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 .
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on January 25, 2024, February 24, 2025, and July 30, 2025 comply with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Citations which have not been considered, have not been considered because they do not comply with 37 CFR 1.98(b) which states “The date of publication supplied must include at least the month and year of publication, except that the year of publication (without the month) will be accepted if the applicant points out in the information disclosure statement that the year of publication is sufficiently earlier than the effective U.S. filing date and any foreign priority date so that the particular month of publication is not in issue”.
Status of Claims
Claims 1, 2 and 5-22 are pending in this application.
35 USC § 101 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
Claim Rejections - 35 USC § 101
35 U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 22 is rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to a program. The scope of a program is broad enough to include either a program by itself, and/or a signal per se, both of which are non-statutory.
In order to overcome the rejection, the examiner suggest amending the preamble as follows: “A non-transitory computer readable medium storing thereon a computer program, which when executed by a computer, performs a method comprising”.
Claim Rejections - 35 USC § 102
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 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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 19-22 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Capron-Richard et al. (U.S. Patent Application Publication No. US 2019/0328489 A1) (hereafter referred to as “Capron-Richard”).
With regard to claim 1, Capron-Richard describes detecting, based on machine learning algorithm (see Figure 45 and refer for example to paragraphs [0012], [0265] and [0275]), a plurality of cephalometric landmarks on the 3D CBCT image to derive 13 parameters for orthodontic diagnosis (see Figures 8 and 32D, and refer for example to paragraphs [0164] through [0196]); and deriving 13 parameters corresponding to distances or angles between the detected plurality of cephalometric landmarks (see Figures 32A and 32D, and refer for example to paragraphs [0135] through [0146], paragraphs [0174] through [0187] and paragraph [0212]).
With regard to claim 19, Capron-Richard describes diagnosing a facial profile of the patient or an occlusal state in response to the derived 13 parameters (refer to paragraphs [0002], [0006], [0224], [0240], [0265], [0275], [0278], [0287] and [0212]).
As to claim 20, Capron-Richard describes wherein when the patient's occlusal state is diagnosed corresponding to the derived 13 parameters, a state in which an antero-posterior occlusal position of the maxilla and mandible is in a relatively normal category, a state in which the maxilla relatively protrudes relative to the mandible, and a state in which the mandible relatively protrudes relative to the maxilla are classified and diagnosed, respectively (refer for example to paragraphs [0002], [0006], [0224], [0240], [0265], [0275], [0278], [0287] and [0212]).
With regard to claim 21, Capron-Richard describes wherein when a facial profile of the patient is diagnosed corresponding to the derived 13 parameters, a state in which a length of the facial portion is in a normal category, a state in which the length of the facial portion is shorter than the normal category, and a state in which the length of the facial portion is longer than the normal category are classified and diagnosed respectively (refer for example to paragraphs [0002], [0006], [0224], [0240], [0265], [0275], [0278], [0287] and [0212]).
As to claim 22, Capron-Richard describes a program (see Figures 8 and 32D, and refer for example to paragraphs [0203], [0311] and [0313]) for deriving cephalometric parameters for orthodontic diagnosis that is installed on a computing device or a computable cloud server and is programmed to automatically perform (see Figure 45 and refer for example to paragraphs [0012], [0265] and [0275]) of detecting a plurality of cephalometric landmarks as output data after obtaining the 3D CBCT image for orthodontic diagnosis results in the method deriving cephalometric parameters for orthodontic diagnosis of claim 1 (see Figures 8 and 32D, and refer for example to paragraphs [0164] through [0196]); and deriving 13 parameters corresponding to distances or angles between the detected plurality of cephalometric landmarks (see Figures 32A and 32D, and refer for example to paragraphs [0135] through [0146], paragraphs [0174] through [0187] and paragraph [0212]).
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 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(a) 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 5 is rejected under 35 U.S.C. §103(a) as being unpatentable over Capron-Richard et al. (U.S. Patent Application Publication No. US 2019/0328489 A1) in view of Abraham et al. (U.S. Patent Application Publication No. US 2018/0061054 A1) (hereafter referred to as “Abraham”).
