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.
Claims 1-7, 9, 10, 13-15, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Candelore (US 10887525) in view of Kasilya Sudarsan (US 10565696).
Regarding claim 1, Candelore teaches, a method (abstract: method for delivery of notifications for feedback over visual quality of images), comprising: obtaining an image captured by an image capture device (Fig. 1, el. 102a: electronic apparatus 102 may include an image sensor 102a and Col. 3, lines 36-38);
obtaining data indicating a plurality of image parameter values (abstract: The electronic apparatus selects a set of scoring parameters for the image frame from a plurality of scoring parameters based on the determined category. The set of scoring parameters corresponds to a defined visual quality for the determined category and Col. 5, lines 25-29 and lines 55-67), wherein each parameter value of the plurality of image parameter values corresponds to a respective image parameter of the captured image (Col. 5, lines 55-67);
and responsive to a score of the plurality of scores not satisfying a threshold score (Col. 5, lines 54-67 through Col. 6, lines1-9: the electronic apparatus 102 may be further configured to estimate score information for the selected set of scoring parameters based on a deviation of a value of at least one scoring parameter of the selected set of scoring parameters from a set of threshold values. Hereinafter, “at least one scoring parameter” may be interchangeably referred to as “one or more scoring parameters”. As an example, the estimated score information may be a score value that may be an aggregate of score values for each of the selected set of scoring parameters. In certain cases, the set of threshold values may be pre-set or selected based on user inputs from the user 110 for each scoring parameter of the plurality of scoring parameters. The deviation may correspond to an amount by which a value of each of the one or more scoring parameters deviates from a corresponding threshold value for a corresponding scoring parameter. The estimated score information may be indicative of a first visual quality of the first image frame and how much the first visual quality deviates from the defined visual quality and col. 12, lines 28-51: The set of scoring parameters may correspond to a set of threshold values associated with a defined visual quality for the determined first category. For example, a threshold value for the focus value as a scoring parameter in the self-portrait category may be “f/5.6” and a threshold value for the red eye value as a scoring parameter in the self-portrait category may be “20), causing a corrective action associated with the image parameter corresponding to the image parameter value from which the score was derived to be performed (Col. 16, lines 60-66 and claim 16: wherein the notification comprises a plurality of user instructions to assist a user to correct at least one issue associated with the deviation of the value of the at least one scoring parameter).
Candelore does not explicitly teach converting each image parameter value of the plurality of image parameter values into a respective vector space; converting each image parameter value in the respective vector space of the plurality of image parameter values in the respective vector spaces into a respective raw score of a plurality of raw scores as claimed.
Kasilya Sudarsan in the same art of endeavor teaches (abstract: method also includes determining one or more image quality parameter weights based on the image capture mode. The method further includes capturing an image. The method additionally includes determining an image quality score for the image based on the one or more image quality parameter weights. The method also includes providing feedback for the image based on the image quality score and suggestion to improve quality score, Fig. 8), converting each image parameter value of the plurality of image parameter values into a respective vector space; converting each image parameter value in the respective vector space of the plurality of image parameter values in the respective vector spaces into a respective raw score of a plurality of raw scores (Col. 18, lines 25-40: each pixel of an image may have three color components (e.g., red, green, and blue). In some approaches, the image quality scorer 122 may sum each of the pixel color components in an image to determine image color components (e.g., R, G, and/or B) Image color component ratios (e.g., (R/G) and/or (B/G)) may be determined (e.g., calculated, computed, evaluated, etc.) for an entire image. An average of the image color component ratios (e.g., a scene average of (R/G) and (B/G)) may also be determined. A range (e.g., 0.9 to 1.1) for the image color component ratio average may be a target range for an image to be considered to have good color balance. For example, a target range for a scene average of (R/G) and (B/G) may be within the range of 0.9 to 1.1. One or more color components (e.g., image color component ratio average) may be utilized to determine a color balance (and/or automatic white balance (AWB), etc., for example) score. And Col. 20, lines 15-60: In Equation (1), x.sub.avg is the image quality score (e.g., weighted average of image quality parameter score(s)), N is the number of image quality parameter scores (e.g., number of scores corresponding