DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The Amendment filed 25 March 2026 has been entered and considered. Claims 1-7 and 10-20 have been amended. Claims 1-20 are all the claims pending in the application. Claims 1-20 are rejected. All new grounds of rejection set forth in the present action were necessitated by Applicant’s claim amendments; accordingly, this action is made final.
Response to Amendment
Prior Art Rejections
In view of the amendments to independent Claims 1, 10, and 16, and their dependent claims by extension, the rejection under 35 USC 102(a)(2) using previously cited art Pamuru is withdrawn. However, a rejection under 35 USC 103 is presented using previously cited art Pamuru ‘318 in view of Pamuru ‘373.
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 1-9 and 16-20 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.
Claims 1-9 and 16-20 are directed to a computer system or stored computer program, but have been amended to require “two or more vehicle dealers”. This appears to be an intended use of the system rather than a feature that would be ascertainable from the program itself. It is unclear what programming or program feature this corresponds to. How would an observer determine (from just a real-world program) if the program infringes/anticipates the claim?
Dependent claims from 1 and 16 inherit these problems and are likewise rejected.
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.
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.
Claims 1-20 as best understood are rejected under 35 U.S.C. 103 as being unpatentable over Pamuru (U.S. Patent App. Pub No. 2023/0274318 A1, hereafter referred as Pamuru ‘318) in view of Pamuru et al. (U.S. Patent App. Pub No. 2023/0315373 A1, hereafter referred as Pamuru ‘373).
Regarding Claim 1:
Pamuru ‘318 teaches a system for generating image metadata (Pamuru ‘318: Par. [0007]; the system and method uses artificial intelligence machine learning technologies to receive images, automatically analyze them and automatically classify them, adding metadata to the images associated with the analysis and classification), the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories (Pamuru ‘318: Par. [0033]; a computer system, such as those known in the art, executes instructions stored on a computer-readable storage medium), configured to: obtain, from two or more vehicle dealers, a plurality of images associated with a plurality of vehicles, wherein the plurality of images comprise a first set of vehicle images from a first vehicle dealer and a second set of vehicle images from a second vehicle dealer (Pamuru ‘318: Par. [0060-0061]; using computing device 16 to capture a plurality of images 30 of at a first item, preferably a first vehicle 31, with multiple images being taken; wants the system to identify in the images 30 of various vehicles 40; Pamuru contemplates that the art generally has multiple sellers ([0002], multiple sellers and buyers from a website; [0043], use of the singular in Pamuru is intended to include the plural; no discussion of features to exclude non-related parties from listing vehicles; it would have been obvious to one having ordinary kill in the art to provide a single website to connect multiple buyers and sellers in order to maximize the value of the site to both buyers and sellers); generate, for the plurality of images, metadata associated with the plurality of image (Pamuru ‘318: Par. [0070]; after adding an identified object to object chart 96, AI/ML module 74 adds a metadata value to the associated image file (not shown) associating the first image with the object identification, indicating details about the identified object, such as the type of object, its location, whether it was removed from the image, etc.), wherein the one or more processors, to generate the metadata, are configured to generate the metadata in accordance with a machine learning model and in accordance with one or more characteristics associated with the plurality of images (Pamuru ‘318: Par. [0066]; upon receipt of images 30, system 10 uses artificial intelligence machine learning “AI/ML” module 74 to automatically analyze features, as objects 76 for identification in images 30; the objects may be any features of the item located in the image 30, such as wheels 90, bumpers 112, logos 114, rear lights 116, side mirrors 118, door handle 120, windows 122, etc.); and modify the plurality of images in accordance with the metadata (Pamuru ‘318: Par. [0066]; the process for modifying and sequencing the images 30 for display using system 10 is preferably completely automated).
Pamuru ‘318 fails to further teach wherein the one or more processors, to modify the plurality of images, are configured to arrange the plurality of images such that a primary image for each vehicle of the plurality of vehicles is associated with a same vehicle angle.
