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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/09/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Preliminary Amendment
The preliminary amendments filed 12/09/2024 have been acknowledged.
Claims 1-15 have been amended.
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.
Claim 7 recites the limitation " said outputted skin parameter " There is insufficient antecedent basis for this limitation in the claim. The dependency chain of claim 7 includes dependency of claim 6 and claim 1. No claim in this dependency discloses “skin parameter”. “Skin parameter” is mentioned in claim 2 and follows claim 2’s downstream dependency chain. Claim 2 is outside the dependency chain of claim 7. Therefore claimed “said outputted skin parameter” lacks antecedent basis.
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.
Claim 13 is rejected under 35 U.S.C. 101 because the claims appear to be directed to a software embodiment and not to hardware embodiment The claim appears to be directed towards a software embodiment. Claim 13 recites “A computer program product comprising computer program code which,” Software, alone, are not physical components and thus are not statutory since software do not define any structural and functional interrelationships between the computer programs and other claimed elements of a computer, which permit the computer' s program functionality to be realized. Hence, the stated functions comprise software and is thus not directed to a hardware embodiment. Data structures not claimed as embodied in computer readable media are descriptive material per se and are not statutory because they are not capable of causing functional change in the computer. See e.g., Warmerdam, 33 F.3d at 1361, 31, USPQ2d at 1760 (claim to a data structure per se held non-statutory). Such claimed data structures do not define any structural and functional interrelationships between data and other claimed aspects of the invention, which permit the data structure' s functionality to be realized. In contrast, a claimed computer readable medium encoded with a data structure defines structural and functional interrelationships between the data structure and the computer software and hardware components which permit the data structure' s functionality to be realized, and is thus statutory.
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, 9-15 are rejected under 35 U.S.C. 103 as being unpatentable over Saiko et al (Saiko hereinafter US 20210259625 A1) in view of Xiong et al (Xiong hereinafter US 20090263013 A1)
As per claim 1
Saiko teaches computer implemented method suitable for monitoring at least one skin region of a subject capable of moving relative to a source of ambient light (Paragraph [0067] “computer implemented method suitable for monitoring at least one skin region of a subject capable of moving relative to a source of ambient light” Any person being monitored is a “subject capable of moving” relative to an ambient light source.) a sequence of video frames (200) of the subject captured while a light emitter is controlled to provide a varying illumination to the subject (Paragraph [0018] “In accordance with a further aspect, the illumination unit further comprises at least one of (i) a controller to control illumination of the one or more light sources” Paragraph [0122] “The tissue visualization app 112 can process an image and can also operate in video mode to process a series of images or video frames.” ) generating using a varying exposure between video frames of a first part of the sequence of video frames caused by said varying illumination during the capture of said first part, a first ambient light-corrected image (Fig. 7 Paragraph [0218] “upon activation of the image capture unit 103, the computing device 108 takes several images. In an example embodiment, one or more images are taken with flash,” Paragraph [0221] computing device 108 (e.g., a mobile device 108) uses image subtraction to remove ambient light in the image. In this case, the image without external illumination (no flash) is subtracted from images with external illumination (with flash)) generating , using a varying exposure between video frames of a second part of the sequence of video frames caused by said varying illumination during the capture of said second part, a second ambient light-corrected image (Paragraph [0159] “ FIG. 7 plots the flash 702 coordinated by illumination unit 104, … As shown at 702, the illumination schema (cycle) consists of m flashes (with m=2 in the example of FIG. 7) and one period without flash…Cycles can be repeated continuously during video mode capturing. Paragraph [0221] “computing device 108 (e.g., a mobile device 108) uses image subtraction to remove ambient light in the image. In this case, the image without external illumination (no flash) is subtracted from images with external illumination (with flash). “ ) the second part being temporally distinct from the first part (Figure 7, Paragraph [0159] “Cycles can be repeated continuously during video mode capturing”)
Although Saiko states in paragraph [0093] that “ Data processing unit 220 may use Machine Learning (including supervised ML and unsupervised ML) to extract information from collected images and other data. In particular, data processing unit 220 can build and train models, which can discriminate between various conditions and provide users with additional information. In some embodiments, data processing unit 220 uses convolutional neural networks for automatic or semi-automatic detection and/or classification of the skin or wound conditions. In some embodiments, ML models built and trained using other tools may be deployed to data processing unit 220 for image/data detection/classification, such as from an external system 106. “ Saiko does not explicitly teach segmenting the first ambient light-corrected image to generate a first segmented image in which the at least one skin region is identified nor segmenting the second ambient light-corrected image to generate a second segmented image in which said at least one skin region is identified.
