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
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 8, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mohamed Abdolell et al (US 20230071400 A1) in view of Dongpei Su (US 10831417 B1).
Regarding claim 1, Abdolell et al discloses a system for validating image content and formatting (¶ [137-138]), the system comprising:
one or more processors (¶ [152]); and
one or more memories configured to store instructions that, when executed by the one or more processors, perform operations (¶ [187-188]) comprising:
inputting an image into one or more machine learning models to obtain one or more predictions indicating whether the image conforms to one or more predetermined parameters (¶ [189]), wherein each machine learning model of the one or more machine learning models is trained to predict a corresponding predetermined parameter of the one or more predetermined parameters (¶ [190-191]);
determining that a prediction of the one or more predictions indicates that the image does not conform to the corresponding predetermined parameter (¶ [198]);
determining whether the prediction is associated with a second machine learning model that is enabled to modify the image to conform the image to the corresponding predetermined parameter (¶ [219-220]);
based on determining that the prediction is associated with the second machine learning model that is enabled to modify the image to conform the image to the corresponding predetermined parameter, inputting the image into the second machine learning model to obtain a final image, wherein the second machine learning model is trained to modify images to conform with the corresponding predetermined parameter (¶ [220] and ¶ [196]).
Abdolell et al fails to explicitly disclose providing the final image to be printed.
Su, in the same field of endeavor of utilizing trained models to conform images to specified parameters (Col. 11 lines 32-40), teaches providing the final image to be printed (Col. 31 lines 43-45).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the system as disclosed by Abdolell et al comprising one or more processors performing operations comprising inputting an image into one or more machine learning models to obtain predictions indicating whether an image conforms to one or more predetermined parameters to utilize the teachings of Su which teaches providing the final image to be printed to improve the quality of digital images from source images exhibiting defects.
Regarding 2, Abdolell et al discloses a method for validating image content and formatting (see rejection of claim 1), the method comprising:
inputting an image into one or more machine learning models to obtain one or more predictions indicating whether the image conforms to one or more predetermined parameters, wherein each machine learning model of the one or more machine learning models is trained to predict a corresponding predetermined parameter of the one or more predetermined parameters (see rejection of claim 1);
determining that a prediction of the one or more predictions indicates that the image does not conform to the corresponding predetermined parameter (see rejection of claim 1);
determining whether the prediction is associated with a second machine learning model that is enabled to modify the image to conform the image to the corresponding predetermined parameter (see rejection of claim 1);
based on determining that the prediction is associated with the second machine learning model that is enabled to modify the image to conform the image to the corresponding predetermined parameter, inputting the image into the second machine learning model to obtain a final image, wherein the second machine learning model is trained to modify images to conform with the corresponding predetermined parameter (see rejection of claim 1); and
providing the final image to be printed (see rejection of claim 1).
Regarding claim 8, Abdolell et al discloses the method of claim 2, wherein inputting the image into the one or more machine learning models to obtain the one or more predictions indicating whether the image conforms to the one or more predetermined parameters further comprises:
determining a plurality of predetermined parameters from available parameters (¶ [172]);
identifying a plurality of machine learning models corresponding to the plurality of predetermined parameters (¶ [189-190]); and
inputting the image into each machine learning model of the plurality of machine learning models (¶ [206-207]).
Regarding claim 13, Abdolell et al discloses one or more non-transitory, computer-readable media storing instructions thereon that cause one or more processors to perform operations (¶ [123-124]) comprising:
inputting an image into one or more machine learning models to obtain one or more predictions indicating whether the formatted image conforms to one or more predetermined parameters, wherein each machine learning model of the one or more machine learning models is trained to predict a corresponding predetermined parameter of the one or more predetermined parameters (see rejection of claim 1);
determining that a prediction of the one or more predictions indicates that the image does not conform to the corresponding predetermined parameter (see rejection of claim 1);
determining whether the prediction is associated with a second machine learning model that is enabled to modify the image to conform the image to the corresponding predetermined parameter (see rejection of claim 1);
based on determining that the prediction is associated with the second machine learning model that is enabled to modify the image to conform the image to the corresponding predetermined parameter, inputting the image into the second machine learning model to obtain a final image, wherein the second machine learning model is trained to modify images to conform with the corresponding predetermined parameter (see rejection of claim 1); and
providing the final image to be printed (see rejection of claim 1).
