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 .
Allowable Subject Matter
Claims 3-7, 13-15 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
With regards to claims 3 and 19, several of the features of these claims were known in the art as evidenced by Chukka et al (US PG Pub. No. 2019/0392578), which anticipates parent claims 1 and 18, respectively. In particular, Chukka discloses an individual region of the number of regions is analyzed using a segmentation model that is implemented with respect to each region of the number of regions at: ¶¶ [0077]-[0078]; ¶¶ [0082]-[0088]; ¶ [0127]. Chukka further discloses determining individual probabilities for individual pixels included in the number of regions indicating that the biological condition is present with respect to the subject at: ¶¶ [0137]-[0139](“At each pixel location in the probability map a vector of class probabilities (vector size is equal to the number of regions) is generated. The class probability for a class gives the probability of a pixel belonging to that particular class.”) However, Chukka does not disclose that the individual probabilities for individual pixels included in the number of regions indicating that the biological condition is present with respect to the subject were an output of the segmentation model.
With regards to claims 4-5 and 20, these claims depend from claims 3 and 19, respectively, therefore incorporate the features of that claim that were found allowable.
With regards to claim 6, several of the features of this claim were known in the art as evidenced by Chukka et al (US PG Pub. No. 2019/0392578), which anticipates parent claim 1. In particular, Chukka discloses analyzing, by the computing system, the feature map and the classification output using a support vector machine (SVM) or Adaboost model to generate a report that is displayed by one or more display devices and that includes information about the image and that includes the classification output for the sample at: ¶¶ [0144]-[0147]. However, Chukka does not disclose analyzing, by the computing system, the feature map and the classification output using a large-vision-language model to generate a report that is displayed by one or more display devices and that includes information about the image and that includes the classification output for the sample.
With regards to claim 7, this claim depends from claim 6 and therefore incorporates the features of that claim that were found allowable.
With regards to claim 13, several of the features of this claim were known in the art as evidenced by Chukka et al (US PG Pub. No. 2019/0392578), which anticipates parent claim 1. In particular, Chukka discloses a segmentation model at ¶¶ [0132]-[0133]. However, Chukka does not disclose performing a training process for a segmentation model using curated training data that includes a number of training images that have been annotated, wherein the training process is performed to achieve a threshold value for a combination of focal loss and dice loss for the segmentation model and the number of training images correspond a plurality of initial images of tissue of training subjects such that the number of training images have a greater level of magnification and a lower level of resolution than the plurality of initial images.
With regards to claim 14, which anticipates parent claim 11. In particular, Chukka discloses analyzing, by the computing system, the feature map and the classification output using a support vector machine (SVM) or Adaboost model to generate a report that is displayed by one or more display devices and that includes information about the image and that includes the classification output for the sample at: ¶¶ [0144]-[0147]. However, Chukka does not disclose performing an additional training process for a classification model that implements a transformer-based architecture, wherein the additional training process is performed with respect to threshold value of cross-entropy loss and with label smoothing.
With regards to claim 15, this claim depends from claim 14 and therefore incorporates the features of that claim that were found allowable.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 11, 16 and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chukka et al (US PG Pub. No. 2019/0392578).
With regards to claim 1, Chukka discloses obtaining, by a computing system including one or more computing devices having one or more processors and memory, image data that includes an image of a sample obtained from a subject at: ¶¶ [0050]-[0054]; see, also: ¶ [0003]; ¶¶ [0173]-[0175].
Chukka discloses determining, by the computing system, a plurality of areas (“tissue regions”) of the image that correspond to tissue of the subject at: ¶¶ [0055]-[0056](“[T]he input images are masked such that only tissue regions are present in the images… [A] segmentation technique is used to generate the tissue region masked images by masking tissue regions from non-tissue regions in the input images.”)
Chukka discloses determining, by the computing system, a number of regions (e.g., “H&E image segmentation”) of the image that each include a group of individual areas of the plurality of areas at: ¶¶ [0077]-[0078]; ¶¶ [0082]-[0088]; ¶ [0127].
Chukka discloses analyzing, by the computing system, an individual region (e.g., “identified tissue type”) of the number of regions to determine a probability that one or more features of the individual region correspond to a biological condition being present with respect to the subject at: ¶¶ [0137]-[0139](“At each pixel location in the probability map a vector of class probabilities (vector size is equal to the number of regions) is generated. The class probability for a class gives the probability of a pixel belonging to that particular class.”)
Chukka discloses generating, by the computing system, a feature map that includes probabilities for the individual regions of the number of regions that the biological condition is present with respect to the subject at: ¶¶ [0137]-[0139](“ Following H&E image segmentation, a probability map is generated and may be based only on the identified tissue types…”)
Chukka discloses analyzing, by the computing system, the feature map to determine a classification output related to the biological condition being present with respect to the subject at: ¶¶ [0144]-[0147](“After H&E image features, biomarker image features, and probability image features are derived, they are merged together and used to classifying nuclei within at least one of the input images.”)