Xiong teaches segmenting the first ambient light-corrected image to generate a first segmented image in which the at least one skin region is identified (Figure 1. Paragraph [0017] “Image processing portion 110 includes an illuminant compensation module 115 to adjust the raw pixel data to compensate for different lighting conditions” Paragraph [0018] “A feature tone detection module 120 detects features that are correlated to specific tones and outputs mask bits that identify whether individual pixels have the feature tone. The output 140 of the image processing portion is an image having illuminant compensated pixel data with additional feature tone identification mask bits. The feature tone identification mask bits may be used, for example, in subsequent image feature identification.” Paragraph [0019] “ the feature tone identification mask bits are embedded in the illumination compensate pixel data (in the first color space). “ Paragraph [0029] “The output of the image processing system is an additional mask bit indicating whether the pixel is a skin tone pixel. In one embodiment, each pixel has a mask bit assigned to it to indicate whether the pixel was identified as having a skin tone. For example, with 8 bit pixel data an extra bit can be assigned as a skin tone identification bit. Consequently, every individual pixel carries its own skin tone identification mask bit (or bits). However, it will also be understood that the mask bits of an image could be separately extracted” This shows ) segmenting the second ambient light-corrected image to generate a second segmented image in which said at least one skin region is identified. (Figure 1. Paragraph [0017] “Image processing portion 110 includes an illuminant compensation module 115 to adjust the raw pixel data to compensate for different lighting conditions” Paragraph [0018] “A feature tone detection module 120 detects features that are correlated to specific tones and outputs mask bits that identify whether individual pixels have the feature tone. The output 140 of the image processing portion is an image having illuminant compensated pixel data with additional feature tone identification mask bits. The feature tone identification mask bits may be used, for example, in subsequent image feature identification.” Paragraph [0019] “ the feature tone identification mask bits are embedded in the illumination compensate pixel data (in the first color space). Paragraph [0024] “For a variety of applications it is a good approximation to assume that a feature of interest for which feature tone detection is being performed is likely to have a contiguous region of pixels. That is, the feature of interest is unlikely to have voids below some criteria (such as single pixel voids) and is also unlikely to be of interest if it is an isolated island of pixels below a certain minimum number of pixels “ “ Paragraph [0028] “The skin tone detection module 325 has a first sub-module 330 to change the color space of a copy of the pixel data into the HSV color space, a second sub-module 335 to identify skin tones based on ranges of hue and saturation, and a third sub-module 340 to perform de-noising and integrate skin tone identification bit masks into a blob of illumination compensated pixel data” Paragraph [0029] “The output of the image processing system is an additional mask bit indicating whether the pixel is a skin tone pixel. In one embodiment, each pixel has a mask bit assigned to it to indicate whether the pixel was identified as having a skin tone. For example, with 8 bit pixel data an extra bit can be assigned as a skin tone identification bit. Consequently, every individual pixel carries its own skin tone identification mask bit (or bits). However, it will also be understood that the mask bits of an image could be separately extracted”. Xiong’s feature tone detection mask module receives illumination compensated image information and performs pixel wise skin vs non skin classification. Each pixel receives a mask bit indicating whether it is skin. Xiong allows the mask bits to be “separately extracted” for image processing. Xiong assigns pixels of an image to different classes according to whether they belong to skin. Under broadest reasonable interpretation Xiong’s skin tone detection mask reasonably constitutes a “segmented image” because it spatially partitions the pixels of the captured image into skin and non-skin classes which identifies the region occupied. A person of ordinary skill in understands that applying this operation to Saiko’s subsequent corrected imaging produces the claimed second segmentation image)
In a combined teaching Saiko teaches a computer implemented method for imagining a subject’s skin tissue in which an illumination unit is coordinated with an imaging unknit to provide varying illumination that is continuously applied during video mode acquisition . Saiko gets images with and without external illumination and subtracting the image acquired without external illumination from images acquired with external illumination to remove ambient light allowing for ambient light correction. Saiko’s video acquisition mode allows for distinct video portions in time. Xiong compliments by teaching skin tone detection on images that were compensated for illumination. Xiong creates image masks for the skin by identifying individual pixels as skin or non-skin and generates the mask indicating corresponding pixels for skin. Applying Xiong’s skin segmentation to Saiko's ambient light corrected images generated from first and temporally distinct second portions of the video sequence results in respective first and second segmented images identifying the skin being monitored.
