ACKNOWLEDGEMENTS
This Office Action addresses U.S. Application No. 18/405876 (“’876 Application” or “instant application”). Based upon a review of the instant application, the actual filing date of the instant application is January 5, 2024. For reissue applications filed on or after September 16, 2012, all references to 35 U.S.C. 251 and 37 CFR 1.172, 1.175, and 3.73 are to the current provisions.
The instant application is a reissue application of US Patent 11,266,345 (’345 Patent”). The ‘345 Patent was filed as US application 17/260664 (‘664 application), which is a national stage entry of PCT/CA2019/050981, filed July 16, 2019 entitled “APPARATUS FOR VISUALIZATION OF TISSUE.”
Based upon Applicant’s statements as set forth in the instant application and after the
Examiner’s independent review of the ‘345 Patent itself and its prosecution history, the Examiner finds that he cannot locate any ongoing proceeding before the Office or current ongoing litigation involving the ‘345 Patent. Also based upon the Examiner’s independent review of the ‘345 Patent itself and the prosecution history, the Examiner cannot locate any previous reexaminations or supplemental examinations.
The Examiner acknowledges the protest filed 4/1/2024.
II. STATUS OF CLAIMS
The ‘345 Patent issued with claims 1-20 (“Patented Claims”). The Preliminary Amendment filed with this application cancels claims 1-20 and adds claims 21-67.
a. Claims 21-67 (“Pending Claims”).
b. Claims 21-67 are examined (“Examined Claims”)
III. PRELIMINARY AMENDMENT OF 1/5/2024
The amendment to the claims of 1/5/2024 has been entered and considered. The Examiner notes that since claims 1-20 were cancelled by a certificate from a post grant review, the text of the claims should be presented and lined through.
IV. PRIORITY AND CONTINUING DATA
The ‘345 patent is a national stage entry of PCT/CA2019/050981, filed July 16, 2019. The ‘345 patent further claims the benefit of US Provisional Applications 62/698799, filed July 16, 2018. Because the effective filing date is after March 16, 2013, the AIA sections of 35 USC 102, 103, and 112 apply to this proceeding.
In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the prior applications. Also, in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record in the prior applications are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the prior applications need not be resubmitted in this application unless Applicant(s) desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicant(s) are reminded that the prosecution histories of the prior applications are relevant in this application.
V. REISSUE DECLARATION
The reissue declaration filed 1/5/2024 is approved.
VI. REJECTIONS UNDER 35 USC 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
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.
Claims 32, 40, 53, and 59 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As to claims 32 and 53, MPEP 2161.01 I notes that textual support is insufficient to provide an adequate written an adequate written description of the claimed invention. Rather, the disclosure must show the steps or algorithm to perform the invention in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed invention at the time of filing. At present, the disclosure provides almost no details regarding how the system and method approximate a shape of the body part and how the image is rescaled to compensate for deviations from a working distance. As such, the claims lack an adequate written description.
Similarly, with respect to claims 40 and 59, the steps or algorithm for compensating for melanin are lacking from the disclosure.
Clarification is required.
Claim 37 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claim 37 states that the bandpass filter has a pass band of 425-750 nm. However, claim 37 depends on claim 36, which uses a wavelength from 395-415 nm. It would seem, then, that the band pass filter would block the measurement light, as such it is unclear how the device would operate. Clarification is required.
Claims 21-67 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 21, the recitation of processor in line 10 renders the claim indefinite in that it is unclear whether processor referred to is the processor of the tissue visualization system or the processor of the tissue imaging system.
Claims 42 and 61 are rejected in that in lines 9 and 10, the claim states that the image capture unit … is communicatively coupled to the computing device. However, the image capture unit is part of the computing device. It is unclear how the image capture unit is coupled to itself.
Claims 22-41, 43-61, and 63-67 are rejected as being dependent on a rejected base claim.
VII. REJECTIONS UNDER 35 USC 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21-67 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more (See MPEP 2106.04(a)).
Using claim 42 as an exemplary claim:
Claim 42 recites the limitations:
positioning a portable computing device at a distance from a target tissue for capturing measurement data …
capturing the measurement data comprising the plurality of images …
consecutively shining m flashes of light from the illumination unit …
pre-processing …. the measurement data by
registering the plurality of images
subtracting a first image of the plurality of images … from one or more second images …
transmitting the pre-processed measurement data to a tissue visualization system
extracting … indications of health indicators from the pre-processed measurement data by executing convolutional neural network to detect and classify a wound in the target area
generating … interface elements corresponding to the tissue health indicators
transmitting … the interface elements to the portable computing device for display
The first step in the 101 analysis, step 1 in MPEP 2106, is whether the claimed invention is in one of the 4 statutory classes of invention. Here, the claim is drawn to a method of generating visualizations of tissue. A method is one of the 4 statutory classes of invention. Hence, step 1 is satisfied.
