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 .
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, 11, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rami Koujan et al., US PGPUB No. 20260187881 A1, hereinafter Rami-Koujan, and further in view of Vorobiov et al., US Patent No. 12505629 B1, hereinafter Vorobiov.
Regarding claim 13, Rami-Koujan discloses a computing device (Rami-Koujan; a computer device [¶ 0114-0115], as illustrated within Fig. 6; moreover, system [¶ 0030, ¶ 0042, and ¶ 0097], as illustrated within Fig. 1) comprising:
a memory (Rami-Koujan; the computer device [as addressed above] comprises a memory [¶ 0115-0116], as illustrated within Fig. 6); and
a processor (Rami-Koujan; the computer device [as addressed above] comprises a processor [¶ 0114-0116], as illustrated within Fig. 6) configured to:
receive a first image of a subject person and a selection of a clothing item (Rami-Koujan; the processor [as addressed above] configure to receive a 1st image (i.e. input image) of a subject person and a selection of a clothing item [¶ 0143-0144]);
determine, using a first machine-learning model and the first image, measurements of the subject person (Rami-Koujan; the processor [as addressed above] configure to determine measurements of the subject person using a 1st ML model and the 1st image (i.e. input image) [¶ 0148 and ¶ 0150], as illustrated within Fig. 10; wherein, measurements are implicit given key points [¶ 0162]), the measurements relatable to one or more dimensions of the clothing item (Rami-Koujan; the measurements relatable to one or more dimensions of the clothing item [¶ 0162-0164]);
determine, using a second machine-learning model, a fit of the clothing item on the subject person based on a second image of the clothing item worn by another person and the measurements of the subject person (Rami-Koujan; the processor [as addressed above] configure to determine a fit of the clothing item on the subject person [¶ 0150 and ¶ 0152-0153] based on a 2nd image (i.e. similar images) of the clothing item and the measurements of the subject person using a ML model [¶ 0154-0156 and ¶ 0161-0162]; moreover, using more than one ML model (e.g. another or 2nd ML mode) corresponding to other portions of the NN [¶ 0150], multiple NN configurations [¶ 0162 and ¶ 0174-0175], and/or iterative training [¶ 0153 and ¶ 0156-0159]); and
display, via a display, a third image of a portrayal of the subject person wearing the clothing item with the fit portrayed in the second image (Rami-Koujan; the processor [as addressed above] configure to display a 3rd image (I.e. generated image) of a portrayal of the subject person wearing the clothing item with the fit portrayed in the 2nd image [¶ 0148-0149 and ¶ 0152-0153], as illustrated within Fig. 10 and Fig. 12, via a display [¶ 0171 and ¶ 0117], as illustrated within Fig. 13; moreover, display corresponds to a computer device and/or HMD [¶ 0097 and ¶ 0099-0100] in relation with a display [¶ 0101 and ¶ 0111]).
Rami-Koujan fails to explicitly teach a second machine-learning model.
However, Vorobiov teaches to: determine, using a second machine-learning model, a fit of the clothing item on the subject person based on a second image of another person and the measurements of the subject person (Vorobiov; determine a fit of the clothing item on the subject person based on a 2nd image by another person and the measurements of the subject person using a 2nd ML model [Col. 5, line 65 to Col. 6, line 25 and Col. 10, lines 7-10]; wherein, another person corresponds to different users [Col. 11, lines 11-53] in relation with identifying keypoints [Col. 11, line 54 to Col. 12, line 25]).
Rami-Koujan and Vorobiov are considered to be analogous art because both pertain to generating and/or managing data in relation with providing media data to a user, wherein one or more computerized units are utilized in order to produce computerized clothing models.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Rami-Koujan, to incorporate to: determine, using a second machine-learning model, a fit of the clothing item on the subject person based on a second image of another person and the measurements of the subject person (as taught by Vorobiov), in order to provide an article of adaptive clothing (Vorobiov; [Col. 1, line 6-14, Col. 2, lines 36-55, and Col. 4, lines 1-17]).
