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 .
This Office Action is responsive to communications filed on 12/15/2023. Claims 1-20 are pending in the instant application. Claims 1, 10 and 18 are independent. An Office Action on the merits follows here below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/13/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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 1, 2, 10, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Mahoor (US 20170053398 A1) in combination with Jansen (US 20200372295 A1).
Regarding Claim 1: Mahoor discloses a method for feature extraction from an image (Refer to para [005, 007 and 008]; “Embodiments of the methods and systems may include using at least one additional feature extraction operation.”) comprising: calculating entropy information for a plurality of regions of the image (Refer to para [086]; “Then various embodiments statistics are calculated from the co-occurrence matrix of the Shearlet coefficients. These statistics may include entropy, autocorrelation, contrast, correlation, cluster prominence, cluster shade, dissimilarity, energy, homogeneity, maximum probability, sum of squared variance, sum average, sum variance, sum entropy, difference variance, difference entropy, information measure of correlation, inverse difference, and inverse difference momentum. Other statistics may also be included. In some embodiments, a principle component analysis (PCA) may be performed on the statistics, to keep only the most significant factors within the set of statistics.”) computing a hyperparameter for a clustering algorithm based on the entropy information (Refer to para [089]; “This motivates basing algorithms for morphological feature extraction processes on cell nuclei detection. To achieve this, various embodiments use a Mean Shift clustering algorithm for the task of color approximation and then apply thresholding in the Hue/Saturation/Value (HSV) color space to distinguish cell nuclei from other parts of the tissue.”); segmenting the image into a plurality of clusters using the clustering algorithm and the hyperparameter (Refer to para [089 and 116]; “For sampling images for training and testing of our classification algorithms throughout all experiments we use the leave one out (LOO) technique. A total of 40 images were used as an evaluation set for tuning SVM hyperparameter C. After the best C was found, the remaining 60 images were used for training and test using LOO. For training 59 images are used, and one image is used for test. This is run 60 times so that every image has been tested. For each image, after extracting features the process chooses the first few eigenvectors that capture at least 90% of the total variance using Principle Component Analysis (PCA) method. The process uses a one-against-all multiclass classification method.”) and extracting a feature set of the image based on the plurality of clusters (Refer to para [074]; “Optionally, other embodiments may use one or more other additional feature extraction operations. One such feature extraction operation 824 comprises extracting morphological features, such as nuclei of malignant cells, by performing image segmentation on the digital image. Embodiments may use a preliminary segmentation together with a mean shift algorithm in image segmentation to obtain values corresponding to the number of nuclei in malignant cells.”).
Mahoor more than fairly discloses cluster calculations as detailed at para [086]. “Then various embodiments statistics are calculated from the co-occurrence matrix of the Shearlet coefficients. These statistics may include entropy, autocorrelation, contrast, correlation, cluster prominence, cluster shade, dissimilarity, energy, homogeneity, maximum probability, sum of squared variance, sum average, sum variance, sum entropy, difference variance, difference entropy, information measure of correlation, inverse difference, and inverse difference momentum. Other statistics may also be included.”
Mahoor does not expressly recite “a plurality of clusters.”
Jansen teaches “systems and methods for training a clustering and/or embedding model.”
Jansen teaches (Refer to para [025 and 029]; “In other words, the respective cluster assignment can probabilistically map (e.g., soft-encode) each input to the plurality of clusters. As such, the cluster assignments can identify-similarities between various inputs or input elements, such as similar objects or features within images, similar sounds within audio, and/or correlations between statistical data points…one or more components of the clustering loss function can be scaled by respective hyperparameters. For example, the second entropy can be scaled by a diversity hyperparameter. The diversity hyperparameter can be used to adjust the relative effects of the clustering loss function terms that respectively promote the two objectives. As such, the diversity hyperparameter can be used to adjust or tune the loss provided by the clustering loss function and the resulting behavior of the models trained based on the clustering loss function (e.g., the machine-learned embedding model and/or the clustering model). The diversity hyperparameter can be selected to produce the desired balance between the first objective of minimizing the average entropy of the input data points and the second objective of preventing collapse of the mapping produced by the clustering model into the trivial solution in which all inputs are mapped to a single cluster.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Mahoor by substituting clustering processing with the expressly plurality of clusters taught by Jansen as detailed above.