The next step in the analysis, step 2A prong one, is whether the claim is directed to judicial exception, i.e. a law of nature, a natural phenomenon, or an abstract idea. Here, pre-processing, transmitting, extracting, and generating are mathematical steps comprising an algorithm to provide tissue health indicators, which is a mathematical process. Alternatively, given the measurement data, the extracting and generating steps can be performed mentally. Both mental processes and mathematical processes are judicial exceptions that recite an abstract idea. See MPEP 2164.
In Step 2A, prong two of the analysis, the claim is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. There are several additional elements. First, the positioning, capturing, and shining steps amount to data gathering, which is insignificant pre-solution activity. In addition, the transmitting for display is an additional step. This step is merely insignificant post-solution activity. Additionally, the structure recited in the claim is recited at a high level of generality and does not amount to significantly more than the exception. Finally, the neural network is merely applying existing technology to a new field, and does not integrate the exception into a practical application. As such, the answer to step 2A, prong 2, is no.
The final step of the analysis, step 2B, where the claim is evaluated to determine whether the recited additional elements amount to significantly more than the judicial exception. Here, the additional steps simply recite well-understood, routine, and conventional processing data. Hence, the steps do not recite an inventive concept.
As such, claim 42 is directed to an abstract idea and is not patent eligible.
As to claims 43-60, the claims recite additional more mental and/or mathematical steps and structure recited at a high level of generality and therefore are still drawn to a judicial exception.
Claims 21-41 and 61-67 are also rejected for similar reasons to those presented above.
Hence, claims 21-67 are non-statutory.
VIII. REJECTIONS UNDER 35 USC 251
Claims 21-67 are rejected under 35 U.S.C. 251 as being improperly broadened. A claim is broader in scope than the original claims if it contains within its scope any conceivable product or process which would not have infringed the original patent. A claim is broadened if it is broader in any one respect even though it may be narrower in other respects.
With respect to claim 21, the patent claim 8 recited that the tissue imaging system both pre-processed and processed the measurement data to generate tissue health indicators. However, new claim 21 states that the tissue imaging system pre-processes the data and transmits the data to the tissue visualization system for processing to generate tissue health indicators. Since the tissue imaging system no longer generates tissue health indicators, current claim 21 is broader than patent claim 8. As noted in MPEP 1449.01(I)(A.)(B)(3), that when a PTAB trial certificate issues after the filing of a reissue application, broadening is prohibited. As such, since claim 21 has been broadened, claims 21-41 are rejected under 35 USC 251 as being improperly broadened1.
Claims 42 and 61 have been broadened in that changing the proper distance to distance in line 2 broadens the claim. As such claims 42-67 are rejected under 35 USC 251 as being improperly broadened.
The Examiner notes that in response to this rejection, Applicant should provide a mapping of the current claims to the patented claims, showing that all of the patent claim features have been included in the current claims.
IX. ART REJECTIONS
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.
Claim(s) 21, 34, 38, 41, 42, 57, and 60 are rejected under 35 U.S.C. 103 as being unpatentable over Cross et al US PG PU 2019/0216326 in view of Darty US PG PUB 2017/0224260 and Budman US PG PUB 2018/0279943.
As to claim 21, Cross teaches:
21. A system for visualization of tissue health indicators, the system comprising:
The system of Cross, shown in figure 1 is used to determine marker maps, showing markers of physiological condition (see paragraph [0015]). Each marker is a tissue health indicator (paragraph [0066]).
