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 .
Claims 1-20 are pending.
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.
Claims 1, 3-5, 7, 10-11, 13-15, 17, 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1 and 11, the following 2-step analysis is applied for analyzing the 35 U.S.C. § 101 subject matter eligibility of the claims.
Step 1: The Statutory Categories
Claim 1 recites a "process," which is a statutory category under 35 U.S.C. § 101. Claim 11 recites a "machine," which is a statutory category under 35 U.S.C. § 101.
Step 2A: The Judicial Exceptions
Prong 1: do the claims recite an exception?
Claims 1 and 11 are directed to the abstract idea of organizing and processing data (splitting an image into regions, applying different algorithms to each, and combining results), which is a mental process and mathematical concept that can be performed mentally or with pen and paper.
Prong 2: is the exception integrated into a practical application?
The claims do not integrate the abstract idea into a practical application because the steps are recited at a high level without specific technological improvements, merely using generic computing elements (e.g., processor, image sensor) to apply the idea, without transforming or improving the technology beyond the abstraction.
Step 2B: The Inventive Concept
Do the claims amount to "significantly more" than the exception?
The additional elements in the claims (e.g., analyzing to split regions, applying algorithms, generating image) are well-understood, routine, and conventional activities in image processing, as evidenced by prior art, and do not provide an inventive concept.
Conclusion: Claims 1 and 11 are directed to an abstract idea and lack an inventive concept. Claims 1 and 11 are rejected as ineligible subject matter under 35 U.S.C. § 101.
Regarding dependent claims 3-5, 7, 10, 13-15, 17 and 20: limitations in these dependent claims have been examined in a similar way as to the above independent claims. It was found that claims 3-5, 7, 10, 13-15, 17 and 20 are ineligible subject matter under 35 U.S.C. § 101:
Claims 3 & 13: ineligible. Merely claims a desired abstract result ("increasing... image quality") without reciting the specific algorithmic rules to achieve it.
Claims 4 & 14: ineligible. Claims a desired result ("maintaining... image quality") without providing the "how."
Claims 5 & 15: ineligible. Claims a relative desired result (making one quality greater than another) rather than the specific technology and/or mathematical rules used to get there.
Claims 7 & 17: ineligible. The "preset condition" is claimed too broadly and functionally without defining the specific rule.
Claims 10 & 20: ineligible. While it targets a specific technical problem (noncontiguous artifacts from splitting images), simply stating "utilize a smooth algorithm" is too generic and lacks the specific mathematical rules.
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 Claim(s) 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 Claim(s) particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 12 is/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 pre-AIA the applicant regards as the invention.
Claim 12 recites limitation “12. The operation device of claim 1”. There is insufficient antecedent basis for this limitation in the claim. It appears to read “12. The operation device of claim 11”.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1-5, 7-15 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Maleh et al (US20070183661) in view of Demircin et al (US20110235706).
Regarding claims 1 and 11, El-Maleh teaches an image processing method comprising:
analyzing an unprocessed image to split the unprocessed image into a first region and a second region;
(El-Maleh, “ROI object segmentation enables selected ROI or “foreground” objects of a video sequence to be extracted from non-ROI or “background” areas of the video sequence”, [0007]; analyzing a video frame (unprocessed image) to split/segment it into an ROI object (first region) and non-ROI/background areas (second region). The extraction of the foreground from the background constitutes splitting the image; “An ROI object may be referred to as a “foreground” object within a video frame and non-ROI areas may be referred to as “background” areas within the video frame. ROI object segmentation enables selected foreground objects of a video sequence that may be of interest to a viewer to be extracted from the background of the video sequence”, [0002]; this further supports the analysis and splitting of the image into distinct regions)
applying a first image processing algorithm to the first region for acquiring a first processed result;
(El-Maleh, “The ROI-enabled video encoder may allocate additional coding bits to the ROI object of the video fram2 ...”; pplying a specific processing algorithm (encoding with additional bits) to the first region (ROI object) to acquire a processed result (high quality encoded ROI); Demircin, “quantization scale for the image frame based on ROI priorities and ROI statistics is calculated. Further, quantization scales for ROI and non-ROI based on ROI priorities are determined”, [0009]; Demircin complements El-Maleh by disclosing a specific algorithmic implementation for processing the ROI; calculating a specific quantization scale for the ROI constitutes applying a first image processing algorithm; “Similarly, the relation between the rate and quantization scale for ROI areas is: ...”, eq. (9), [0058]; applying the specific mathematical algorithm to the ROI region)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the rate-distortion optimized quantization determination techniques of Demircin into El-Maleh in order to provide a specific, computationally efficient algorithmic implementation for the "preferential encoding" disclosed in El-Maleh, thereby ensuring the segmented first region (ROI) is processed with a finer quantization scale for higher fidelity while the second region (non-ROI) is processed with a coarser scale to efficiently manage the overall bit budget. The combination of El-Maleh and Demircin also teaches other enhanced capabilities.