Regarding claim 19, Abdolell et al discloses the one or more non-transitory, computer-readable media of claim 13 (see rejection of claim 13), wherein the operations inputting the image into the one or more machine learning models to obtain the one or more predictions indicating whether the image conforms to the one or more predetermined parameters further cause the one or more processors to perform operations comprising:
determining a plurality of predetermined parameters from available parameters (see rejection of claim 8);
identifying a plurality of machine learning models corresponding to the plurality of predetermined parameters (see rejection of claim 8); and
inputting the image into each machine learning model of the plurality of machine learning models (see rejection of claim 8).
Claims 6, 7, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Abdolell et al in view of Su as applied to claim 2 above, and further in view of Carlos A. Davila et al (US 20160278427 A1).
Regarding claim 6, Abdolell et al discloses the method of claim 2 (see rejection of claim 2).
Abdolell et al fails to explicitly disclose causing a user device to generate for display a prompt prompting a user to select the image for printing on a physical object; and receiving the image from the user device.
Davila et al, in the same field of endeavor of searching, selecting and outputting a desired image (¶ [55]), teaches generating for display a prompt prompting a user to select the image for printing on a physical object (¶ [53-55]); and receiving the image from the user device (¶ [53-54]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the system as disclosed by Abdolell et al comprising one or more processors performing operations comprising inputting an image into one or more machine learning models to obtain predictions indicating whether an image conforms to one or more predetermined parameters to utilize the teachings of Davila et al which teaches generating for display a prompt prompting a user to select the image for printing on a physical object and receiving the image from the user device to enable the user the ability to modify designs without having to move through multiple screens.
Regarding claim 7, Abdolell et al discloses the method of claim 2, further comprising:
Abdolell et al fails to explicitly disclose causing a user device to generate for display a request for a user to describe the image to be printed on a physical object; receiving a description from the user device; and retrieving the image based on the description.
Davila et al teaches a user device to generate for display a request for a user to describe the image to be printed on a physical object (¶ [96]); receiving a description from the user device (¶ [96]); and retrieving the image based on the description (¶ [100]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the system as disclosed by Abdolell et al comprising one or more processors performing operations comprising inputting an image into one or more machine learning models to obtain predictions indicating whether an image conforms to one or more predetermined parameters to utilize the teachings of Davila et al which teaches a user device to generate for display a request for a user to describe the image to be printed on a physical object, receiving a description from the user device, and retrieving the image based on the description to optimize browsing and selection of desired image data and provide an efficient storage and retrieval process.
Regarding claim 17, Abdolell et al discloses the one or more non-transitory, computer-readable media of claim 13 (see rejection of claim 13), wherein the operations further cause the one or more processors to perform operations comprising:
causing a user device to generate for display a prompt prompting a user to select the image for printing on a physical object (see rejection of claim 6); and
receiving the image from the user device (see rejection of claim 6).
Regarding claim 18, Abdolell et al discloses the one or more non-transitory, computer-readable media of claim 13 (see rejection of claim 13), wherein the operations further cause the one or more processors to perform operations comprising:
causing a user device to generate for display a request for a user to describe the image to be printed on a physical object (see rejection of claim 7);
receiving a description from the user device; and retrieving the image based on the description (see rejection of claim 7).
Allowable Subject Matter
Claims 3-5, 9-11, 14-16 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMARES Q WASHINGTON whose telephone number is (571) 270-1585. The examiner can normally be reached Mon-Fri 8:30am-4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi M. Sarpong can be reached at (571) 270-3438. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMARES Q WASHINGTON/Primary Examiner, Art Unit 2681
June 18, 2026
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