A tissue visualization system comprising a processor and a non-transitory computer-readable medium; and
The analysis server 14 of Cross, which is a processor in and of itself, includes a processor 50. Further, Cross has a non-transitory memory (paragraph [0057]).
a tissue imaging system comprising:
Paragraph [0071] of Cross discloses a portable imaging device to be used for tissue assessment.
a portable computing device comprising a processor and a non-transitory computer-readable memory storing a tissue visualization application;
Cross shows a portable computing device 12 that includes a processor 30. Further, Cross teaches that the image acquisition module 48 is implemented as a software application (paragraph [0089], which would require a non-transitory medium to store the application.
an image capture unit: and
the tissue imaging system of Cross includes a camera 32.
an illumination unit, wherein the illumination unit comprises one or more narrow band light sources;
the system of Cross includes an illumination unit, light source unit 10, that comprises multiple LEDs having discrete wavelengths (see paragraph [0017], for example).
wherein the processor is configured to access and execute in accordance with the tissue visualization application to:
As noted above, in Cross, the image acquisition module operates as a software application (paragraph [0089]). As such, the processor must access and execute the software.
cause the image capture unit and the illumination unit to capture measurement data comprising a plurality of images for a target area of tissue
In paragraph [0071], Cross teaches that when the user wishes to image a tissue region, the user can obtain a plurality of images while the light source illuminates the tissue region to obtain each image. Also extra images are taken without illumination to provide a reference image. See also paragraph [0148] which teaches that M image data sets are obtained.
wherein the processor causes the illumination unit to shine m flashes of light on the target area of the tissue at n predetermined wavelengths during capture of the measurement data,
As noted in paragraph [0089], Cross teaches that the device captures N images with the camera 32. Further, the illumination is performed at discrete wavelengths (see paragraph [0017]). Finally, in paragraph [0075], Cross teaches that the light has a finite length. Shining light with a finite length is a flash.
wherein each of the m flashes of light has an identical duration,
In the circumstance where one flash is used while the image is obtained, then the flash obviously has an identical duration. Further, in paragraph [0075], Cross teaches that the duration of the light signals are controlled and the same is taught in paragraph [0147]). It would be apparent that when making repeated measurements, the same conditions should be present and the light should have the same duration.
each of the m flashes are performed consecutively,
again, when there is one flash per image set, the flash is “consecutive.” Further, in paragraph [0107], Cross teaches sequentially activating the LEDs to obtain the multiple image sets M.
at least some of the m flashes are synchronized with capture of one or more of the plurality of images, n/4 ≤ m ≤ n, where m is the number of flashes in one cycle and n is a number of wavelengths.
As discussed above, there are multiple discrete wavelengths (paragraph [0064]), but it appears from paragraph [0064] that each image is obtained at a different wavelength. However, with one flash and one wavelength, the formula is satisfied. Further as discussed above, the camera and light sources are synchronized (see paragraph [0071]).
pre-process the measurement data to generate pre-processed measurement data at least in part by:
Paragraph [0102] teaches that the image data is pre-processed
registering the plurality of images; and
Cross teaches in paragraph [0111] that act 154 of the image processing method includes registering the image sets.
subtracting a first image of the plurality of images taken without illumination from the illumination unit from one or more second images of the plurality of images taken with illumination from the illumination unit; and
Cross teaches this is paragraph [0112] discussing act 156 of the image processing method.
transmit the pre-processed measurement data to the tissue visualization system over a network;
Cross teaches in paragraph [0150] that the image datasets are stored and transmitted to the analysis sever 14 via a communications network 16. However, in Cross, the pre-processing is done at the analysis server 14 (see paragraph [0066] which states that all of figures 2 and 3A are performed in the image processing module [0066]). As such, Cross does not send signals that have been pre-processed as discussed above to the tissue visualization device 14. However, Darty teaches in paragraph [0099] that it is known to preprocess the data at the imaging device and then send the data to a remote processing device for analysis. As such, it would have been obvious to modify Cross to preprocess the images at the imaging device and send preprocessed data to the analysis server, as it is merely the substitution of one known processing technique for another.
wherein the tissue visualization system is configured to: receive the pre-processed measurement data
Image analysis module 68 of Cross analyzes the preprocessed images. As such, the analysis system must receive the images from the mobile device.
generate tissue health indicators from the pre-processed measurement data at least in part by executing a convolutional neural network to detect and classify a wound in the target area of the tissue;
In paragraph [0066], Cross teaches using a neural network to process the image data to obtain measures that are correlated with tissue viability. Further, in paragraph [0117], Cross teaches using a neural network to obtain an indicator outcome of the tissue, which is a health indicator. Neither paragraph mentions a convolutional neural network. However, Budman teaches in paragraph [0061] that convolutional neural networks are known neural networks used in skin imaging. As such, it would have been obvious to Cross to use a convolutional neural network, as it is merely the use of a well-known neural network for the task of Cross.
generate interface elements corresponding to the tissue health indicators; and
based on the analysis, Cross teaches displaying an interface element, for example words to be displayed, indicating the results of the analysis (see paragraphs [0096] and [0101]). Further Cross generates a warning corresponding to the health indicators (see paragraph [0103]).
transmit the interface elements to the tissue imaging system for display
the warning is sent to the mobile device where it is used to notify the user, for example, by displaying it.