The combination of El-Maleh and Demircin further teaches:
applying a second image processing algorithm different from the first image processing algorithm to the second region for acquiring a second processed result; and
(El-Maleh, “... and allocate a reduced number of coding bits to non-ROI areas of the video frame”, [0037]; applying a different processing parameter (reduced coding bits) to the second region (non-ROI areas) compared to the first region. This constitutes a different image processing algorithm or at least the application of the algorithm with distinctly different parameters; Demircin, “Consider the case when there are only two areas (i) ROI area with quality enhancement α1, and (ii) non-ROI area. Then, by setting the distortion in the ROI area to a factor of α1 lesser than the distortion in the non-ROI area we can ensure that ROI area is represented with higher fidelity than the non-ROI area”, [0047]; applying a second processing algorithm (encoding with higher distortion/different quantization) to the non-ROI region; “relation between the quantization scales for the different ROI areas”, eq. (7); Demircin provides the specific algorithmic relationship where the quantization scale (processing algorithm) for the non-ROI (represented as the P+1th area) is different from the ROI)
generating a processed image via the first processed result and the second processed result.
(El-Maleh, “The encoded video frame may then be transmitted over a wired or wireless communication channel to another communication device”, [0037]; generating a final processed image (the encoded video frame) which contains the results of the ROI processing (additional bits) and the non-ROI processing (reduced bits); Demircin, “Further, the image frame is encoded at step 335 and compressed bit streams of the image are generated at step 340”, [0061]; generating the final processed output (compressed bit streams) using the different quantization scales determined for the ROI and non-ROI)
Regarding claims 2 and 12, the combination of El-Maleh and Demircin teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 1, further comprising:
setting at least one region of interest inside the unprocessed image as the first region; and
defining a remaining region inside the unprocessed image outside the at least one region of interest as the second region, or defining all region inside the unprocessed image as the second region;
wherein computation power of the first image processing algorithm is greater than computation power of the second image processing algorithm.
(El-Maleh, Fig. 2B; “ROI object segmentation enables selected ROI or “foreground” objects of a video sequence that may be of interest to a viewer to be extracted from non-ROI or “background” areas of the video sequence”, [abstract]; setting the ROI as the first region and the non-ROI/background as the second region; “ The video sequence encoder may allocate more resources to the segmented ROI object to code the ROI object with higher quality for transmission to a recipient”, [0030]; the algorithm applied to the first region (ROI) utilizes "more resources" (greater computation power/bits) compared to the second region)
Regarding claims 3 and 13, the combination of El-Maleh and Demircin teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 1, further comprising:
increasing a first image quality of the first region by the first image processing algorithm to acquire the first processed result.
(El-Maleh, “... code the ROI object with higher quality for transmission to a recipient”, [0030]; increasing the image quality of the first region; “Accordingly, preferential allocation of coding bits to ROI objects can be helpful in improving the visual quality of the ROI object ...”, [0037]; improving the visual quality of the first region)
Regarding claims 4 and 14, the combination of El-Maleh and Demircin teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 3, further comprising:
maintaining a second image quality of the second region by the second image processing algorithm to acquire the second processed result.
(Demircin, Fig. 2; “De-blocking filter 270 operates to remove visual artifacts that may be present in the reconstructed macro-blocks received on path 267. The artifacts may be introduced in the encoding process due, for example, to the use of different modes of encoding. Artifacts may be present, for example, at the boundaries/edges of the received macro-blocks, and de-blocking filter 270 operates to smoothen the edges of the macro-blocks to improve visual quality”; [0034]; De-blocking filter 270 may be the second image processing algorithm that make the quality of the ROI objects and the non-ROI objects in the image better than per-filtering; based on the filtering characteristics, the filtered results may be at least maintaining or enhancing the quality of the non-ROI objects)
Regarding claims 5 and 15, the combination of El-Maleh and Demircin teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 3, further comprising:
enhancing a second image quality of the second region by the second image processing algorithm to acquire the second processed result, wherein the first image quality is greater than or different from the second image quality.