Xiong identifies an issue where “when the same skin surface is lit with different illuminants the UV components of the captured image will vary, which in turn may cause errors in the skin tone detection algorithm” then provides the solution that Performing skin tone detection after illumination compensation (e.g., after AWB) makes the detection robust to changes in lighting conditions.”
Accordingly, a person of ordinary skill in the art, at the time this invention was effectively filed would have found it obvious to modify Saiko’s tissue imagining system to perform the skin tone segmentation taught by Xiong on Saiko’s ambient light corrected images. Xiong recognizes that variations in illumination can cause errors in skin tone detection and provides a solution to remedy. A person of ordinary skill in the art would have been motivated to apply Xiong’s mask processing pipeline after Saiko’s ambient light removal to reliably identify the skin region despite variations in illumination. This gives a predictable result of generating pixel wise masks that distinguish the skin region from non-skin portions of Saiko’s corrected issues. Saiko’s corrected images are input to the downstream processing Xiong says benefits from receiving illumination compensated image data giving the modification a natural flow. A person of ordinary skill in the art understands this modification leads to reliable identification and isolation of the monitored skin region in Saiko’s ambient light corrected image despite varying illumination. At the same time, it reduces interference from non-skin portions via segmentation.
As per claim 9
Saiko and Xiong teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1.
Saiko teaches wherein the varying illumination is a pulsed illumination (Figure 7)
As per claim 10
Saiko and Xiong teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1.
Saiko teaches wherein a frame rate of the sequence of video frames is at least 24 frames per second. (Paragraph [0160] “In a video mode, the framerate can be selected as fps=2*f/k (e.g., 30, 24, 20, 15, 12, and 10 fps for North America and 25, 20, and 10 fps for Europe). “)
As per claim 11
Saiko and Xiong teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1.
Saiko teaches wherein the video frames are rolling shutter camera-captured video frames. (Paragraph [0024] “In accordance with an aspect, the computing device comprises a mobile device and the image capture unit is a camera integrated with the mobile device.” Paragraph [0070] “image capturing unit 103 may comprise an internal (built-in to computing device 108) or external device capable of capturing images. In an example embodiment, image capturing unit 103 comprises a 3 channel (RGB) or 4 channel (RGB-NIR) camera.” Paragraph [0101] “The image capture unit 103 may comprise a smartphone camera (front or back), for example.” As of the year 2026 there are no true commercially available global shutter smartphones. Therefore, it is within reason to assume a person of ordinary skill in the art is aware Saiko’s smartphone being used as an imagining unit comprises a rolling shutter camera. )
As per claim 12
Saiko and Xiong teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1.
Saiko teaches wherein a timing of the varying illumination relative to a video frame capture timing is such that each of the video frames comprises more exposed areas caused by the varying illumination and less exposed areas whose exposure is due only to the source of ambient light (Figure 7) the more exposed and the less exposed areas being positioned differently between the video frames of the first part (Figure 7) and the more exposed and the less exposed areas being positioned differently between the video frames of the second part (Figure 7) Paragraph [0160] “The exposure time (T) for each frame (in milliseconds) can be selected as T=k/2*f, where k is an integer, and f is the utility frequency for a particular country in Hz (e.g., 60 Hz for North America, 50 Hz for Europe). In a video mode, the framerate can be selected as fps=2*f/k (e.g., 30, 24, 20, 15, 12, and 10 fps for North America and 25, 20, and 10 fps for Europe). The frame rate of 20 fps (T=50 ms) is an example selection. It can work without any configurations with external light sources connected to any electrical grid (50 Hz or 60 Hz). Other frame rates can also be used. Paragraph [0161] The duration of each flash can be T or any whole number multiple of T. ) wherein the first ambient light-corrected image is generated based on a comparison between the more exposed areas and the less exposed areas of the first part of the sequence of video frames (Paragraph [0221] “In some embodiments, computing device 108 (e.g., a mobile device 108) uses image subtraction to remove ambient light in the image. In this case, the image without external illumination (no flash) is subtracted from images with external illumination (with flash).”) wherein the second ambient light-corrected image (202B) is generated based on a comparison between the more exposed areas (212B) and the less exposed areas (214B) of the second part of the sequence of video frames. Paragraph [0221] “In some embodiments, computing device 108 (e.g., a mobile device 108) uses image subtraction to remove ambient light in the image. In this case, the image without external illumination (no flash) is subtracted from images with external illumination (with flash).”)