Regarding claim 1, the rejection of claim 1 is addressed within the rejection of claim 13, due to the similarities claim 1 and claim 13 share, therefore refer to the rejection of claim 13 regarding the rejection of claim 1. Although, claim 1 and claim 13 may not be identical, they are considerably comparable or substantially equivalent given their overlapping subject matter. Thus, it is reasonable to reject claim 1 based on the teachings and rational in relation with the prior art within the rejection of claim 13.
Regarding claim 11, Rami-Koujan in view of Vorobiov further discloses the method of claim 1, wherein the selection of the clothing item indicates a selected size of the clothing item (Vorobiov; the selection of the clothing item indicates a selected size of the clothing item [Col. 5, lines 18-38 and Col. 6, lines 26-44]; wherein, sizes of a garment is chosen based on a size of a person [Col. 11, line 11-40]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Rami-Koujan as modified by Vorobiov, to incorporate the selection of the clothing item indicates a selected size of the clothing item (as taught by Vorobiov), in order to provide an article of adaptive clothing (Vorobiov; [Col. 1, line 6-14, Col. 2, lines 36-55, and Col. 4, lines 1-17]).
Regarding claim 20, the rejection of claim 20 is addressed within the rejection of claim 13, due to the similarities claim 20 and claim 13 share, therefore refer to the rejection of claim 13 regarding the rejection of claim 20. Although, claim 20 and claim 13 may not be identical, they are considerably comparable or substantially equivalent given their overlapping subject matter. However, the subject matter/limitations not addressed by claim 13 is/are addressed below.
Rami-Koujan discloses one or more computer-readable storage media storing instructions that, responsive to execution by a processing device, causes the processing device to perform (Rami-Koujan; one or more computer-readable storage media storing instructions that causes the processing device to perform responsive to execution by a processing device [¶ 0114-0116]).
(further refer to the rejection of claim 13)
Claim(s) 2-7 and 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rami-Koujan in view of Vorobiov as applied to claim(s) 1 and 13 above, and further in view of Shuvi et al., US PGPUB No. 20220189087 A1, hereinafter Shuvi.
Regarding claim 14, Rami-Koujan in view of Vorobiov further discloses the computing device of claim 13, wherein:
the first machine-learning model comprises a parametric model that generates a representation of the subject person using a human mesh model with measurements of the subject person (Rami-Koujan; the 1st ML model [as addressed within the parent claim(s)] comprises a parametric model that generates a representation of the subject person using a human model (corresponding to pose and/or bounding-box model) with measurements of the subject person [¶ 0022-0024 and ¶ 0028-0029], as illustrated within Fig. 10 and Fig. 12; moreover, determined characteristics of input data [¶ 0026-0027] in relation with a pose generator and bounding-box generator [¶ 0162], as illustrated within Fig. 11; and moreover, generator NN [¶ 0168]); and
the machine-learning model comprises a convolutional neural network that transfers the fit of the clothing item in the second image to the portrayal of the subject person wearing the clothing item in the third image (Rami-Koujan; the ML model comprises a CNN (i.e. convolution layers) [¶ 0150] that transfers the fit of the clothing item in the 2nd image (i.e. similar images) to the portrayal of the subject person wearing the clothing item in the 3rd image (i.e. generated image) [¶ 0154-0156 and ¶ 0161-0162]; moreover, an implicit 2nd ML model [as addressed within the parent claim(s)]), training data for the machine-learning model including pairs of images of persons wearing garments to learn to transfer the fit of the garments between the persons (Rami-Koujan; training data for the ML model including pairs of images of persons wearing garments to learn to transfer the fit of the garments between the persons [¶ 0156-0159 and ¶ 0171]; additionally, training component [¶ 0024-0027] for generative virtual clothing try-on [¶ 0065 and ¶ 0142-0144]; moreover, the implicit 2nd ML model [as addressed above]).