The arguments advanced in section 10 above, as to the applicability of Capron-Richard, are incorporated herein.
In regard to claim 5, Capron-Richard provides for using a dental panoramic image to detect individual regions of entire set of teeth, detecting, for each detected individual region of the entire set of teeth, teeth landmarks representing positions of the teeth, analyzing the positions of the detected teeth landmarks to classify the entire set of teeth into upper teeth and lower teeth, numbering each of right upper teeth, left upper teeth, right lower teeth, and left lower teeth sequentially based on a horizontal distance from the midline of a facial portion to he detected teeth landmarks, and analyzing the numbered teeth to detect a plurality of cephalometric landmarks for deriving a parameter from a specific tooth, including an incisor, a canine, and a first molar (see Figures 1, 2, 5, 6 and 8, and refer for example to paragraphs [0097] through [0113]).
Capron-Richard provides for using a computer system that executes various image processing/computer algorithms in order to accomplish a plurality of 3-D cephalometric analysis tasks, specifically using artificial intelligence algorithm and machine learning approaches (refer to paragraphs [0103], [0265] and [0305]), but does not expressly describe applying a region based convolutional neural network machine learning model to the dental panoramic image to detect individual regions of entire set of teeth, such a technique is well known and widely utilized in the prior art.
Abraham discloses a system for automatic cephalometric analysis using machine learning based on convolutional neural networks (see Figures 2, 3 and 5 through 7, and refer for example to paragraphs [0037] through [0044], paragraphs [0051] through [0058], and paragraphs [0063] through [0067]).
Given the teachings of the two references and the same environment of operation, namely that of systems for cephalometric analysis using machine learning, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using a convolutional neural network machine learning in the manner described by Abraham according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by (refer for example to paragraphs [0022] and [0023]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
Claims 2 and 6-18 are rejected under 35 U.S.C. §103(a) as being unpatentable over Capron-Richard et al. (U.S. Patent Application Publication No. US 2019/0328489 A1) in view of the cited Wikipedia reference (which is supported by the cited Oxford Reference).
The arguments advanced in section 10 above, as to the applicability of Capron-Richard, are incorporated herein.
As to claim 2, Capron-Richard describes wherein, in order to provide information on the orthodontic diagnosis, the 13 parameters include a degree of protrusion of a maxilla, a degree of protrusion of a mandible, a degree of protrusion of chin, a degree of displacement of a center of a mandible, a degree of displacement of the midline of upper central incisors, and a degree of displacement of the midline of lower central incisors, a vertical distance from the true horizontal plane (THP) passing through nasion, which is the most concave point between a frontal bone and a nasal bone, to a tip of a right upper canine, a vertical distance from the THP to a tip of a left upper canine, a vertical distance from the THP to a right upper first molars, a vertical distance from the THP to a left upper first molars, a degree of inclination of a upper central incisor, a degree of inclination of a lower central incisor, and a degree of inclination of the mandible with respect to the THP (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
With regard to claim 6, Capron-Richard describes wherein the machine learning algorithm (see Figure 45 and refer for example to paragraphs [0012], [0265] and [0275]) detects nasion, which is the most concave point between a frontal bone and a nasal bone, and A-point (A), which is the deepest portion of a line connecting an anterior nasal spine in the maxilla and a prosthion, in the CBCT image in the sagittal plane, and wherein the degree of protrusion of a maxilla is derived by measuring a distance between the nasion true vertical plane (NTVP), which is a vertical plane passing through nasion, and A-point (refer to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
As to claim 7, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone, and B-point, which is the deepest portion connecting an infradentale and Pog, which is the most prominent point of chin, in the CBCT image in the sagittal plane, and wherein the degree of protrusion of the mandible is derived by measuring a distance between the nasion true vertical plane (NTVP), which is a vertical plane passing through nasion, and B-point (refer to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
In regard to claim 8, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone, and pogonion, which is the most prominent point of chin, in the CBCT image in the sagittal plane, and wherein the degree of protrusion of chin is derived by measuring a distance between the nasion true vertical plane (NTVP), which is a vertical plane passing through nasion, and pogonion (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
With regard to claim 9, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone, and menton, which is the lowest point of the mandible, in the CBCT image in the coronal plane, and wherein the degree of displacement of a center of the mandible is derived by measuring a distance between the nasion true vertical plane (NTVP), which is a vertical plane passing through nasion, and menton (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