The suggestion/motivation for combining the teachings of Mahoor and Jansen would have been in order to “facilitate the discovery of natural partitions or clusters in the data without requiring a pre-existing embedding to seed the clustering objective. ” (at para [077], Jansen).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mahoor and Jansen in order to obtain the specified claimed elements of Claim 1. 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 2: Jansen teaches each of the plurality of clusters represents a feature in the image (Refer to para [068]; “In some implementations, the machine-learned embedding model 202 can be configured to receive a plurality of inputs 204 and to respectively process each input 204 to produce respective embeddings 206. An embedding 206 is a mapping of the (discrete) inputs 204 to continuous vectors or tensors of real numbers (e.g., weights) in a representational space. Embeddings can describe or represent features or patterns within the inputs 204.”).
Regarding Claim 10: Mahoor discloses a system for feature extraction (Refer to para [005 and 007]; “The computer system performs a feature extraction operation using the digital image of the tissue sample.”) comprising: a memory storing instructions (Refer to para [147]; “examples of the methods may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary tasks may be stored in a non-transitory computer-readable medium such as a storage medium.”); and at least one processor coupled to the memory and configure to execute the instructions to (Refer to para [132]; “The components shown may be implemented in hardware, such as by one or more processors, application specific integrated circuits (ASICs), or on reconfigurable circuits such as field programmable gate arrays (FPGAs). The system modules may also be implemented as program modules on a dedicated computing system, or as software program modules on a computer system.”): calculate entropy information by determining an entropy value for each of a plurality of regions of an image (Refer to para [086]; “Then various embodiments statistics are calculated from the co-occurrence matrix of the Shearlet coefficients. These statistics may include entropy, autocorrelation, contrast, correlation, cluster prominence, cluster shade, dissimilarity, energy, homogeneity, maximum probability, sum of squared variance, sum average, sum variance, sum entropy, difference variance, difference entropy, information measure of correlation, inverse difference, and inverse difference momentum. Other statistics may also be included. In some embodiments, a principle component analysis (PCA) may be performed on the statistics, to keep only the most significant factors within the set of statistics.”) determine a hyperparameter for a clustering algorithm based on the entropy information (Refer to para [089]; “This motivates basing algorithms for morphological feature extraction processes on cell nuclei detection. To achieve this, various embodiments use a Mean Shift clustering algorithm for the task of color approximation and then apply thresholding in the Hue/Saturation/Value (HSV) color space to distinguish cell nuclei from other parts of the tissue.”) segmenting the image into a plurality of clusters using the clustering algorithm and the hyperparameter (Refer to para [089 and 116]; “For sampling images for training and testing of our classification algorithms throughout all experiments we use the leave one out (LOO) technique. A total of 40 images were used as an evaluation set for tuning SVM hyperparameter C. After the best C was found, the remaining 60 images were used for training and test using LOO. For training 59 images are used, and one image is used for test. This is run 60 times so that every image has been tested. For each image, after extracting features the process chooses the first few eigenvectors that capture at least 90% of the total variance using Principle Component Analysis (PCA) method. The process uses a one-against-all multiclass classification method.”) and extracting a feature set of the image based on the plurality of clusters (Refer to para [074]; “Optionally, other embodiments may use one or more other additional feature extraction operations. One such feature extraction operation 824 comprises extracting morphological features, such as nuclei of malignant cells, by performing image segmentation on the digital image. Embodiments may use a preliminary segmentation together with a mean shift algorithm in image segmentation to obtain values corresponding to the number of nuclei in malignant cells.”).
Mahoor more than fairly discloses cluster calculations as detailed at para [086]. “Then various embodiments statistics are calculated from the co-occurrence matrix of the Shearlet coefficients. These statistics may include entropy, autocorrelation, contrast, correlation, cluster prominence, cluster shade, dissimilarity, energy, homogeneity, maximum probability, sum of squared variance, sum average, sum variance, sum entropy, difference variance, difference entropy, information measure of correlation, inverse difference, and inverse difference momentum. Other statistics may also be included.”
Mahoor does not expressly recite “a plurality of clusters.”
Jansen teaches “systems and methods for training a clustering and/or embedding model.”
