DETAILED ACTION
Notice of 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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
Claims 1-18 are pending.
Claims 1, 4- 5, 10, 13 and 16 are objected to.
Claims 1-18 are rejected.
Priority
This application US 18/182,846 (03/13/2023) claims benefit of US Application 63/319,424 (03/14/2022) as reflected in the filing receipt mailed on 04/03/2023. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-18 is 03/14/2022.
Claim objections
Claims 1, 4- 5, 10, 13 and 16 are objected to because of the following informalities. Appropriate correction is required.
Claim 1 is objected to for reciting a comma at the end of the first "acquiring" step which should be replaced by a semicolon for proper claim language.
Claim 1 is objected to for reciting a comma at the end of the "analyzing" step – after "and" (penultimate claim element) which should be removed for proper grammar. Claim 13 repeats the same issue at the end of the "recognize" step (fourth claim element).
Claim 1 is objected to for reciting "wherein the plurality of categories of tissue comprise" (line 3 of the first "acquiring" step) which should read "wherein the plurality of categories of tissue comprises" for proper noun-verb agreement.
In claim 1, the recited "the regions" (lines 2 and 3 of second performing step and lines 1 and 3 of analyzing step) should read "the one or more regions" for proper claim language.
In claim 1, the recited "the hyperspectral images" (line 2 of second acquiring step, line 2 of first performing step, lines 1 and 2 of second performing step, lines 1, 3 and 4 of the analyzing step and lines 1, 2, 4 and 5 of the assigning step) should read "the plurality of hyperspectral images" for proper claim writing. Claim 4 repeats the same issue. In contrast, claim 13 does not present similar issues.
Claims 5 and 10 are objected to for not reciting a colon after "at least one of" for proper claim language.
In claim 10, the recited "the image processing steps" should read "the one or more image processing steps" for proper claim language.
Claim 13 is objected to for reciting "where the plurality of patterns of data correspond" (line 2 of the recognize step) which should read "where the plurality of patterns of data corresponds" for proper noun-verb agreement.
Claim 13 is objected to for reciting "the tissue sample" (last line of the recognize step) which should read "the bodily tissue sample" for proper claim language.
In claim 16, the recited "wherein execution" should read "wherein the execution" for proper claim language.
Claim Rejections - 35 USC § 112(b)
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 6-18 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
In claims 6 and 11, the recited "wherein the one or more unsupervised exploratory analyses comprise principal components analysis and hierarchical cluster analysis" is indefinite. To overcome this rejection, the applicant should amend claims 6 and 11 to recite "wherein the analyses comprise two or more analyses, and wherein the two or more analyses comprise X and Y." Claims 7 and 12 are rejected for similar reasons for the recitation of “wherein the one or more supervised discriminatory analyses comprise…”.
Claim 8 is indefinite because it lacks a positive active step relating back to the preamble. The preamble recites "a method for stratifying precancerous tissues utilizing a discriminatory model", however the body of the claim recites a positive active step drawn to "generate a discriminatory model." Therefore, it is unclear as to whether the method is drawn to utilizing a model or generating a model.
Claim 13 recites “the risk” which is indefinite because it lacks antecedent basis. "A risk" has not been previously recited. The examiner suggests amending claim 13 to recite "a risk" to overcome the rejection.
Claim 16 recites “the one or more images of bodily tissues” which is indefinite because it lacks antecedent basis. "One or more images of bodily tissues" has not been previously recited. The examiner suggests amending claim 13 to recite "the one or more hyperspectral images of bodily tissues" or similar terms that clarifies the antecedent basis to overcome the rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a method (claims 1-12) and a system (claims 13-18); each of which falls within one of the categories of statutory subject matter.
[Step 1: claims 1-18: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) as well as a law of nature or a natural phenomenon are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "performing one or more unsupervised exploratory analyses on the hyperspectral images to generate labeled hyperspectral images" (independent claim 1);
• "performing one or more supervised discriminatory analyses on the hyperspectral images of the regions comprising cancerous tissues and the hyperspectral images of the regions comprising benign tissues to generate a discriminatory model" (independent claim 1);
• "analyzing the hyperspectral images of the regions comprising precancerous tissues with the discriminatory model to determine whether each of the hyperspectral images of the regions comprising precancerous tissues are most similar to the hyperspectral images of the cancerous tissues or to the hyperspectral images of the benign tissues" (independent claim 1);
• "applying one or more image processing steps to the hyperspectral images" (claim 4);
• "performing one or more unsupervised exploratory analyses on the plurality of images of tissues to generate a plurality of labeled images" (independent claim 8);
• "performing one or more supervised discriminatory analyses on the plurality of labeled images to generate a discriminatory model" (independent claim 8); and
• "applying one or more image processing steps to the plurality of images of tissues" (claim 9).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed by mathematical operation. The step directed to “executing an algorithm to apply processing steps and organize images into categories” requires mathematical techniques as the only supported embodiments because it describes a mathematical technique (MPEP 2106.04(a)(2) pertains). Further support for the mathematical techniques used in the claims is provided in the specification at [0067], which discloses an algorithm implemented to analyze hyperspectral images of OED tissues to stratify those tissues by their risk of becoming cancerous. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "assigning the precancerous tissues to a high-risk stratum when the hyperspectral images of the precancerous tissues are most similar to the hyperspectral images of the cancerous tissues, and assigning the precancerous tissues to a low-risk stratum when the hyperspectral images of the precancerous tissues are most similar to the hyperspectral images of the benign tissues" (independent claim 1);
• "assigning the precancerous tissues to one of a plurality of intermediate strata between the ‘low-risk’ stratum and the ‘high-risk’ stratum, wherein each stratum in the of intermediate strata corresponds to one of the categories of intermediate dysplastic tissue" (claim 3);
• "recognize a plurality of patterns of data in the plurality of hyperspectral images, where the plurality of patterns of data correspond to one or more chemical or biological features of the tissue sample" (independent claim 13); and
• "organize the plurality of hyperspectral images into one of a plurality of categories, wherein each of the plurality of categories corresponds to one or more of the plurality of patterns of data" (independent claim 13).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind (i.e. concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) or because the method only requires a user to manually determine action based on an added number. Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of recognize patterns within the data and organize data into categories.
