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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 – 8 and 11 – 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yan (Sensors, Published on May 6, 2021, cited in the IDS filed on 12/7/23).
With respect to independent claim 1, Yan teaches A method of analyzing an ex-vivo tissue sample in Fig. 3, comprising:
sequentially interrogating the tissue sample a plurality of times see Fig. 3; dark image and white images, each sequential interrogation using at least one excitation light within a plurality of excitation lights and each said excitation light within the plurality of excitation lights centered on a respective wavelength distinct from the respective centered wavelengths of the other excitation lights total of 30 spectral bands were investigated using 365 nm and 495 nm; see p. 7, last paragraph and Table 3,
wherein at least one of the excitation light centered wavelengths is configured to produce autofluorescence (AF) emissions from one or more biomolecules associated with the tissue sample, and at least one of the excitation light centered wavelengths is configured to produce diffuse reflectance signals from the tissue sample see p. 5, the last paragraph;
using at least one photodetector CMOS sensor on p. 4 the first paragraph to detect the AF emissions, or the diffuse reflectance signals, or both from the tissue sample, and to produce photodetector signals representative of the detected said AF emissions, or the detected said diffuse reflectance signals, or both see p. 8, the first paragraph;
processing the photodetector signals attributable to the AF emissions using at least one first classifier trained with a plurality of predetermined AF data sets to determine one or more first data sets indicative of biomolecules present within the tissue sample; processing the photodetector signals attributable to the diffuse reflectance signals using at least one second classifier trained with a plurality of predetermined diffuse reflectance signal data sets to determine one or more second data sets; and determining a type of the tissue sample using the one or more first data sets and the one or more second data sets see p. 5 and p. 8, the first paragraph.
With respect to dependent claim 2, Yan teaches on. P. 7 (gray-level images) wherein the photodetector signals attributable to the diffuse reflectance signals provide microstructural information relating to the tissue sample.
With respect to dependent claim 3, Yan teaches on p. 7 (morphologies) wherein the photodetector signals attributable to the diffuse reflectance signals provide morphological information relating to the tissue sample.
With respect to dependent claim 4, Yan teaches on p. 4, the last paragraph wherein the plurality of predetermined diffuse reflectance signal data sets used to train the second classifier include data sets attributable to known tissue types including benign tissue, fibrous tissue, adipose tissue, diseased tissue, and tissue morphologies.
With respect to dependent claim 5, Yan teaches on p. 2, the second paragraph wherein the plurality of predetermined AF data sets used to train the first classifier include data sets attributable to known biomolecules.
With respect to dependent claim 6, Yan teaches p. 2, the second paragraph wherein the known biomolecules include at least one of tryptophan, collagen, NADH, FAD, elastin, or hemoglobin.
With respect to dependent claim 7, Yan teaches p. 14, the second paragraph wherein the at least one first classifier includes a plurality of first classifiers and each producing a said first data set; and the step of processing the photodetector signals attributable to the AF emissions further includes providing the plurality of first data sets to a first metaclassifier, and the step of determining the type of the tissue sample utilizes an output of the first metaclassifier.
With respect to dependent claim 8, Yan teaches p. 14, the first paragraph wherein the at least one first classifier includes a plurality of first classifiers and each producing a said first data set, and the at least one second classifier includes a plurality of second classifiers and each producing a said second data set; and the step of processing the photodetector signals attributable to the AF emissions further includes providing the plurality of first data sets to a first metaclassifier, and the step of processing the photodetector signals attributable to the diffuse reflectance signals includes providing the plurality of second data sets to the first metaclassifier; and the step of determining the type of the tissue sample utilizes an output of the first metaclassifier.
With respect to dependent claim 11, Yan teaches on p. 13, the last paragraph – p. 14, the first paragraph wherein the step of determining the type of the tissue sample utilizes a third classifier that utilizes the one or more first data sets from the at least one first classifier and the one or more second data sets from the at least one second classifier in a cascading manner.
