegmann also teaches that “the sample volume that is to be cleared away is removed by laser-machining along the defined laser-machining path” and that the removed volume is cleared away by evacuating the processing chamber with a pump (par[0022]).
The set-free target volume corresponds to the claimed target tissue fragment, and the surrounding cleared-away material corresponds to the claimed waste fragments because it is material removed from around the target volume during cutting/machining. Therefore, Zust in view of Stegmann teaches or suggests that cutting the sample creates a plurality of target tissue fragments of a first size and a plurality of waste fragments of a second size.
Regarding claim 28, Zust in view of Stegmann teaches the method of claim 27 as discussed above. Stegmann teaches a target volume and surrounding material that is removed around the target volume. In particular, Stegmann teaches that “the target volume first is set free within a larger material space” (par [0007]). Stegmann further teaches that “the area around the target volume is cleared away with a wide reach” and that, to arrive at a slab-shaped sample, material is cleared away around the contours of a rectangular block until five sides of the sample block are set free (par [0036]). Stegmann also teaches removing the “sample volume that is to be cleared away” by laser-machining along the defined laser-machining path (par [0022]).
The target volume corresponds to the target tissue fragments of the first size, and the surrounding cleared-away material corresponds to the waste fragments of the second size. It would have been obvious that the surrounding cleared-away material may form waste fragments larger than the target tissue fragments because Stegmann teaches setting the target volume free within a larger material space and clearing away the area around the target volume “with a wide reach” (par [0007], [0036]).
Claim 29-30, 32-34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zust et al. in view of Stegmann, and further in view of Zahniser (US 2008/0137938).
Regarding claim 29, Zust in view of Stegmann teaches the method of claim 24 as discussed above. Zahniser teaches, before capturing an image of a sample for analysis, moving the sample to a first distance from a camera, capturing a first focus image of the sample with the camera, determining a first focus value of the first focus image, moving the sample to a second distance from the camera, capturing a second focus image of the sample with the camera, determining a second focus value of the second focus image, and determining a focused distance from the camera based on the first focus value and the second focus value.
In particular, Zahniser teaches a specimen imaging apparatus having a camera 48 and microscope 50, wherein an automated microscope may include an autofocusing mechanism 54, and wherein the stage transports the specimen slide into and within the optical path of the microscope (par [0023]-[0024]). Zahniser further teaches that, in automated microscopy, “the computer generally finds the optimal focal plane for a given location within a sample by varying the focal height, acquiring an image at each height,” and calculating “a score for each acquired image” using autofocus functions, wherein “the highest scoring image corresponds to the ideal focal height” (par [0006]). The different focal heights correspond to different distances between the sample and the camera/optics.
Zahniser further teaches obtaining images at different focal heights and determining focus scores for those images. Zahniser teaches “obtaining three digital images of the specimen at different focal heights,” calculating “a Brenner auto-focus score for each of the three digital images,” fitting a function to the focal heights and corresponding scores, acquiring a new image at a new focal height based on the function, calculating a new Brenner score, and acquiring additional images at different focal heights until the ideal focal height is reached (par [0015]). Zahniser also teaches that multiple digital images are acquired at different focal heights, a Brenner score is calculated for each image, and a curve is generated that allows the peak of the Brenner function, i.e., the optimal or ideal focal height, to be estimated (par [0049]).
Zahniser further teaches moving the sample based on the determined focus information. Zahniser teaches that once an estimated displacement value is obtained, “the specimen 14 (via the moveable stage 40) can be moved toward or further away from the device optics” (par [0052]). Zahniser further teaches that after the specimen and/or optics is moved to adjust to the predicted correct focal plane, another image of the specimen may be obtained, and if the new position is still away from the optimal focal position, the specimen and/or optics may be moved a certain amount as directed by the new score or ratio until the device reaches the optimal or near-optimal focal plane (par [0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method of Zust and Stegmann to include the autofocus/focused-distance determination taught by Zahniser before capturing the first image of the first cross-section. One of ordinary skill in the art would have been motivated to make the combination because Zust teaches capturing images of newly produced cut surfaces for image processing, Stegmann teaches identifying a target area and determining a machining path based on an image, and Zahniser teaches that automated imaging systems require rapid focusing to obtain high-quality images of a sample or regions of interest (par [0003]-[0004]). The combination would have involved applying a known autofocus technique to the similar specimen imaging method of Zust and Stegmann to improve the quality of the image used for target-area identification and cutting-parameter determination.