Jansen teaches (Refer to para [025 and 029]; “In other words, the respective cluster assignment can probabilistically map (e.g., soft-encode) each input to the plurality of clusters. As such, the cluster assignments can identify-similarities between various inputs or input elements, such as similar objects or features within images, similar sounds within audio, and/or correlations between statistical data points…one or more components of the clustering loss function can be scaled by respective hyperparameters. For example, the second entropy can be scaled by a diversity hyperparameter. The diversity hyperparameter can be used to adjust the relative effects of the clustering loss function terms that respectively promote the two objectives. As such, the diversity hyperparameter can be used to adjust or tune the loss provided by the clustering loss function and the resulting behavior of the models trained based on the clustering loss function (e.g., the machine-learned embedding model and/or the clustering model). The diversity hyperparameter can be selected to produce the desired balance between the first objective of minimizing the average entropy of the input data points and the second objective of preventing collapse of the mapping produced by the clustering model into the trivial solution in which all inputs are mapped to a single cluster.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Mahoor by substituting clustering processing with the expressly plurality of clusters taught by Jansen as detailed above.
The suggestion/motivation for combining the teachings of Mahoor and Jansen would have been in order to “facilitate the discovery of natural partitions or clusters in the data without requiring a pre-existing embedding to seed the clustering objective. ” (at para [077], Jansen).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mahoor and Jansen in order to obtain the specified claimed elements of Claim 10. 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 11: Jansen teaches each of the plurality of clusters represents a feature in the image (Refer to para [068]; “In some implementations, the machine-learned embedding model 202 can be configured to receive a plurality of inputs 204 and to respectively process each input 204 to produce respective embeddings 206. An embedding 206 is a mapping of the (discrete) inputs 204 to continuous vectors or tensors of real numbers (e.g., weights) in a representational space. Embeddings can describe or represent features or patterns within the inputs 204.”).
Regarding Claim 18: Mahoor discloses a non-transitory machine readable medium that (Refer to para [147]; “When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary tasks may be stored in a non-transitory computer-readable medium such as a storage medium. Processors may perform the described tasks.”) when executed by at least one processor of a computing system (Refer to para [132]; “The components shown may be implemented in hardware, such as by one or more processors, application specific integrated circuits (ASICs), or on reconfigurable circuits such as field programmable gate arrays (FPGAs). The system modules may also be implemented as program modules on a dedicated computing system, or as software program modules on a computer system.”) causes the computing system to perform a method comprising:
performing a plurality of entropy scans of an image (Refer to para [055]; “FIG. 5 illustrates an embodiment of a method 500 for using Shearlet transforms and coefficients to diagnose and classify images of stained tissue samples. Method 500 may be performed using a system that can be used to obtain, stain, scan, and analyze tissue samples, such as system 100 of FIG. 1.”) the plurality of entropy scans providing entropy information by determining an entropy value for each of a plurality of regions of the image (Refer to para [086]; “Then various embodiments statistics are calculated from the co-occurrence matrix of the Shearlet coefficients. These statistics may include entropy, autocorrelation, contrast, correlation, cluster prominence, cluster shade, dissimilarity, energy, homogeneity, maximum probability, sum of squared variance, sum average, sum variance, sum entropy, difference variance, difference entropy, information measure of correlation, inverse difference, and inverse difference momentum. Other statistics may also be included. In some embodiments, a principle component analysis (PCA) may be performed on the statistics, to keep only the most significant factors within the set of statistics.”) and deriving a hyperparameter for a clustering algorithm based on the entropy information and extract a feature set of the image (Refer to para [074]; “Optionally, other embodiments may use one or more other additional feature extraction operations. One such feature extraction operation 824 comprises extracting morphological features, such as nuclei of malignant cells, by performing image segmentation on the digital image. Embodiments may use a preliminary segmentation together with a mean shift algorithm in image segmentation to obtain values corresponding to the number of nuclei in malignant cells.”) by applying the clustering algorithm to the image using the derived hyperparameter (Refer to para [089]; “This motivates basing algorithms for morphological feature extraction processes on cell nuclei detection. To achieve this, various embodiments use a Mean Shift clustering algorithm for the task of color approximation and then apply thresholding in the Hue/Saturation/Value (HSV) color space to distinguish cell nuclei from other parts of the tissue.”).