Dependent claims 2, 5-7, 10-12 and 16-18 recite further steps that limit the judicial exceptions in independent claims 1, 8 and 13 and, as such, also are directed to those abstract ideas. For example, claim 2 recites further details about the plurality of categories of tissues; claims 5 and 10 recite further details about the image processing steps; claims 6 and 11 recite further details about the one or more unsupervised exploratory analyses; claims 7 and 12 recite further details about the one or more supervised discriminatory analyses; claim 16 recites further details about the execution of the machine learning algorithm and claims 17-18 recite further details about the plurality of categories that the hyperspectral images are organized into.
Furthermore, the instant claims recite a natural correlation by correlating the infrared spectra of images of bodily tissue samples naturally found in the body with its strata classification for the risk of the precancerous tissues becoming cancerous. (see MPEP 2106.04(b).I).
In addition, the limitations of claims 13-18, recite a natural product because the claims are drawn to one or more tissue sections of the bodily tissue sample, which encompasses naturally occurring tissue (Critchley-Thorne ("A tissue systems pathology test detects abnormalities associated with prevalent high-grade dysplasia and esophageal cancer in Barrett's esophagus." Cancer Epidemiology, Biomarkers & Prevention 26.2:240-248 (2017), as cited on the attached Form PTO-892)). While this tissue is claimed in combination with a microscope and computer linked to it, it does not change the structure of the tissue such that one of skill in the art would recognize it as markedly different from its naturally occurring counterpart. Therefore, these limitations correspond to a natural product. As such, claims 13-18 recite a natural product.
[Step 2A Prong One: claims 1-18: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
MPEP 2106.04.II.B states that if multiple exceptions that are distinct from each other in a claim are recited, then examiners should select one of the exceptions (i.e. a law of nature, a natural phenomenon, or an abstract idea) and conduct an eligibility analysis for that selected exception. If the claim does not recite any additional element or combination of elements that integrate the selected exception into a practical application and also does not recite any additional elements or combination of additional elements that amount to significantly more than the judicial exception, then the claim should be considered ineligible. In view of this and for the purposes of examination, the abstract idea judicial exception is selected.
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2).
Instant claims 1-18 recite additional elements that are not abstract ideas:
• "acquiring one or more tissue samples, wherein each tissue sample comprises one or more regions of tissue, further wherein each region of tissue comprises one of a plurality of categories of tissue, wherein the plurality of categories of tissue comprise cancerous tissue, benign tissue, and precancerous tissue" (independent claim 1);
• "acquiring a plurality of hyperspectral images of the one or more regions of the one or more tissue samples, wherein the hyperspectral images comprise a plurality of infrared spectra" (independent claim 1);
• "acquiring a plurality of images of tissues of a tissue sample, each of which correspond to one of the plurality of categories of tissues" (independent claim 8);
• "one or more tissue sections of the bodily tissue sample comprising at least one section" (independent claim 13);
• "a Fourier transform infrared (FTIR) microscope structured and operable to acquire a plurality of hyperspectral images of the at least one section, such that each of the plurality of hyperspectral images is acquired from a region of cancerous tissue or a region of precancerous tissue in the at least one section" (independent claim 13); and
• "a computer-based system communicatively linked to the FTIR microscope" (independent claim 13).
Dependent claim 14 recites further details about the one or more tissue sections. Dependent claim 15 recites further details about the shapes and compositions of the first section and the at least one section.
Considerations under Step 2A, Prong Two
The recited limitations in claims 1-18 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
The recited use of an "FTIR microscope" to "acquire" images of tissue samples read on data gathering activities since acquired data is used as input for the subsequent mathematical calculations; not amounting to a practical application. The type of data doesn’t change that it is mere data gathering or conventional computer receiving means.
Furthermore, as discussed above, the limitations for the system are a natural product (claims 13-18). The "one or more tissue sections of the bodily tissue sample" is an additional element however, it is a natural product, thus it is also a judicial exception and it cannot provide a practical application to the abstract idea judicial exceptions. Judicial exceptions cannot provide a practical application. MPEP 2106.04.II.A.2 states that another judicial exception is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). Therefore the natural product of the system cannot integrate the recited judicial exception into a practical application
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-18: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1-18 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
The computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)).
The specification as published also notes that any suitable machine learning model may be used without limitation at [0067]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
With respect to the instant claims, the prior art review to Baker ("Using Fourier transform IR spectroscopy to analyze biological materials." Nature protocols 9.8:1771-1791 (2014); newly cited) discloses that using an "FTIR microscope" to "acquire" images of tissue samples is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, pg. 1771 Abstract.
Furthermore, as discussed above, the limitations of the tissue sections in the system are a natural product (claims 13-18), which is a judicial exception. MPEP 2106.05.I states that an inventive concept under Step 2B cannot be furthered by a judicial exception itself. Therefore the natural product composition cannot provide an inventive concept that provides significantly more than the judicial exception itself.
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-18: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
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)(l) 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang ("Oral cancer discrimination and novel oral epithelial dysplasia stratification using FTIR imaging and machine learning." Diagnostics 11.11:2133 (2021) – Published on 11/17/2021), as cited on the attached Form PTO-892.