As to claim 10, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone in the CBCT image in the coronal plane, and a midpoint of upper central incisors in the dental panoramic image, and wherein the degree of displacement of the midline of the upper central incisors is derived by measuring a distance between the nasion true vertical plane (NTVP), which is a vertical plane passing through nasion, and the midpoint of the upper central incisors (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
In regard to claim 11, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone in the CBCT image in the coronal plane, and a central point of lower central incisors in the dental panoramic image, and wherein the degree of displacement of the midline of the lower central incisors is derived by measuring a distance between the nasion true vertical plane (NTVP), which is a vertical plane passing through nasion, and the midpoint of the lower central incisors (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
With regard to claim 12, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone in the CBCT image in the coronal plane, and the cusp tip of the right upper canine in the dental panoramic image, and wherein the vertical distance between the true horizontal plane (THP), which is a horizontal plane passing through nasion, and the cusp tip of the right upper canine is derived (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
In regard to claim 13, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone in the CBCT image in the coronal plane, and the cusp tip of the left upper canine in the dental panoramic image, and wherein the vertical distance between the true horizontal plane (THP), which is a horizontal plane passing through nasion, and the cusp tip of the left upper canine is derived (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
As to claim 14, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone in the CBCT image in the coronal plane, and the mesio-buccal cusp tip of a right upper first molar in the dental panoramic image, and wherein, through a distance between the true horizontal plane (THP), which is a horizontal plane passing through nasion, and the mesio-buccal cusp tip of the right upper first molar, the vertical distance from the THP to the right upper first molars is derived (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
In regard to claim 15, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone in the CBCT image in the coronal plane, and the mesio-buccal cusp tip of a left upper first molar in the dental panoramic image, and wherein, through a distance between the true horizontal plane (THP), which is a horizontal line passing through nasion, and the mesio-buccal cusp tip of the left upper first molar, the vertical distance from the THP to the left upper first molar is derived (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
With regard to claim 16, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone in the CBCT image in the coronal plane, and the crown tip of the upper incisor and the root tip of the upper incisor in the incisor image in a cross-sectional view, and wherein the degree of inclination of the upper central incisor is derived through an angle between the true horizontal plane (THP), which is a horizontal plane passing through nasion, and a vector connecting the crown tip of the upper incisor and the root tip of the upper incisor (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result..
As to claim 17, Capron-Richard describes wherein the machine learning algorithm detects menton, which is the lowest point in the mandible, and gonion, which is a point of maximum curvature in the mandibular angle, in the CBCT image in the sagittal plane, and the crown tip of the lower incisor and the root tip of the lower incisor in the incisor image in a cross- sectional view, and wherein the degree of inclination of the lower central incisor is derived through an angle between a MeGo line connecting menton and gonion and a vector connecting the crown tip of the lower incisor and the root tip of the lower incisor (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
In regard to claim 18, Capron-Richard describes wherein the machine learning algorithm detects nasion, which is the most concave point between a frontal bone and a nasal bone, menton, which is the lowest point in the mandible, and gonion, which is a point of maximum curvature in the mandibular angle, in the CBCT image in the sagittal plane, and wherein, through an angle between the true horizontal plane (THP), which is a horizontal plane passing through nasion, and a MeGo line connecting menton and gonion, the degree of inclination of the mandible with respect to the THP is derived (refer for example to paragraph [0004] and paragraphs [0168] through [0196]).
Although Capron-Richard does not show expressly describe some of the cephalometric landmarks, however such cephalometric landmarks are well known and widely utilized in the prior art (as attested to by the cited references).
Given the teachings of the references, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Capron-Richard system using other cephalometric landmarks according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested, which fails to patentably distinguish over the prior art absent some novel and unexpected result.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen (‘252), (‘254) and (‘976), Masoud, Inglese, Kim, Kang and Juneja all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/JOSE L COUSO/Primary Examiner, Art Unit 2667
December 4, 2025