DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) filed on 5/23/2024 and 10/8/2024 were considered and placed on the file of record by the examiner.
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)(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 1-4, 6-13, 17, 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Saltz et al. (US 2020/0388029).
Regarding claim 1, Saltz teaches a method, comprising: receiving a digital pathology image of a lung adenocarcinoma (LUAD) tissue sample (see figure 1B, para. 0257, 0298, Saltz discusses classifying cells in a dataset of images of lung adenocarcinoma tissue);
inputting the digital pathology image into an artificial intelligence model (see figure 1B, figure 1C, figure 5B, para. 0033, 0088, 0178, Saltz discusses convolutional neural network (CNN) used to classify tissue images; see figure 3B, claim 14, para. 0024, 0166, 0180, 0210, Saltz discusses receiving tissue image data from patients to train a model and then implementing the model on newly scanned patients); and
grading, using the artificial intelligence model, one or more tumors within the LUAD tissue sample (see figure 8B, para. 0056, Saltz discusses identification, quantification, and refined characterization of tumors, see para. 0169, Saltz discusses analyzing LUAD images; see para. 0166, Saltz discusses a trained model is used to classify new data; see para. 0284-0287, 0291, 0306, Saltz discusses LUAD tumor grades and CNN tumor recognition).
Regarding claim 2, Saltz teaches further comprising segmenting, using the artificial intelligence model, the one or more tumors in the digital pathology image (see para. 0025-0026, Saltz discusses classifying segmented data using CNNs).
Regarding claim 3, Saltz teaches wherein the step of grading, using the artificial intelligence model, the one or more tumors comprises assigning each of the one or more tumors to one of a plurality of classes (see para. 0025-0026, Saltz discusses classifying segmented data using CNNs).
Regarding claim 4, Saltz teaches wherein the step of grading, using the artificial intelligence model, the one or more tumors comprises assigning one or more areas within each of the one or more tumors to one of a plurality of classes on a pixel-by-pixel basis or a cell-by-cell basis (see para. 0025-0026, 0117, 0332, Saltz discusses CNN outputs pixel-wise segmentation results).
Regarding claim 6, Saltz teaches wherein the classes comprises one or more of normal alveolar, normal bronchiolar, Grade 1 LUAD, Grade 2 LUAD, Grade 3 LUAD, Grade 4 LUAD, and Grade 5 LUAD (see para. 0286, Saltz discusses tumor grade classification).
Regarding claim 7, Saltz teaches wherein the step of grading, using the artificial intelligence model, the one or more tumors comprises generating graphical display data for a pseudo color map of the one or more tumors (see para. 0223-0224, Saltz discusses displaying a two-color heatmap of tumor classified regions with Tumor-infiltrating lymphocytes (TILs). TILs are immune cells (primarily T cells) that migrate from the blood into a tumor, indicating a direct, active immune response against cancer. High densities of TILs suggest a "hot" or inflamed tumor).
Regarding claim 8, Saltz teaches further comprising analyzing the one or more tumors (see para. 0023, Saltz discusses analyzing tumor tissue image data).
Regarding claim 9, Saltz teaches wherein the step of analyzing the one or more tumors comprises counting the one or more tumors or characterizing an intratumor heterogeneity of the one or more tumors (see para. 0332, Saltz discusses counting each tumor type and spatial structure pattern).
Regarding claim 10, Saltz teaches further comprising performing an immuno-histochemistry (IHC) analysis of the one or more tumors (see figure 8C, para. 0292, Saltz discusses performing an immuno-histochemistry (IHC) analysis).
Regarding claim 11, Saltz teaches wherein the artificial intelligence model is a machine learning model (see para. 0097, Saltz discusses fine-tuned constructed CNN for supervised classification).
Regarding claim 12, Saltz teaches wherein the machine learning model is a supervised machine learning model (see para. 0097, Saltz discusses fine-tuned constructed CNN for supervised classification).
Regarding claim 13, Saltz teaches wherein the supervised machine learning model is a convolutional neural network (CNN) (see para. 0097, Saltz discusses fine-tuned constructed CNN for supervised classification).