As to claim 34:
34. The system of claim 21, wherein the portable computing device comprises a mobile device and the image capture unit is a camera integrated with the mobile device.
Cross teaches that the portable computing device is a mobile device 12 with an integrated camera 32.
As to claim 38:
38. The system of claim 21, wherein one or more narrow band light sources are configured to provide at least two flashes having wavelengths in a 450 nm – 750 nm range during the capture of the measurement data; and
wherein the tissue visualization system is configured to extract indications of oxygenation based at least in part on or more images of the plurality of images captured during the at least two flashes having a wavelength in the 450 nm – 750 nm range
Cross teaches that the light has a wavelength 600-1000 nm and more specifically at 620, 630 and 700 nm, for example (paragraph [0017] and [[0082]). This is two wavelengths in the range. As discussed above, the light is emitted in flashes. Further, in paragraphs [0067] and [0069], Cross teaches that it extracts indications of oxygen saturation.
As to claim 41
41. The system of claim 21, wherein one or more narrow band light sources are configured to provide at least two flashes having wavelengths in a 450 nm – 750 nm range and at least one flash having a 970± 10 nm wavelength during the capture of the measurement data; and
wherein the tissue visualization system is configured to extract indications of water content based at least on or more images of the plurality of images captured during the at least two flashes having a wavelength in the 450 nm – 750 nm range and the at least one flash having a 970± 10 nm wavelength.
Cross teaches that the light has a wavelength 600-1000 nm and more specifically at 620, 630 and 700 nm, for example (paragraph [0017] and [[0082]). This is two wavelengths in the range of 450-750 nm. Further, Cross teaches using 940 ±10 nm as a wavelength. Also, Cross teaches multiple discrete wavelengths in the 600-10000 nm range. This covers the 970 nm limitation from the claims. Further, Cross teaches measuring water content based on the measurement (paragraph [0066]).
As to claim 42, Cross teaches:
42. A method for visualizations of tissue, the method:
The method of Cross, for visualizing tissue, is shown in figures 2 and 3a.
positioning a portable computing device at a distance from a target area of tissue for capturing measurement data comprising a plurality of images of the target area, wherein the portable computing device comprises a processor and a non-transitory computer-readable memory storing computer-executable instructions comprising tissue visualization application;
Cross shows a portable computing device 12 that includes a processor 30. Further, Cross teaches that the image acquisition module 48 is implemented as a software application (paragraph [0089], which would require a non-transitory medium to store the application. Further, Cross teaches using the portable computing device by placing the device near a target area to obtain images (step 104, paragraph [10107]).
capturing the measurement data comprising the plurality of images for the target area of the tissue by an image capture unit of the portable computing device and an illumination unit, where the image capture unit and the illumination unit are communicatively coupled to the computing device;
the tissue imaging system of Cross includes a camera 32. an illumination unit, light source unit 10, which communicate to the that comprises multiple LEDs having discrete wavelengths (see paragraph [0017], for example) that communicate with the portable computing device (see figure 1). Further, in paragraph [0071], Cross teaches that when the user wishes to image a tissue region, the user can obtain a plurality of images while the light source illuminates the tissue region to obtain each image. Also extra images are taken without illumination to provide a reference image. See also paragraph [0148] which teaches that M image data sets are obtained.
consecutively shining m flashes of light having an identical duration on the target area of the tissue at n predetermined wavelengths from the illumination unit comprising one or more narrow band light sources, wherein at least some of the m flashes are synchronized with capture of one or more of the plurality of images, n/4 ≤ m ≤ n, where m is the number of flashes in one cycle and n is a number of wavelengths.
As noted in paragraph [0089], Cross teaches that the device captures N images with the camera 32. Further, the illumination is performed at discrete wavelengths (see paragraph [0017]). Finally, in paragraph [0075], Cross teaches that the light has a finite length. Shining light with a finite length is a flash. In the circumstance where one flash is used while the image is obtained, then the flash obviously has an identical duration. Further, in paragraph [0075], Cross teaches that the duration of the light signals are controlled and the same is taught in paragraph [0147]). It would be apparent that when making repeated measurements, the same conditions should be present and the light should have the same duration. Furthermore, when there is one flash per image set, the flash is “consecutive.” Further, in paragraph [0107], Cross teaches sequentially activating the LEDs to obtain the multiple image sets M. As discussed above, there are multiple discrete wavelengths (paragraph [0064]), but it appears from paragraph [0064] that each image is obtained at a different wavelength. However, with one flash and one wavelength, the formula is satisfied. Further as discussed above, the camera and light sources are synchronized (see paragraph [0071]).