With respect to dependent claim 12, Yan teaches on p. 15, the last paragraph wherein the step of processing the photodetector signals attributable to the AF emissions and the step of processing the photodetector signals attributable to the diffuse reflectance signals further includes providing a first output from the at least one first classifier and a second output from the at least one second classifier to a second level classifier in an ensemble classifier architecture; and the step of determining the type of the tissue sample utilizes a third output from the second level classifier.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan, and further in view of Kumar (US 2021/0140882 A1, cited in the same IDS).
The teaching of Yan has been discussed above.
With respect to dependent claim 9, Yan is silent with processing the photodetector signals attributable to the diffuse reflectance signals using at least one third classifier trained with a plurality of predetermined morphology signal data sets to determine one or more third data sets indicative of morphologies present within the tissue sample; and the step of determining the type of the tissue sample further uses the one or more third data sets indicative of morphologies present within the tissue sample.
Kumar, a pertinent art, teaches in paragraph [0097] the above limitation. In view of this, it would be obvious at the time of the claimed invention was filed to modify the teaching of Yan in order to further include SVMs such as in Kumar for additional classifier specifically interrogating morphology, and therefore more accurate classification of cancerous cells.
Claim(s) 13 – 20 and 23 – 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan.
With respect to independent claim 13, Yan teaches a system for analyzing an ex-vivo tissue sample, comprising:
an excitation light source LEDs on p. 3, the last paragraph; see Fig. 1 configured to selectively produce a plurality of excitation lights, each said excitation light centered on a wavelength distinct from the centered wavelength of the other said excitation lights centered about 405 nm and 365 nm; see Fig. 1,
wherein at least one of the excitation light centered wavelengths is configured to produce autofluorescence (AF) emissions from one or more biomolecules associated with a bladder wall tissue, and diffuse reflectance signals from the tissue sample, the system configured so that the plurality of excitation lights are incident to the tissue sample see p. 5, the last paragraph; p. 2, the second paragraph;
at least one photodetector CMOS on p. 4, the first paragraph configured to detect the AF emissions, or the diffuse reflectance signals, or both from the tissue sample as a result of the respective incident excitation light, and produce signals representative of the detected said AF emissions, or the detected said diffuse reflectance signals, or both; and
a system controller the imaging module on p. 4, the first paragraph in communication with the excitation light source, the at least one photodetector, and the system controller to: control the excitation light unit to sequentially produce the plurality of excitation lights; control the at least one photodetector to detect the AF emissions, or the diffuse reflectance signals, or both from the tissue sample, and to produce photodetector signals representative of the detected said AF emissions, or the detected said diffuse reflectance signals, or both; process the photodetector signals attributable to the AF emissions using at least one first classifier trained with a plurality of predetermined AF data sets to determine one or more first data sets indicative of biomolecules present within the tissue sample; process the photodetector signals attributable to the diffuse reflectance signals using at least one second classifier trained with a plurality of predetermined diffuse reflectance signal data sets to determine one or more second data sets; and determine a type of the tissue sample using the one or more first data sets and the one or more second data sets.
Yan is silent with a non-transitory memory storing instructions, which instructions when executed cause the system controller. However, Yan discloses executing all the above. In view of this, it would be obvious at the time of the claimed invention was filed to modify the teaching of Yan in order to perform automated procedures in investing desired tissue sample. This is in consistency with the Supreme Court Decision of the KSR. V. International Co.: Obvious to try – choosing form a finite number of predictable results.
With respect to dependent claim 14, Yan teaches on p. 7 (gray-level images) wherein the photodetector signals attributable to the diffuse reflectance signals provide microstructural information relating to the tissue sample.
With respect to dependent claim 15, Yan teaches on p. 7 wherein the photodetector signals attributable to the diffuse reflectance signals provide morphological information relating to the tissue sample.
