DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/20/2025 was filed after the mailing date of the non-final on 05/20/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Claims 1-2, 5, 8, 10, 12-13, 15-16, 19-20, 22-26, and 31-34 were previously pending and subject to a non-final action 05/20/2025. In the response filed 08/20/2025, claims 31 and 33 were amended. Therefore, claims 1-2, 5, 8, 10, 12-13, 15-16, 19-20, 22-26, and 31-34 are currently pending and subject to the final action below.
Response to Arguments
Applicant’s arguments, see page 8, filed 08/20/2025, with respect to claim 33 under 112 (f) claim interpretation have been fully considered and are persuasive. The 112 (f) of claim 33 has been withdrawn.
Applicant’s arguments, see page 8, filed 08/20/2025, with respect to claim 31 under 35 U.S.C 101 rejection have been fully considered and are persuasive. The 101 rejection of claim 31 has been withdrawn.
Applicant’s arguments, see pages 8-11, filed 08/20/2025, with respect to claims 1-2, 5, 8, 10, 12-13, 15-16, 19-20, 22-26, and 31-34 under35 U.S.C. 103 have been fully considered but they are not persuasive
Applicant’s argument 1: Applicant's attorney submits that (the combination of) Strasfeld and Whitney does not teach or suggest all of the elements as recited in this claim. As the examiner has inherently stated, claim 1 is novel over Strasfeld alone. For example, claim 1 recites that a verification indicator is determined based on a quality of the segmentation. In contrast, Strasfeld does not teach, or even mention, any evaluation of the quality of the segmentation, let alone the determination of any verification indicator derived therefrom.
Claim 1 recites that the luminescence image is displayed with the detection segment that is highlighted according to the verification indicator. In contrast, because Strasfeld does not teach, or even mention, any verification indicator, the display of the images in Strasfeld is not, and cannot be, influenced by such a verification indicator. Consequently, claim 1 is patentable over Strasfeld, which, at least for the purpose of these remarks, is considered the prior art that is closest to the patent application and its claims. For example, as indicated above, distinguishing features of claim 1 with respect to Strasfeld include, inter alia, the determination of the verification indicator based on the quality of the segmentation and the display of the luminescence image with the detection segment that is highlighted according to the verification indicator.
And even considering Whitney in combination with Strasfeld, still this combination would not have motivated a POSITA to develop the subject matter recited in claim 1.
Examiner Response 1: After careful consideration and review of prior art, the examiner respectfully disagrees with applicant’s arguments.
Strasfeld teaches: segmenting, by the computing device, the analysis region into a detection segment and a non-detection segment representative of detection of the luminescence agent and of non-detection of the luminescence agent, respectively, according to the luminescence values only of the analysis region, (Strasfeld − [Col. 9 ll. 32-36] filtering the enhanced image received from block 602. A detection process may be trained to determine filter settings using identified cancer images. Comparisons between healthy and non-healthy tissue may be used to set parameters (e.g., train) the detection process to identify residual cancer cells in the image. [Col. 10 ll. 24-26] a feature meets the criteria, its pixels are given a value of “1” for highlighting. [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted.)
and displaying, by the computing device, the luminescence image with the detection segment being highlighted according to the verification indicator. (Strasfeld − [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted. The two semi-circular spots 1302 in FIG. 13B are cancer cells according to pathology results and confirms the accuracy of the imaging analysis technique described above in identifying the two cancer spot features 1302.)
Strasfeld teaches, separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted.
Furthermore, Whitney teaches: segmenting, by the computing device, the analysis region into a detection segment and a non-detection segment representative of detection of the luminescence agent and of non-detection of the luminescence agent, respectively, according to the luminescence values only of the analysis region, (Whitney − Fig. 3 [0044] (B) high tumor-contrast following MMP-dependent cleavage, separating Cy5 from Cy7. Separating high contrast agent of tumor tissue from normal tissue.)
determining, by the computing device, a verification indicator based on a quality indicator of said segmenting the analysis region, (Whitney − [0045] FIG. 4 describes (A, B) RACPP injection produces greater radiometric fluorescent signal in HNSCC tumor versus normal tissue [0293] A mean Cy5/Cy7 ratio was calculated for segments containing histologically-confirmed tumor and, separately, tumor-free segments. A verification of tumor to tumor-free segments ratio is performed. This is referred to tumor to background/target to background ratio verification indicator.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Strasfeld and Whitney as each invention teaches defining tumor cells within tissue using image analysis. Adding the teaching of Whitney provide Strasfeld with a plurality of radiometric analysis tumor to background ratio in determining tumor cells within a tissue. One of ordinary skill in the art would have been motivated to improve prediction of tumor cell within a region of interest.
Examiner Notes
Luminescence imaging and fluorescence imaging are both methods for generating images based on light emission. Applicant’s specification use each term interchangeable
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 5, 8, 10, 12, 16, 19-20, 22-26, and 31-34 are rejected under 35 U.S.C. 103 as being unpatentable over Strasfeld (US PAT: 11426075 B1, Filed Date: Aug. 23, 2017) in view of Whitney (US PGPUB: 20160160263 A1, Filed Date: Oct 2, 2015).