pre-processing, by the portable measurement device using the tissue visualization application, the measurement data to generate pre-processed measurement data at least in part by:
Paragraph [0102] teaches that the image data is pre-processed
registering the plurality of images; and
Cross teaches in paragraph [0111] that act 154 of the image processing method includes registering the image sets.
subtracting a first image of the plurality of images taken without illumination from the illumination unit from one or more second images of the plurality of images taken with illumination from the illumination unit; and
Cross teaches this is paragraph [0112] discussing act 156 of the image processing method.
transmitting the pre-processed measurement data from the portable computing device to a tissue visualization system comprising a processor and a non-transitory computer readable medium over a network;
The analysis server 14 of Cross, is a tissue visualization system which includes a processor 50. Further, Cross has a non-transitory memory (paragraph [0057]). Cross teaches in paragraph [0150] that the image datasets are stored and transmitted to the analysis sever 14 via a communications network 16. However, in Cross, the pre-processing is done at the analysis server 14 (see paragraph [0066] which states that all of figures 2 and 3A are performed in the image processing module [0066]). As such, Cross does not send signals that have been pre-processed as discussed above to the tissue visualization device 14. However, Darty teaches in paragraph [0099] that it is known to preprocess the data at the imaging device and then send the data to a remote processing device for analysis. As such, it would have been obvious to modify Cross to preprocess the images at the imaging device and send preprocessed data to the analysis server, as it is merely the substitution of one known processing technique for another.
extracting, by the tissue visualization system, indications of tissue health indicators from the pre-processed measurement data at least in part by executing a convolutional neural network to detect and classify a wound in the target area of the tissue;
Image analysis module 68 of Cross analyzes the preprocessed images. As such, the analysis system must receive the images from the mobile device. In paragraph [0066], Cross teaches using a neural network to process the image data to obtain measures that are correlated with tissue viability. Further, in paragraph [0117], Cross teaches using a neural network to obtain an indicator outcome of the tissue, which is a health indicator. Neither paragraph mentions a convolutional neural network. However, Budman teaches in paragraph [0061] that convolutional neural networks are known neural networks used in skin imaging. As such, it would have been obvious to Cross to use a convolutional neural network, as it is merely the use of a well-known neural network for the task of Cross.
generating, by the tissue visualization system, interface elements corresponding to the tissue health indicators; and
based on the analysis, Cross teaches displaying an interface element, for example words to be displayed, indicating the results of the analysis (see paragraphs [0096] and [0101]). Further Cross generates a warning corresponding to the health indicators (see paragraph [0103]).
transmitting, by the tissue visualization system the interface elements to the portable computing device for display.
the warning is sent to the mobile device where it is used to notify the user, for example, by displaying it.
As to claim 57:
57. The method of claim 42, wherein consecutively shining m flashes of light having an identical duration on the target area of the tissue comprises shining at least two flashes having wavelengths in a 450 nm – 750 nm range during the capture of the measurement data; and further comprising
extracting indications of oxygenation based at least in part on or more images of the plurality of images captured during the at least two flashes having a wavelength in the 450 nm – 750 nm range
Cross teaches that the light has a wavelength 600-1000 nm and more specifically at 620, 630 and 700 nm, for example (paragraph [0017] and [[0082]). This is two wavelengths in the range. As discussed above, the light is emitted in flashes. Further, in paragraphs [0067] and [0069], Cross teaches that it extracts indications of oxygen saturation.
As to claim 60
60. The method of claim 42, wherein consecutively shining m flashes of light having an identical duration on the target area of the tissue comprises shining at least two flashes having wavelengths in a 450 nm – 750 nm range and at least one flash having a 970± 10 nm wavelength during the capture of the measurement data; and
extracting indications of water content based at least on or more images of the plurality of images captured during the at least two flashes having a wavelength in the 450 nm – 750 nm range and the at least one flash having a 970± 10 nm wavelength.
Cross teaches that the light has a wavelength 600-1000 nm and more specifically at 620, 630 and 700 nm, for example (paragraph [0017] and [[0082]). This is two wavelengths in the range of 450-750 nm. Further, Cross teaches using 940 ±10 nm as a wavelength. Also, Cross teaches multiple discrete wavelengths in the 600-10000 nm range. This covers the 970 nm limitation from the claims. Further, Cross teaches measuring water content based on the measurement (paragraph [0066]).
