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 Objections
Claims 1, 3, 20 and 22 are objected to because of the following informalities:
Claim 1 recites “Machine-readable instructions saved on non-transient memory for execution by data processor…which instructions when executed by the data processor implement at least the following steps”. The phrase machine-readable instructions saved on non-transient memory for execution by data processor is unclear as to whether the claim is directed to a storage medium or to the instructions themselves. The examiner suggests a non-transitory memory storing instructions that, when executed by a data processor. Clarification is required.
Claim 3 recites determine a darkest level of lesion skin tone; since claim 3 depends on claim 2, and claim 2 already recites determine a darkest level of the lesion skin tone. Claim 3 should be amended to recite determine the darkest level of lesion skin tone.
Claim 20 recites the field of view of a camera; it should be changed to a field of view of a camera.
Claim 22 recites prompt the user review the image; it should be changed to prompt a user review the image.
Appropriate correction is required.
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 3 and 4 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.
The term “substantially” in claim 3 is a relative term which renders the claim indefinite. The term “substantially” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The term “proximal” in claim 4 is a relative term which renders the claim indefinite. The term “proximal” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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, 5-17 and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Skladnev et al. (US 2004/0264749) in view of Chen et al. (Color analysis of skin lesion regions for melanoma discrimination in clinical images, Skin Res Technol. 2003) in view of Vezhnevets et al. (“GrowCut”- Interactive Multi-Label N-D Image Segmentation By Cellular Automata, 2004) in view of Do et al. (US 2017/0231550).
Regarding claim 1, Skladnev et al. (hereinafter Skladnev) discloses machine-readable instructions saved on non-transient memory for execution by data processor (Skladnev, [0130], “The software may be stored in a computer readable medium, including the storage devices described below, for example, The software is loaded into the computer from the computer readable medium, and then executed by the computer”), for processing a captured image, which includes a lesion (Skladnev, [0007], “when an image is captured of a lesion”), to determine a boundary of the lesion (Skladnev, [0011], “the determination of a boundary of a lesion”), which instructions when executed by the data processor implement at least the following steps:
displaying the image, which image includes a lesion under investigation and includes a portion of skin adjacent to the lesion (Skladnev, [0075], “using the system 100 has the capacity to move the camera assembly 104 about the patient and into an appropriate position over the lesion 103 and when satisfied with the position (as represented by a real-time image displayed on the display 114), may capture the particular image by depression of the switch 138 which actuates the control signal 134 to cause the frame capture board 108 to capture the image”);
determining within the image a boundary of where lesion skin pixels meet adjacent skin pixels (Skladnev, [0096], “provide a mask image, SRG mask 658, shown in FIG. 17 which represents the specific boundary of the image as a result of seeded region growing that is construed to be "lesion"”);
Skladnev does not expressly disclose “determining a lesion skin reference tone within the image, which comprises selecting one or more representative pixels within the lesion”;
Chen et al. (hereinafter Chen) discloses determining a lesion skin reference tone within an image, which comprises selecting one or more representative pixels within a lesion (Chen, Lesion region analysis, [0001], “all eight connected neighbors of each boundary pixel located in the lesion interior are identified”. In addition, in Percentage melanoma color feature, [0001], “The percent melanoma color and color clustering ratio features are computed after the relative color histogram bins have been labeled and the lesion region for feature analysis has been identified”);
determining an adjacent skin reference tone of skin adjacent to the lesion which is also in the image, which comprises selecting one or more representative pixels not of the lesion (Chen, Relative color and surrounding skin color, [0001], “Surrounding skin color is represented as the average RGB value of pixels outside the lesion but within a circular region with center point at the lesion centroid”);
analyzing tone levels of pixels within the image individually and/or in groups to determine said pixels of the image as either lesion tone pixels or adjacent skin tone pixels (Chen, Methods, [0001], “determine a representative or average surrounding skin color value for normalizing the skin lesion color…identify colors characteristic of melanomas and benign lesions from the cumulative histogram, (6) identify the portion of the skin lesion to be used for computing color features”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the lesion boundary determination of Skladnev by determining the lesion boundary using the determined lesion skin tone and adjacent skin tone as taught by Chen. The motivation for doing so would have been improving accuracy of lesion boundary detection under different skin tones.