Regarding independent claim 1, Strasfeld teaches: A method for imaging a field of view comprising a target containing a luminescence substance, (Strasfeld − [Col. 5 ll. 24-30] the medical imaging device 110, the handheld imaging device may be a hand-held fluorescence imaging device with a photosensitive detector sensitive to fluorescence signals corresponding to a photons emitted from fluorescence of certain fluorescent imaging agent with which the cancer cells are labelled with.)
wherein the method comprises: providing, to a computing device, (Strasfeld − [Col. 4, ll 11-27, 35-40 ] FIG. 2 is a block diagram of a cancer cell detection system 200 that is an example of the system 100 of FIG. 1. Some of the components are the same and thus the same reference numbers are used. In the example in FIG. 2, the image analysis system 201 includes an interconnect 220 used to interface with and coordinate with various different components in the system.)
a luminescence image of the field of view, (Strasfeld − [Col. 11, ll. 55-57] FIG. 8 is an image 800 taken from a fluorescent imaging device according to an exemplary embodiment. [Col. 5 ll. 44-51] images being captured by a handheld imaging device may comprise pixels with brightness or intensity levels. The intensity values of each pixel on the captured image in a portion of the tissue within the total field of view of the imaging device.)
the luminescence image comprising a plurality of luminescence values representative of a luminescence light being emitted by the luminescence substance from corresponding locations of the field of view, (Strasfeld − [Col. 5 ll. 44-51] images being captured by a handheld imaging device may comprise pixels with brightness or intensity levels. The intensity values of each pixel on the captured image in a portion of the tissue within the total field of view of the imaging device. [Col. 13 ll. 1-20] FIG. 12B and FIG. 12D, a small circle 1206 highlights the portion of the original input image 800 selected for analysis containing two bright spots 1202 corresponding to the two features that exceed the intensity threshold and the size threshold for screening cancer cell features (see e.g., features 1015 and 1003 in FIG. 10C and FIG. 10D). The small circle area 1206 is enlarged in FIG. 12D. The two bright spot features 1202 outline groups of pixels identified as potential cancer features.)
setting, by the computing device, an analysis region a portion of the luminescence image surrounding a suspected representation of the target, (Strasfeld − [Col. 11., ll. 5-40] User interface 700 in FIG. 7 also includes a user control area 703 hosting a plurality of interactive controls responsive to an operator input through one or more input devices such as input devices 105 in FIG. 2. User control area 703 include controls (e.g., buttons) to initiate actions; user to adjust the parameters used in the image analysis system 201, for example,… size threshold that are optimized for the image analysis system 201 as disclosed in the sections above to perform cancer cell feature identification.)
segmenting, by the computing device, the analysis region into a detection segment and a non-detection segment representative of detection of the luminescence agent and of non-detection of the luminescence agent, respectively, according to the luminescence values only of the analysis region, (Strasfeld − [Col. 9 ll. 32-36] filtering the enhanced image received from block 602. A detection process may be trained to determine filter settings using identified cancer images. Comparisons between healthy and non-healthy tissue may be used to set parameters (e.g., train) the detection process to identify residual cancer cells in the image. [Col. 10 ll. 24-26] a feature meets the criteria, its pixels are given a value of “1” for highlighting. [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted.)
and displaying, by the computing device, the luminescence image with the detection segment being highlighted according to the verification indicator. (Strasfeld − [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted. The two semi-circular spots 1302 in FIG. 13B are cancer cells according to pathology results and confirms the accuracy of the imaging analysis technique described above in identifying the two cancer spot features 1302.)
Strasfeld does not explicitly teach: determining, by the computing device, a verification indicator based on a quality indicator of said segmenting the analysis region,
However, Whitney teaches: segmenting, by the computing device, the analysis region into a detection segment and a non-detection segment representative of detection of the luminescence agent and of non-detection of the luminescence agent, respectively, according to the luminescence values only of the analysis region, (Whitney − Fig. 3 [0044] (B) high tumor-contrast following MMP-dependent cleavage, separating Cy5 from Cy7. Separating high contrast agent of tumor tissue from normal tissue.)
determining, by the computing device, a verification indicator based on a quality indicator of said segmenting the analysis region, (Whitney − [0045] FIG. 4 describes (A, B) RACPP injection produces greater radiometric fluorescent signal in HNSCC tumor versus normal tissue [0293] A mean Cy5/Cy7 ratio was calculated for segments containing histologically-confirmed tumor and, separately, tumor-free segments. A verification of tumor to tumor-free segments ratio is performed. This is referred to tumor to background/target to background ratio verification indicator.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Strasfeld and Whitney as each invention teaches defining tumor cells within tissue using image analysis. Adding the teaching of Whitney provide Strasfeld with a plurality of radiometric analysis tumor to background ratio in determining tumor cells within a tissue. One of ordinary skill in the art would have been motivated to improve prediction of tumor cell within a region of interest.