As per claim 13
Saiko and Xiong teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1.
Saiko teaches A computer program product comprising computer program code which, when executed on a computing device having a processing system, causes the processing system to perform all of the steps of the method (Paragraph [0241] “The embodiments of the devices, systems, and methods described herein may be implemented in a combination of both hardware and software. These embodiments may be implemented on programmable computers, each computer including at least one processor, a data storage system (including volatile memory or non-volatile memory or other data storage elements or a combination thereof), and at least one communication interface.”
Paragraph [0242] “Program code is applied to input data to perform the functions described herein and to generate output information. The output information is applied to one or more output devices” Paragraph [0067] “…non-transitory computer readable storage medium or memory…” )
As per claim 14
Saiko and Xiong teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1.
Claim 14 is the system claim that parallels method claim 1 and will be rejected under the same premise.
As per claim 15
Saiko and Xiong teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1.
Saiko teaches The system according to claim 14, wherein the processing system is further configured to control the image capture unit to capture said sequence of video frames; and/or to control the light emitter to provide said varying illumination to the subject. Paragraph [0105] “A controller 301 causes light sources to flash in a predetermined fashion. The controller 301 can receive commands from I/O unit 304 or be triggered manually (e.g., using a button). The controller 301 can be based on any type of general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), a reconfigurable processor, a programmable read-only memory (PROM), or any combination thereof. In an example embodiment, the controller 301 is based on a microcontroller.”
Claims 2-5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Saiko et al (Saiko hereinafter US 20210259625 A1) in view of Xiong et al (Xiong hereinafter US 20090263013 A1) in further view of Serval et al (Serval hereinafter US 20240065554 A1)
As per claim 2
The Saiko/Xiong system teaches all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection
Neither Saiko or Xiong teaches extracting from one or more of the first and second segmented images at least one skin parameter; and outputting the at least one skin parameter.
Serval teaches extracting from one or more of the first and second segmented images at least one skin parameter and outputting the at least one skin parameter. (Fig 1B, Paragraph [0074] “The segmentation may be used to identify one or more regions and features within respective input images of each dataset.” Paragraph [0102] “Further, at 510, the method 500 includes extracting skin areas from the detected body part. Extracting skin areas include performing image segmentation on the detected body part image to locate and identify boundaries of different features within the detected body part image. That is, one or more features of the detected body part may be segmented and various features of the body part may be classified including skin areas…After segmentation, the skin areas are extracted and used for subsequent skin analysis, and the remaining areas corresponding to one or more…An example processed image including the relevant skin areas after extraction for analysis of redness is shown at FIG. 7A. facial image segmentation generates an output including one or more boundaries according to respective contours of the one or more facial features.)
Accordingly, a person of ordinary skill in the art would have found it obvious, at the time this invention was effectively filed to modify the Saiko/Xiong methodology with Serval’s concept of extracting segmented skin areas for subsequent skin analysis to obtain and output skin parameters from the identified skin region. Saiko allows for illumination corrected skin images and Xiong segments those images to identify skin region while Serval teaches using the resulting segmented skin areas for analysis of skin characteristics such as redness. Applying Serval’s known skin analysis processing to Xiong’s Segmented output would be a coherent and natural us of the identified skin region in the pipeline and would predictably provide skin specific parameters while excluding non skin portions of the image. A person of ordinary skill in the art understands the advantage of reliable skin parameter analysis by limiting the analysis to actual identified skin regions and excludes irrelevant regions.