Vorobiov further teaches the first machine-learning model comprises a parametric model that generates a representation of the subject person using a human mesh model with measurements of the subject person (Vorobiov; the 1st ML model (i.e. 1st model generator) comprises a parametric model that generates a representation of the subject person using a human mesh model with measurements of the subject person [Col. 6, line 26 to Col. 7, line 58]; wherein, keypoint(s) [id.] in relation with a mesh [Col. 7, line 59 to Col. 8, line 8 and Col. 12, lines 26-46]; moreover, ML driven modeling [Col. 9, line 43 to Col. 10, line 47 and Col. 15, lines 12-27]; additionally, pose estimations [Col. 8, line 53 to Col. 9, line 13]); and
the second machine-learning model comprises a convolutional neural network that transfers the fit of the clothing item in the second image to the portrayal of the subject person wearing the clothing item in the third image (Vorobiov; the 2nd ML model comprises a CNN that transfers the fit of the clothing item in the 2nd image to the portrayal of the subject person wearing the clothing item in the 3rd image [Col. 8, lines 9-52 and Col. 9, lines 29-42]; wherein, additional ML driven modeling [Col. 10, line 48 to Col. 11, line 10]; moreover, generation of an image associated with a virtual try-on [Col. 13, lines 24-56]), training data (Vorobiov; training using image data [Col. 10, lines 7-47 and Col. 15, lines 12-27]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Rami-Koujan as modified by Vorobiov and Shuvi, to incorporate the first machine-learning model comprises a parametric model that generates a representation of the subject person using a human mesh model with measurements of the subject person; and the second machine-learning model comprises a convolutional neural network that transfers the fit of the clothing item in the second image to the portrayal of the subject person wearing the clothing item in the third image, training data (as taught by Vorobiov), in order to provide an article of adaptive clothing (Vorobiov; [Col. 1, line 6-14, Col. 2, lines 36-55, and Col. 4, lines 1-17]).
Rami-Koujan as modified by Vorobiov fails to explicitly disclose training data for the second machine-learning model including pairs of images of persons wearing garments to learn to transfer the fit of the garments between the persons.
However, Shuvi teaches the second machine-learning model comprises a convolutional neural network that transfers the fit of the clothing item in the second image to the portrayal of the subject person wearing the clothing item in the third image (Shuvi; the 2nd ML model comprises a CNN that transfers the fit of the clothing item in the 2nd image to the portrayal of the subject person wearing the clothing item in the 3rd image [¶ 0034-0036]; moreover, virtual try-on [¶ 0059]), training data for the second machine-learning model including pairs of images of persons wearing garments to learn to transfer the fit of the garments between the persons (Shuvi; training data for the 2nd ML model including pairs of images of persons wearing garments to learn to transfer the fit of the garments between the persons [¶ 0113-0115]; wherein, module(s) within the virtual try-on module involve training data using an input image(s) [¶ 0120-0121, ¶ 0126, ¶ 0134-0135, and ¶ 0137]).
Rami-Koujan in view of Vorobiov and Shuvi are considered to be analogous art because they pertain to generating and/or managing data in relation with providing media data to a user, wherein one or more computerized units are utilized in order to produce computerized clothing models.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Rami-Koujan as modified by Vorobiov, to incorporate the second machine-learning model comprises a convolutional neural network that transfers the fit of the clothing item in the second image to the portrayal of the subject person wearing the clothing item in the third image, training data for the second machine-learning model including pairs of images of persons wearing garments to learn to transfer the fit of the garments between the persons (as taught by Shuvi), in order to provide realistic computer graphics for virtual try-on clothing (Shuvi; [¶ 0001-0002, ¶ 0031-0033, and ¶ 0035]).
Regarding claim 15, Rami-Koujan in view of Vorobiov and Shuvi teaches the computing device of claim 14, wherein the fit of the clothing item includes one or more of a garment length, a relative size of the clothing item on the other person (Vorobiov; the fit of the clothing item includes (one or more of) a relative size of the clothing item on the other person [Col. 6, lines 26-44, Col. 11, lines 11-40, and Col. 12, lines 7-25]), a draping of the clothing item on the other person, tucked in versus untucked, or sleeves rolled up versus unrolled.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Rami-Koujan as modified by Vorobiov and Shuvi, to incorporate the fit of the clothing item includes one or more of a garment length, a relative size of the clothing item on the other person, a draping of the clothing item on the other person, tucked in versus untucked, or sleeves rolled up versus unrolled (as taught by Vorobiov), in order to provide an article of adaptive clothing (Vorobiov; [Col. 1, line 6-14, Col. 2, lines 36-55, and Col. 4, lines 1-17]).