Regarding dependent claim 2, depends on claim 1, Strasfeld teaches: displaying the luminescence image with the detection segment being highlighted selectively according to the verification indicator. (Strasfeld − [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted. The two semi-circular spots 1302 in FIG. 13B are cancer cells according to pathology results and confirms the accuracy of the imaging analysis technique described above in identifying the two cancer spot features 1302.)
Regarding dependent claim 5, depends on claim 1, Strasfeld teaches: displaying, by the computing device, the luminescence image with the detection segment being highlighted progressively with a highlighting intensity depending on the verification indicator. (Strasfeld − [Col. 3, ll. 5-7] FIG. 10B is a slice of data plot showing cross-sectional signal intensity values along the pixels on a horizontal line FIG. 10A. [Col. 8 ll. 23-30] For each group of residual cancer cell features based on a comparison between signal intensity of pixels substantially within and outside the feature. Only features with contrast above a predetermined threshold are identified as cancer cells)
Regarding dependent claim 8, depends on claim 1, Strasfeld teaches: determining, by the computing device, the quality indicator according to a comparison between a content of the detection segment and a content of the non-detection segment. (Strasfeld − [Col. 9 ll. 32-36] filtering the enhanced image received from block 602. A detection process may be trained to determine filter settings using identified cancer images. Comparisons between healthy and non-healthy tissue may be used to set parameters (e.g., train) the detection process to identify residual cancer cells in the image. [Col. 10 ll. 24-26] a feature meets the criteria, its pixels are given a value of “1” for highlighting.)
Regarding dependent claim 10, depends on claim 1, Strasfeld teaches: determining, by the computing device, the quality indicator according to a content only of the detection segment, particularly according to a continuity of the detection segment. (Strasfeld − [Col. 3, ll. 5-7] FIG. 10B is a slice of data plot showing cross-sectional signal intensity values along the pixels on a horizontal line FIG. 10A. [Col. 8 ll. 23-30] For each group of residual cancer cell features based on a comparison between signal intensity of pixels substantially within and outside the feature. Only features with contrast above a predetermined threshold are identified as cancer cells)
Regarding dependent claim 12, depends on claim 1, Strasfeld teaches: initializing, by the computing device, the analysis region to a portion of a context space equal to the luminescence image. (Strasfeld − [Col. 11., ll. 5-40] User interface 700 in FIG. 7 also includes a user control area 703 hosting a plurality of interactive controls responsive to an operator input through one or more input devices such as input devices 105 in FIG. 2. User control area 703 include controls (e.g., buttons) to initiate actions; user to adjust the parameters used in the image analysis system 201, for example,… size threshold that are optimized for the image analysis system 201 as disclosed in the sections above to perform cancer cell feature identification. User control parameter used in the image analysis system to adjust the size threshold (portion) of region to analyze.)
Regarding dependent claim 16, depends on claim 12, Strasfeld teaches: initializing, by the computing device, the analysis region to a portion of the context space having predefined initial shape, initial size and/or initial position. (Strasfeld − [Col. 11., ll. 5-40] User interface 700 in FIG. 7 also includes a user control area 703 hosting a plurality of interactive controls responsive to an operator input through one or more input devices such as input devices 105 in FIG. 2. User control area 703 include controls (e.g., buttons) to initiate actions; user to adjust the parameters used in the image analysis system 201, for example,… size threshold that are optimized for the image analysis system 201 as disclosed in the sections above to perform cancer cell feature identification.)
Regarding dependent claim 19, depends on claim 12, Strasfeld teaches: initializing, by the computing device, the analysis region to a best candidate region selected among a plurality of candidate regions of the context space being candidate to initialize the analysis region according to corresponding contents thereof. (Strasfeld − [Col. 11., ll. 5-40] User interface 700 in FIG. 7 also includes a user control area 703 hosting a plurality of interactive controls responsive to an operator input through one or more input devices such as input devices 105 in FIG. 2. User control area 703 include controls (e.g., buttons) to initiate actions; user to adjust the parameters used in the image analysis system 201, for example,… size threshold that are optimized for the image analysis system 201 as disclosed in the sections above to perform cancer cell feature identification.)
Regarding dependent claim 20, depends on claim 19, Strasfeld teaches: segmenting, by the computing device, each of the candidate regions into a candidate detection segment and a candidate non-detection segment representative of detection of the luminescence agent and of non-detection of the luminescence agent, respectively, according to the luminescence values only of the candidate region, calculating, by the computing device, corresponding candidate quality indicators of said segmenting the candidate regions, and selecting, by the computing device (100), the best candidate region having a best one of the candidate quality indicators. (Strasfeld − [Col. 9 ll. 32-36] filtering the enhanced image received from block 602. A detection process may be trained to determine filter settings using identified cancer images. Comparisons between healthy and non-healthy tissue may be used to set parameters (e.g., train) the detection process to identify residual cancer cells in the image. [Col. 10 ll. 24-26] a feature meets the criteria, its pixels are given a value of “1” for highlighting. [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted.)