Skladnev as modified by Chen teaches each of the one or selected representative pixels of the lesion (Chen, Lesion region analysis, [0001], “all eight connected neighbors of each boundary pixel located in the lesion interior are identified”) and the one or more selected representative pixels not of the lesion (Chen, Relative color and surrounding skin color, [0001], “Surrounding skin color is represented as the average RGB value of pixels outside the lesion but within a circular region with center point at the lesion centroid”); Skladnev as modified by Chen does not expressly disclose “progressively away from each of the one or selected representative pixels of the lesion and the one or more selected representative pixels not of the lesion, respectively”;
Vezhnevets et al. (hereinafter Vezhnevets) discloses progressively away from each of the one or selected representative pixels of foreground and background, respectively (Vezhnevets, 2.1 Basic method, [0006], “That is why we called the method ‘GrowCut’. The rules of bacteria growth and competition are obvious - at each discrete time step, each cell tries to ‘attack’ its neighbors”. In addition, in 2.3 User interaction, [0003], “The segmentation is started by specifying the initial seeds. This is done by user’s strokes with ‘object’ and ‘background’ brushes (see figure 1.b - red pixels correspond to ‘object’ brush strokes, blue-to ‘background’ strokes). Each paint stroke of a defined brush sets the initial labels and strengths of seed pixels”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to determine the boundary of the lesion of Skladnev using the segmentation processing including the seed pixel inside the object of interest is specified, and then neighboring pixels are iteratively added to the growing region, as taught by Vezhnevets. The motivation for doing so would have been providing an accurate and automated boundary determination.
Skladnev teaches determine the boundary; Skladnev as modified by Chen and Vezhnevets does not expressly disclose “a two-stage process, in which a first stage comprises calculating an initial boundary at a first image resolution and then calculating a refined boundary at a second image resolution, wherein the second image resolution is higher than the first image resolution”;
Do et al. (hereinafter Do) discloses a two-stage process, in which a first stage comprises calculating an initial boundary at a first image resolution and then calculating a refined boundary at a second image resolution, wherein the second image resolution is higher than the first image resolution (Do, [0018], “segmenting the image further comprising a first segmenting and a second segmenting…The first segmenting process may be a coarse segmenting to determine any uncertain regions of the image, and the second segmenting process may be a fine segmenting carried out on the coarse segmentation to refine the uncertain regions to obtain segment boundary details. Uncertain regions may be an image region in the original resolution image where pixel labels are uncertain after the first coarse segmentation”. In addition, in paragraph [0074], “the input image is down-sampled, an approximate mole location is determined using the down-sampled image, and a region enclosing the mole is determined and cropped from the high-resolution input image”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Skladnev to incorporate the two-stage boundary determination process of Do including determining an initial lesion location using a low-resolution image and refining the lesion location based on the initial location using a corresponding high-resolution image. The motivation for doing so would have been reducing time processing and memory to store image.
Regarding claim 5, Skladnev as modified by Chen with the same motivation from claim 1 discloses determine an adjacent skin reference tone of skin adjacent to the lesion by identifying at least one pixel which is away from the lesion and within the image (Chen, Relative color and surrounding skin color, [0001], “Surrounding skin color is represented as the average RGB value of pixels outside the lesion but within a circular region with center point at the lesion centroid”).
Regarding claim 6, Skladnev as modified by Chen with the same motivation from claim 1 discloses the at least one pixel is analyzed in each of multiple locations in the image (Chen, Lesion region analysis, [0001], “all eight connected neighbors of each boundary pixel located in the lesion interior are identified”. In addition, in Relative color and surrounding skin color, [0001], “Surrounding skin color is represented as the average RGB value of pixels outside the lesion but within a circular region with center point at the lesion centroid”).
Regarding claim 7, Skladnev discloses generate an initial determined boundary of the lesion (Skladnev, [0104], “initially a small lesion boundary representing those darkest portions of the lesion which are generally indicative of cancerous growth”).
Regarding claim 8, Skladnev as modified by Chen with the same motivation from claim 1 discloses analyze the tonality of pixels within the image (Chen, Lesion region analysis, [0001], “all eight connected neighbors of each boundary pixel located in the lesion interior are identified”. In addition, in Percentage melanoma color feature, [0001], “The percent melanoma color and color clustering ratio features are computed after the relative color histogram bins have been labeled and the lesion region for feature analysis has been identified”), starting from at least two different locations with at least one being in the lesion and one being outside of the lesion (Chen, Lesion region analysis, [0001], “all eight connected neighbors of each boundary pixel located in the lesion interior are identified”. In addition, in Relative color and surrounding skin color, [0001], “Surrounding skin color is represented as the average RGB value of pixels outside the lesion but within a circular region with center point at the lesion centroid”).