Regarding claim 30, Zahniser teaches moving the sample to the focused distance from the camera and, with the sample positioned at the focused distance from the camera, capturing a focused image of the sample with the camera. As discussed above, Zahniser teaches estimating the optimal or ideal focal height based on focus scores and adjusting the focal length/focal height using the generated curve (par [0049]). Zahniser further teaches moving the specimen toward or further away from the device optics based on an estimated displacement value (par [0052]) and obtaining another image after the specimen and/or optics is moved to adjust to the predicted correct focal plane (par [0053]). The image obtained at the predicted correct focal plane corresponds to the claimed focused image.
In the combined method of Zust, Stegmann, and Zahniser, the focused image is the first image of the first cross-section because Zahniser’s autofocus procedure is performed before the image is used for Stegmann’s target-area identification and cutting-parameter/path determination.
Regarding claim 32, Zahniser teaches determining that a sample is loaded improperly when focus values are below or outside a threshold. Zahniser teaches that a focus score may be used to determine whether a digital image is accepted or rejected, and that if the displacement falls outside a predetermined threshold value, the image may be rejected and a new image may be acquired at a different focal height (par [0045]). Zahniser further teaches that the focus score may be obtained for images from specimens loaded into the specimen imaging apparatus, that particular slides may be reprocessed if their focus scores are unacceptable, and that if a rolling or cumulative score crosses a predetermined threshold, the slides may be deemed suspect and rescanned (par [0045]). Zahniser also teaches that an automated imaging device may track images that fall outside a range of acceptable scores, and if the number of images failing the criteria is above a certain number, the slide may be reprocessed or flagged (par [0046]).
Therefore, Zahniser teaches using focus values/scores compared to a threshold to determine that the loaded specimen/slide is suspect or unacceptable for imaging. It would have been obvious to determine that the sample is loaded improperly when multiple focus values are below or outside a threshold because consistently unacceptable focus values for the loaded sample would indicate that the sample/slide is not properly positioned for focused imaging.
Regarding claim 33, Zust in view of Stegmann and Zahniser teaches the method of claim 30 as discussed above. Stegmann teaches identifying the first target area using the focused image because Stegmann teaches that an image of the object is made, “object portions that are of interest” can be identified, and the object zone to be prepared is delineated by superimposing a boundary demarcation on the image of the object using an operating software program (par [0019]). In the combined method, the image used by Stegmann to identify the object portion of interest is the focused image obtained using Zahniser’s autofocus technique before the first cross-sectional image is used for target identification.
Regarding claim 34, Zahniser teaches determining the focused distance by collecting focus values at a range of distances and identifying an optimal focus position based on the focus values. Zahniser teaches that, in automated microscopy, the computer finds the optimal focal plane for a given location by varying the focal height, acquiring an image at each height, and calculating a score for each acquired image, wherein the highest scoring image corresponds to the ideal focal height (par [0006]). Zahniser further teaches obtaining three digital images of a specimen at different focal heights, calculating a Brenner auto-focus score for each image, fitting a function to the focal heights and corresponding scores, acquiring a new image at a new focal height based on the function, and repeating until the ideal focal height is reached (par [0015]). Zahniser also teaches acquiring multiple digital images at different focal heights, calculating a Brenner score for each image, generating a curve that fits the scores, and estimating the peak of the Brenner function corresponding to the optimal or ideal focal height (par [0049]). Accordingly, Zahniser teaches collecting focus values at a range of distances and identifying an optimal focus position based on the focus values.
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Zust et al. in view of Stegmann and Zahniser, and further in view of Han et al. (WO 2021/174078) (Han).
Regarding claim 31, Zust in view of Stegmann and Zahniser teaches the method of claim 30 as discussed above. Han teaches measuring a surface/cross-sectional area of a sample based on image information. In particular, Han teaches two-dimensional visual depictions of volumetric density information along respective target sections through a target sample, and also teaches “an indication of cross-sectional areas 307” representing the intersection of a region of interest with target sections that intersect the region of interest (par [0052]). The cross-sectional area corresponds to a surface area of the sample because it is the area of the exposed section/target-section surface represented in the image.