to selected image quality parameters), i is an index value, x.sub.i is the one or more image quality parameter scores (corresponding to one or more selected image quality parameters, for example), and w.sub.i is the one or more image quality parameter weights (corresponding to one or more selected image quality parameters, for example). In some approaches, image quality parameter selection may be performed by applying image quality parameter weight(s) with a value of zero to any unselected image quality parameters in the image quality score calculation. In other approaches, image quality parameter score(s) and/or weight(s) corresponding to any unselected image quality parameters may be excluded from the calculation. As described herein, the image quality score may be calculated in several different ways (based on mode, for example) using the formula given in Equation (1), the image quality scorer 122 may determine a score level based on the image quality score. For example, the range of possible image quality scores may be partitioned (e.g., divided) into two or more score levels. For instance, the image quality score (e.g., overall score) may be categorized into five levels. A first score level (e.g., L1) may include image quality scores of 100. An image quality score in the first level may indicate very good image quality (where no retake is needed, for example). A second score level (e.g., L2) may include image quality scores from 90-99. An image quality score in the second level may indicate good image quality (where no retake is needed, for example). A third score level (e.g., L3) may include image quality scores from 50-89. An image quality score in the third level may indicate fair or average image quality (where a retake with minor changes may be beneficial and/or suggested, for example). A fourth score level (e.g., L4) may include image quality scores from 10-49. An image quality score in the fourth level may indicate poor image quality (where a retake may be beneficial with clear suggestion(s), for example). A fifth score level (e.g., L5) may include image quality scores from 0-9. An image quality score in the fifth level may indicate very poor image quality (where a retake may be beneficial, suggested, and/or mandatory, for example). In some configurations of the systems and methods disclosed herein, the electronic device 102 may automatically retake (e.g., immediately retake) one or more images in a case that the image quality score is within one or more ranges (e.g., in score levels L5, L4, and/or L3). It should be noted that the number of score levels may vary and/or the image quality scores for each score level may vary).
Therefore, it would have been obvious to one with ordinary skill in the art before the filing date of the claimed invention to modify Candelore with Kasilya Sudarsan by Converting an image to a vector and computing a raw score will improve the system and transforms raw pixel data into a compact, stable, and machine-friendly format. This improves efficiency, scalability, and accuracy in tasks like similarity search, classification, and matching, while making the system more robust to variations in lighting, viewpoint, and background.
Regarding claim 2, Candelore in view of Kasilya Sudarsan teaches, wherein the image parameter corresponding to the image parameter value from which the raw score was derived comprises an exposure of the captured image (Kasilya Sudarsan: Col. 4, line 57).
Regarding claim 3, Candelore in view of Kasilya Sudarsan teaches, wherein the image parameter corresponding to the image parameter value from which the raw score was derived comprises a color accuracy of the captured image (Kasilya Sudarsan: Col. 4, line 62-63).
Regarding claim 4, Candelore in view of Kasilya Sudarsan teaches, wherein the image parameter corresponding to the image parameter value from which the raw score was derived comprises a sharpness of the captured image (Kasilya Sudarsan: Col. 4, line 30).
Regarding claim 5, Candelore in view of Kasilya Sudarsan teaches, wherein the vector space comprises a just-noticeable difference (JND) space (Candelore: Fig. 11, minimum threshold 1109c).
Regarding claim 6, Candelore in view of Kasilya Sudarsan teaches, wherein causing the performance of the corrective action comprises causing a command to be provided to the image capture device that adjusts the image parameter corresponding to the image parameter value from which the raw score was derived (Kasilya Sudarsan Fig. 8, el. 824: The electronic device 102 may provide 824 one or more suggestions to improve the image quality score. This may be accomplished as described in connection with FIG. 1. For example, the electronic device 102 may provide one or more minor suggestions for the L3 score level, one or more significant suggestions for the L4 score level, and/or one or more major suggestions for the L5 score level. In some configurations, the electronic device 102 may suggest one or more changes in capture mode (e.g., “Image blur is high; switch to action mode?”), image quality parameter(s) (e.g., “Image is underexposed; ignore exposure score?”), and/or image quality parameter weight(s) (e.g., “Background isn't sharp; lower sharpness weight?”). It should be noted that providing 822 feedback and providing 824 one or more suggestions may be combined and/or may be parts of providing feedback in some configurations).