Skladnev as modified by Chen and Vezhnevets with the same motivation from claim 1 discloses sequentially analyze (Vezhnevets, 2.1 Basic method, [0006], “That is why we called the method ‘GrowCut’. The rules of bacteria growth and competition are obvious - at each discrete time step, each cell tries to ‘attack’ its neighbors”. In addition, in 2.3 User interaction, [0003], “The segmentation is started by specifying the initial seeds. This is done by user’s strokes with ‘object’ and ‘background’ brushes (see figure 1.b - red pixels correspond to ‘object’ brush strokes, blue-to ‘background’ strokes). Each paint stroke of a defined brush sets the initial labels and strengths of seed pixels”).
Regarding claim 9, Skladnev the initial boundary is determined as where lesion pixels neighbor adjacent skin tone pixels (Skladnev, [0096], “provide the image of FIG. 16 which provides, at the centre at the image, a clear representation of pixels that are construed to be "lesion" surrounded by pixels that are construed to be "skin". FIG. 16 can therefore be flirter processed to provide a mask image, SRG mask 658, shown in FIG. 17 which represents the specific boundary of the image as a result of seeded region growing that is construed to be "lesion"”);
Skladnev as modified by Chen with the same motivation from claim 1 discloses lesion tone pixels (Chen, Lesion region analysis, [0001], “all eight connected neighbors of each boundary pixel located in the lesion interior are identified”. In addition, in Percentage melanoma color feature, [0001], “The percent melanoma color and color clustering ratio features are computed after the relative color histogram bins have been labeled and the lesion region for feature analysis has been identified”)
Regarding claim 10, Skladnev as modified by Chen, Vezhnevets and Do with the same motivation from claim 1 discloses determine a refined lesion boundary which has a higher accuracy than the initial boundary (Do, [0018], “The first segmenting process may be a coarse segmenting to determine any uncertain regions of the image, and the second segmenting process may be a fine segmenting carried out on the coarse segmentation to refine the uncertain regions to obtain segment boundary details”. In addition, in paragraph [0206], “we need to refine the labels of uncertain pixels in an efficient and accurate way”).
Regarding claim 11, Skladnev teaches the initial boundary (Skladnev, [0011], “determination of a boundary of a lesion”);
Skladnev as modified by Chen, Vezhnevets and Do with the same motivation from claim 1 discloses determine the refined lesion boundary by using a higher resolution version of the image as compared to a resolution of image used to determine the initial boundary, and by using the initial boundary (Do, [0018], “segmenting the image further comprising a first segmenting and a second segmenting…The first segmenting process may be a coarse segmenting to determine any uncertain regions of the image, and the second segmenting process may be a fine segmenting carried out on the coarse segmentation to refine the uncertain regions to obtain segment boundary details. Uncertain regions may be an image region in the original resolution image where pixel labels are uncertain after the first coarse segmentation”. In addition, in paragraph [0074], “the input image is down-sampled, an approximate mole location is determined using the down-sampled image, and a region enclosing the mole is determined and cropped from the high-resolution input image”).
Regarding claim 12, Skladnev as modified by Chen, Vezhnevets and Do with the same motivation from claim 1 discloses map the initial boundary onto the higher resolution of the image (Do, [0017], “identifying the lesion in the image. Such a processing comprises down-sampling the image”. In addition, in paragraph [0018], “The first segmenting process may be a coarse segmenting to determine any uncertain regions of the image, and the second segmenting process may be a fine segmenting carried out on the coarse segmentation to refine the uncertain regions to obtain segment boundary details. Uncertain regions may be an image region in the original resolution image where pixel labels are uncertain after the first coarse segmentation”).
Regarding claim 13, Skladnev as modified by Chen, Vezhnevets and Do with the same motivation from claim 1 discloses generate a cropped version of the image which includes the lesion (Do, [0074], “the input image is down-sampled, an approximate mole location is determined using the down-sampled image, and a region enclosing the mole is determined and cropped from the high-resolution input image”).
Regarding claim 14, Skladnev as modified by Chen, Vezhnevets and Do with the same motivation from claim 1 discloses generate the cropped image once the lesion has been mapped onto the higher resolution version (Do, [0142], “after we obtain an approximate location of the lesion, using the low-resolution image as reference, we crop the corresponding ROI from the original high-resolution image”).
Regarding claim 15, Skladnev as modified by Chen, Vezhnevets and Do with the same motivation from claim 1 discloses the cropped version of the image is generated using location of the initial boundary (Do, [0074], “the input image is down-sampled, an approximate mole location is determined using the down-sampled image, and a region enclosing the mole is determined and cropped from the high-resolution input image”).
Regarding claim 16, Skladnev as modified by Chen, Vezhnevets and Do with the same motivation from claim 1 discloses generate the cropped image as having its major portion depicting the lesion and a minor portion depicting skin surrounding the lesion (Do, [0142], “The border of a synthetic ROI, obtained after applying the coarse lesion segmentation, together with the actual contour are illustrated in FIG. 7(b)”).