Mahoor more than fairly discloses cluster calculations as detailed at para [086]. “Then various embodiments statistics are calculated from the co-occurrence matrix of the Shearlet coefficients. These statistics may include entropy, autocorrelation, contrast, correlation, cluster prominence, cluster shade, dissimilarity, energy, homogeneity, maximum probability, sum of squared variance, sum average, sum variance, sum entropy, difference variance, difference entropy, information measure of correlation, inverse difference, and inverse difference momentum. Other statistics may also be included.”
Mahoor does not expressly recite “a plurality of clusters.”
Jansen teaches “systems and methods for training a clustering and/or embedding model.”
Jansen teaches (Refer to para [025 and 029]; “In other words, the respective cluster assignment can probabilistically map (e.g., soft-encode) each input to the plurality of clusters. As such, the cluster assignments can identify-similarities between various inputs or input elements, such as similar objects or features within images, similar sounds within audio, and/or correlations between statistical data points…one or more components of the clustering loss function can be scaled by respective hyperparameters. For example, the second entropy can be scaled by a diversity hyperparameter. The diversity hyperparameter can be used to adjust the relative effects of the clustering loss function terms that respectively promote the two objectives. As such, the diversity hyperparameter can be used to adjust or tune the loss provided by the clustering loss function and the resulting behavior of the models trained based on the clustering loss function (e.g., the machine-learned embedding model and/or the clustering model). The diversity hyperparameter can be selected to produce the desired balance between the first objective of minimizing the average entropy of the input data points and the second objective of preventing collapse of the mapping produced by the clustering model into the trivial solution in which all inputs are mapped to a single cluster.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Mahoor by substituting clustering processing with the expressly plurality of clusters taught by Jansen as detailed above.
The suggestion/motivation for combining the teachings of Mahoor and Jansen would have been in order to “facilitate the discovery of natural partitions or clusters in the data without requiring a pre-existing embedding to seed the clustering objective. ” (at para [077], Jansen).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mahoor and Jansen in order to obtain the specified claimed elements of Claim 18. It is for at least the aforementioned reasons that the Examiner has reached a conclusion of obviousness with respect to the claim in question.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mahoor (US 20170053398 A1) in combination with Jansen (US 20200372295 A1) and further in view of Doumbouya et al. (US 20210117778 A1).
Regarding Claim 7: Mahoor and Jansen in combination disclose all the claimed elements as rejected above. Mahoor and Jansen in combination does not expressly teach a “Mean Shift clustering algorithm.”
Doumbouya teaches “performing semantic coherence analysis on a deep neural network (DNN).”
As is already well known in the art, Doumbouya more specifically teaches “… a coherence score will indicate a semantic of features produced by an intermediate layer for performing the second inference task…such as “the neural network is applied to the input data set to obtain output features vectors of an intermediate layer.”
Doumbouya teaches a clustering algorithm is based on a Mean Shift clustering algorithm (Refer to para [054]; “In some embodiments, determination of the mutual information metric may include performing a clustering the output feature vectors according to a clustering technique, such as K-Means clustering, mean shift clustering, density-based spatial clustering, and the like.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Mahoor and Jansen by adding “a process of computing a mutual information metric used to evaluate neural network layers in SCA, according to some embodiments.” as taught by Doumbouya.
The suggestion/motivation for combining the teachings of Mahoor, Jansen and Doumbouya would have been in order to enhance a clustering technique such that “the clustering may be specified to produce the same number of clusters as the number of distinct class labels used for the input dataset.” (at para [058]; Doumbouya).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mahoor, Jansen and Doumbouya in order to obtain the specified claimed elements of Claim 7. 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: Mahoor and Jansen in combination disclose all the claimed elements as rejected above. Mahoor and Jansen in combination does not expressly teach a “Mean Shift clustering algorithm.”
Doumbouya teaches “performing semantic coherence analysis on a deep neural network (DNN).”
As is already well known in the art, Doumbouya more specifically teaches “… a coherence score will indicate a semantic of features produced by an intermediate layer for performing the second inference task…such as “the neural network is applied to the input data set to obtain output features vectors of an intermediate layer.”