Claims 22, 24-27, 29, 43, 45-48, and 50 are rejected over Cross in view of Darty and Budman, as applied to claims 21, 34, 38, 41, 42, 57, and 60 above, further in view of Freeman US PG PUB 2007/0016079
As to claims 22 and 43:
22. The system of claim 21, wherein generating tissue health indicators from the pre-processed measurement data further comprises extracting a concentration of one or more tissue chromophores.
Claim 43. The method of claim 42, wherein extracting indications of tissue health indicators from the pre-processed measurement data further comprises extracting a concentration of one or more tissue chromophores.
Cross discloses that N is the number of chromophores to be solved and N is the number of physiologic markers being measured (paragraphs [0072] and [0145], and that the physiological markers include one or more of the items listed in paragraph [0082]). However, it does not specifically state that it measures the concentration of the listed parameters. Freeman teaches
in paragraph [0046], for example, that it is known to determine oxyhemoglobin concentration from an image. As such, it would have been obvious to modify Cross to determine the concentration, as it is merely the substitution of one known parameter for another.
As to claims 24 and 45:
24. The system of claim 22, wherein extracting tissue health indicators from the pre-processed measurement data comprises determining perfusion and oxygenation based at least in part on the concentration of the one or more tissue chromophores.
45. The method of claim 43, wherein extracting indications of tissue health indicators from the pre-processed measurement data comprises determining perfusion and oxygenation based at least in part on the concentration of the one or more tissue chromophores.
Cross teaches measuring perfusion and oxygenation in paragraphs [0064]-[0068].
As to claims 25 and 46:
25. The system of claim 22, wherein extracting a concentration of one or more chromophores comprises determining an absorption coefficient.
46. The method of claim 43, wherein extracting a concentration of one or more chromophores comprises determining an absorption coefficient.
As part of the measurement process, Cross determines absorption coefficients (see paragraph [0080]).
As to claims 26 and 47:
26. The system of claim 22, wherein generating tissue health indicators from the pre-processed measurement data comprises extracting indications of oxyhemoglobin and deoxyhemoglobin.
47. The method of claim 43, wherein extracting indications of tissue health indicators from the pre-processed measurement data comprises extracting indications of oxyhemoglobin and deoxyhemoglobin.
Cross extracts information relevant to oxyhemoglobin and deoxyhemoglobin (see paragraphs [0067], [0069], and [0082]).
As to claims 27 and 48:
27. The system of claim 26, wherein generating tissue health indicators from pre-processed measurement data further comprises extracting an indication of melanin.
48. The method of claim 47, wherein extracting indications of tissue health indicators from pre-processed measurement data further comprises extracting an indication of melanin.
Cross extracts information relevant to melanin (see paragraphs [0067] and [0082]).
As to claims 29 and 50:
29. The system of claim 26, wherein generating tissue health indicators from pre-processed measurement data further comprises extracting an indication of water content.
50. The method of claim 47, wherein extracting indications of tissue health indicators from pre-processed measurement data further comprises extracting an indication of water content.
Cross extracts information relevant to water content (see paragraphs [0067] and [0082]).
Claims 23 and 44 are rejected over Cross in view of Darty, Budman, and Freeman, as applied to claims 22, 24-27, 29, 43, 45-48, and 50 above, further in view of Nakajima US PG PUB 2016/0374565.
23. The system of claim 22, wherein extracting a concentration of one or more chromophores comprises executing a least squares fit algorithm to extract the concentration of the one or more tissue chromophores.
44. The method of claim 22, wherein extracting a concentration of one or more chromophores comprises executing a least squares fit algorithm to extract the concentration of the one or more tissue chromophores.
The combination does not teach extracting the features using a least squares fit algorithm. However, this is well known, as shown by Nakajima in paragraph [0047]. As such, it would have been obvious to modify Cross to use a least squares algorithm extract the chromophores, as it is merely the use of a known model for generating the information of Cross.
Claims 28, 30, 49, 51 are rejected over Cross in view of Darty, Budman, and Freeman, as applied to claims 22, 24-27, 29, 43, 45-48, and 50 above, further in view of Anderson US Patent 5897924.
28. The system of claim 26, wherein extracting indications of oxyhemoglobin and deoxyhemoglobin comprises using a Beer-Lambert model or modified Beer-Lambert model to extract the indications of oxyhemoglobin and deoxyhemoglobin.
