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 .
Status of the Claims
Claims 1-38 are pending in the present application, with claims 1, 15, and 27 being independent.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 20 June 2025 and 17 July 2025 have been considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Double Patenting
Claims 1-15 and 27 of this application is patentably indistinct from claims 1-15 and 27 of Application No. 19/072009 Pursuant to 37 CFR 1.78(f), when two or more applications filed by the same applicant or assignee contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-15 and 27 are provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-15 and 27 of copending Application No. 19/072009 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 16-26 and 28-38 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2-12 of copending Application No. 19/072009 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 16-26 and 28-38 are obvious variants of claims 2-12 of the reference application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Interpretation
The following interpretations are being applied to the claims:
As used herein, "headgear" refers to the straps, bands, or other components used to secure the user interface to the user's nose, mouth, or both, see paragraph 41.
As used herein, the "conduit" refers to a tube that connects the user interface (e.g., a CPAP mask) to the respiratory therapy device (e.g., the flow generator) and provides airflow to the user, from the flow generator, via the interface, see paragraph 41.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-38 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 1, given the broadest reasonable interpretation of the claimed limitations in the light of the corresponding disclosure, the scope of the claimed limitation is unclear. For instance, it is not immediately clear as to:
What is meant by the 3D mesh being scaled to a size of the head of the user. The disclosure sets forth “In some embodiments, a deep learning model is trained end-to-end to generate three-dimensional meshes having appropriate size or scale, relative to the user’s face. That is, the mesh may be scaled according to the size of the user’s face, ensuring that measurements taken based on the mesh...are accurate”, see paragraph 22. Paragraph 34 sets forth “In some embodiments, the mesh component 125 uses a camera model to scale the mesh 130...Using this information, in some embodiments, the mesh component 125 can determine the scale of the face or features therein. For example, after determining that the user's nose is N millimeters away from the camera and that the POV of the camera is X degrees, the mesh component 125 may determine the actual size of the user's nose (e.g., in millimeters)”. Paragraph 34 recites “In some embodiments, rather than using a camera model to generate or scale the mesh, the mesh component 125 may use a deep learning model that generates an appropriately scaled mesh 130”. Paragraph 75 recites “as discussed above, the mesh generated by the machine learning model is already scaled to the size of the user's head and/or facial features. That is, because the model may be trained end-to-end using relatively dense labels (e.g., dense point clouds and/or meshes), the model may inherently learn to predict the scale of the face, without separately predicting how the camera affects the perceived size (e.g., based on POV and/or distance). For example, in some embodiments, the machine learning model learns to respect or recreate the identity (e.g., facial shape) of the person regardless of any variations in angle, background, POV of the camera, distance of the camera, and the like (in a similar manner to how some facial recognition models work). In this way, the generated mesh may be inherently scaled correctly by the mode”. Paragraph 76 recites “In some embodiments, if the mesh is not inherently scaled, the measurement system may then scale the output mesh using such a camera model”. The specification appears to set forth that the mesh is scaled to a size relative to the head of the user using either camera model or the deep learning model. However, it is not clear in the claim language as to a) if the scaling is done by the machine learning model or a camera model or how the scaling is to a size of the had of a user. The examiner respectfully requests the applicant clarify the scope of the claimed limitation. For the purposes of further examination, the examiner is interpreting the scaling to be relative to the size of a head of the user and for the scaling to be done by the deep learning/machine learning model to better comport with applicant’s originally filed disclosure. The examiner is also interpreting the generated mesh depicting a head of a user to also depict the face of the user.