Pamuru ‘373, like Pamuru ‘318, is directed to generating vehicle image metadata. Pamuru ‘373 does teach wherein the one or more processors, to modify the plurality of images, are configured to arrange the plurality of images such that a primary image for each vehicle of the plurality of vehicles is associated with a same vehicle angle (Pamuru ‘373: Par. [0005] and [0055]; Certain sellers may desire a group of displayed product images all show the same perspective of the product offerings. While initially capturing all of the images from the desired perspective addresses this issue, sometimes a seller receives videos, or images from multiple sources that may include multiple images taken from a plurality of perspectives. It would therefore be desirable to provide a system that allows a seller to automatically select guidelines for images that the system automatically applies to a group of images to automatically sort into images showing product from a predetermined perspective; User interface 24 preferably automatically indicates to the dealer 28 the images and perspectives desired for display).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pamuru ‘318 to utilize the desired image perspective technique, as taught by Pamuru ‘373, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. As taught by Pamuru ‘373, the proposed modification allows for uniformity, saves time, and improves the quality of the displayed images to enhance the overall potential customer experience (Pamuru ‘373: Par. [0008]).
In regards to Claim 2, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the system of claim 1, wherein the one or more processors, to generate the metadata, are configured to generate metadata that indicates image quality, wherein the image quality corresponds to at least one of a blur characteristic, a shadow characteristic, a lighting characteristic, or an external object characteristic (Pamuru ‘373: Par. [0009]; the machine learning and artificial intelligence technology associated with the system and method can differentiate between images and identify features within images; such features may include a desired perspective, inconsistent backgrounds, low detail, blurriness, shadows, glare, reflections, unwanted information, unwanted elements, poles, trees, lack or focus, poor resolution, rain, snow, fumes, smoke, mud, unwanted banners, unwanted overlays, etc.).
In regards to Claim 3, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the system of claim 1, wherein the one or more processors, to generate the metadata, are configured to generate metadata that indicates a view of a vehicle included in an image, wherein the view of the vehicle corresponds to an exterior view of the vehicle, a view of an exterior component of the vehicle, an interior view of the vehicle, a view of an interior component of the vehicle, or a poster view of the vehicle (Pamuru ‘318: Par. [0071-0072] and Fig. 10; as shown in FIG. 10, classifications 136 may include descriptions of the image 30, such as what portion of the subject is shown in the image 30 and from what angle; AI/ML module 74 may determine, based on previously identified objects 76 in first image 52 that the classification 136 from list 134 most closely matching first image 52 is 3_4th_passenger_side_rear, indicating that first image 52 shows a three-quarters image of vehicle 40 taken from the rear passenger side of vehicle 40).
In regards to Claim 4, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the system of claim 1, wherein the one or more processors, to generate the metadata, are configured to generate metadata that indicates an angle of a vehicle included in an image, wherein the angle of the vehicle corresponds to a select angle of a plurality of configured vehicle angles (Pamuru ‘318: Par. [0071-0072] and Fig. 10; as shown in FIG. 10, classifications 136 may include descriptions of the image 30, such as what portion of the subject is shown in the image 30 and from what angle; AI/ML module 74 may determine, based on previously identified objects 76 in first image 52 that the classification 136 from list 134 most closely matching first image 52 is 3_4th_passenger_side_rear, indicating that first image 52 shows a three-quarters image of vehicle 40 taken from the rear passenger side of vehicle 40).
In regards to Claim 5, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the system of claim 1, wherein the one or more processors, to modify the plurality of images, are configured to modify a size of the plurality of images or to modify a background of the plurality of images (Pamuru ‘318: Par. [0075]; if module 74 determines 156 images 30 are to be segmented 164 from backgrounds 158, module 74 begins to segment vehicles 40 within images 30 from their associated backgrounds 158).
In regards to Claim 6, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the system of claim 5, wherein the one or more processors, to modify the size of the plurality of images, are configured to modify the size of the plurality of images in accordance with a standard image size (Pamuru ‘318: Par. [0077]; AI/ML module 74 automatically uses maximum height and width parameters predetermined by vehicle dealer 28 or otherwise, to resize the image 165 within the predefined size parameters), and wherein the one or more processors, to modify the background of the plurality of images, are configured to remove the background of the plurality of images or to add a new background to the plurality of images (Pamuru ‘318: Par. [0075]; if module 74 determines 156 images 30 are to be segmented 164 from backgrounds 158, module 74 begins to segment vehicles 40 within images 30 from their associated backgrounds 158).