Regarding claim 17, Saltz teaches wherein the digital pathology image is a hematoxylin & eosin (H&E) stained slide image (see para. 0103, Saltz discusses Convolutional Neural Network (CNN) model identifying lymphocyte-infiltrated regions in whole slide tissue images (WSis) of Hematoxylin and Eosin (H&E) stained tissue specimens. Tumor-infiltrating lymphocytes (TILs) are immune cells (primarily T cells) that migrate from the blood into a tumor, indicating a direct, active immune response against cancer. High densities of TILs suggest a "hot" or inflamed tumor).
Regarding claim 19, Saltz teaches wherein the LUAD tissue sample is from a human (see figure 8D, para. 0180, Saltz discusses tissue sample image slides from patients in hospitals).
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.
Claims 5, 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Saltz et al. (US 2020/0388029) in view of Raharja et al. (US 2023/0377155).
Regarding claim 5, Saltz does not expressly disclose further comprising, identifying, based at least on the pixel-by-pixel or cell-by-cell assignments, one or more genes of interest or one or more drivers of tumor progression. However, Raharja teaches further comprising, identifying, based at least on the pixel-by-pixel or cell-by-cell assignments, one or more genes of interest or one or more drivers of tumor progression (see para. 0085, Raharja discusses identifying a cancer biomarker gene that indicates a pathological or physiological progression or disease).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Saltz with Raharja to derive at the invention of claim 5. The result would have been expected, routine, and predictable in order to perform tumor status classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Saltz in this manner in order to improve tumor status classification by detecting genes associated with tumors because it enables precise diagnosis and provides information on how aggressively a tumor might grow or spread. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Saltz, while the teaching of Raharja continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of classifying grades and progression of tumors. The Saltz and Raharja systems perform tumor classification, therefore one of ordinary skill in the art would have reasonable expectation of success in the combination. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 14, Saltz does not expressly disclose wherein the supervised machine learning model comprises one or more Residual Neural Network (ResNet) layers or components. However, Raharja teaches wherein the supervised machine learning model comprises one or more Residual Neural Network (ResNet) layers or components (see para. 0158, Raharja discusses a CNN with a residual network architecture).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Saltz with Raharja to derive at the invention of claim 14. The result would have been expected, routine, and predictable in order to perform tumor status classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Saltz in this manner in order to improve tumor status classification by implementing Residual Neural Networks ResNet to identify tumors in medical imaging because ResNets effectively capture complex, intricate tumor features allowing high-accuracy classification. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Saltz, while the teaching of Raharja continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of classifying grades and progression of tumors implementing a ResNet. The Saltz and Raharja systems perform tumor classification, therefore one of ordinary skill in the art would have reasonable expectation of success in the combination. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 15, Raharja teaches wherein the supervised machine learning model further comprises one or more atrous convolutional layers (see para. 0132, Raharja discusses a CNN with atrous convolution layer).
The same motivation of claim 14 is applied to claim 15. Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Saltz with Raharja to derive at the invention of claim 15. The result would have been expected, routine, and predictable in order to perform tumor status classification.
Regarding claim 16, Raharja teaches wherein the supervised machine learning model further comprises one or more transposed convolutional layers (see para. 0134, Raharja discusses a CNN with layer comprising the dilated convolutions is located prior to the pooling and upsampling layers).
The same motivation of claim 14 is applied to claim 16. Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Saltz with Raharja to derive at the invention of claim 16. The result would have been expected, routine, and predictable in order to perform tumor status classification.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Saltz et al. (US 2020/0388029) in view of Kennedy et al. (US 2021/0262040).
Regarding claim 18, Saltz does not expressly disclose wherein the LUAD tissue sample is from a mouse. However, Kennedy teaches wherein the LUAD tissue sample is from a mouse (see para. 0065, 0122, 0127, Kennedy discusses analyzing mammal tissue samples from subjects, such as humans and mice for lung cancer).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Saltz with Kennedy to derive at the invention of claim 18. The result would have been expected, routine, and predictable in order to perform tumor status classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Saltz in this manner in order to improve tumor status classification by extracting data from mammal tissue samples from subjects, such as humans and mice. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Saltz, while the teaching of Kennedy continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of classifying grades and progression of tumors in mammals. The Saltz and Kennedy systems perform tumor classification, therefore one of ordinary skill in the art would have reasonable expectation of success in the combination. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Kennedy et al. (US 2021/0262040) in view of Saltz et al. (US 2020/0388029).