Claim 1 recites:
acquiring one or more tissue samples, wherein each tissue sample comprises one or more regions of tissue, further wherein each region of tissue comprises one of a plurality of categories of tissue, wherein the plurality of categories of tissue comprise cancerous tissue, benign tissue, and precancerous tissue,
acquiring a plurality of hyperspectral images of the one or more regions of the one or more tissue samples, wherein the hyperspectral images comprise a plurality of infrared spectra;
performing one or more unsupervised exploratory analyses on the hyperspectral images to generate labeled hyperspectral images;
performing one or more supervised discriminatory analyses on the hyperspectral images of the regions comprising cancerous tissues and the hyperspectral images of the regions comprising benign tissues to generate a discriminatory model;
analyzing the hyperspectral images of the regions comprising precancerous tissues with the discriminatory model to determine whether each of the hyperspectral images of the regions comprising precancerous tissues are most similar to the hyperspectral images of the cancerous tissues or to the hyperspectral images of the benign tissues; and,
assigning the precancerous tissues to a high-risk stratum when the hyperspectral images of the precancerous tissues are most similar to the hyperspectral images of the cancerous tissues, and assigning the precancerous tissues to a low-risk stratum when the hyperspectral images of the precancerous tissues are most similar to the hyperspectral images of the benign tissues
• Wang teaches methods and systems for oral cancer discrimination and oral epithelial dysplasia stratification using FTIR imaging and machine learning (pg. 1 Title) providing a classification strategy for risk stratification of oral epithelial dysplasia precancerous samples (i.e. reading on acquiring a plurality of hyperspectral images of the one or more regions of the one or more tissue samples, wherein the hyperspectral images comprise a plurality of infrared spectra) (pg. 3 para. 1); wherein unsupervised exploratory analyses comprise principal components analysis and hierarchical cluster analysis (i.e. reading on performing one or more unsupervised exploratory analyses on the hyperspectral images to generate labeled hyperspectral images) (pg. 5 para. 2 ) and following exploratory analyses, discriminant machine learning models were built using three different supervised algorithms for discrimination between HK and OSCC samples (i.e. reading on performing one or more supervised discriminatory analyses on the hyperspectral images of the regions comprising cancerous tissues and the hyperspectral images of the regions comprising benign tissues to generate a discriminatory model) (pg. 6 para. 1); wherein a comparison of the three class average spectra for the hyperplasia/ hyperkeratosis (HK), oral epithelial dysplasia (OED), and oral squamous cell carcinoma (OSCC) samples reveals an overall reduction in proteins and an increase in the nucleic acids as the oral tissues progress from HK to OED and further to OSCC (pg. 13 para. 1) which can be used for accurate risk assessment for OED samples and early detection for OSCC (i.e. reading on similarity to HK representing low risk and similarity to OSCC representing high risk based on the progression described – hence assigning the precancerous tissues to a high-risk stratum when the hyperspectral images of the precancerous tissues are most similar to the hyperspectral images of the cancerous tissues, and assigning the precancerous tissues to a low-risk stratum when the hyperspectral images of the precancerous tissues are most similar to the hyperspectral images of the benign tissues) (pg. 14 para. 3); wherein 46 representative spectra from 12 HK and 11 OSCC tissue samples were used as training data to build the discriminant models (i.e. reading on acquiring one or more tissue samples, wherein each tissue sample comprises one or more regions of tissue, further wherein each region of tissue comprises one of a plurality of categories of tissue, wherein the plurality of categories of tissue comprise cancerous tissue, benign tissue, and precancerous tissue) (pg. 6 para. 2) and the best performing model was further used to classify the 22 representative spectra from the 11 OED samples (i.e. reading on analyzing the hyperspectral images of the regions comprising precancerous tissues with the discriminatory model to determine whether each of the hyperspectral images of the regions comprising precancerous tissues are most similar to the hyperspectral images of the cancerous tissues or to the hyperspectral images of the benign tissues) (pg. 7 para. 1).
Claim 2 recites:
wherein the plurality of categories of tissues further comprises one or more categories of intermediate dysplastic tissues, wherein each category of intermediate dysplastic tissues has a set of defining cytological criteria and an associated level of risk of the category of intermediate dysplastic tissue becoming cancerous
• Wang teaches methods and systems for oral cancer discrimination and oral epithelial dysplasia stratification using FTIR imaging and machine learning (pg. 1 Title) providing a classification strategy for risk stratification of oral epithelial dysplasia precancerous samples (pg. 3 para. 1); wherein the 11 oral epithelial dysplasia samples looked very similar in their morphological appearance and were all pathologically diagnosed as having a moderate-to-severe grade of dysplasia and classified by the model based on their FTIR spectral information (i.e. reading on wherein the plurality of categories of tissues further comprises one or more categories of intermediate dysplastic tissues, wherein each category of intermediate dysplastic tissues has a set of defining cytological criteria and an associated level of risk of the category of intermediate dysplastic tissue becoming cancerous) (pg. 13 para. 6)
Claim 3 recites:
further comprising assigning the precancerous tissues to one of a plurality of intermediate strata between the ‘low-risk’ stratum and the ‘high-risk’ stratum, wherein each stratum in the of intermediate strata corresponds to one of the categories of intermediate dysplastic tissue
• Wang teaches methods and systems for oral cancer discrimination and oral epithelial dysplasia stratification using FTIR imaging and machine learning (pg. 1 Title) providing a classification strategy for risk stratification of oral epithelial dysplasia precancerous samples (pg. 3 para. 1); wherein the 11 oral epithelial dysplasia samples looked very similar in their morphological appearance and were all pathologically diagnosed as having a moderate-to-severe grade (i.e. reading on between the ‘low-risk’ stratum and the ‘high-risk’ stratum) of dysplasia and classified by the model based on their FTIR spectral information (i.e. reading on wherein each stratum in the of intermediate strata corresponds to one of the categories of intermediate dysplastic tissue) (pg. 13 para. 6).
Claim 4 recites:
further comprising applying one or more image processing steps to the hyperspectral images
Claim 5 recites:
wherein the one or more image processing steps comprise at least one of conversion between absorbance and transmission data, selection of relevant data regions, digital filtering, light-scattering correction, baseline correction, and normalization
• Wang teaches that spectral preprocessing was applied in the form of baseline correction to remove background absorption interference, and vector normalization to correct for sample thickness variations (i.e. reading on claims 4-5) (pg. 12 para. 3).