In regards to Claim 7, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the system of claim 1, wherein the one or more processors, to modify the plurality of images, are configured to select the primary image for each vehicle, wherein the primary image for each vehicle displays each vehicle in accordance with the same vehicle angle, wherein the same vehicle angle comprises a configured vehicle angle (Pamuru ‘373: Par. [0005] and [0055]; Certain sellers may desire a group of displayed product images all show the same perspective of the product offerings. While initially capturing all of the images from the desired perspective addresses this issue, sometimes a seller receives videos, or images from multiple sources that may include multiple images taken from a plurality of perspectives. It would therefore be desirable to provide a system that allows a seller to automatically select guidelines for images that the system automatically applies to a group of images to automatically sort into images showing product from a predetermined perspective; User interface 24 preferably automatically indicates to the dealer 28 the images and perspectives desired for display).
In regards to Claim 8, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the system of claim 1, wherein the one or more processors are further configured to identify an image having an image quality that does not satisfy an image quality threshold (Pamuru ‘373: Par. [0004]; a system that allows a seller to automatically identify guidelines for images which a system automatically applies to a group of images to automatically sharpen and/or upscale as desired by the seller; it would also be desirable to allow a seller to select guidelines for images that a system automatically applies to a group of images to automatically identify and remove noise and undesirable artifacts such as fingerprints or smudges on the product, glare, reflections, etc.).
In regards to Claim 9, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the system of claim 1, wherein the one or more processors, to obtain the plurality of images, are configured to receive the plurality of images without receiving any metadata associated with the plurality of images (Pamuru ‘318: Par. [0060]; using computing device 16 to capture a plurality of images 30 of at a first item, preferably a first vehicle 31, with multiple images being taken).
Regarding Claim 10:
Pamuru ‘318 as modified by Pamuru ‘373 further teaches a method of generating image metadata (Pamuru ‘318: Par. [0007]; the system and method uses artificial intelligence machine learning technologies to receive images, automatically analyze them and automatically classify them, adding metadata to the images associated with the analysis and classification), comprising: obtaining, from two or more vehicle dealers, a plurality of images associated with a plurality of vehicles, wherein the plurality of images comprise a first set of vehicle images from a first vehicle dealer and a second set of vehicle images from a second vehicle dealer (Pamuru ‘318: Par. [0060-0061]; using computing device 16 to capture a plurality of images 30 of at a first item, preferably a first vehicle 31, with multiple images being taken; wants the system to identify in the images 30 of various vehicles 40; obvious to one skilled in the art that the plurality of images from a plurality of vehicle dealers); generating, for the plurality of images, metadata associated with the plurality of images (Pamuru ‘318: Par. [0070]; after adding an identified object to object chart 96, AI/ML module 74 adds a metadata value to the associated image file (not shown) associating the first image with the object identification, indicating details about the identified object, such as the type of object, its location, whether it was removed from the image, etc.), wherein generating the metadata comprises generating the metadata in accordance with a machine learning model and in accordance with one or more characteristics associated with the plurality of images (Pamuru ‘318: Par. [0066]; upon receipt of images 30, system 10 uses artificial intelligence machine learning “AI/ML” module 74 to automatically analyze features, as objects 76 for identification in images 30; the objects may be any features of the item located in the image 30, such as wheels 90, bumpers 112, logos 114, rear lights 116, side mirrors 118, door handle 120, windows 122, etc.); and modifying the plurality of images in accordance with the metadata (Pamuru ‘318: Par. [0066]; the process for modifying and sequencing the images 30 for display using system 10 is preferably completely automated), wherein the modifying the plurality of images comprises arranging the plurality of images such that a primary image for each vehicle of the plurality of vehicles is associated with a same vehicle angle (Pamuru ‘373: Par. [0005] and [0055]; Certain sellers may desire a group of displayed product images all show the same perspective of the product offerings. While initially capturing all of the images from the desired perspective addresses this issue, sometimes a seller receives videos, or images from multiple sources that may include multiple images taken from a plurality of perspectives. It would therefore be desirable to provide a system that allows a seller to automatically select guidelines for images that the system automatically applies to a group of images to automatically sort into images showing product from a predetermined perspective; User interface 24 preferably automatically indicates to the dealer 28 the images and perspectives desired for display).
In regards to Claim 11, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the method of claim 10, wherein generating the metadata comprises generating metadata that indicates image quality, wherein the image quality corresponds to at least one of a blur characteristic, a shadow characteristic, a lighting characteristic, or an external object characteristic (Pamuru ‘373: Par. [0009]; the machine learning and artificial intelligence technology associated with the system and method can differentiate between images and identify features within images; such features may include a desired perspective, inconsistent backgrounds, low detail, blurriness, shadows, glare, reflections, unwanted information, unwanted elements, poles, trees, lack or focus, poor resolution, rain, snow, fumes, smoke, mud, unwanted banners, unwanted overlays, etc.).