With regards to claim 2, Chukka discloses the image corresponds to at least one histology slide (“Immunohistochemical (IHC) slide”) generated from the sample obtained from the subject at: ¶¶ [0050]-[0054]; see, also: ¶ [0003]
With regards to claim 11, the steps performed by the apparatus of this claim are anticipated by Chukka for the same reasons as were provided in the discussion of claim 1, which recites a method performing these same steps.
With regards to claim 16, Chukka discloses generating one or more user interfaces that include the classification output at: ¶ [0043]; ¶ [0069].
With regards to claim 18, the steps stored in the computer readable medium of this claim are anticipated by Chukka for the same reasons as were provided in the discussion of claim 1, which recites a method performing these same steps.
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 8-10, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chukka et al (US PG Pub. No. 2019/0392578) in view of Setlur et al (US Patent No. 10,817,527).
With regards to claim 8, Chukka inherently discloses receiving, by the computing system, a request to launch an application when it discloses performing its methods using a computer program at ¶¶ [0173]-[0175]. A computer program must necessarily be launched before it may perform functions. Regarding the text and speech inputs, and as was discussed above with respect to claim 1, although Chukka discloses an application that analyzes images of tissue of subjects, it does not specify performing operations based upon text or voice inputs. However, these limitations were known in the art:
Setlur discloses obtaining, by the computing system and within the application, at least one of text input or audio input via one or more input devices of a computing device at 5:5-31. Setlur further disclose analyzing, by the computing system, the at least one of the text input or the audio input to determine one or more operations to perform with respect to the image at 5:5-31 and 8:13-9:62. At the time of the filing of the present application, it would have been obvious to a person of ordinary skill in the art to use natural language processing comprising speech or text, as taught by Setlur, when running the application taught by Chukka on a computing device. The motivation for doing so comes from Setlur, which discloses, “Natural language processing allows users to communicate naturally with a computing device (e.g., a computing device 200) in order to explore the data ( e.g., communicate via a microphone of the computing device and/or via text).” (11:15-37). Therefore, it would have been obvious to combine Setlur with Chukka to obtain the invention specified in this claim.
With regards to claim 9, Chukka discloses obtaining additional input that includes at least one of text annotations or video annotations at ¶¶ [0059]-[0060]. And, Chukka discloses measuring distances between a number of attributes of the image at: ¶ [0092]; ¶ [0104]; ¶ [0106].
With regards to claim 10 Chukka discloses using a computer program to determine the classification output related to the biological condition being present with respect to the subject at ¶¶ [0144]-[0147](“After H&E image features, biomarker image features, and probability image features are derived, they are merged together and used to classifying nuclei within at least one of the input images.”) Setlur discloses the at least one of the text input or the audio input includes program commands. The motivation for the combination is the same as previously presented.
With regards to claim 17, the steps performed by the apparatus of this claim are obvious over the combination of Chukka and Setlur for the same reasons as were provided in the discussion of claim 9, which recites a method performing these same steps.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Chukka et al (US PG Pub. No. 2019/0392578) in view of Muhammad et al (US PG Pub. No. 2025/0125054).
With regards to claim 12, Chukka discloses the image corresponds to at least one histology slide (“Immunohistochemical (IHC) slide”) generated from the sample obtained from the subject at: ¶¶ [0050]-[0054]; see, also: ¶ [0003]. Chukka discloses the biological condition classified within the histology slide (“Immunohistochemical (IHC) slide”) corresponds to a cancer at ¶ [0003], ¶ [0016] and ¶ [0160]. Chukka does not specify the cancer classified within the histology slide (“Immunohistochemical (IHC) slide”) corresponds to a skin cancer or a colorectal cancer. However, histology slides (“Immunohistochemical (IHC) slide”) corresponding to a skin cancer or a colorectal cancer were known in the art as evidenced by Muhammad et al (US PG Pub. No. 2025/0125054) at ¶ [0007]. At the time of the filing of the present application, it would have been obvious to a person of ordinary skill in the art to use the system for computer scoring immunohistochemistry slides taught by Chukka with histology slides comprising colorectal or skin cancer. The motivation for doing so comes from , which discloses, “Manual selection of the FOVs or ROIs and counting is highly subjective and biased to the readers, as different readers may select different FOVs or ROIs to count. Hence, an immunoscore study is not necessarily reproducible in a manual process.” (¶ [0004]). Therefore, it would have been obvious to combine Muhammad with Chukka to obtain the invention specified in this claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID F DUNPHY whose telephone number is (571)270-1230. The examiner can normally be reached 9 am - 5 pm.
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/DAVID F DUNPHY/Primary Examiner, Art Unit 2673
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