Regarding dependent claim 22, depends on claim 1, Strasfeld teaches: displaying, by the computing device, the luminescence image with a representation of the analysis region, (Strasfeld − [Col. 11., ll. 5-40] User interface 700 in FIG. 7 also includes a user control area 703 hosting a plurality of interactive controls responsive to an operator input through one or more input devices such as input devices 105 in FIG. 2. User control area 703 include controls (e.g., buttons) to initiate actions; user to adjust the parameters used in the image analysis system 201, for example,… size threshold that are optimized for the image analysis system 201 as disclosed in the sections above to perform cancer cell feature identification. User control parameter used in the image analysis system to adjust the size threshold (portion) of region to analyze.) and receiving, by the computing device, a manual adjustment of the analysis region. (Strasfeld − [Col. 11., ll. 5-40] User control area 703 include controls (e.g., buttons) to initiate actions; user to adjust the parameters used in the image analysis system 201, for example,… size threshold that are optimized for the image analysis system 201 as disclosed in the sections above to perform cancer cell feature identification. User control parameter used in the image analysis system to adjust the size threshold (portion) of region to analyze.)
Regarding dependent claim 23, depends on claim 1, Strasfeld teaches: receiving, by the computing device, a confirmation of the analysis region after a manual movement of a content of the field of view. (Strasfeld − [Col. 11., ll. 5-40] User interface 700 in FIG. 7 also includes a user control area 703 hosting a plurality of interactive controls responsive to an operator input through one or more input devices such as input devices 105 in FIG. 2. User control area 703 include controls (e.g., buttons) to initiate actions; user to adjust the parameters used in the image analysis system 201, for example,… size threshold that are optimized for the image analysis system 201 as disclosed in the sections above to perform cancer cell feature identification. User control parameter used in the image analysis system to adjust the size threshold (portion) of region to analyze.)
Regarding dependent claim 24, depends on claim 1, Strasfeld teaches: providing, to the computing device, one or more further luminescence images of the field of view, each of the further luminescence images comprising corresponding further luminescence values representative of the luminescence light being emitted by the luminescence substance from the locations of the field of view, (Strasfeld − [Col. 3 ll 55-60] a computer-based hardware element 103 that is specially programmed to process one or more images in order to identify residual cancer features in the images. [Col. 5 ll. 44-51] images being captured by a handheld imaging device may comprise pixels with brightness or intensity levels. The intensity values of each pixel on the captured image in a portion of the tissue within the total field of view of the imaging device.)
segmenting, by the computing device, corresponding further analysis regions of the further luminescence images corresponding to the analysis region each into a further detection segment and a further non-detection segment representative of detection of the luminescence agent and of non-detection of the luminescence agent, (Strasfeld − [Col. 9 ll. 32-36] filtering the enhanced image received from block 602. A detection process may be trained to determine filter settings using identified cancer images. Comparisons between healthy and non-healthy tissue may be used to set parameters (e.g., train) the detection process to identify residual cancer cells in the image. [Col. 10 ll. 24-26] a feature meets the criteria, its pixels are given a value of “1” for highlighting. [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted.)
respectively, according to the further luminescence values only of the further analysis region, (Strasfeld − [Col. 9 ll. 32-36] filtering the enhanced image received from block 602. A detection process may be trained to determine filter settings using identified cancer images. Comparisons between healthy and non-healthy tissue may be used to set parameters (e.g., train) the detection process to identify residual cancer cells in the image. [Col. 10 ll. 24-26] a feature meets the criteria, its pixels are given a value of “1” for highlighting. [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted.)
Strasfeld does not explicitly teach: determining, by the computing device, corresponding further quality indicators of said segmenting the further analysis regions, and determining, by the computing device, the verification indicator further based on the further quality indicators.
However, Whitney teaches: determining, by the computing device, corresponding further quality indicators of said segmenting the further analysis regions, and determining, by the computing device, the verification indicator further based on the further quality indicators.(Whitney − Fig. 3 [0044] (B) high tumor-contrast following MMP-dependent cleavage, separating Cy5 from Cy7. Separating high contrast agent of tumor tissue from normal tissue. [0045] FIG. 4 describes (A, B) RACPP injection produces greater radiometric fluorescent signal in HNSCC tumor versus normal tissue [0293] A mean Cy5/Cy7 ratio was calculated for segments containing histologically-confirmed tumor and, separately, tumor-free segments. A verification of tumor to tumor-free segments ratio is performed. This is referred to tumor to background/target to background ratio verification indicator.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Strasfeld and Whitney as each invention teaches defining tumor cells within tissue using image analysis. Adding the teaching of Whitney provide Strasfeld with a plurality of radiometric analysis tumor to background ratio in determining tumor cells within a tissue. One of ordinary skill in the art would have been motivated to improve prediction of tumor cell within a region of interest.