Claims 6 and 11 recite:
wherein the one or more unsupervised exploratory analyses comprise principal components analysis and hierarchical cluster analysis
• Wang teaches the oral cancer discrimination and oral epithelial dysplasia stratification using FTIR imaging and machine learning (pg. 1 Title); wherein unsupervised exploratory analyses comprise principal components analysis and hierarchical cluster analysis (pg. 5 para. 2 )
Claims 7 and 12 recite:
wherein the one or more supervised discriminatory analyses comprise partial least squares discriminant analysis, support vector machines discriminant analysis, and extreme gradient boosting discriminant analysis
• Wang teaches that following exploratory analyses, discriminant machine learning models were built using three different supervised algorithms for discrimination between HK and OSCC samples (i.e. reading on wherein the one or more supervised discriminatory analyses comprise partial least squares discriminant analysis, support vector machines discriminant analysis, and extreme gradient boosting discriminant analysis) (pg. 6 para. 1).
Claim 8 recites:
acquiring a plurality of images of tissues of a tissue sample, each of which correspond to one of the plurality of categories of tissues;
performing one or more unsupervised exploratory analyses on the plurality of images of tissues to generate a plurality of labeled images; and
performing one or more supervised discriminatory analyses on the plurality of labeled images to generate a discriminatory model
• Wang teaches oral cancer discrimination and oral epithelial dysplasia stratification using FTIR imaging and machine learning (pg. 1 Title) providing a classification strategy for risk stratification of oral epithelial dysplasia precancerous samples (i.e. reading on acquiring a plurality of hyperspectral images of the one or more regions of the one or more tissue samples, wherein the hyperspectral images comprise a plurality of infrared spectra) (pg. 3 para. 1); wherein unsupervised exploratory analyses comprise principal components analysis and hierarchical cluster analysis (i.e. reading on performing one or more unsupervised exploratory analyses on the hyperspectral images to generate labeled hyperspectral images) (pg. 5 para. 2 ) and following exploratory analyses, discriminant machine learning models were built
using three different supervised algorithms for discrimination between HK and OSCC samples (i.e. reading on performing one or more supervised discriminatory analyses on the hyperspectral images of the regions comprising cancerous tissues and the hyperspectral images of the regions comprising benign tissues to generate a discriminatory model) (pg. 6 para. 1).
Claim 9 recites:
further comprising applying one or more image processing steps to the plurality of images of tissues
Claim 10 recites:
wherein the image processing steps comprise at least one of conversion between absorbance and transmission data, selection of relevant data regions, digital filtering, light-scattering correction, baseline correction, and normalization
• Wang teaches that spectral preprocessing was applied in the form of baseline correction to remove background absorption interference, and vector normalization to correct for sample thickness variations (i.e. reading on claims 9-10) (pg. 12 para. 3).
Claim 13 recites:
one or more tissue sections of the bodily tissue sample comprising at least one section
a Fourier transform infrared (FTIR) microscope structured and operable to acquire a plurality of hyperspectral images of the at least one section, such that each of the plurality of hyperspectral images is acquired from a region of cancerous tissue or a region of precancerous tissue in the at least one section; and
a computer-based system communicatively linked to the FTIR microscope, the computer-based system structured and operable to execute a machine learning algorithm to:
recognize a plurality of patterns of data in the plurality of hyperspectral images, where the plurality of patterns of data correspond to one or more chemical or biological features of the tissue sample; and,
organize the plurality of hyperspectral images into one of a plurality of categories, wherein each of the plurality of categories corresponds to one or more of the plurality of patterns of data
• Wang teaches methods and systems for oral cancer discrimination and oral epithelial dysplasia stratification using FTIR imaging and machine learning (pg. 1 Title) providing a classification strategy for risk stratification of oral epithelial dysplasia precancerous samples (pg. 3 para. 1); wherein FTIR images of tissue sections (i.e. reading on one or more tissue sections of the bodily tissue sample comprising at least one section) were acquired in transmission mode using a Perkin Elmer FTIR Spectrum Spotlight imaging system (Spectrum one, Spotlight 300, Perkin Elmer, Waltham, MA, USA) (i.e. reading on a computer-based system communicatively linked to the FTIR microscope) (pg. 3 para. 3); wherein 46 representative spectra from 12 HK and 11 OSCC tissue samples were used as training data to build the discriminant models (i.e. reading on a Fourier transform infrared (FTIR) microscope structured and operable to acquire a plurality of hyperspectral images of the at least one section, such that each of the plurality of hyperspectral images is acquired from a region of cancerous tissue or a region of precancerous tissue in the at least one section) (pg. 6 para. 2); wherein following exploratory analyses, discriminant machine learning models were built using three different supervised algorithms for discrimination between HK and OSCC samples (i.e. reading on recognize a plurality of patterns of data in the plurality of hyperspectral images, where the plurality of patterns of data correspond to one or more chemical or biological features of the tissue sample; and, organize the plurality of hyperspectral images into one of a plurality of categories, wherein each of the plurality of categories corresponds to one or more of the plurality of patterns of data) (pg. 6 para. 1).
Claim 14 recites:
wherein the one or more tissue sections comprises a first section, and further wherein the system further comprises an optical microscope structured and operable to acquire optical image data of the first section of the tissue sample, such that regions of cancerous or precancerous tissue in the first section can be identified
• Wang teaches a series of FTIR imaging areas defined in each sample section in reference to the hematoxylin and eosin staining image of an adjacent section of the same sample (pg. 1 Abstract); wherein sections were imaged with a light microscope and the digital images were annotated areas of interest based on histopathological evaluation and used as references for FTIR imaging (i.e. reading on wherein the one or more tissue sections comprises a first section, and further wherein the system further comprises an optical microscope structured and operable to acquire optical image data of the first section of the tissue sample, such that regions of cancerous or precancerous tissue in the first section can be identified) (pg. 3 para. 2).