As per claim 3
The Saiko/Xiong/Serval system teaches all claim limitations previously rejected in claim 1’s 103 rejection. See claim 2’s 103 rejection
Serval teaches wherein the skin parameter is a measure of one or more of: skin colour, skin texture, skin shape, and skin optical reflectance. (Paragraph [102] “After segmentation, the skin areas are extracted and used for subsequent skin analysis, and the remaining areas corresponding to one or more…An example processed image including the relevant skin areas after extraction for analysis of redness is shown at FIG. 7A. facial image segmentation generates an output including one or more boundaries according to respective contours of the one or more facial features” Paragraph [0109] “some examples, a number of areas for a particular skin feature (e.g., a number of redness areas) among the plurality of skin features may be detected based on the segmented features. In some examples, a number of areas of each skin feature may be detected based on the segmented features.” Paragraph [0122] “in one example, one or more skin features may be segmented and the segmentation indication (e.g., indications of a boundary of the skin feature) may be displayed on the display portion. Further, the local features, including feature size, volume, a number of features, location of the features and occupancy of the features in each of these locations” )
As per claim 4
The Saiko/Xiong/Serval system teaches all claim limitations previously rejected in claim 1’s 103 rejection. See claim 2’s 103 rejection
Serval teaches determining, an image quality metric, wherein the extracting or outputting of said skin parameter is implemented responsive to the image quality metric meeting or exceeding a predetermined threshold (Paragraph [0198] “the processor 22 may be configured to automatically perform skin analysis responsive to detecting the presence of a user within a threshold distance from the mirror, as discussed above with respect to FIGS. 2-8E” The threshold distance is being interpreted as an ”image quality metric”. The distance the user has to the mirror effects the quality of analysis. Therefore, a user must be within or greater than the threshold distance to automatically trigger skin analysis which includes skin segmentation and subsequent feature extraction. )
As per claim 5
The Saiko/Xiong/Serval system teaches all claim limitations previously rejected in claim 4’s 103 rejection. See claim 4’s 103 rejection
Serval teaches The computer implemented method according to comprising issuing responsive to the image quality metric failing to meet said predetermined threshold a report indicating unreliable image data (Paragraph: “Further, in some examples, if the distance of the user is greater than a threshold distance, an indication may be provided (via the display, or speaker coupled to the skin analysis system) to guide the user to remain within the threshold distance.” Staying within the threshold distance of an image based system implies the need of quality/accurate images. Being outside the threshold distance range implies unreliable imagining. ) or outputting, responsive to the image quality metric meeting/exceeding the predetermined threshold, said skin parameter relevant to the at least one skin region (Paragraph [0198] “processor 22 may be configured to automatically perform skin analysis responsive to detecting the presence of a user within a threshold distance from the mirror, as discussed above with respect to FIGS. 2-8E)
As per claim 8
The Saiko/Xiong system teaches all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection
Serval teaches obtaining RGB values of the first segmented image (204A) and/or the second segmented mage (204B); and converting the RGB values to CIE L*a*b* values. (Paragraph [0094] “ The camera may be an RGB camera, for example. The one or more images of the user includes images of the body part for which skin analysis is desired. “ Paragraph [0096] “a first set of images may be acquired with UV light and no polarizing filters for sebum distribution analysis and a second set of images may be acquired with daylight RGB and polarized filter for redness analysis” Paragraph [0104] “Returning to FIG. 5A, upon extracting the skin areas for analysis, the method 500 proceeds to 512. At 512, the )method 500 includes encoding the extracted image onto a multi-dimensional array. In one example, the method 500 includes encoding the image with extracted skin areas on to a four dimensional matrix. The four dimensional matrix includes two dimensions for space vector (X, Y), and two dimensions for the AB CIE 1976 color space”
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Saiko et al (Saiko hereinafter US 20210259625 A1) in view of Xiong et al (Xiong hereinafter US 20090263013 A1) in further view of Lipson et al (Lipson hereinafter US 20110262028 A1)
As per claim 6
The Saiko/Xiong system teaches all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection
Saiko nor Xiong teach wherein the first and second segmented images each comprise a first skin region) and a second skin region the method comprising: comparing the first skin region identified from the first segmented image to the first skin region identified from the second segmented image to determine a first image consistency parameter; comparing second skin region identified from the first segmented image to the second skin region identified from the second segmented image to determine a second image consistency parameter; and comparing the first and second image consistency parameters.