How the mesh is modified to remove one or more facial expressions. The specification appears to indicate in at least paragraphs 24, 25, 39, and 77 that facial expressions are removed to modify the mesh to place the face in a neutral position by deformation or using a shape model. The disclosure does not appear to indicate how the invention would work with simply removing an expression among a plurality of expressions, since “removing facial expressions may result in improved accuracy, as compared to taking measurements from a mesh depicting one or more facial expressions. Further by removing the expression dynamically from the mesh itself, the measurement system may avoid the need to request additional images from the user (e.g., asking the user to take another picture without smiling. This can improve the user experience and reduce the time consumed by the measurement process”, see paragraph 77. The examiner respectfully requests the applicant clarify the scope of the claimed limitation. For the purposes of further examination, the examiner is interpreting the claimed limitation to modify the 3D mesh to remove facial expressions to place the face depicted in the mesh to have a neutral expression. The next limitation is being interpreted as determining a set of facial measurements based on the modified three-dimensional mesh having a neutral expression.
What is meant by selecting a user interface for the user based on the set of facial measurements. The disclosure provides examples of user interfaces being a nasal mask, a full-face mask, and/or a nasal pillow mask)/a mask for respiratory therapy (e.g., CPAP), see for instance, paragraphs 25, 41, and 45. However, the disclosure does not set forth a definition or clarify the scope of user interfaces as claimed. It is also unclear as to how the generically recited set of facial measurements are used to select a given user interface. It appears from at least paragraph 78, the set of facial measurements correspond to different facial features, such as the nose, mouth, nostrils, etc. The examiner respectfully requests the applicant clarify the scope of the claimed invention. For the purposes of further examination, the examiner is interpreting the user interface to be a physical mask used for respiratory therapy (i.e., wherein the user interface is a physical mask used for respiratory therapy) and for the set of facial measurements to include measurements for a set of facial features.
Claims 15 and 27 recite substantially similar subject matter as to that recited in claim 1 and are accordingly rejected using substantially similar rationale as to that which was set forth for claim 1.
Claims depending thereon do not cure the noted deficiencies and are accordingly rejected using substantially similar rationale as to that which set forth for the claims from which they depend.
With respect to claim 2, given the broadest reasonable interpretation of the claimed limitations in the light of the corresponding disclosure, the scope of the claimed limitation is unclear. For instance, it is not immediately clear as to what is meant by pillow size in the context of the claims as currently drafted. It appears from paragraphs 45 and 67 the pillow size is related to a nasal pillow mask – not any physical mask used for respiratory therapy. The examiner respectfully requests the applicant clarify the scope of the claimed limitation. For the purposes of further examination, the examiner is interpreting the claim to recite in part wherein the physical mask is a pillow nasal mask and the recommended pillow size is for the pillow nasal mask.
Claims 16 and 28 recite substantially similar subject matter as to that recited in claim 3 and are accordingly rejected using substantially similar rationale as to that which was set forth for claim 2.
Claims depending thereon do not cure the noted deficiencies and are accordingly rejected using substantially similar rationale as to that which set forth for the claims from which they depend.
With respect to claim 3, given the broadest reasonable interpretation of the claimed limitations in the light of the corresponding disclosure, the scope of the claimed limitation is unclear. For instance, it is not immediately clear as to how the first ellipse fits within the context of the claim, if the comprise language goes with the nostril measurements or the ellipse, and what constitutes the various items with respect to the claim. For instance, a major axis with respect to what? If it is the ellipse, then is it the nostrils themselves? A nose size? Etc. Paragraph 41 for instance, appears to tie the items listed to the ellipse and the ellipse to the nostril(s) of the user. The examiner respectfully requests the applicant clarify the scope of the claimed limitation. For the purposes of further examination, the examiner is interpreting the claim along the lines of paragraph 41.
Claims 17 and 29 recite substantially similar subject matter as to that recited in claim 3 and are accordingly rejected using substantially similar rationale as to that which was set forth for claim 3.