Regarding claim 17, Skladnev as modified by Chen with the same motivation from claim 1 discloses apply pixel tone analysis (Chen, Lesion region analysis, [0001], “all eight connected neighbors of each boundary pixel located in the lesion interior are identified”. In addition, in Percentage melanoma color feature, [0001], “The percent melanoma color and color clustering ratio features are computed after the relative color histogram bins have been labeled and the lesion region for feature analysis has been identified”);
Skladnev as modified by Chen, Vezhnevets and Do with the same motivation from claim 1 discloses cropped image and thereby determine the refined boundary (Do, [0017], “identifying the lesion in the image. Such a processing comprises down-sampling the image”. In addition, in paragraph [0018], “The first segmenting process may be a coarse segmenting to determine any uncertain regions of the image, and the second segmenting process may be a fine segmenting carried out on the coarse segmentation to refine the uncertain regions to obtain segment boundary details. Uncertain regions may be an image region in the original resolution image where pixel labels are uncertain after the first coarse segmentation”).
Regarding claim 23, Skladnev as modified by Chen, Vezhnevets and Do with the same motivation from claim 1 discloses generate a lower resolution version of the captured image, which lower resolution image is used to determine an initial lesion boundary (Do, [0074], “the input image is down-sampled…an approximate mole location is determined using the down-sampled image, and a region enclosing the mole is determined”).
Regarding claim 24, SKladnev discloses a user device which is loaded with the machine-readable instructions of claim 1 (Skladnev, [0130], “The methods described here may be practiced using a general-purpose computer system 1800, such as that shown in FIG. 18…the steps of the methods are effected by instructions in the software that are carried out by the computer”).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Skladnev et al. (US 2004/0264749) in view of Chen et al. in view of Vezhnevets et al. in view of Do et al. (US 2017/0231550), as applied to claim 1, in further view of Wong et al. (US 2005/0123211).
Regarding claim 2, Skladnev as modified by Chen with the same motivation from claim 1 discloses determine a level of lesion skin tone (Chen, Lesion region analysis, [0001], “all eight connected neighbors of each boundary pixel located in the lesion interior are identified”. In addition, in Percentage melanoma color feature, [0001], “The percent melanoma color and color clustering ratio features are computed after the relative color histogram bins have been labeled and the lesion region for feature analysis has been identified”);
Skladnev as modified by Chen, Vezhnevets and Do does not expressly disclose “a darkest level”;
Wong et al. (hereinafter Wong) discloses a darkest pixel (Wong, [0042], “the darkest pixel of an image is analyzed to check whether a color casting defect exists in a shadow region of the image”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chen to determine the lesion skin tone using the darkest pixel analysis in the image, as taught by Wong. The motivation for doing so would have been allowing more accurate lesion segmentation.
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Skladnev et al. (US 2004/0264749) in view of Chen et al. in view of Vezhnevets et al. in view of Do et al. (US 2017/0231550) in view of Wong et al. (US 2005/0123211), as applied to claim 2, in further view of Ancin et al. (US 6,038,340).
Regarding claim 3, Skladnev as modified by Chen, Vezhnevets, Do and Wong with the same motivation from claim 2 teaches determine a darkest level of lesion skin tone by identifying at least one pixel from within the lesion region;
Skladnev as modified by Chen, Vezhnevets, Do and Wong does not expressly disclose “determined as being the pixel from a subset which has substantially the darkest level of tone of the lesion”;
Ancin et al. (hereinafter Ancin) discloses determine pixels from a subset which has the most black pixels (Ancin, col 6. 9-18, “in step 610 with pixel clustering routines 340 determining the black cluster having the most black pixels…If multiple clusters are identified, then in step 620 pixel clustering routines 340 compare the clusters' centroid RGB values to select the darkest of the clusters as the black cluster”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use the concept disclosed in Ancin of selecting the darkest of the clusters as the black cluster to determine the darkest level of lesion skin tone as taught by Skladnev as modified by Chen, Vezhnevets, Do and Wong. The motivation for doing so would have been providing ability to measure darkest portion of the lesion and facilitating more accurate lesion analysis.
Regarding claim 4, Skladnev discloses the set of pixels is at or proximal to a central region of the image (Skladnev, [0027], “forming from said histogram a (first) mask to identify, in the transformation space, relative locations of lesion pixels, skin pixels and unknown pixels”. In addition, in paragraph [0113], “identifiable "lesion" class is seen in the centre corresponding to the colour of seed”).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Skladnev et al. (US 2004/0264749) in view of Chen et al. in view of Vezhnevets et al. in view of Do et al. (US 2017/0231550), as applied to claim 1, in further view of Upendran et al. (US 2018/0225870).