In regards to Claim 12, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the method of claim 10, wherein generating the metadata comprises generating metadata that indicates a view of an object included in an image, wherein the view of the object corresponds to an exterior view of the object, a view of an exterior component of the object, an interior view of the object, a view of an interior component of the object, or a poster view of the object (Pamuru ‘318: Par. [0071-0072] and Fig. 10; as shown in FIG. 10, classifications 136 may include descriptions of the image 30, such as what portion of the subject is shown in the image 30 and from what angle; AI/ML module 74 may determine, based on previously identified objects 76 in first image 52 that the classification 136 from list 134 most closely matching first image 52 is 3_4th_passenger_side_rear, indicating that first image 52 shows a three-quarters image of vehicle 40 taken from the rear passenger side of vehicle 40).
In regards to Claim 13, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the method of claim 10, wherein generating the metadata comprises generating metadata that indicates an angle of an object included in an image, wherein the angle of the object corresponds to a select angle of a plurality of configured object angles (Pamuru ‘318: Par. [0071-0072] and Fig. 10; as shown in FIG. 10, classifications 136 may include descriptions of the image 30, such as what portion of the subject is shown in the image 30 and from what angle; AI/ML module 74 may determine, based on previously identified objects 76 in first image 52 that the classification 136 from list 134 most closely matching first image 52 is 3_4th_passenger_side_rear, indicating that first image 52 shows a three-quarters image of vehicle 40 taken from the rear passenger side of vehicle 40).
In regards to Claim 14, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the method of claim 10, wherein modifying the plurality of images comprises at least one of modifying a size of the plurality of images or modifying a background of the plurality of images (Pamuru ‘318: Par. [0075]; if module 74 determines 156 images 30 are to be segmented 164 from backgrounds 158, module 74 begins to segment vehicles 40 within images 30 from their associated backgrounds 158).
In regards to Claim 15, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the method of claim 10, wherein modifying the plurality of images comprises selecting the primary image for each vehicle, wherein the primary image for each vehicle displays each vehicle in accordance with the same vehicle angle, wherein the same vehicle angle comprises a configured vehicle angle. (Pamuru ‘373: Par. [0005] and [0055]; Certain sellers may desire a group of displayed product images all show the same perspective of the product offerings. While initially capturing all of the images from the desired perspective addresses this issue, sometimes a seller receives videos, or images from multiple sources that may include multiple images taken from a plurality of perspectives. It would therefore be desirable to provide a system that allows a seller to automatically select guidelines for images that the system automatically applies to a group of images to automatically sort into images showing product from a predetermined perspective; User interface 24 preferably automatically indicates to the dealer 28 the images and perspectives desired for display).
Regarding Claim 16:
Pamuru ‘318 as modified by Pamuru ‘373 further teaches a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device (Pamuru ‘318: Par. [0033]; a computer system, such as those known in the art, executes instructions stored on a computer-readable storage medium), cause the device to: obtain, from two or more vehicle dealers, a plurality of images associated with a plurality of vehicles, wherein the plurality of images comprise a first set of vehicle images from a first vehicle dealer and a second set of vehicle images from a second vehicle dealer; (Pamuru ‘318: Par. [0060-0061]; using computing device 16 to capture a plurality of images 30 of at a first item, preferably a first vehicle 31, with multiple images being taken; wants the system to identify in the images 30 of various vehicles 40; obvious to one skilled in the art that the plurality of images from a plurality of vehicle dealers); generate, for the plurality of images, metadata associated with the plurality of images (Pamuru ‘318: Par. [0070]; after adding an identified object to object chart 96, AI/ML module 74 adds a metadata value to the associated image file (not shown) associating the first image with the object identification, indicating details about the identified object, such as the type of object, its location, whether it was removed from the image, etc.), wherein the one or more instructions, that cause the device to generate the metadata, cause the device to generate the metadata in accordance with a machine learning model and in accordance with one or more characteristics associated with the plurality of images (Pamuru ‘318: Par. [0066]; upon receipt of images 30, system 10 uses artificial intelligence machine learning “AI/ML” module 74 to automatically analyze features, as objects 76 for identification in images 30; the objects may be any features of the item located in the image 30, such as wheels 90, bumpers 112, logos 114, rear lights 116, side mirrors 118, door handle 120, windows 122, etc.); and modify the plurality of images in accordance with the metadata (Pamuru ‘318: Par. [0066]; the process for modifying and sequencing the images 30 for display using system 10 is preferably completely automated), wherein the one or more instructions, that cause the device to modify the plurality of images, cause the device to arrange the plurality of images such that a primary image for each vehicle of the plurality of vehicles is associated with a same vehicle angle (Pamuru ‘373: Par. [0005] and [0055]; Certain sellers may desire a group of displayed product images all show the same perspective of the product offerings. While initially capturing all of the images from the desired perspective addresses this issue, sometimes a seller receives videos, or images from multiple sources that may include multiple images taken from a plurality of perspectives. It would therefore be desirable to provide a system that allows a seller to automatically select guidelines for images that the system automatically applies to a group of images to automatically sort into images showing product from a predetermined perspective; User interface 24 preferably automatically indicates to the dealer 28 the images and perspectives desired for display).