With respect to claim 5, given the broadest reasonable interpretation of the claimed limitations in the light of the corresponding disclosure, the scope of the claimed limitation is unclear. For instance, it is not immediately clear as to what is meant by generating a recommended conduit size for the user interface based on the set of facial measurements. As set forth above, the specification defines conduit as a tube that connects the user interface (e.g., a CPAP mask) to the respiratory therapy device (e.g., the flow generator) and provides airflow to the user, from the flow generator, via the interface. Is the size the length of the conduit? The diameter of the tube? Is it something else related to the conduit? The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
Claims 19 and 31 recite substantially similar subject matter as to that recited in claim 5 and are accordingly rejected using substantially similar rationale as to that which was set forth for claim 5.
With respect to claim 6, given the broadest reasonable interpretation of the claimed limitations in the light of the corresponding disclosure, the scope of the claimed limitation is unclear. For instance, it is not immediately clear as to what is meant by generating a recommended headgear size for the user interface based on the set of facial measurements. As set forth above, the specification defines headgear as referring to the straps, bands, or other components used to secure the user interface to the user's nose, mouth, or both, see paragraph 40. Since head gear is related to multiple items, what is the size referencing here? The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
Claims 20 and 32 recite substantially similar subject matter as to that recited in claim 6 and are accordingly rejected using substantially similar rationale as to that which was set forth for claim 6.
Claims depending thereon do not cure the noted deficiencies and are accordingly rejected using substantially similar rationale as to that which set forth for the claims from which they depend.
With respect to claim 9, given the broadest reasonable interpretation of the claimed limitations in the light of the corresponding disclosure, the scope of the claimed limitation is unclear. For instance, it is not immediately clear as to how the 3D data points for a head of the training user fits with the training images depicting the training user. Are they both inputs to training the ML model? Is the 3D data points output and fed back in to the ML model? The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
Claims 23 and 35 recite substantially similar subject matter as to that recited in claim 9 and are accordingly rejected using substantially similar rationale as to that which was set forth for claim 9.
Claims depending thereon do not cure the noted deficiencies and are accordingly rejected using substantially similar rationale as to that which set forth for the claims from which they depend.
With respect to claim 10, given the broadest reasonable interpretation of the claimed limitations in the light of the corresponding disclosure, the scope of the claimed limitation is unclear. For instance, it is not immediately clear as to what comprises the camera model and how the first ML model does not use the camera model in the generation of the 3D mesh. The disclosure does not appear to describe a ML model using a camera model – in paragraph 34 it is described as “rather than using a camera model to generate or scale the mesh, the mesh component may use a deep learning model that generates an appropriately scaled mesh”. The examiner respectfully requests the applicant clarify the scope of the claimed limitation.
Claims 24 and 36 recite substantially similar subject matter as to that recited in claim 10 and are accordingly rejected using substantially similar rationale as to that which was set forth for claim 10.
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.
Claim(s) 1-4, 11, 13, 15-18, 25, 27-29, and 37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tan-Ngo et al. (US PG Publication 2024/0170139) in view of Fonte et al. (US PG Publication 2015/0055086).
Regarding claim 1, Tan-Ngo teaches a method, comprising: accessing a set of two-dimensional images of a user (A client device may be configured to received, via an image capture device, one or more images of a facial device user, see for instance paragraph 41, 52, 66, 83. The image data (e.g., photo) may illustrate anatomical features, such as the user’s nose, mouth, chin, cheeks, forehead, eyes, among other features, see for instance, paragraph 66. The image data can represent 2D data, see for instance, paragraph 82);
generating, based on processing the set of two-dimensional images using a first machine learning model, a three-dimensional mesh depicting a head of the user, wherein the three-dimensional mesh is scaled to a size of the head of the user (see for instance, paragraphs 82-88. A 3D mesh of the user’s face can be generated at a scale corresponding to the relative size of the user’s face/head, see for instance, paragraphs 82-86, 92, and 101 and fig. 10-13);
modifying the three-dimensional mesh to remove one or more facial expressions; determining a set of facial measurements based on the modified three-dimensional mesh (see for instance, paragraphs 39, 110, 113, 126, 128, 138, and 140-145); and
selecting a user interface for the user based on the set of facial measurements (see for instance, paragraphs 81, 82, 143, 149, 151-154).