Regarding claim 19, Skladnev disclose a graphic user interface (Skladnev, [0127], “user interface of the computer system 1800 may be supplemented by a slider-type control which has an effective range of”), and the lesion in the image to be taken (Skladnev, [0080], “In FIG. 5, the representation of the captured image 502 is shown which incorporates a number of colour calibration regions 504, 506, 508 and 510 arranged in the periphery or corners of the image”);
Skladnev as modified by Chen, Vezhnevets and Do does not expressly disclose “generate centering graphics to guide a user to center the image to be taken”;
Upendran et al. (hereinafter Upendran) discloses generate centering graphics to guide a user to center the image to be taken (Upendran, [0037], “a plurality overlay guides to capture the entire building including all sides/corners with various perspectives of the building…The user would substantially align (e.g., center and level) the front building image with the corresponding overlay by moving the capture device and take the picture when substantially aligned”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to capture the skin image of Skladnev using the graphic guide to align the overlay with the object image, as taught by Upendran. The motivation for doing so would have been facilitating accurate image capture.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Skladnev et al. (US 2004/0264749) in view of Chen et al. in view of Vezhnevets et al. in view of Do et al. (US 2017/0231550) in view of Upendran et al. (US 2018/0225870), as applied to claim 19, in further view of Huffman et al. (US 11,310,433).
Regarding claim 20, Skladnev teaches the field of view of a camera (Skladnev, [0073], “a camera assembly 104 is directed at a portion of a patient 102 in order to capture an image of the skin of the patient 102 and for which dermatological examination is desired”); Skladnev as modified by Chen, Vezhnevets, Do and Huffman with the same motivation from claim 19 teaches provide the centering graphics overlaid (Upendran, [0037], “a plurality overlay guides to capture the entire building including all sides/corners with various perspectives of the building…The user would substantially align (e.g., center and level) the front building image with the corresponding overlay by moving the capture device and take the picture when substantially aligned”);
Skladnev as modified by Chen, Vezhnevets, Do and Huffman does not expressly disclose “a magnified part of the field of view of a camera”;
Huffman et al. (hereinafter Huffman) discloses a magnified part of the field of view of a camera (Huffman, col 6. 18-29, “In FIG. 2A, the image on display 200 of imaging device 100 is a more zoomed-out image of…a more zoomed-in, higher zoom level when imaging device 100 is held farther from user 100, such as assumed in the image depiction of FIG. 2B”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to capture the skin image of Sklandev using the magnified field of view of the camera as taught by Huffman. The motivation for doing so would have been enable more accuracy image analysis.
Claims 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Skladnev et al. (US 2004/0264749) in view of Chen et al. in view of Vezhnevets et al. in view of Do et al. (US 2017/0231550), as applied to claim 1, in further view of Collins et al. (US 2006/0274928).
Regarding claim 21, Skladnev discloses generate an output an image which includes a determined lesion boundary and the lesion (Skladnev, [0096], “FIG. 16 can therefore be flirter processed to provide a mask image, SRG mask 658, shown in FIG. 17 which represents the specific boundary of the image as a result of seeded region growing that is construed to be "lesion"”);
Skladnev as modified by Chen, Vezhnevets and Do is silent with respect to “the determined lesion boundary is displayed as superimposed on the image of the lesion”;
Collins et al. (hereinafter Collins) discloses a determined lesion boundary is displayed as superimposed on an image of the lesion (Collins, [0053], “as shown in FIG. 4A. Each candidate image 402 is a composite image with the original image superimposed thereon a possible lesion boundary 404”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to display the lesion boundary of Sklandnev as a composite image with the lesion boundary superimposed on the original image as taught by Collins. The motivation for doing so would have been facilitating user review and verification.
Regarding claim 22, Skladnev as modified by Chen, Vezhnevets, Do and Collins with the same motivation from claim 21 discloses prompt the user review the image, and provide an input in relation to an assessment of a perceived accuracy of the boundary (Collins, [0053], “The user may also select a segmented image from one of the other candidate images 408. The user may identify a selection to the system by, for example, double-clicking a segmentation candidate. Optionally, a user can reject any or all of the displayed candidates and review the complete set of segmentation results. This allows the user to visually examine all segmentation results and pick one suitable candidate based on the user's own experience and judgment”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ZHAI whose telephone number is (571)270-3740. The examiner can normally be reached 9AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ke Xiao can be reached at (571) 272 - 7776. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE ZHAI/ Primary Examiner, Art Unit 2611
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