Doumbouya teaches a clustering algorithm is based on a Mean Shift clustering algorithm (Refer to para [054]; “In some embodiments, determination of the mutual information metric may include performing a clustering the output feature vectors according to a clustering technique, such as K-Means clustering, mean shift clustering, density-based spatial clustering, and the like.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Mahoor and Jansen by adding “a process of computing a mutual information metric used to evaluate neural network layers in SCA, according to some embodiments.” as taught by Doumbouya.
The suggestion/motivation for combining the teachings of Mahoor, Jansen and Doumbouya would have been in order to enhance a clustering technique such that “the clustering may be specified to produce the same number of clusters as the number of distinct class labels used for the input dataset.” (at para [058]; Doumbouya).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mahoor, Jansen and Doumbouya in order to obtain the specified claimed elements of Claim 15. It is for at least the aforementioned reasons that the Examiner has reached a conclusion of obviousness with respect to the claim in question.
Claims 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Mahoor (US 20170053398 A1) in combination with Jansen (US 20200372295 A1) and further in view of Pandya et al. (US 20230363836 A1).
Regarding Claim 9: Mahoor and Jansen in combination disclose all the claimed elements as rejected above. Mahoor and Jansen in combination does not expressly teach “Shannon entropy” processing.
Pandya teaches “a medical device, a surgical system and at least a surgical robot…” capable to “generate entropy pixels representing the entropy of pixels in frames depicting a scene of interest.”
Pandya more specifically teaches “Machine learning-based techniques can be used as an alternative, or in addition to, entropy-based image processing techniques for detecting bleeding…” wherein the entropy information is based on a Shannon entropy values for each of the plurality of regions (Refer to para [088]; “the value of the first entropy pixel 216 is based on a Shannon entropy of the first detection window 212. In various cases, the value of the first entropy pixel 216 is based on a local entropy with respect to the first reference pixel 214.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Mahoor and Jansen by enhancing through “a surgical device [that] provides information about the source, location, and magnitude of a surgical injury causing bleeding.” as taught by Pandya.
The suggestion/motivation for combining the teachings of Mahoor, Jansen and Pandya in order to
“automatically and accurately identify a source of bleeding within an intraoperative environment, and in some cases, can do so faster and more accurately than a surgeon. ” at para [039], Pandya.
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mahoor, Jansen and Pandya in order to obtain the specified claimed elements of Claim 9. 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 17: Mahoor and Jansen in combination disclose all the claimed elements as rejected above. Mahoor and Jansen in combination does not expressly teach “Shannon entropy” processing.
Pandya teaches “a medical device, a surgical system and at least a surgical robot…” capable to “generate entropy pixels representing the entropy of pixels in frames depicting a scene of interest.”
Pandya more specifically teaches “Machine learning-based techniques can be used as an alternative, or in addition to, entropy-based image processing techniques for detecting bleeding…” wherein the entropy information is based on a Shannon entropy values for each of the plurality of regions (Refer to para [088]; “the value of the first entropy pixel 216 is based on a Shannon entropy of the first detection window 212. In various cases, the value of the first entropy pixel 216 is based on a local entropy with respect to the first reference pixel 214.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Mahoor and Jansen by enhancing through “a surgical device [that] provides information about the source, location, and magnitude of a surgical injury causing bleeding.” as taught by Pandya.
The suggestion/motivation for combining the teachings of Mahoor, Jansen and Pandya in order to
“automatically and accurately identify a source of bleeding within an intraoperative environment, and in some cases, can do so faster and more accurately than a surgeon. ” at para [039], Pandya.
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Mahoor, Jansen and Pandya in order to obtain the specified claimed elements of Claim 17. 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
Claims 3, 4, 5, 6, 8, 12, 13, 14, 16, 19 and 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 prior art either singly or in combination does not teach, disclose or suggest at least the following claim limitation(s): “a first entropy scan and a second entropy scan, wherein the first entropy scan and the second entropy scan are performed in parallel and generating the plurality of regions of the image by defining a first region of the image and iteratively expanding the first region to a subset of the plurality of regions, wherein the plurality of regions comprises the first region and the subset of the plurality of regions.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Mopur (US 20240012852 A1)
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MIA M. THOMAS
Primary Examiner
Art Unit 2665
/MIA M THOMAS/Primary Examiner
Art Unit 2665