Regarding dependent claim 25, depends on claim 1, Strasfeld teaches: determining, by the computing device, a segmentation threshold according to a statistical distribution of the luminescence values in the analysis region, (Strasfeld − [Col. 5 ll. 44-51] images being captured by a handheld imaging device may comprise pixels with brightness or intensity levels. The intensity values of each pixel on the captured image in a portion of the tissue within the total field of view of the imaging device. [Col. 13 ll. 1-20] FIG. 12B and FIG. 12D, a small circle 1206 highlights the portion of the original input image 800 selected for analysis containing two bright spots 1202 corresponding to the two features that exceed the intensity threshold and the size threshold for screening cancer cell features (see e.g., features 1015 and 1003 in FIG. 10C and FIG. 10D). The small circle area 1206 is enlarged in FIG. 12D. The two bright spot features 1202 outline groups of pixels identified as potential cancer features.)
and segmenting, by the computing device, the analysis region into the detection segment and the non-detection segment according to a comparison of the luminescence values of the analysis region with the segmentation threshold. (Strasfeld − [Col. 9 ll. 32-36] filtering the enhanced image received from block 602. A detection process may be trained to determine filter settings using identified cancer images. Comparisons between healthy and non-healthy tissue may be used to set parameters (e.g., train) the detection process to identify residual cancer cells in the image. [Col. 10 ll. 24-26] a feature meets the criteria, its pixels are given a value of “1” for highlighting. [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted.)
Regarding dependent claim 26, depends on claim 1, Strasfeld teaches: wherein the method is for imaging a body-part of a patient in a medical application, the target being defined by a target condition of the body- part. (Strasfeld − [Col. 3 ll. 60-67] The images may be captured in-situ at the surgical site 186 where the tumor is resected using a medical imaging device 110 and transmitted in real-time to the image analysis system 101 via a data connection 111.)
Regarding independent claim 31, Strasfeld teaches: A computer program product comprising a tangible, non-transitory computer readable storage medium embodying a computer program loadable into a working memory of a computing device thereby (Strasfeld − [Col. 5 ll. 24-30] the medical imaging device 110, the handheld imaging device may be a hand-held fluorescence imaging device with a photosensitive detector sensitive to fluorescence signals corresponding to a photons emitted from fluorescence of certain fluorescent imaging agent with which the cancer cells are labelled with.)
configuring the computing device to perform a method for imaging a field of view comprising a target containing a luminescence substance, (Strasfeld − [Col. 5 ll. 24-30] the medical imaging device 110, the handheld imaging device may be a hand-held fluorescence imaging device with a photosensitive detector sensitive to fluorescence signals corresponding to a photons emitted from fluorescence of certain fluorescent imaging agent with which the cancer cells are labelled with.)
the method comprising: providing a luminescence image of the body-part, (Strasfeld − [Col. 3 ll. 60-67] The images may be captured in-situ at the surgical site 186 where the tumor is resected using a medical imaging device 110 and transmitted in real-time to the image analysis system 101 via a data connection 111. It should be appreciated that any suitable computer readable storage media may be used in the image analysis system as memory 233 for storage of image data and program instructions.)
the luminescence image comprising a plurality of luminescence values representative of a luminescence light being emitted by the luminescence substance from corresponding locations of the body-part, (Strasfeld − [Col. 5 ll. 44-51] images being captured by a handheld imaging device may comprise pixels with brightness or intensity levels. The intensity values of each pixel on the captured image in a portion of the tissue within the total field of view of the imaging device. [Col. 13 ll. 1-20] FIG. 12B and FIG. 12D, a small circle 1206 highlights the portion of the original input image 800 selected for analysis containing two bright spots 1202 corresponding to the two features that exceed the intensity threshold and the size threshold for screening cancer cell features (see e.g., features 1015 and 1003 in FIG. 10C and FIG. 10D). The small circle area 1206 is enlarged in FIG. 12D. The two bright spot features 1202 outline groups of pixels identified as potential cancer features.)
setting an analysis region to a portion of the luminescence image surrounding a suspected representation of the target, (Strasfeld − [Col. 11., ll. 5-40] User interface 700 in FIG. 7 also includes a user control area 703 hosting a plurality of interactive controls responsive to an operator input through one or more input devices such as input devices 105 in FIG. 2. User control area 703 include controls (e.g., buttons) to initiate actions; user to adjust the parameters used in the image analysis system 201, for example,… size threshold that are optimized for the image analysis system 201 as disclosed in the sections above to perform cancer cell feature identification.)
segmenting the analysis region into a detection segment and a non-detection segment representative of detection of the luminescence agent and of non-detection of the luminescence agent, respectively, according to the luminescence values only of the analysis region, (Strasfeld − [Col. 9 ll. 32-36] filtering the enhanced image received from block 602. A detection process may be trained to determine filter settings using identified cancer images. Comparisons between healthy and non-healthy tissue may be used to set parameters (e.g., train) the detection process to identify residual cancer cells in the image. [Col. 10 ll. 24-26] a feature meets the criteria, its pixels are given a value of “1” for highlighting. [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted.)
and displaying the luminescence image with the detection segment being highlighted according to the verification indicator. (Strasfeld − [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted. The two semi-circular spots 1302 in FIG. 13B are cancer cells according to pathology results and confirms the accuracy of the imaging analysis technique described above in identifying the two cancer spot features 1302.)