Regarding claim 7, Candelore in view of Kasilya Sudarsan teaches, wherein causing the performance of the corrective action comprises adjusting the image parameter of the captured image corresponding to the image parameter value from which the raw score was derived (Kasilya Sudarsan Fig. 8, el. 822-824 and Candelore: col. 16, lines 65-66: the processor 204 may be configured to adjust the brightness of the first image frame 402).
Regarding claim 9, Candelore in view of Kasilya Sudarsan teaches, combining the plurality of raw scores as a weighted average; and causing the weighted average to be presented on a user interface (UI) (Candelore: Fig. 4, el. 408b).
Regarding claim 10, see claim 1 rejection.
Regarding claim 13, see claim 5 rejections.
Regarding claim 14, see claim 6 rejections.
Regarding claim 15, see claim 7 rejections.
Regarding claim 17, see claim 1 rejection.
Regarding claim 18, see claim 2 rejections.
Regarding claim 19, see claim 5 rejections.
Regarding claim 20, see claim 6 rejections.
Claims 8, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Candelore (US 10887525) in view of Kasilya Sudarsan (US 10565696) in view of Suszek (US 20190347771).
Regarding claim 8, Candelore in view of Kasilya Sudarsan teaches, the claimed method.
Candelore in view of Kasilya Sudarsan does not teach causing a virtual meeting user interface (UI) to present the captured image, with the adjusted image parameter, in a first region of the virtual meeting UI during a virtual meeting between a plurality of participants, wherein the first region corresponds to a participant of the plurality of participants.
Suszek teaches virtual meeting user interface (UI) to present the captured image, with the adjusted image parameter, in a first region of the virtual meeting UI during a virtual meeting between a plurality of participants, wherein the first region corresponds to a participant of the plurality of participants (Paragraph 42, 66 and Fig. 10).
Therefore, it would have been obvious to one with ordinary skill in the art before the filing date of the claimed invention to modify Candelore with Kasilya Sudarsan with Suszek in order to improve the system and enhance user’s experience.
Regarding claim 16, see claim 8 rejections.
Claims 11, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Candelore (US 10887525) in view of Kasilya Sudarsan (US 10565696) in view of Cohen (US 20260086229).
Regarding claim11, Candelore in view of Kasilya Sudarsan teaches, wherein the image parameter corresponding to the image parameter value from which the raw score was derived comprises different elements (see claims 2-4).
Candelore in view of Kasilya Sudarsan does not teach a noise of the captured image as claimed.
Cohen teaches image quality parameters including noise of image and artifacts (Paragraph 58).
Therefore, it would have been obvious to one with ordinary skill in the art before the filing date of the claimed invention to modify Candelore with Kasilya Sudarsan with Cohen in order to improve the system and enhance image quality.
Regarding claim12, Candelore in view of Kasilya Sudarsan teaches, wherein the image parameter corresponding to the image parameter value from which the raw score was derived comprises different elements (see claims 2-4).
Candelore in view of Kasilya Sudarsan does not teach a number of artifacts present in the captured image as claimed.
Cohen teaches image quality parameters including image artifacts (Paragraph 58).
Therefore, it would have been obvious to one with ordinary skill in the art before the filing date of the claimed invention to modify Candelore with Kasilya Sudarsan with Cohen in order to improve the system and enhance image quality.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA EL-ZOOBI whose telephone number is (571)270-3434. The examiner can normally be reached Monday-Friday 7-4.
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/MARIA EL-ZOOBI/Primary Examiner, Art Unit 2692
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