Regarding claim 16, Rami-Koujan in view of Vorobiov and Shuvi further discloses the computing device of claim 14, wherein determining the fit of the clothing item (Rami-Koujan; determining the fit of the clothing item [as addressed within the parent claim(s)]).
Shuvi further teaches extracting a correlation between a shape of the clothing item and a body shape of the other person as a style code (Shuvi; extracting a correlation between a shape of the clothing item and a body shape of the other person as a style code (i.e. masking) [¶ 0119-0121 and ¶ 0124]);
transferring the style code to a parsing map that reflects how the clothing item fits on a human body (Shuvi; transferring the style code (i.e. masking) to a parsing map (i.e. correlation map) that reflects how the clothing item fits on a human body [¶ 0122-0123], as illustrated within Fig. 11), the parsing map providing geometric constraints to retain the fit of the clothing item from the second image (Shuvi; the parsing map (i.e. correlation map) [as addressed above] providing geometric constraints to retain the fit of the clothing item from the 2nd image [¶ 0120 and ¶ 0122-0123]); and
generating, using a third machine-learning model, a warped clothing item from a flat representation of the clothing item based on the parsing map (Shuvi; generating a warped clothing item from a flat representation of the clothing item [¶ 0141-0145] based on the parsing map (i.e. correlation map) using a 3rd ML model [¶ 0122-0125, and ¶ 0128], as illustrated within Fig. 11; moreover, NN structure [¶ 0120 and ¶ 0140]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Rami-Koujan as modified by Vorobiov and Shuvi, to incorporate extracting a correlation between a shape of the clothing item and a body shape of the other person as a style code; transferring the style code to a parsing map that reflects how the clothing item fits on a human body, the parsing map providing geometric constraints to retain the fit of the clothing item from the second image; and generating, using a third machine-learning model, a warped clothing item from a flat representation of the clothing item based on the parsing map (as taught by Shuvi), in order to provide realistic computer graphics for virtual try-on clothing (Shuvi; [¶ 0001-0002, ¶ 0031-0033, and ¶ 0035]).
Regarding claim 17, Rami-Koujan in view of Vorobiov and Shuvi further discloses the computing device of claim 16, wherein the third machine-learning model comprises a convolutional neural network and a transformer that are trained independently from the second machine-learning model using parsing maps from unpaired data (Shuvi; the 3rd ML model comprises a CNN and a transformer that are trained independently [¶ 0128-0130, ¶ 0138, and ¶ 0140] from the 2nd ML model using parsing maps from unpaired data [¶ 0120-0122 and ¶ 0124-0125], as illustrated within Fig. 22; wherein, the training module is independent one or more NN; additionally, operation of training a harmonization module [¶ 0134-0135]).
Regarding claim 2, the rejection of claim 2 is addressed within the rejection of claim 14, due to the similarities claim 2 and claim 14 share, therefore refer to the rejection of claim 14 regarding the rejection of claim 2.
Regarding claim 3, the rejection of claim 3 is addressed within the rejection of claim 14, due to the similarities claim 3 and claim 14 share, therefore refer to the rejection of claim 14 regarding the rejection of claim 3.
Regarding claim 4, the rejection of claim 4 is addressed within the rejection of claim 15, due to the similarities claim 4 and claim 15 share, therefore refer to the rejection of claim 15 regarding the rejection of claim 4.
Regarding claim 5, the rejection of claim 5 is addressed within the rejection of claim 16, due to the similarities claim 5 and claim 16 share, therefore refer to the rejection of claim 16 regarding the rejection of claim 5.
Regarding claim 6, the rejection of claim 6 is addressed within the rejection of claim 16, due to the similarities claim 6 and claim 16 share, therefore refer to the rejection of claim 16 regarding the rejection of claim 6.