Strasfeld does not explicitly teach: determining a verification indicator based on a quality indicator of said segmenting the analysis region,
However, Whitney teaches: segmenting the analysis region into a detection segment and a non-detection segment representative of detection of the luminescence agent and of non-detection of the luminescence agent, respectively, according to the luminescence values only of the analysis region, (Whitney − Fig. 3 [0044] (B) high tumor-contrast following MMP-dependent cleavage, separating Cy5 from Cy7. Separating high contrast agent of tumor tissue from normal tissue.)
determining a verification indicator based on a quality indicator of said segmenting the analysis region, (Whitney − [0045] FIG. 4 describes (A, B) RACPP injection produces greater radiometric fluorescent signal in HNSCC tumor versus normal tissue [0293] A mean Cy5/Cy7 ratio was calculated for segments containing histologically-confirmed tumor and, separately, tumor-free segments. A verification of tumor to tumor-free segments ratio is performed. This is referred to tumor to background/target to background ratio verification indicator.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Strasfeld and Whitney as each invention teaches defining tumor cells within tissue using image analysis. Adding the teaching of Whitney provide Strasfeld with a plurality of radiometric analysis tumor to background ratio in determining tumor cells within a tissue. One of ordinary skill in the art would have been motivated to improve prediction of tumor cell within a region of interest.
Regarding independent claim 32, Strasfeld teaches: A system or imaging a field of view comprising a target containing a luminescence substance, wherein the system comprises means for providing a luminescence image of the field of view, (Strasfeld − [Col. 5 ll. 24-30] the medical imaging device 110, the handheld imaging device may be a hand-held fluorescence imaging device with a photosensitive detector sensitive to fluorescence signals corresponding to a photons emitted from fluorescence of certain fluorescent imaging agent with which the cancer cells are labelled with.)
the luminescence image comprising a plurality of luminescence values representative of a luminescence light being emitted by the luminescence substance from corresponding locations of the field of view, (Strasfeld − [Col. 5 ll. 44-51] images being captured by a handheld imaging device may comprise pixels with brightness or intensity levels. The intensity values of each pixel on the captured image in a portion of the tissue within the total field of view of the imaging device. [Col. 13 ll. 1-20] FIG. 12B and FIG. 12D, a small circle 1206 highlights the portion of the original input image 800 selected for analysis containing two bright spots 1202 corresponding to the two features that exceed the intensity threshold and the size threshold for screening cancer cell features (see e.g., features 1015 and 1003 in FIG. 10C and FIG. 10D). The small circle area 1206 is enlarged in FIG. 12D. The two bright spot features 1202 outline groups of pixels identified as potential cancer features.)
means for setting an analysis region to a portion of the luminescence image surrounding a suspected representation of the target, (Strasfeld − [Col. 11., ll. 5-40] User interface 700 in FIG. 7 also includes a user control area 703 hosting a plurality of interactive controls responsive to an operator input through one or more input devices such as input devices 105 in FIG. 2. User control area 703 include controls (e.g., buttons) to initiate actions; user to adjust the parameters used in the image analysis system 201, for example,… size threshold that are optimized for the image analysis system 201 as disclosed in the sections above to perform cancer cell feature identification.)
means for segmenting the analysis region into a detection segment and a non- detection segment representative of detection of the luminescence agent and of non- detection of the luminescence agent, respectively, according to the luminescence values only of the analysis region, (Strasfeld − [Col. 9 ll. 32-36] filtering the enhanced image received from block 602. A detection process may be trained to determine filter settings using identified cancer images. Comparisons between healthy and non-healthy tissue may be used to set parameters (e.g., train) the detection process to identify residual cancer cells in the image. [Col. 10 ll. 24-26] a feature meets the criteria, its pixels are given a value of “1” for highlighting. [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted.)
and means for displaying the luminescence image with the detection segment being highlighted according to the verification indicator. (Strasfeld − [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted. The two semi-circular spots 1302 in FIG. 13B are cancer cells according to pathology results and confirms the accuracy of the imaging analysis technique described above in identifying the two cancer spot features 1302.)
Strasfeld does not explicitly teach: means for determining a verification indicator based on a quality indicator of said segmenting the analysis region,
However, Whitney teaches: means for segmenting the analysis region into a detection segment and a non- detection segment representative of detection of the luminescence agent and of non- detection of the luminescence agent, respectively, according to the luminescence values only of the analysis region,(Whitney − Fig. 3 [0044] (B) high tumor-contrast following MMP-dependent cleavage, separating Cy5 from Cy7. Separating high contrast agent of tumor tissue from normal tissue.)
means for determining a verification indicator based on a quality indicator of said segmenting the analysis region, (Whitney − [0045] FIG. 4 describes (A, B) RACPP injection produces greater radiometric fluorescent signal in HNSCC tumor versus normal tissue [0293] A mean Cy5/Cy7 ratio was calculated for segments containing histologically-confirmed tumor and, separately, tumor-free segments. A verification of tumor to tumor-free segments ratio is performed. This is referred to tumor to background/target to background ratio verification indicator.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Strasfeld and Whitney as each invention teaches defining tumor cells within tissue using image analysis. Adding the teaching of Whitney provide Strasfeld with a plurality of radiometric analysis tumor to background ratio in determining tumor cells within a tissue. One of ordinary skill in the art would have been motivated to improve prediction of tumor cell within a region of interest.