Regarding claim 21, Kennedy teaches a method, comprising: training a machine learning model with a dataset, the dataset comprising a plurality of mouse model digital pathology images, each of the mouse model digital pathology images being of a respective lung adenocarcinoma (LUAD) tissue sample from a mouse (see para. 0065, 0122, 0127, Kennedy discusses analyzing mammal tissue samples from subjects, such as humans and mice for lung cancer; see para. 0128, lung cancer-classifier may be trained on a data set comprising expression levels of the plurality of informative-genes in biological samples obtained from a plurality of subjects identified as having lung cancer);
inputting the digital pathology image into the trained machine learning model (see para. 0124-0127, Kennedy discusses implementing a trained neural network to identify lung cancer in new subjects).
Kennedy does not expressly disclose grading, using the trained machine learning model, one or more tumors within the LUAD tissue sample from the human.
However, Saltz teaches receiving a digital pathology image of a LUAD tissue sample from a human (see figure 3B, claim 14, para. 0024, 0166, 0180, 0210, Saltz discusses receiving tissue image data from patients to train a model and then implementing the model on newly scanned patients);
inputting the digital pathology image into the trained machine learning model (see figure 3B, para. 0026, 0205, 0306, Saltz discusses receiving human tissue image data and inputting the data into a trained CNN to segment and classify tumors);
grading, using the trained machine learning model, one or more tumors within the LUAD tissue sample from the human (see para. 0276-0277, Saltz discusses color scale indicating representative regions of lymphocyte density associated with a tissue sample. Tumor-infiltrating lymphocytes (TILs). TILs are immune cells (primarily T cells) that migrate from the blood into a tumor, indicating a direct, active immune response against cancer. High densities of TILs suggest a "hot" or inflamed tumor; See para. 0166, Saltz discusses a trained model is used to classify new data; see para. 0286-0287, 0306, Saltz discusses LUAD tumor grades and CNN tumor recognition).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Kennedy with Saltz to derive at the invention of claim 21. The result would have been expected, routine, and predictable in order to perform tumor status classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Kennedy in this manner in order to improve tumor status classification by detecting genes associated with tumors because it enables precise diagnosis and provides information on how aggressively a tumor might grow or spread. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Kennedy, while the teaching of Saltz continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of classifying grades and progression of tumors. The Kennedy and Saltz systems perform tumor classification, therefore one of ordinary skill in the art would have reasonable expectation of success in the combination. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Allowable Subject Matter
Claim 20 is allowed.
The following is an examiner’s statement of reasons for allowance:
The present invention pertains to grading, using the artificial intelligence model, one or more tumors. The following is an examiner's statement of reasons for allowance: The present invention is directed towards tumor classification by projecting a plurality of respective coordinates of the positively and negatively stained cells within the second LUAD tissue sample onto the one or more tumors within the first LUAD tissue sample.
Orrock (US 2012/0092247) teaches displays a plurality of immuno stains (see para. 0078).
Yip (US 11,348,239) teaches displaying a probability map overlaid on the H&E image (see col. 8 lines 47-59).
However, Orrock and Yip fail to address:
“receiving a first digital pathology image of a first lung adenocarcinoma (LUAD) tissue sample, the first digital pathology image being a hematoxylin & eosin (H&E) stained slide image;
inputting the first digital pathology image into an artificial intelligence model;
grading, using the artificial intelligence model, one or more tumors within the first LUAD tissue sample;
segmenting, using the artificial intelligence model, the one or more tumors in the digital pathology image;
receiving a second digital pathology image comprising a second lung adenocarcinoma (LUAD) tissue sample, the second digital pathology image being an immuno-stained slide image;
identifying and classifying a plurality of positively and negatively stained cells within the second LUAD tissue sample;
co-registering the first and second digital pathology images; and
projecting a plurality of respective coordinates of the positively and negatively stained cells within the second LUAD tissue sample onto the one or more tumors within the first LUAD tissue sample.”
These distinct features are in each independent claim and renders them allowable.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Grove (US 20150356730) discusses generating a quantitative score for the one or more image features, wherein the quantitative score for the one or more image features are associated with tumor severity.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNY A CESE whose telephone number is (571). The examiner can normally be reached on Monday – Friday, 9am – 4pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached on (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Kenny A Cese/
Primary Examiner, Art Unit 2663