49. The method of claim 47, wherein extracting indications of oxyhemoglobin and deoxyhemoglobin comprises using a Beer-Lambert model or modified Beer-Lambert model to extract the indications of oxyhemoglobin and deoxyhemoglobin.
The combination does not teach extracting the features using the Beer-Lambert model. However, this is well known, as shown by Anderson in column 2, lines 51-66. As such, it would have been obvious to modify Cross to use a Beer-Lambert model to extract the chromophores, as it is merely the use of a known model for generating the information of Cross.
30. The system of claim 21, wherein generating tissue health indicators from pre-processed measurement data further comprises extracting tissue absorption coefficients from the pre-processed measurement data using a tissue optical model or a tissue light propagation model.
51. The method of claim 42, wherein extracting indications of tissue health indicators from pre-processed measurement data further comprises extracting tissue absorption coefficients from the pre-processed measurement data using a tissue optical model or a tissue light propagation model.
The combination does not use a tissue model or light propagation model to extract absorption coefficients. However, Anderson teaches as much in column 2 line 61- column 3 line 8. As such, it would have been obvious to use such a model, as it is merely the use of a known technique to calculate the values of Cross.
Claims 31 and 52 are rejected over Cross in view of Darty and Budman, as applied to claims 21, 34, 38, 41, 42, 57, and 60 above, further in view of Fawzy US PG PUB 2018/0188108.
31. The system of claim 21, wherein pre-processing the measurement data to generate the pre-processed measurement data further comprises recalibrating each image of the plurality of images according to control parameters related to intensity of illumination using a self-reference object positioned within the target area.
52. The method of claim 42, wherein pre-processing the measurement data to generate the pre-processed measurement data further comprises recalibrating each image of the plurality of images according to control parameters related to intensity of illumination using a self-reference object positioned within the target area.
The combination does not calibrate using a self-reference object, as claimed. However, Fawzy teaches in paragraph [0007], for example, pre-measurement calibration using a white diffuse reflectance disk. As such, it would have been obvious to modify Cross to use such a disk, as it is merely the substitution of one known calibration technique for another.
Claims 35-37 and 55-56 are rejected over Cross in view of Darty and Budman, as applied to claims 21, 34, 38, 41, 42, 57, and 60 above, further in view of Dacosta US PG PUB 2017/0236281.
As to claims 35 and 55:
35. The system of claim 21, wherein one or more narrow band light sources are configured to provide at least one flash having a 405± 10 nm wavelength.
55. The method of claim 42, wherein consecutively shining m flashes of light having an identical duration on the target area of tissue comprises shining at least one flash having a 405± 10 nm wavelength.
Cross teaches that the light has a wavelength of 600-1000 nm. It does not teach the 405 nm light. However, Dacosta teaches an alternate optical device and method of imaging a wound to provide information on the condition of the wound that uses 405nm to image the tissue (paragraph [0061], for example). As such, it would have been obvious to modify the combination above to use the imaging method of Dacosta to generate one of the image data sets, to provide more information about the condition of the patient and therefore prevent a more complete picture of the patient’s condition.
As to claims 36 and 56:
36. The system of claim 35, wherein the tissue visualization system is configured to extract indications of bacterial burden based at least in part on one or more images of the plurality of images captured during thee at least one flash having a 405± 10 nm wavelength.
56. The method of claim 55, further comprising extracting indications of bacterial burden based at least in part on one or more images of the plurality of images captured during thee at least one flash having a 405± 10 nm wavelength.
Dacosta further teaches that its method produces an indicator of bacterial load, i.e. bacterial burden (see paragraph [0057] for example).
As to claim 37:
37. The system of claim 36, wherein the tissue visualization system further comprises an emission filter covering an aperture of the image capture unit, wherein the emission filter is selected from:
a long pass filter with a cut-on frequency of 450±25 nm; or
a bandpass filter allowing a transmission through the emission filter in a 425-750 nm wavelength range and having a lower cut-on wavelength of 450±25 nm.
Cross teaches using a long pass filter in paragraph [0142]. The exact specifications of the filter would have been obvious to one of ordinary skill in the art in the combination with Dacosta.
Claims 39 and 58 are rejected over Cross in view of Darty and Budman, as applied to claims 35-37 and 55-56 above, further in view of Mozdierz US PG PUB 2018/0235484
39. The system of claim 21, wherein one or more narrow band light sources are configured to provide at least two flashes having wavelengths in a 450 nm – 750 nm range during the capture of the measurement data to capture one or more reflectance images for the target area; and
wherein the processor of the portable computing device is configured to access and execute instructions in accordance with the tissue visualization application to compare the one or more reflectance images for the target area with one or more images of a control site.