Claim 15 recites:
wherein the shapes and compositions of the first section and the at least one section are substantially similar, such that the regions of cancerous or precancerous tissue in the first section correspond spatially to the regions of cancerous or precancerous tissue in the second at least one section
• Wang teaches methods and systems for oral cancer discrimination and oral epithelial dysplasia stratification using FTIR imaging and machine learning (pg. 1 Title) providing a classification strategy for risk stratification of oral epithelial dysplasia precancerous (pg. 3 para. 1); wherein the samples were cut into 5µm sections using a manual microtome (i.e. reading on wherein the shapes and compositions of the first section and the at least one section are substantially similar) (pg. 3 para. 2); wherein sections were imaged with a light microscope and the digital images were annotated areas of interest based on histopathological evaluation and used as references for FTIR imaging (i.e. reading on regions of cancerous or precancerous tissue in the first section correspond spatially to the regions of cancerous or precancerous tissue in the second at least one section) (pg. 3 para. 2).
Claim 16 recites:
wherein execution of the machine learning algorithm utilizes statistical methods including supervised discriminatory analyses to organize each of the one or more images of bodily tissues into one of a plurality of categories
• Wang teaches methods and systems for oral cancer discrimination and oral epithelial dysplasia stratification using FTIR imaging and machine learning (pg. 1 Title) providing a classification strategy for risk stratification of oral epithelial dysplasia precancerous (pg. 3 para. 1); wherein following exploratory analyses, discriminant machine learning models were built using three different supervised algorithms for discrimination between HK and OSCC samples: partial least squares discriminant analysis, support vector machines discriminant analysis and extreme gradient boosting discriminant analysis (i.e. reading on wherein execution of the machine learning algorithm utilizes statistical methods including supervised discriminatory analyses to organize each of the one or more images of bodily tissues into one of a plurality of categories) (pg. 6 para. 1).
Claim 17 recites:
wherein the plurality of categories comprises categories that correspond to benign, precancerous, and cancerous tissue categories
• Wang teaches methods and systems for oral cancer discrimination and oral epithelial dysplasia stratification using FTIR imaging and machine learning (pg. 1 Title) providing a classification strategy for risk stratification of oral epithelial dysplasia precancerous (pg. 3 para. 1); wherein unsupervised exploratory analyses comprise principal components analysis and hierarchical cluster analysis (pg. 5 para. 2 ) and following exploratory analyses, discriminant machine learning models were built using three different supervised algorithms for discrimination between HK and OSCC samples (pg. 6 para. 1); wherein a comparison of the three class average spectra for the hyperplasia/ hyperkeratosis (HK), oral epithelial dysplasia (OED), and oral squamous cell carcinoma (OSCC) samples (i.e. reading on benign, precancerous, and cancerous tissue categories as in claim 17) reveals an overall reduction in proteins and an increase in the nucleic acids as the oral tissues progress from HK to OED and further to OSCC (pg. 13 para. 1).
Claim 18 recites:
wherein the plurality of categories further comprises multiple distinct precancerous tissue categories.
• Wang teaches methods and systems for oral cancer discrimination and oral epithelial dysplasia stratification using FTIR imaging and machine learning (pg. 1 Title) providing a classification strategy for risk stratification of oral epithelial dysplasia precancerous (pg. 3 para. 1); wherein a comparison of the three class average spectra for the hyperplasia/ hyperkeratosis (HK), oral epithelial dysplasia (OED), and oral squamous cell carcinoma (OSCC) samples (i.e. reading on benign, precancerous, and cancerous tissue categories as in claim 17) reveals an overall reduction in proteins and an increase in the nucleic acids as the oral tissues progress from HK to OED and further to OSCC (pg. 13 para. 1); wherein 46 representative spectra from 12 HK and 11 OSCC tissue samples were used as training data to build the discriminant models (pg. 6 para. 2) and the best performing model was further used to classify the 22 representative spectra from the 11 OED samples (pg. 7 para. 1); wherein within the 11 OED samples, 6 OED samples were classified as “HK-grade” and 4 OED samples were classified as “OSCC-grade” (i.e. reading on multiple distinct precancerous tissue categories as in claim 18) (pg. 13 para. 6).
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 1-6, 8-11 and 13-18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ingham ("A novel FTIR analysis method for rapid high-confidence discrimination of esophageal cancer." Infrared Physics & Technology 102:103007 (2019)) in view of Critchley-Thorne ("A tissue systems pathology test detects abnormalities associated with prevalent high-grade dysplasia and esophageal cancer in Barrett's esophagus." Cancer Epidemiology, Biomarkers & Prevention 26.2:240-248 (2017)), as cited on the attached Form PTO-892.
Claim 1 recites:
acquiring one or more tissue samples, wherein each tissue sample comprises one or more regions of tissue, further wherein each region of tissue comprises one of a plurality of categories of tissue, wherein the plurality of categories of tissue comprise cancerous tissue, benign tissue, and precancerous tissue,
acquiring a plurality of hyperspectral images of the one or more regions of the one or more tissue samples, wherein the hyperspectral images comprise a plurality of infrared spectra;
performing one or more unsupervised exploratory analyses on the hyperspectral images to generate labeled hyperspectral images;
performing one or more supervised discriminatory analyses on the hyperspectral images of the regions comprising cancerous tissues and the hyperspectral images of the regions comprising benign tissues to generate a discriminatory model;
analyzing the hyperspectral images of the regions comprising precancerous tissues with the discriminatory model
• Ingham teaches a metrics-based analysis method for distinguishing between cancer stromal cells (pg. 1 Abstract) for identification of patients with risk of progressing to dysplasia and cancer (pg. 1 col. 2 para. 1); wherein a score is associated to each metric to quantify how well the metric discriminates between cell types (pg. 3 col. 2 para. 2) after analyzing FTIR images acquired with a spectral range from 990 cm−1 to 3800 cm−1 with a resolution of 2 cm−1 (i.e. reading on analyzing the hyperspectral images of the regions comprising precancerous tissues with the discriminatory model) (pg. 2 col. 2 para. 3); wherein the metrics-based analysis score comprised the rate at which the cell type is labeled correctly (i.e. reading on performing one or more unsupervised exploratory analyses on the hyperspectral images to generate labeled hyperspectral images) (pg. 3 col. 2 para. 3); wherein two esophageal cancer cell lines (OE19 and OE21) and two esophageal myofibroblast cells lines (denoted cancer associated (CAM) and adjacent tissue(ATM)) samples were obtained from the same patient via surgery (i.e. reading on acquiring one or more tissue samples, wherein each tissue sample comprises one or more regions of tissue, further wherein each region of tissue comprises one of a plurality of categories of tissue, wherein the plurality of categories of tissue comprise cancerous tissue, benign tissue, and precancerous tissue) (pg. 3 col. 2 para. 1); wherein FTIR imaging microscope hyperspectral images of obtained tissues were produced (i.e. reading on acquiring a plurality of hyperspectral images of the one or more regions of the one or more tissue samples, wherein the hyperspectral images comprise a plurality of infrared spectra) (pg. 3 col. 2 para. 3). Ingham also teaches a Random Forest classification algorithm used to construct a classifier to discriminate between the different samples (i.e. reading on performing one or more supervised discriminatory analyses on the hyperspectral images of the regions comprising cancerous tissues and the hyperspectral images of the regions comprising benign tissues to generate a discriminatory model) (pg. 5 col. 1 para. 1).