With respect to dependent claim 16, Yan teaches p. 4, the last paragraph wherein the plurality of predetermined diffuse reflectance signal data sets used to train the second classifier include data sets attributable to known tissue types including benign tissue, fibrous tissue, adipose tissue, diseased tissue, and tissue morphologies.
With respect to dependent claim 17, Yan teaches on p. 2, the second paragraph wherein the plurality of predetermined AF data sets used to train the first classifier include data sets attributable to known biomolecules.
With respect to dependent claim 18, Yan teaches on p. 2, the second paragraph wherein the known biomolecules include at least one of tryptophan, collagen, NADH, FAD, elastin, or hemoglobin.
With respect to dependent claim 19, Yan teaches p. 14, the first paragraph wherein the at least one first classifier includes a plurality of first classifiers and each producing a said first data set; and wherein the instructions that when executed cause the system controller to process the photodetector signals attributable to the AF emissions, further cause the system controller to provide the plurality of first data sets to a first metaclassifier, and the determination of the tissue sample type utilizes an output of the first metaclassifier.
With respect to dependent claim 20, Yan teaches p. 8, the second paragraph and p. 13, the last paragraph – p. 14, the first paragraph wherein the at least one first classifier includes a plurality of first classifiers and each producing a said first data set, and the at least one second classifier includes a plurality of second classifiers and each producing a said second data set; and wherein the instructions that when executed cause the system controller to process the photodetector signals attributable to the AF emissions, further cause the system controller to provide the plurality of first data sets to a first metaclassifier; and wherein the instructions that when executed cause the system controller to process the photodetector signals attributable to the diffuse reflectance signals, further cause the system controller to provide the plurality of second data sets to the first metaclassifier; and wherein the instructions that when executed cause the system controller to determine the type of the tissue sample utilizes an output of the first metaclassifier.
With respect to dependent claim 23, Yan teaches on p. 14, the first paragraph wherein the instructions that when executed cause the system controller to determine the type of the tissue sample utilizes a third classifier that utilizes the one or more first data sets from the at least one first classifier and the one or more second data sets from the at least one second classifier in a cascading manner.
With respect to dependent claim 24, Yan teaches on p. 14, in Figure caption of Fig. 13 and on p. 15, the last paragraph wherein the instructions that when executed cause the system controller to process the photodetector signals attributable to the AF emissions and to process the photodetector signals attributable to the diffuse reflectance signals further cause the system to provide a first output from the at least one first classifier and a second output from the at least one second classifier to a second level classifier in an ensemble classifier architecture; and wherein the instructions that when executed cause the system controller to determine the type of the tissue sample utilizes a third output from the second level classifier.
Allowable Subject Matter
Claims 10 and 21 – 22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
With respect to dependent claim 10, the prior art of record fails to teach or reasonably suggest:
wherein the step of processing the photodetector signals attributable to the AF emissions using at least one first classifier further includes providing the plurality of first data sets to a first metaclassifier, the step of processing the photodetector signals attributable to the diffuse reflectance signals using at least one second classifier includes providing the plurality of second data sets to the first metaclassifier, and the step of processing the photodetector signals attributable to the diffuse reflectance signals using at least one third classifier trained with a plurality of predetermined morphology signal data sets includes providing the plurality of third data sets to the first metaclassifier; and the step of determining the type of the tissue sample utilizes an output of the first metaclassifier.
With respect to dependent claim 21 and its dependent claim 22, the prior art of record fails to teach or reasonably suggest:
wherein the instructions that when executed further cause the system controller to process the photodetector signals attributable to the diffuse reflectance signals using at least one third classifier trained with a plurality of predetermined morphology signal data sets to determine one or more third data sets indicative of morphologies present within the tissue sample; and wherein the instructions that when executed cause the system controller to determine the type of the tissue sample further uses the one or more third data sets indicative of morphologies present within the tissue sample.
Conclusion
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KIHO KIM, Ph.D.
Primary Examiner
Art Unit 2884
/Kiho Kim/Primary Examiner, Art Unit 2884