Regarding claim 7, the rejection of claim 7 is addressed within the rejection of claim 17, due to the similarities claim 7 and claim 17 share, therefore refer to the rejection of claim 17 regarding the rejection of claim 7.
Claim(s) 8, 10, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rami-Koujan in view of Vorobiov as applied to claim(s) 6 and 16 above, and further in view of Sugita et al., US PGPUB No. 20170140574 A1, hereinafter Sugita.
Regarding claim 18, Rami-Koujan in view of Vorobiov and Shuvi further discloses the computing device of claim 16, wherein the third image is generated using a generative adversarial neural network that synthesizes the warped clothing item on the portrayal of the subject person (Rami-Koujan; wherein the 3rd image (i.e. generated image) is generated using a generative adversarial NN [¶ 0065 and ¶ 0150-0151] that synthesizes the warped clothing item on the portrayal of the subject person [¶ 0025-0026, ¶ 0155, and ¶ 0171]; wherein, generative clothing is modified to fit a person [id.]; and wherein, adversarial corresponds to the relationships between a generator and a discrimator [¶ 0149-0151] in which differences are determined [¶ 0153 and ¶ 0157-0158]; additionally, generative AI [¶ 0172-0176] in relation with generating an output image [¶ 0179]).
Rami-Koujan as modified by Vorobiov and Shuvi fails to explicitly disclose the portrayal of the subject person being projected onto the human mesh model.
However, Sugita teaches wherein the third image is generated that synthesizes the warped clothing item on the portrayal of the subject person (Sugita; the 3rd image is generated that synthesizes the warped/deformed clothing item on the portrayal of the subject person [¶ 0076-0077], as illustrated within Fig. 10; moreover, deformation [¶ 0090-0092]), the portrayal of the subject person being projected onto the human mesh model (Sugita; the portrayal of the subject person being projected onto the human mesh model [¶ 0062 and ¶ 0076]; moreover, mesh data generator [¶ 0055-0056] and deformation [¶ 0057]).
Regarding claim 8, the rejection of claim 8 is addressed within the rejection of claim 18, due to the similarities claim 8 and claim 18 share, therefore refer to the rejection of claim 18 regarding the rejection of claim 8.
Regarding claim 10, the rejection of claim 10 is addressed within the rejection of claim 18, due to the similarities claim 10 and claim 18 share, therefore refer to the rejection of claim 18 regarding the rejection of claim 10.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rami-Koujan in view of Vorobiov and Shuvi as applied to claim(s) 6 and 16 above, and further in view of Korszun, US Patent No. 5680528 A1, hereinafter Korszun.
Regarding claim 19, Rami-Koujan in view of Vorobiov and Shuvi further discloses the computing device of claim 16, wherein the warped clothing item (Rami-Koujan; the warped clothing item [¶ 0025-0026 and ¶ 0155]).
Shuvi further discloses the warped clothing item is further generated based on one or more dimensions of the clothing item (Shuvi; the warped clothing item is further generated based on one or more dimensions of the clothing item [¶ 0122-0124] that include warp and size a garment to make it fit [¶ 0139]).
Rami-Koujan as modified by Vorobiov and Shuvi fails to explicitly disclose one or more dimensions of the clothing item that include at least two of shoulder width, waist width, waist circumference, inseam length, hip circumference, sleeve length, collar opening diameter, chest width, or chest diameter.
However, Korszun teaches the warped clothing item is further generated based on one or more dimensions of the clothing item that include at least two of shoulder width, waist width, waist circumference, inseam length, hip circumference, sleeve length, collar opening diameter, chest width, or chest diameter (Korszun; the warped clothing item is further generated based on one or more dimensions of the clothing item that include at least two of shoulder width, waist width, waist circumference, inseam length, hip circumference, sleeve length, collar opening diameter, chest width, or chest diameter [Col. 14, line 55 to Col. 15, line 50], as illustrated within Figs. 39B-40; moreover, online process [id.]; wherein, warped corresponds to geometrically transformed [Col. 6, lines 39-53]; moreover, balanced warping [Col. 10, lines 26-48] in relation with prototype measurements [Col. 8, lines 10-67]).