(Demircin, see comments on claim 4)
Regarding claims 7 and 17, the combination of El-Maleh and Demircin teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 1, further comprising:
adjusting a number or a size of the first region in accordance with a preset condition.
(El-Maleh, “The size, shape and position of ROI object 24 may be fixed or adjustable, and may be defined, described or adjusted in a variety of ways”, [0048]; the size of the first region is adjustable; “multi-face separation module 70 separates the sets of eye and mouth candidates into groups corresponding to the different faces ... First, the total number of faces included within the video frame is unknown”, [0083]; adjusting the number of regions based on the number of faces detected; “Based on the geometric locations of the facial features and an ROI geometric model, the ROI object shape is approximated”, [0043]; adjusting the size based on a preset condition, specifically the "ROI geometric model" and feature locations)
Regarding claims 8 and 18, the combination of El-Maleh and Demircin teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 7, wherein the image processing method is applied to an operation device, and the preset condition is computation constraint of the operation device or a target feature inside the unprocessed image.
(El-Maleh, “the ROI-enabled video encoder may reside within a wireless mobile terminal”, [0036]; applying the method to an operation device; “System 14 then performs feature verification based on geometric properties and shape characteristics of human facial features ...”, [0043]; the preset condition is a "target feature" (facial features/geometric properties) used to determine the ROI shape/size)
Regarding claims 9 and 19, the combination of El-Maleh and Demircin teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 8, wherein the preset condition is the ever-changing computation constraint, and the image processing method adjusts the first region in accordance with a manually-input control command or a control command automatically analyzed by the preset condition.
(El-Maleh, “System 14 may determine a computational complexity of the received frame (46) ... Therefore, system 14 may decide to perform intra-mode segmentation when the computational complexity is above a pre-determined level “, [0057]; analyzing ever-changing computation constraint (frame complexity) automatically to adjust process, affecting region handling)
Regarding claims 10 and 20, the combination of El-Maleh and Demircin teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 1, further comprising:
utilizing a smooth algorithm to merge the first processed result and the second processed result for eliminating noncontiguous artifact of the processed image.
(Demircin, Fig. 3B; “An abrupt change in quantization scale between ROI and non-ROI areas will result in sudden change in quality between adjacent macro blocks. This will result in subjective quality degradations. In order to overcome this problem an additional guard band ... is defined ... Within the guard band the quantization scale is varied gradually from QROI to Qnon-ROI”, [0064]; utilizing a smoothing algorithm (gradually varying quantization in a guard band) to eliminate noncontiguous artifacts (abrupt changes/sudden change in quality) when merging the results)
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Maleh et al (US20070183661) in view of Demircin et al (US20110235706) and further in view of Molina et al (US20180225522).
Regarding claims 6 and 16, the combination of El-Maleh and Demircin teaches its/their respective base claim(s).
The combination does not expressly disclose but Molina teaches the image processing method of claim 1, further comprising:
setting the first processed result acquired by the first image processing algorithm applied to the first region as prior information; and
the second image processing algorithm enhancing an image quality of the second region in accordance with the prior information to acquire the second processed result.
(Molina, Fig. 5; “1 - Calculate the histogram of the input image pixels contained in region of interest pIr (i) and use this information to obtain the corresponding transformation function Tr; 2 - Apply this transformation Tr to the entire input image I or sub-region ro of the input image”, [0057]; Molina teaches this step-by-step: (1) first Region (ROI): It identifies a specific "region of interest" (r); (2) first Algorithm & Result (Prior Information): it analyzes only that ROI (the first algorithm) to calculate a specific "transformation function Tr" (the first result); this function represents the ideal contrast settings for the important part of the image; (3)second Region & Enhancement: It then takes that function (Tr) and applies it to the rest of the image or a larger area (ro). This means the background (second region) is enhanced using the specific "prior information" learned from the ROI)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the histogram-based enhancement technique of Molina into the ROI-based video system of El-Maleh and Demircin in order to improve the overall visual consistency and contrast of the video frame; specifically, applying the transformation function derived from the statistical analysis of the priority ROI (first region) to the background (second region) ensures that the background remains visually coherent with the subject of interest, thereby enhancing the perceptual quality for the viewer in surveillance or video telephony applications without requiring independent, computationally intensive analysis of the background statistics. The combination of El-Maleh, Demircin and Molina also teaches other enhanced capabilities.
Conclusion
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 4/4/2026
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