It would have been obvious to one of ordinary skill in the art before the effective filing date to further modify the method of Zust, Stegmann, and Zahniser to measure a surface area of the sample based on the focused image, as taught by Han. One of ordinary skill in the art would have been motivated to make the combination because Zust teaches producing and imaging cut surfaces of a specimen, Stegmann teaches identifying target regions in an image for sample preparation, Zahniser teaches acquiring a focused image, and Han teaches using image data to show cross-sectional areas of regions of interest in tissue samples. The combination would have involved applying a known image-analysis measurement technique to the focused cross-sectional image of the combined method to characterize the target/sample area before cutting.
Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over Zust et al. in view of Stegmann and Zahniser, and further in view of Kong et al. (US 2010/0175520) (Kong).
Regarding claim 35, Zust in view of Stegmann and Zahniser teaches the method of claim 29 as discussed above. Kong teaches that the sample is a live tissue sample. In particular, Kong teaches that the invention is directed to a vibrating microtome and “a method of its use in the preparation of live or fixed tissue slices” (par [0002]). Kong further teaches that the method and device are “particularly intended for use in the slicing of live or fixed tissue slices which are not hard or rigid,” that such specimens “are not embedded in paraffin and are not frozen,” and that the microtome cuts slices having higher live/dead cell ratios, which is an indication of slice viability (par [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method of Zust, Stegmann, and Zahniser for use with a live tissue sample, as taught by Kong, because Kong teaches that vibrating microtomes are used to prepare live or fixed tissue slices and improve live tissue slice viability. The combination would have involved applying the known live-tissue slicing technique of Kong to the similar microtome/imaging method of Zust, as modified by Stegmann and Zahniser, to improve preparation and imaging of viable tissue samples in a predictable way.
Claim 36-37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zust et al. in view of Stegmann, Zahniser, and Kong, and further in view of Han.
Regarding claim 36, Zust in view of Stegmann, Zahniser, and Kong teaches the method of claim 35 as discussed above. Han teaches that the live tissue sample is a resection tissue sample because Han teaches improved analysis of “surgically explanted pathology samples or other varieties of tissue sample” by guiding sectioning using high-resolution volumetric imaging data (Abstract). Han further teaches that surgeons remove tumor masses or other tissue from the body and that a pathologist analyzes the “explanted tissue” to determine whether the entire target has been removed, including sectioning the sample to optically inspect a target within the tissue sample (par [0002]-[0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to apply the image-guided cutting method of Zust, Stegmann, Zahniser, and Kong to a resection tissue sample, as taught by Han, because Han teaches guiding sectioning of surgically explanted pathology samples to evaluate target tissue and margins. The combination would have involved applying a known image-guided pathology-sectioning application to the similar tissue-cutting and imaging method of the combined system to improve analysis of surgically removed tissue samples.
Regarding claim 37, Zust in view of Stegmann, Zahniser, Kong, and Han teaches the method of claim 36 as discussed above. Han teaches determining an orientation/location of sectioning based on cross-sectional shape information. In particular, Han teaches specifying or automatically determining the location and/or angle of a target section through a tissue sample based on volumetric density information (par [0048]). Han further teaches that an algorithm can generate a target section through the tissue sample such that the area or greatest dimension of a cross-section through a region of interest is maximized or increased relative to alternative sections, so that the region of interest, when imaged along the target section, is accurately represented with respect to “size, shape, texture, or other properties” (par [0048]). Han also teaches that images rendered from volumetric imaging data may be used to identify regions of interest and/or the location/angle of target sections, and to select a location/angle relative to the tissue sample to which to control a sectioning tool (par [0066]).
The determined location/angle of the target section corresponds to the claimed rotational orientation because it determines the orientation of the sample/sectioning operation relative to the cross-sectional shape information of the sample. The area, greatest dimension, and shape information of the cross-section correspond to the claimed cross-sectional shape of the sample in the first image.