Rami-Koujan in view of Vorobiov and Shuvi and Korszun are considered to be analogous art because they pertain to providing and manipulating media data, wherein one or more computerized units are utilized in order to produce virtualized clothing models.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Rami-Koujan as modified by Vorobiov and Shuvi, to incorporate the warped clothing item is further generated based on one or more dimensions of the clothing item that include at least two of shoulder width, waist width, waist circumference, inseam length, hip circumference, sleeve length, collar opening diameter, chest width, or chest diameter (as taught by Korszun), in order to provide realistic visualizations/graphics for virtual try-on clothing (Korszun; [Col. 1, lines 19-31 and lines 43-60 and Col. 4, line 62-67]).
Regarding claim 9, the rejection of claim 9 is addressed within the rejection of claim 19, due to the similarities claim 9 and claim 19 share, therefore refer to the rejection of claim 19 regarding the rejection of claim 9.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rami-Koujan in view of Vorobiov and Shuvi as applied to claim(s) 10 above, and further in view of Vilcovsky et al., US PGPUB No. 20130229482 A1, hereinafter Vilcovsky.
Regarding claim 12, Rami-Koujan in view of Vorobiov and Shuvi further discloses the method of claim 10, wherein a suggestion for a different size of the clothing item or a different clothing item with a better fit is displayed along with the third image (Vorobiov; an implicit suggestion for a different clothing item (given a list of garment images) with a subjectively better fit is displayed along with the 3rd image [Col. 4, lines 18-57 and Col. 13, lines 24-42], as illustrated within Fig. 1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Rami-Koujan as modified by Vorobiov and Shuvi, to incorporate a suggestion for a different size of the clothing item or a different clothing item with a better fit is displayed along with the third image (as taught by Vorobiov), in order to provide an article of adaptive clothing (Vorobiov; [Col. 1, line 6-14, Col. 2, lines 36-55, and Col. 4, lines 1-17]).
However, Vilcovsky teaches wherein a suggestion for a different size of the clothing item or a different clothing item with a better fit is displayed along with the third image (Vilcovsky; a suggestion for a different clothing item with a subjectively better fit [¶ 0117-0118] is displayed along with the 3rd image [¶ 0079 and ¶ 0082], as illustrated within Fig. 2B and Fig. 3C; additionally, compare views [id.]).
Rami-Koujan in view of Vorobiov and Shuvi and Vilcovsky are considered to be analogous art because they pertain to control and providing media data, wherein one or more computerized units are utilized in order to produce virtualization effects.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Rami-Koujan as modified by Vorobiov and Shuvi, to incorporate a suggestion for a different size of the clothing item or a different clothing item with a better fit is displayed along with the third image (as taught by Vilcovsky), in order to provide an improved user shopping experience (Vilcovsky; [¶ 0006-0007]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(regarding virtual clothing and try-on / fit)
Biggs et al. (US PGPUB No. 20240202809 A1);
Rami Koujan et al. (US PGPUB No. 20250037333 A1);
Haaramo et al. (US PGPUB No. 20140010449 A1);
Assouline et al. (US Patent No. 12682508 B2);
Sun et al. (US PGPUB No. 20250278910 A1);
Jain et al. (US PGPUB No. 20250005824 A1);
Davis et al. (US PGPUB No. 20240331211 A1);
Zhou et al. (US Patent No. 11321769 B2); and
Koh et al. (US PGPUB No. 20190357615 A1).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892, Notice of Reference Cited for a listing of analogous art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Charles Lloyd Beard whose telephone number is (571)272-5735. The examiner can normally be reached Monday - Friday, 8:00 AM - 5: 00 PM, alternate Fridays EST.
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, Tammy Goddard can be reached at (571) 272-7773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHARLES LLOYD. BEARD
Primary Examiner
Art Unit 2611
/CHARLES L BEARD/Primary Examiner, Art Unit 2611
Prosecution Insights