determine whether each of the hyperspectral images of the regions comprising precancerous tissues are most similar to the hyperspectral images of the cancerous tissues or to the hyperspectral images of the benign tissues; and,
assigning the precancerous tissues to a high-risk stratum when the hyperspectral images of the precancerous tissues are most similar to the hyperspectral images of the cancerous tissues, and assigning the precancerous tissues to a low-risk stratum when the hyperspectral images of the precancerous tissues are most similar to the hyperspectral images of the benign tissues
• Ingham does not teach the recitation above. However, Critchley-Thorne teaches a risk classifier based upon quantitative tissue image features to predict risk for high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) in Barrett's esophagus patients (pg. 240 Abstract); wherein classification was based on multivariate models for diagnosis of HGD/EAC evaluated first in relation to pathologic diagnosis alone (i.e. reading on determine whether each of the hyperspectral images of the regions comprising precancerous tissues are most similar to the hyperspectral images of the cancerous tissues) then in relation to risk classes and pathologic diagnosis in non-progressors (i.e. reading on to determine whether each of the hyperspectral images of the regions comprising precancerous tissues are most similar to the hyperspectral images of the benign tissues) and prevalent cases (pg. 250 Table S2); wherein said models were used to incorporated a 3-tier risk stratification to classify patients as low-, intermediate-, or high-risk for HGD/EAC (i.e. reading on assigning the precancerous tissues to a high-risk stratum when the hyperspectral images of the precancerous tissues are most similar to the hyperspectral images of the cancerous tissues, and assigning the precancerous tissues to a low-risk stratum when the hyperspectral images of the precancerous tissues are most similar to the hyperspectral images of the benign tissues) (pg. 246 col. 1para. 1).
Claim 2 recites:
wherein the plurality of categories of tissues further comprises one or more categories of intermediate dysplastic tissues, wherein each category of intermediate dysplastic tissues has a set of defining cytological criteria and an associated level of risk of the category of intermediate dysplastic tissue becoming cancerous
• Ingham does not teach the recitation above. However, Critchley-Thorne teaches a risk classifier based upon quantitative tissue image features to predict risk for high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) in Barrett's esophagus patients (pg. 240 Abstract) to incorporate a 3-tier risk stratification to classify patients as low-, intermediate-, or high-risk for HGD/EAC (i.e. reading on one or more categories of intermediate dysplastic) (pg. 246 col. 1para. 1); wherein classification comprised a 15-feature 3-tier risk classifier process derived from 9 biomarkers and morphology criteria (i.e. reading on a set of defining cytological criteria and an associated level of risk of the category of intermediate dysplastic tissue becoming cancerous) (pg. 241 Fig. 1).
Claim 3 recites:
further comprising assigning the precancerous tissues to one of a plurality of intermediate strata between the ‘low-risk’ stratum and the ‘high-risk’ stratum, wherein each stratum in the of intermediate strata corresponds to one of the categories of intermediate dysplastic tissue
• Ingham does not teach the recitation above. However, Critchley-Thorne teaches the risk classification model using risk cutoff values applied to the risk score to classify patients for risk of progression: risk class, low if scaled score from 0 to 5.5, intermediate if scaled score from 5.5 to 6.4, and high if scaled score from 6.4 to 10 (i.e. a sample can score any of the value in the range for each level of risk – reading on assigning the precancerous tissues to one of a plurality of intermediate strata between the ‘low-risk’ stratum and the ‘high-risk’ stratum, wherein each stratum in the of intermediate strata corresponds to one of the categories of intermediate dysplastic tissue) (pg. 241 Fig. 1).
Claim 4 recites:
further comprising applying one or more image processing steps to the hyperspectral images
Claim 5 recites:
wherein the one or more image processing steps comprise at least one of conversion between absorbance and transmission data, selection of relevant data regions, digital filtering, light-scattering correction, baseline correction, and normalization
• Ingham teaches analyzing FTIR images acquired with a spectral range from 990 cm−1 to 3800 cm−1 with a resolution of 2 cm−1 (pg. 2 col. 2 para. 3); wherein the quality check utilized a threshold based on the height of the amide I band with spectra having absorbance between 0.03 and 1.00 being retained and infrared spectra being corrected for resonant scattering (i.e. reading on more image processing steps to the hyperspectral images as in claim 4 and conversion between absorbance and transmission data as in claim 5) (pg. 2 col. 2 para. 3).