Lipson teaches wherein the first and second segmented images (Paragraph [0080] “Either after or before the alignment step, the primary and target images are each divided or segmented into a plurality of sub regions”) each comprise a first skin region and a second skin region (“Paragraph [0016] The at least one plug-in module can provide application information used by the image processing system to process images having…a face” Paragraph [0083] “For instance, when comparing facial images, the structure of the face is very important. In all cases there should be regions corresponding to the eyes, nose, mouth, cheeks, etc.” Paragraph [0014] “Repeating (h)-(j) for each of a predetermined number of regions in the target image.” ) comparing the first skin region identified from the first segmented image of the first skin region identified from the second segmented image to determine a first image consistency parameter (Figure 3 label 50, Figure 3A Paragraph [0014] “assigning a score indicating a difference between the one or more properties in the primary image region within the selected image and the corresponding one or more properties in the target image region, (h) selecting a next region in the target image, (i) comparing one or more properties of the primary image region within the selected image to a corresponding one or more properties in the next target image region, and (j) assigning a score indicating a difference between the one or more properties in the primary image region within the selected image and the corresponding one or more properties in the next target image region”) comparing the second skin region identified from the first segmented image to the second skin region identified from the second segmented image to determine a second image consistency parameter (Figure 3 label 50, Figure 3A Paragraph [0014] “assigning a score indicating a difference between the one or more properties in the primary image region within the selected image and the corresponding one or more properties in the target image region, (h) selecting a next region in the target image, (i) comparing one or more properties of the primary image region within the selected image to a corresponding one or more properties in the next target image region, and (j) assigning a score indicating a difference between the one or more properties in the primary image region within the selected image and the corresponding one or more properties in the next target image region” Paragraph [0080] “Either after or before the alignment step, the primary and target images are each divided or segmented into a plurality of sub regions”) and comparing the first and second image consistency parameters. (Figure 5, Paragraph [0114] “Referring now to FIG. 5, a the processing steps to make a symmetric measurement between two images is shown. The method described in FIG. 3-3B computes how similar a primary image is to a target image. In FIG. 4, a comparison was made between the primary image 70 and the target image 80 (i.e. primary image 70 was compared with target image 80). Based on this comparison, a similarity score was computed….Based on this comparison, a second similarity score can be computed. It is possible that the second similarity score (computed by comparing primary image 80 with target image 70) will be different than the first similarity score (computed by comparing primary image 70 with target image 80…Processing begins in step 90 where a first image (image A) is matched to a second image (e.g., image B) and an aggregate score is computed. The processing performed to match image A to image B is the processing described above in conjunction with FIGS. 3-4B...Processing then flows to step 92 in which the second image (i.e. image B) is matched to the first image (i.e. image A) and a second aggregate score is computed…the scores from the two searches are combined to provide a composite score. The scores may be combined, for example, by simply computing a mathematical average or alternatively, the scores” Paragraph [0121] “ One could also retain the scores for each property in steps 90 and 92 of FIG. 5. Step 94 could average the score for each property independently and then combine the results into an aggregate match for image A to B and B to A….” )
Accordingly, a person of ordinary skill in the art, at the time this invention was effectively filed would have found it obvious to modify the Saiko/Xiong system to incorporates Lipson’s concept of cross image regional scoring technique to the segmented images produced by the Saiko/Xiong to improve the reliability of the skin monitoring. Saiko provides a temporally distinct ambient light corrected images while Xiong identifies corresponding regions in in the skin mask segmentation. Lipson teaches comparing corresponding regions across two images, generating a score reflecting their degree of similarity or difference (i.e. reliability of similarity) and then the generated scores for the multiple regions are compared and evaluated to identify the best match. . Applying Lipson’s technique would reduce the influence of residual illumination or motion variation on the Saiko/Xiong system. A person of ordinary skill in the art would also appreciate the fact that one of Lipson’s properties that are used to compare regional difference scores is of luminance. This gives a direct technical connection to Saiko/Xiong’s illumination varying imagining. A person of ordinary skill in the art understands the advantage here is improved monitoring reliability via identification of which skin region remains most consistent across successive images despite illumination and or motion variations.
As per claim 7
Saiko, Xiong, and Lipson teach all claim limitations previously rejected in claim 6’s 103 rejection. See claim 6’s 103 rejection.
Lipson teaches comprising selecting the first skin region or the second skin region based on said comparison of the first and second image consistency parameters, said outputted skin parameter being relevant to the selected skin region (Figure 7B, Figure 11B. Within the Saiko/Xiong/Lipson method a person of ordinary skill in the art would understand that the first image in the list to be the higher ranking reliability/confidence comparative score and find that association of its rank obvious)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE WRENSFORD CODRINGTON whose telephone number is (571)272-8130. The examiner can normally be reached 8:00am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571) 272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHANE WRENSFORD CODRINGTON/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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