In regards to Claim 17, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the non-transitory computer-readable medium of claim 16, wherein the one or more instructions, that cause the device to generate the metadata, cause the device to generate metadata that indicates image quality, wherein the image quality corresponds to at least one of a blur characteristic, a shadow characteristic, a lighting characteristic, or an external object characteristic (Pamuru ‘373: Par. [0009]; the machine learning and artificial intelligence technology associated with the system and method can differentiate between images and identify features within images; such features may include a desired perspective, inconsistent backgrounds, low detail, blurriness, shadows, glare, reflections, unwanted information, unwanted elements, poles, trees, lack or focus, poor resolution, rain, snow, fumes, smoke, mud, unwanted banners, unwanted overlays, etc.).
In regards to Claim 18, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the non-transitory computer-readable medium of claim 16, wherein the one or more instructions, that cause the device to generate the metadata, cause the device to generate metadata that indicates a view of a vehicle of the plurality of vehicles, wherein the view of the vehicle corresponds to an exterior view of the vehicle, a view of an exterior component of the vehicle, an interior view of the vehicle, a view of an interior component of the vehicle, or a poster view of the vehicle (Pamuru ‘318: Par. [0071-0072] and Fig. 10; as shown in FIG. 10, classifications 136 may include descriptions of the image 30, such as what portion of the subject is shown in the image 30 and from what angle; AI/ML module 74 may determine, based on previously identified objects 76 in first image 52 that the classification 136 from list 134 most closely matching first image 52 is 3_4th_passenger_side_rear, indicating that first image 52 shows a three-quarters image of vehicle 40 taken from the rear passenger side of vehicle 40).
In regards to Claim 19, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the non-transitory computer-readable medium of claim 16, wherein the one or more instructions, that cause the device to generate the metadata, cause the device to generate metadata that indicates an angle of a vehicle of the plurality of vehicles, wherein the angle of the vehicle corresponds to the same vehicle angle (Pamuru ‘318: Par. [0071-0072] and Fig. 10; as shown in FIG. 10, classifications 136 may include descriptions of the image 30, such as what portion of the subject is shown in the image 30 and from what angle; AI/ML module 74 may determine, based on previously identified objects 76 in first image 52 that the classification 136 from list 134 most closely matching first image 52 is 3_4th_passenger_side_rear, indicating that first image 52 shows a three-quarters image of vehicle 40 taken from the rear passenger side of vehicle 40).
In regards to Claim 20, Pamuru ‘318 as modified by Pamuru ‘373 further teaches the non-transitory computer-readable medium of claim 16, wherein the one or more instructions, when executed by the one or more processors, further cause the device to identify an image having an image quality that does not satisfy an image quality threshold (Pamuru ‘373: Par. [0004]; a system that allows a seller to automatically identify guidelines for images which a system automatically applies to a group of images to automatically sharpen and/or upscale as desired by the seller; it would also be desirable to allow a seller to select guidelines for images that a system automatically applies to a group of images to automatically identify and remove noise and undesirable artifacts such as fingerprints or smudges on the product, glare, reflections, etc.).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RENAE BITOR whose telephone number is (703)756-5563. The examiner can normally be reached Monday to Friday: 8:00 - 5:30 but off the 1st Friday of the biweek.
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/RENAE A BITOR/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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