Tan-Ngo does not teach the 3D mesh is generated using a first machine learning model or the 3D mesh is modified to remove one or more facial expressions.
In the same art of computer graphics and product sizing, Fonte teaches this “embodiment has the advantage of using a collection of previously acquired images that the user may have at their disposal (eg collection of existing photos, photo gallery, social network or online image gallery photos, etc). Referring to FIG. 21, in this embodiment, a) A computer system receives a collection of images (e.g. 2101, 2102, 2103) of a user 2105, b) The images may be previously tagged with facial recognition data to determine which face in each photo is the user. c) If the images were not previously tagged, then the computer system performs facial recognition, prompting the user to confirm which face is theirs in at least one image or using the highest frequency of detected faces to determine the user from other people in photos, d) The computer system detects facial features (eg various points of the eyes, nose, mouth, ears, chin, etc) in each image of the user and fits a face model 2104 to the image data, e) Optionally, the computer system determines expression in each image (e.g. 2101 vs 2103) and adjusts the face model to a neutral expression, f) The computer system determines the pose of the user's face in each image, g) The computer system reconstructs a single model 2104 of the user's face by fitting a face model across the collection of feature sets and camera positions (2105, 2106, 2107) of the user. The face model is scaled by one of these methods: h) The computer system requests additional data from the user based on previously described methods: Pd input, an image with the reference target, etc. i) The computer system detects known objects in the images to determine a reference size (eg recognize a sheet of paper, a logo, a phone, etc). j) The computer system requests additional image data taken of the user with a reference object, using any other method described herein. k) The face model is inherently scaled due to the shape model containing dimensions that relate shape and size”, see paragraph 160. A series of
images is required around the user's face....features detected in each image, and the relative distances between the features from image-to-image are used to construct a 3D representation, see paragraph 140. A method that combines a generalized shape model with subsequent local SFM refinement may be utilized to enhance local detail of features, such as the nose shape, see for instance, paragraph 140. Machine learning methods are used to train a classifier for detecting a face as well as determining the pose of the head in an image that is post-processed to define various features, see for instance, paragraph 135. Machine learning is used to analyze images to detect facial features and edges, see for instance, paragraph 136. The computer system detects the facial features in the face model, measures the distance between them, and uses the scaled facial feature measurement to scale the face model, see for instance, paragraph 151. Different measurements can be analyzed and determined, see for instance, paragraph 96, 101, 121, 123, 127, and 182-184.
It would have been obvious to one of ordinary skill in the art having the teachings of Tan-Ngo and Fonte in front of them before the effective filing date of the claimed invention to incorporate measurements and 3D reconstruction as taught by Fonte into Tan-Ngo’s selecting a a facial device based on visually determined anatomical dimensions system, as having performing 3D reconstruction from 2D images and using a machine learning model on the data, such as described by Fonte was well known at the time of the effective filing date invention and would have yielded predictable results in combination with Tan-Ngo.
The modification of Tan-Ngo with Fonte would have explicitly allowed the 3D mesh to be generated using 2D images processed using a first machine learning model and modifying the three-dimensional mesh to remove one or more facial expressions.
The motivation for combining Tan-Ngo with Fonte would have been to improve the user experience and enhance functionality, see for instance, Fonte, paragraphs 15 and 160 .
Regarding claim 2, Tan-Ngo in view of Fonte teach the method of claim 1 and further teach wherein selecting the user interface comprises generating a recommended pillow size for the user interface based on a set of nostril measurements of the set of facial measurements (see for instance, paragraph 69, 141, 143, 153). The motivation to combine Tan-Ngo and Fonte is the same as that which was set forth with respect to claim 1.