58. The method of claim 42, wherein consecutively shining m flashes of light having an identical duration on the target area of tissue comprises shining at least two flashes having wavelengths in a 450 nm – 750 nm range during the capture of the measurement data to capture one or more reflectance images for the target area; and fu
comparing the one or more reflectance images for the target area with one or more images of a control site.
Cross teaches that the light has a wavelength 600-1000 nm and more specifically at 620, 630 and 700 nm, for example (paragraph [0017] and [[0082]). This is two wavelengths in the range. As discussed above, the light is emitted in flashes. Further, paragraphs [0112]-[01130 state that the system measures reflectance datasets, i.e. images. Cross compares images at different wavelengths to determine tissue condition (paragraph [0064]). Further, Mozdierz teaches a method of assessing tissues by comparing the images to a control site, healthy tissue (see paragraph [0019], for example). It would have been obvious to modify the combination to compare the images to healthy tissue, as it would provide a normalized vision of the patient’s condition.
Claims 40 and 59 are rejected over Cross in view of Darty and Budman, as applied to claims 21, 34, 38, 41, 42, 57, and 60 above, further in view of Barker et al US PG PUB 2018/0137609.
As to claims 40 and 59:
40. The system of claim 21, wherein one or more narrow band light sources are configured to provide at least three flashes having wavelengths in a 450 nm – 750 nm range during the capture of the measurement data; and
wherein the tissue visualization system is configured to extract indications of oxygenation and perfusion and to compensate for melanin based at least in part one on or more images of the plurality of images captured during the at least three flashes having a wavelength in the 450 nm – 750 nm range
59. The system of claim 42, wherein consecutively shining m flashes of light having an identical duration on the target area of tissue comprises shining at least three flashes having wavelengths in a 450 nm – 750 nm range during the capture of the measurement data; and
extracting indications of oxygenation and perfusion and to compensate for melanin based at least in part one on or more images of the plurality of images captured during the at least three flashes having a wavelength in the 450 nm – 750 nm range
Cross teaches that the light has a wavelength 600-1000 nm and more specifically at 620, 630 and 700 nm, for example (paragraph [0017] and [[0082]). This is three wavelengths in the range. As discussed above, the light is emitted in flashes. The combination does not compensate for melanin. However, Barker teaches that it is known to compensate for melanin in images, as melanin absorbs light and can affect measurement data. As such, it would have been obvious to modify the combination above to compensate for melanin, in order to improve measurement accuracy.
X. ALLOWABLE SUBJECT MATTER
Claims 32, 33, 53, and 54 would be allowable if the 101, 112, and 251 rejections were overcome and if rewritten into independent form.
Claims 61-67 would be allowable if the 101 rejection were overcome.
Claims 32 and 53 define over the art of record. The protest applied Garcia against claim 32. However, Garcia does not teach rescaling each image to compensate for deviations in the working distance between the camera and the approximate shape of the body part. Garcia does approximate an icon over the image of the object being imaged, but it does not rescale the image to compensate for deviations in the working distance, i.e. different distances of different parts of the image. No other art teaches this feature.
Claims 33 and 54 define over the art of record. The protest alleges that Garcia teaches this claim. However, Garcia does not automatically capture image data based on the “recognized” size of the reference object. Rather, the object of Garcia has a known size, but the size is not used to trigger a measurement. Recognizing the size must include some means to sense the size. The size in Garcia is known.
Claims 61-67 define over the art of record in that while, all of the features are individually known, at least 5 references would be needed to make the rejection and, in the Examiner’s opinion, there is insufficient motivation to combine all the references. If the claim were to be amended to have the computing device pre-process and process the measurement data, it would eliminate a reference and the claim might be subject to an art rejection.
XI. CONCLUSION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT L NASSER whose telephone number is (571)272-4731. The examiner can normally be reached M-F 8-6.
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/ROBERT L NASSER/ Primary Examiner, Art Unit 3992
Conferees:
/ADAM L BASEHOAR/Primary Examiner, Art Unit 3992 /ALEXANDER J KOSOWSKI/Supervisory Patent Examiner, Art Unit 3992
1 The Examiner further notes that there has not been any unequivocable intent to broaden shown within 2 years of the issue date of the ‘345 patent.