It would have been obvious to one of ordinary skill in the art before the effective filing date to determine the first set of cutting parameters to include a rotational orientation of the sample based on a cross-sectional shape of the sample in the first image, as taught by Han, because Han teaches determining target-section location/angle based on cross-sectional area/dimension/shape information to improve tissue sectioning and analysis. The combination would have involved applying Han’s known orientation/section-selection technique to the image-guided cutting method of Zust, Stegmann, Zahniser, and Kong to improve selection of the cutting orientation for the target tissue.
Claim 38-39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zust et al. in view of Stegmann, Zahniser, Kong, Han, and further in view of Niehren (US 2009/0140169, IDS).
Regarding claim 38, Zust in view of Stegmann, Zahniser, Kong, and Han teaches the method of claim 37 as discussed above. Niehren teaches rotating a tissue/sample holder supporting the sample about a longitudinal axis of the tissue/sample holder to place the sample at a rotational orientation before cutting. In particular, Niehren teaches an ablation chamber with a sample holder provided therein, on which the sample to be processed is mounted, wherein the sample holder is “movable along an axis V and rotatable about a rotary axis R,” and wherein positioning devices move the sample holder along axis V and rotate it about rotary axis R (par [0038]). Niehren further teaches that the optical axis of the laser stands perpendicular on rotary axis R, and that “rotary axis R and V coincide” (par [0045]). The coinciding axis V/rotary axis R corresponds to the longitudinal axis of the tissue holder because the holder is moved along and rotated about the same holder/sample axis.
It would have been obvious to one of ordinary skill in the art before the effective filing date to rotate the tissue holder supporting the sample about a longitudinal axis to place the sample at the rotational orientation before cutting, as taught by Niehren, because Han teaches selecting a location/angle for sectioning based on image data, and Niehren teaches rotating a sample holder about its rotary axis to position the sample for controlled cutting/ablation. The combination would have involved applying a known sample-holder rotation mechanism to the image-guided tissue cutting method of Zust, Stegmann, Zahniser, Kong, and Han to place the sample at the selected rotational orientation before cutting.
Regarding claim 39, Zust in view of Stegmann, Zahniser, Kong, Han, and Niehren teaches the method of claim 38 as discussed above. Han teaches selecting an orientation/location of sectioning based on image-derived cross-sectional properties, including area, greatest dimension, size, and shape, to improve representation and analysis of the tissue sample (par [0048]). Stegmann teaches selecting a cutting pattern because Stegmann teaches defining the working path of a laser beam by determining the movement pattern by which the laser beam is guided over the object surface to be machined, such as “in parallel rows or in a circular path” (par [0020]). Stegmann further teaches that the size of the second boundary demarcation can be freely selected depending on how much material is to be removed (par [0020]) and that, depending on the desired shape of the sample, the boundary demarcation may have any geometric shape based on the shape of the base surface of the three-dimensional sample body to be prepared (par [0021]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to select the rotational orientation and cutting pattern to improve similarity in sizes of tissue fragments produced by cutting the sample. Han teaches selecting a sectioning orientation based on image-derived cross-sectional size/shape information, and Stegmann teaches selecting cutting patterns and boundary sizes/shapes based on the desired sample geometry. In view of these teachings, selecting an orientation and cutting pattern to produce tissue fragments having improved size similarity would have been an obvious optimization of known image-guided sectioning and pattern-based cutting parameters to obtain more uniform cut tissue portions.
Allowable Subject Matter
Claims 40-43 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art of record does not teach or fairly suggest the claimed arrangement in which the first set of cutting parameters defines scoring cuts in a first dimension and a second dimension to produce a scored tissue sample, and the method further comprises making a slicing cut in a third dimension to detach tissue fragments from the scored tissue sample.
The prior art of record also does not teach or fairly suggest the variable sized scoring pattern having unequal distances between adjacent scores, as recited in claim 41.
Claims 42 and 43 would be allowable at least by virtue of their dependency from claim 40.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIAOYUN R XU, Ph. D. whose telephone number is (571)270-5560. The examiner can normally be reached M-F 8am-5pm.
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, Lyle Alexander can be reached at 571-272-1254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/XIAOYUN R XU, Ph.D./ Primary Examiner, Art Unit 1797
Prosecution Insights