Regarding dependent claim 33, depends on claim 32, Strasfeld teaches: wherein the system comprises an optical acquisition unit for acquiring the luminescence image. (Strasfeld − [Col. 3 ll. 60-67] The images may be captured in-situ at the surgical site 186 where the tumor is resected using a medical imaging device 110 and transmitted in real-time to the image analysis system 101 via a data connection 111. It should be appreciated that any suitable computer readable storage media may be used in the image analysis system as memory 233 for storage of image data and program instructions.)
Regarding independent claim 34, Strasfeld teaches: A medical method comprising: imaging a body-part of a patient comprising a target containing a luminescence substance, wherein said imaging comprises: (Strasfeld − [Col. 3 ll. 60-67] The images may be captured in-situ at the surgical site 186 where the tumor is resected using a medical imaging device 110 and transmitted in real-time to the image analysis system 101 via a data connection 111.)
providing, to a computing device, a luminescence image of the body-part, (Strasfeld − [Col. 3 ll. 60-67] The images may be captured in-situ at the surgical site 186 where the tumor is resected using a medical imaging device 110 and transmitted in real-time to the image analysis system 101 via a data connection 111. It should be appreciated that any suitable computer readable storage media may be used in the image analysis system as memory 233 for storage of image data and program instructions.)
the luminescence image comprising a plurality of luminescence values representative of a luminescence light being emitted by the luminescence substance from corresponding locations of the body-part, (Strasfeld − [Col. 5 ll. 44-51] images being captured by a handheld imaging device may comprise pixels with brightness or intensity levels. The intensity values of each pixel on the captured image in a portion of the tissue within the total field of view of the imaging device. [Col. 13 ll. 1-20] FIG. 12B and FIG. 12D, a small circle 1206 highlights the portion of the original input image 800 selected for analysis containing two bright spots 1202 corresponding to the two features that exceed the intensity threshold and the size threshold for screening cancer cell features (see e.g., features 1015 and 1003 in FIG. 10C and FIG. 10D). The small circle area 1206 is enlarged in FIG. 12D. The two bright spot features 1202 outline groups of pixels identified as potential cancer features.)
setting, by the computing device, an analysis region to a portion of the luminescence image surrounding a suspected representation of the target, (Strasfeld − [Col. 11., ll. 5-40] User interface 700 in FIG. 7 also includes a user control area 703 hosting a plurality of interactive controls responsive to an operator input through one or more input devices such as input devices 105 in FIG. 2. User control area 703 include controls (e.g., buttons) to initiate actions; user to adjust the parameters used in the image analysis system 201, for example,… size threshold that are optimized for the image analysis system 201 as disclosed in the sections above to perform cancer cell feature identification.)
segmenting, by the computing device, the analysis region into a detection segment and a non-detection segment representative of detection of the luminescence agent and of non-detection of the luminescence agent, respectively, according to the luminescence values only of the analysis region, (Strasfeld − [Col. 9 ll. 32-36] filtering the enhanced image received from block 602. A detection process may be trained to determine filter settings using identified cancer images. Comparisons between healthy and non-healthy tissue may be used to set parameters (e.g., train) the detection process to identify residual cancer cells in the image. [Col. 10 ll. 24-26] a feature meets the criteria, its pixels are given a value of “1” for highlighting. [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted.)
and displaying, by the computing device, the luminescence image with the detection segment being highlighted according to the verification indicator, (Strasfeld − [Col. 13 ll. 1-50] FIGS. 12A through 12D show images used to perform local contrast based filtering on the identified features. Since fluorescently labeled cancer cells are expected to be much brighter than the non-cancer background. Examiner Note: Separating by detection process for filtering cancer cells (detection segment) that are highlighted with pixel value as shown in FIGS. 12A through 12D. The non-cancer background are non-detection of the luminescence agent were the pixels are not highlighted. The two semi-circular spots 1302 in FIG. 13B are cancer cells according to pathology results and confirms the accuracy of the imaging analysis technique described above in identifying the two cancer spot features 1302.)
and performing a medical procedure relating to the body-part according to said displaying the luminescence image. (Strasfeld − [Col. 7 ll 34-40] As discussed above, aspects of the present application relate to methods for detecting cancer cells, such as those remaining in a surgical site after removal of bulk tumors, in order to facilitate complete removal of residual cancer cells)
Strasfeld does not explicitly teach: determining, by the computing device, a verification indicator based on a quality indicator of said segmenting the analysis region,
However, Whitney teaches: segmenting, by the computing device, the analysis region into a detection segment and a non-detection segment representative of detection of the luminescence agent and of non-detection of the luminescence agent, (Whitney − Fig. 3 [0044] (B) high tumor-contrast following MMP-dependent cleavage, separating Cy5 from Cy7. Separating high contrast agent of tumor tissue from normal tissue.)respectively, according to the luminescence values only of the analysis region,
determining, by the computing device, a verification indicator based on a quality indicator of said segmenting the analysis region, (Whitney − [0045] FIG. 4 describes (A, B) RACPP injection produces greater radiometric fluorescent signal in HNSCC tumor versus normal tissue [0293] A mean Cy5/Cy7 ratio was calculated for segments containing histologically-confirmed tumor and, separately, tumor-free segments. A verification of tumor to tumor-free segments ratio is performed. This is referred to tumor to background/target to background ratio verification indicator.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Strasfeld and Whitney as each invention teaches defining tumor cells within tissue using image analysis. Adding the teaching of Whitney provide Strasfeld with a plurality of radiometric analysis tumor to background ratio in determining tumor cells within a tissue. One of ordinary skill in the art would have been motivated to improve prediction of tumor cell within a region of interest.