Regarding claim 3, Tan-Ngo in view of Fonte teach the method of claim 2 and further teach wherein the set of nostril measurements define at least a first ellipse and comprise at least one of: (i) a major axis, (ii) a minor axis, (iii) a rotation, or (iv) a distance of the first ellipse from a center of a nose of the user (see for instance, Tan-Ngo, paragraph 118, 126 ). The motivation to combine Tan-Ngo and Fonte is the same as that which was set forth with respect to claim 1.
Regarding claim 4, Tan-Ngo in view of Fonte teach the method of claim 2 and further teach wherein generating the recommended pillow size comprises processing the set of nostril measurements using a second machine learning model (see for instance, paragraphs 43, 63, 72, 153, and 156). The motivation to combine Tan-Ngo and Fonte is the same as that which was set forth with respect to claim 1.
Regarding claim 11, Tan-Ngo in view of Fonte teach the method of claim 1 and further teach wherein the set of two-dimensional images comprise an image depicting a left side of the head of the user, an image depicting a right side of the head of the user, an image depicting a front of the head of the user, and an image depicting a bottom of the head of the user (see for instance, Fonte, paragraphs 120-123, 130, and 160). The motivation to combine Tan-Ngo and Fonte is the same as that which was set forth with respect to claim 1.
Regarding claim 13, Tan-Ngo in view of Fonte teach the method of claim 1, and further teach providing one or more requests for information to the user, wherein the one or more requests for information ask the user to indicate whether they experience difficulty breathing through their nose; receiving, from the user, one or more responses to the one or more requests; and selecting the user interface based further on the one or more responses (see for instance, Tan-Ngo, paragraph 63). The motivation to combine Tan-Ngo and Fonte is the same as that which was set forth with respect to claim 1.
Regarding claims 15 and 27, claim 15 is the processing system claim and claim 27 is the non-transitory computer readable medium claim of method claim 1. Claims 15 and 27 are rejected using substantially similar rationale as to that set forth in the rejection of claim 1. In addition, Tan-Ngo teaches a processing system, comprising: one or more processors; and one or more memories collectively comprising computer-executable instructions which, when executed on any combination of the one or more processors, cause the processing system to perform an operation (see for instance, Tan-Ngo, paragraphs 4 and 6 and Fonte, paragraphs 41-43) and one or more non-transitory computer readable media collectively containing, in any combination, computer program code that, when executed by operation of a computing system, performs an operation (see for instance, Tan-Ngo, paragraphs 4 and 6 and Fonte, paragraphs 41-43).
Regarding claims 16 and 28, claim 16 is the processing system claim and claim 28 is the non-transitory computer readable medium claim of method claim 2. Claims 16 and 28 are rejected using substantially similar rationale as to that set forth in the rejection of claim 2.
Regarding claims 17 and 28, claim 17 is the processing system claim and claim 28 is the non-transitory computer readable medium claim of method claim 3. Claims 17 and 28 are rejected using substantially similar rationale as to that set forth in the rejection of claim 3.
Regarding claims 18 and 29, claim 18 is the processing system claim and claim 29 is the non-transitory computer readable medium claim of method claim 4. Claims 18 and 29 are rejected using substantially similar rationale as to that set forth in the rejection of claim 4.
Regarding claims 25 and 37, claim 25 is the processing system claim and claim 37 is the non-transitory computer readable medium claim of method claim 11. Claims 25 and 37 are rejected using substantially similar rationale as to that set forth in the rejection of claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG Publication 2023/0364365 to Hogg teaches system and methods for user interface comfort evaluation, see for instance, abstract. Note: Hogg is one of the authors on the current application and the publication date falls within one year of the provisional filing date. There is a common assignee between the PG Publication and the current application.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J COBB whose telephone number is (571)270-3875. The examiner can normally be reached Monday - Friday, 11am - 7pm ET.
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, Alicia Harrington can be reached at 571-272-2330. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL J COBB/Primary Examiner, Art Unit 2615