Claims 6 and 11 recite:
wherein the one or more unsupervised exploratory analyses comprise principal components analysis and hierarchical cluster analysis
• Ingham teaches the combination of confocal FTIR microscopy and a hierarchical cluster analysis of second derivative FTIR spectra to distinguish normal and Barrett’s esophageal tissue from adenocarcinoma (pg. 5 col. 2 para. 1) and the preprocessing of infrared spectra using a principal component analysis based noise reduction algorithm (i.e. reading on wherein the one or more unsupervised exploratory analyses comprise principal components analysis and hierarchical cluster analysis) (pg. 2 col. 2 para. 3).
Claim 8 recites:
acquiring a plurality of images of tissues of a tissue sample, each of which correspond to one of the plurality of categories of tissues;
performing one or more unsupervised exploratory analyses on the plurality of images of tissues to generate a plurality of labeled images; and
performing one or more supervised discriminatory analyses on the plurality of labeled images to generate a discriminatory model
• Ingham teaches a metrics-based analysis method for distinguishing between cancer stromal cells (pg. 1 Abstract) for identification of patients with risk of progressing to dysplasia and cancer (pg. 1 col. 2 para. 1); wherein a score is associated to each metric to quantify how well the metric discriminates between cell types (pg. 3 col. 2 para. 2); wherein the metrics-based analysis score comprised the rate at which the cell type is labeled correctly (i.e. reading on performing one or more unsupervised exploratory analyses on the hyperspectral images to generate labeled hyperspectral images) (pg. 3 col. 2 para. 3); wherein two esophageal cancer cell lines (OE19 and OE21) and two esophageal myofibroblast cells lines (denoted cancer associated (CAM) and adjacent tissue(ATM)) samples were obtained from the same patient via surgery (pg. 3 col. 2 para. 1); wherein FTIR imaging microscope hyperspectral images of obtained tissues were produced (i.e. reading on acquiring a plurality of images of tissues of a tissue sample, each of which correspond to one of the plurality of categories of tissues) (pg. 3 col. 2 para. 3). Ingham also teaches a Random Forest classification algorithm used to construct a classifier to discriminate between the different samples (i.e. reading on performing one or more supervised discriminatory analyses on the hyperspectral images of the regions comprising cancerous tissues and the hyperspectral images of the regions comprising benign tissues to generate a discriminatory model) (pg. 5 col. 1 para. 1).
Claim 9 recites:
further comprising applying one or more image processing steps to the plurality of images of tissues
Claim 10 recites:
wherein the image processing steps comprise at least one of conversion between absorbance and transmission data, selection of relevant data regions, digital filtering, light-scattering correction, baseline correction, and normalization
• Ingham teaches analyzing FTIR images acquired with a spectral range from 990 cm−1 to 3800 cm−1 with a resolution of 2 cm−1 (pg. 2 col. 2 para. 3); wherein the quality check utilized a threshold based on the height of the amide I band with spectra having absorbance between 0.03 and 1.00 being retained and infrared spectra being corrected for resonant scattering (i.e. reading on more image processing steps to images as in claim 9 and conversion between absorbance and transmission data as in claim 10) (pg. 2 col. 2 para. 3).
Claim 13 recites:
a Fourier transform infrared (FTIR) microscope structured and operable to acquire a plurality of hyperspectral images of the at least one section, such that each of the plurality of hyperspectral images is acquired from a region of cancerous tissue or a region of precancerous tissue in the at least one section; and
a computer-based system communicatively linked to the FTIR microscope, the computer-based system structured and operable to execute a machine learning algorithm to:
recognize a plurality of patterns of data in the plurality of hyperspectral images, where the plurality of patterns of data correspond to one or more chemical or biological features of the tissue sample; and,
organize the plurality of hyperspectral images into one of a plurality of categories, wherein each of the plurality of categories corresponds to one or more of the plurality of patterns of data
• Ingham teaches a analyzing FTIR images acquired with a spectral range from 990 cm−1 to 3800 cm−1 with a resolution of 2 cm−1 (pg. 2 col. 2 para. 3); wherein two esophageal cancer cell lines (OE19 and OE21) and two esophageal myofibroblast cells lines (denoted cancer associated (CAM) and adjacent tissue(ATM)) samples were obtained from the same patient via surgery (pg. 3 col. 2 para. 1); wherein FTIR imaging microscope hyperspectral images of obtained tissues were produced (i.e. reading on a Fourier transform infrared (FTIR) microscope structured and operable to acquire a plurality of hyperspectral images of the at least one section, such that each of the plurality of hyperspectral images is acquired from a region of cancerous tissue or a region of precancerous tissue in the at least one section) (pg. 3 col. 2 para. 3); wherein a Random Forest classification algorithm is used to construct a classifier to discriminate between the different samples (i.e. reading on machine learning algorithm to recognize a plurality of patterns of data in the plurality of hyperspectral images, where the plurality of patterns of data correspond to one or more chemical or biological features of the tissue sample; and, organize the plurality of hyperspectral images into one of a plurality of categories, wherein each of the plurality of categories corresponds to one or more of the plurality of patterns of data) (pg. 5 col. 1 para. 1); wherein FTIR studies were carried out by the Varian Cary 670-FTIR spectrometer in conjunction with a Varian Cary 620-FTIR imaging microscope produced by Varian (i.e. reading on a computer-based system communicatively linked to the FTIR microscope) (pg. 2 col. 2 para. 3)
one or more tissue sections of the bodily tissue sample comprising at least one section
• Ingham does not teach the recitation above. However, Critchley-Thorne teaches a risk classifier based upon quantitative tissue image features to predict risk for high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) in Barrett's esophagus patients (pg. 240 Abstract); wherein the assay employs multiplexed immunofluorescence labeling of 9 epithelial and stromal biomarkers in serial sections from biopsies (i.e. reading on one or more tissue sections of the bodily tissue sample comprising at least one section) (pg. 241 col. 1 para. 2).