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Strasfeld and Whitney as applied to claim 1 above, and further in view of Kishima (US PGPUB: 20190046041 A1, Filed Date: Oct. 17, 2018).
Regarding dependent claim 13, depends on claim 1, Strasfeld teaches: wherein the field of view contains a region of interest for imaging and one or more foreign objects different from the region of interest, (Strasfeld − [Col. 11, ll. 55-57] FIG. 8 is an image 800 taken from a fluorescent imaging device according to an exemplary embodiment. [Col. 5 ll. 44-51] images being captured by a handheld imaging device may comprise pixels with brightness or intensity levels. The intensity values of each pixel on the captured image in a portion of the tissue within the total field of view of the imaging device.)
the method comprising: providing to the computing device, an auxiliary image of the field of view, (Strasfeld − [Col. 9, ll. 13-15] In some embodiments, videos containing surgical site images captured at a video rate are processed at substantially the same video rate such that the identified cancer cells in each frame of the videos are identified and presented as a video in the display 107.)
Strasfeld does not explicitly teach: determining, by the computing device, a verification indicator based on a quality indicator of said segmenting the analysis region, the auxiliary image comprising a plurality of auxiliary values representative of an auxiliary light, different from the luminescence light, being received from corresponding auxiliary locations of the field of view,
However, Kishima teaches: wherein the field of view contains a region of interest for imaging and one or more foreign objects different from the region of interest, (Kishima − [0056] teaches that needles and clamps are within the field of view. Fig. 1 shows peripheral areas in the overall image field of view, the surgical area, and the ROI that is not the fluorescent area)
the method comprising: providing to the computing device, an auxiliary image of the field of view, the auxiliary image comprising a plurality of auxiliary values representative of an auxiliary light, different from the luminescence light, being received from corresponding auxiliary locations of the field of view, (Kishima − [0032] teaches that the visible light capturing system is capturing video camera information. Fig. 1 shows that it is capturing the same image storing area as the fluorescence system. [0088-0089] teaches the illimitation of visible light via a visible light LED. The display shows the surgical area),
identifying, by the computing device, an auxiliary information region of the auxiliary image representative of the region of interest without the foreign objects according to a content of the auxiliary image, (Kishima − [0032] teaches that the video camera can capture the surgical field and provide an ROI while the operator is considering which area to remove. Fig. 1 does not show the presence of the needle in the display or the surgical area),
identifying, by the computing device, a luminescence information region of the luminescence image corresponding to the auxiliary information region, (Kishima − [0032] teaches that the video camera can capture the surgical field and provide an ROI while the operator is considering which area to remove)
and initializing, by the computing device, the analysis region to a portion of a context space being defined according to the luminescence informative region. (Kishima − [0013] teaches that the cutting out unit can allow the user to select a partial area of the fluorescence image and cut out the selected area of the ROI and allow for it to be displayed.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Strasfeld, Whitney and Kishima as each invention teaches defining tumor cells within tissue using image analysis. One of ordinary skill in the art would have been motivated to improve prediction of tumor cell within a region of interest.
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Strasfeld, Whitney and Kishima as applied to claim 13 above, and further in view of Sjöstrand (US PGPUB: 20190209116 A1, Filed Date: Jun. 7, 2018).
Regarding dependent claim 15, depends on claim 13, Strasfeld does not explicitly teach: wherein the step of identifying the auxiliary informative region comprises: segmenting, by the computing device, the auxiliary image semantically into the auxiliary informative region corresponding to at least one region of interest class of the region of interest and an auxiliary non- informative region corresponding to one or more foreign objects classes of the foreign objects
However, Sjöstrand teaches: wherein the step of identifying the auxiliary informative region comprises: segmenting, by the computing device, the auxiliary image semantically into the auxiliary informative region corresponding to at least one region of interest class of the region of interest and an auxiliary non- informative region corresponding to one or more foreign objects classes of the foreign objects (Sjöstrand − [0030] In certain embodiments, step (d) comprises using the second module to classify each voxel within the initial VOI as corresponding a particular tissue region of a set of (predetermined) different tissue regions {e.g., the set comprising the prostate and, optionally, one or more additional tissue regions;)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Strasfeld, Whitney, Kishima and Sjöstrand as each invention teaches defining tumor cells within tissue using image analysis. One of ordinary skill in the art would have been motivated to improve prediction of tumor cell within a region of interest.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARL E BARNES JR whose telephone number is (571)270-3395. The examiner can normally be reached Monday-Friday 9am-6pm.
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/CARL E BARNES JR/Examiner, Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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