Claim 14 recites:
wherein the one or more tissue sections comprises a first section, and further wherein the system further comprises an optical microscope structured and operable to acquire optical image data of the first section of the tissue sample, such that regions of cancerous or precancerous tissue in the first section can be identified
• Ingham does not teach the recitation above. However, Critchley-Thorne teaches a risk classifier based upon quantitative tissue image features to predict risk for high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) in Barrett's esophagus patients (pg. 240 Abstract); wherein the assay employs multiplexed immunofluorescence labeling of 9 epithelial and stromal biomarkers in serial sections from biopsies (i.e. reading on wherein the one or more tissue sections comprises a first section) (pg. 241 col. 1 para. 2) and whole slide fluorescence scanning—labeled slides are imaged by whole slide fluorescence scanning that generates image data on each biomarker and nuclei (i.e. reading on optical microscope structured and operable to acquire optical image data of the first section of the tissue sample, such that regions of cancerous or precancerous tissue in the first section can be identified) (pg. 241 Fig. 1).
Claim 15 recites:
wherein the shapes and compositions of the first section and the at least one section are substantially similar, such that the regions of cancerous or precancerous tissue in the first section correspond spatially to the regions of cancerous or precancerous tissue in the second at least one section
• Ingham does not teach the recitation above. However, Critchley-Thorne teaches a risk classifier based upon quantitative tissue image features to predict risk for high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) in Barrett's esophagus patients (pg. 240 Abstract); wherein the assay employs multiplexed immunofluorescence labeling of 9 epithelial and stromal biomarkers in serial sections from biopsies (i.e. reading on wherein the one or more tissue sections comprises a first section) (pg. 241 col. 1 para. 2); wherein Fig. 1 shows that said serial sections all present shapes and compositions are substantially similar (pg. 241 Fig. 1); wherein fluorescently labeled slides are imaged by whole-slide fluorescence scanning, and automated image analysis software extracts quantitative expression and localization data on the biomarkers and morphology imaging (i.e. reading on regions of cancerous or precancerous tissue in the first section correspond spatially to the regions of cancerous or precancerous tissue in the second at least one section) (pg. 241 col. 1 para. 2).
Claim 16 recites:
wherein execution of the machine learning algorithm utilizes statistical methods including supervised discriminatory analyses to organize each of the one or more images of bodily tissues into one of a plurality of categories
• Ingham teaches a Random Forest classification algorithm used to construct a classifier to discriminate between the different samples (i.e. reading on wherein execution of the machine learning algorithm utilizes statistical methods including supervised discriminatory analyses to organize each of the one or more images of bodily tissues into one of a plurality of categories) (pg. 5 col. 1 para. 1).
Claim 17 recites:
wherein the plurality of categories comprises categories that correspond to benign, precancerous, and cancerous tissue categories
• Ingham teaches a metrics-based analysis method for distinguishing between cancer stromal cells (pg. 1 Abstract) for identification of patients with risk of progressing to dysplasia (i.e. reading on precancerous state) and cancer (pg. 1 col. 2 para. 1); wherein two esophageal cancer cell lines (OE19 and OE21) (i.e. cancer tissues) and two esophageal myofibroblast cells lines - cancer associated (CAM) and adjacent tissue (ATM) (i.e. CAM reading on precancerous tissues) samples were obtained from the same patient via surgery (pg. 3 col. 2 para. 1); wherein ATMs have been obtained from normal tissue adjacent to the cancer (i.e. ATMs reading on benign tissues) (pg. 2 col. 1 para. 3).
Claim 18 recites:
wherein the plurality of categories further comprises multiple distinct precancerous tissue categories.
• Ingham does not teach the recitation above. However, Critchley-Thorne teaches a risk classifier based upon quantitative tissue image features to predict risk for high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) in Barrett's esophagus patients (pg. 240 Abstract); wherein biopsies with diagnoses of nondysplastic, indefinite for dysplasia or low grade dysplasia (i.e. reading on multiple distinct precancerous tissue categories) from 30 Barrett's esophagus patients with HGD or EAC (pg. 244 col. 1 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1-6, 8-11 and 13-18, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Ingham in view of Critchley-Thorne because all references disclose methods for classifying tissue samples. The motivation would have been to provide an objective method to facilitate earlier identification of esophageal dysplasia and adenocarcinoma patients requiring therapeutic intervention (g. 240 Abstract Critchley-Thorne).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for classifying tissue samples of Ingham to the methods by Critchley-Thorne because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for classifying tissue samples.
B. Claims 7 and 12 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ingham and Critchley-Thorne as applied to claims 1 and 8 above further in view of Baker ("Using Fourier transform IR spectroscopy to analyze biological materials." Nature protocols 9.8:1771-1791 (2014)) in view of Beuque ("Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging." Computers in Biology and Medicine 138:104918 (2021)), as cited on the attached Form PTO-892.
Claims 7 and 12 recite:
wherein the one or more supervised discriminatory analyses comprise partial least squares discriminant analysis, support vector machines discriminant analysis, and extreme gradient boosting discriminant analysis
• Ingham teaches the application of partial least-squares fitting procedure to determine the principal components of the FTIR spectra to analyze tissue data (pg. 2 col. 1 para. 1)
• Neither Ingham or Critchley-Thorne teach "support vector machines discriminant analysis, and extreme gradient boosting discriminant analysis." However, Baker teaches that classification of FTIR data uses popular supervised techniques such as artificial neural networks, support vector machines, linear discriminant classifier and Bayesian inference-base methods (pg. 1781 col. 2 para. 4). Furthermore, Beuque teaches the use of machine learning for tissue type classification regarding the grade of dysplasia using XGBoost (extreme gradient boosting) (pg. 5 col. 2 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claims 7 and 12, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Ingham and Critchley-Thorne in view of Baker and Beuque because all references disclose methods for classifying tissue samples. The motivation would have been to:
• incorporate the analysis of FTIR spectra combined with multivariate data processing (pg. 1771 Abstract Baker).
• incorporate a more accurate method for distinguishing dysplastic grades and patients at progression risk (pg. 1 Abstract Beuque).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for classifying tissue samples of Ingham and Critchley-Thorne to the methods by Baker and Beuque because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for classifying tissue samples.
Conclusion
No claims are allowed.
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/F.F.L./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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